What a plant might be doing

Four ways to count a neighbourhood

How deep does the placement rule look? Counting the organs that carry nine tenths of its profile gives 182 down to 3 as the falloff steepens. Counting the ones that carry half gives 33 down to 1. The weighted mean lag gives 68 down to 11. The four disagree by an order of magnitude about the size and agree exactly about the order.

Worth reading first: A disturbance with a memory · Fitting the exponent · How far a primordium reaches.

The rule places each organ where a sum is smallest — one term per organ already there, each falling off as a power of the distance. The exponent of that power is the only thing in the rule that says how far it looks, and it has been used as a knob: sweep it, and the neighbourhood the rule attends to changes from almost two hundred organs to almost none.

That sweep produced a refutation. A disturbance with a memory was supposed to pass through a rule whose neighbourhood was shallower than the memory and be corrected by one that was deeper, with the crossover tracking the depth. There was no crossover at any depth, and the deep rule passed more rather than less.

The wander climbs because its denominator fallsThree quantities across the same sweep of the rule's depth, each divided by its own value for the shallowest rule so that they share an axis. The wander rises by a factor of 3.7 as the neighbourhood goes from 3 organs to 182. The scatter between neighbouring divergences falls by 1.57, because that is the part of a disturbance a placement rule corrects and a deeper rule corrects it better. What is left — how many degrees of slow drift actually reach the divergences — changes by 1.20, from 1.52 to 1.82 degrees. The rule barely filters a drift at any depth.012342.262.081.480.9030.477organs in the neighbourhoodagainst the shallowest rulewanderscatterdrift througha drift correlated over 33 organs · 3 seeds a pointdrift through: 1.52° to 1.82°
Fig. 1 The result being followed up. Swept across a factor of sixty in the neighbourhood, the ratio the drift is undone by moves the opposite way from the prediction, smoothly and without a crossing.

A negative result that leans on a measured quantity deserves a second measurement of that quantity, which is what this essay is. How deep the rule looks was reported one way; there are three others, and they do not agree — though what they agree about instead turns out to be the more useful answer.

What the profile is

At the moment an organ is placed, the rule evaluates a sum over the organs already there. Each term is one over the distance to the power of the exponent, and the distance is measured across the surface of the stem rather than up it.

The rule, 26 steps in, at a growth of 0.40The next primordium goes where the repulsion is least — the marked minimum at 216°. Nothing in the rule refers to any particular angle.05e+51e+61.5e+60100200300angle around the boundary (°)repulsion from what is theregrowth 0.40 · 14 elements in playthe minimum is where the next one goes
Fig. 2 The rule doing the placing. One organ, one height, and the position that makes a sum smallest.
A stem gathers neighbours linearly; a growing disc barely gathers them at allOn a cylinder of circumference 1 with a rise of 0.005, the nodes within distance d number 2d/0.005 once d exceeds one turn — a fitted exponent of 1.011 and 400 per unit against the 400 the geometry fixes. In the disc model an element of age k sits at radius e^(0.4k), so each doubling of the distance adds the same two or three neighbours rather than twice as many.01234-0.50000.50011.50distance from the node, log₁₀neighbours, log₁₀a stemslope 1a disca logarithmrise 0.005 · 24000 nodes · meristem growth 0.4slope 1.011 against slope 1
Fig. 3 The distances the sum is over, on a settled stem. A handful of near neighbours and a long tail, which is the shape every question here is about.

A “depth” is a summary of that sum: some statement of how many organs are doing the work. The trouble is that the sum has no edge. Every organ ever placed contributes something, so any count of organs requires a decision about how much of the total is enough — and the decision is a free parameter that nobody has ever varied.

A sixfold neighbourhood, and nothing to diluteThe prediction was that a rule with fewer neighbours would convert a jostle into divergence scatter more efficiently. Across a sixfold widening the ratio sits between 0.97 and 1.00, and the one point that differs is the narrowest, at 0.82 — smaller, where the prediction wanted larger. The row underneath is why: past four spacings the rule builds the identical lattice, internode for internode, so there is no neighbourhood left to widen.00.50011246how far the rule looks, in units of the local spacingscatter a jostle adds, over the scatter the same displacement adds after the choiceequal damage0.82 — the wrong wayinternodes that differ between one neighbourhood and the next501→2442→4none4→64 runs per point · window 16–95 nodeseach disagreement is one grid sample
Fig. 4 Why an edge cannot simply be put on the sum by hand: truncating the neighbourhood is a change to the rule, and it changes what the rule produces.
The same rule at p = 1, cut off at two distancesThe top 220 nodes of two stems grown by an identical rule whose energy does not converge. Allowed to see 1/√h neighbours it produces 8/13 at 137.67° with 0.64° of scatter — a lattice no test on this site would question. Allowed 6/√h it produces 39° of scatter and no pattern. The truncation was doing the work.cut at 1/√h8/13 at 137.67°0.64° of scattercut at 6/√hno divergence angle38.50° of scatterexponent 1 · identical but for the neighbourhood0.64° against 38.5°
Fig. 5 And what it changes it to: a truncated rule makes patterns the untruncated one never produces, so the cut-off is not a bookkeeping device.

Four summaries

The first three fix a share and count organs. Rank the terms by size, add them up until the running total reaches the share, and report how many terms that took. The share used before was nine tenths. A half and ninety-nine hundredths are equally defensible.

The fourth uses no share at all. The weighted mean lag is the average distance back, in organs, with each organ weighted by the size of its own term. It is the centre of mass of the profile and it needs no threshold.

exponent half nine tenths ninety-nine hundredths weighted mean
1.5 33 182 284 68.2
2 12 120 263 45.4
3 3 30 149 20.8
4 2 8 54 13.5
6 1 3 9 11.1
Four ways to count the rule's neighbourhood, and one orderingHow many organs the placement rule is looking at, as its falloff exponent is swept from 1.5 to 6, by four different definitions. Counting the organs that carry half the profile gives 33, 12, 3, 2, 1. Counting the organs that carry nine tenths gives 182, 120, 30, 8, 3. Counting the organs that carry ninety-nine hundredths gives 284, 263, 149, 54, 9. Counting the organs that carry the weighted mean lag gives 68, 45, 21, 14, 11. They disagree about the size of the neighbourhood by a factor of 10 — the widest moves by 61 times across the sweep and the narrowest by 6.1 — and every one of them falls as the exponent rises. So no choice among them changes which rule is the deeper, and no redefinition can turn a result about the depth around.131030100300half the profilenine tenthsninety-nine hundredthsweighted mean lag1.52346falloff exponent of the placement ruleorgans in the neighbourhoodrise 0.005 · 5 exponents · top lags 13, 8, 5, 21, 26 at every onegenerated from a stated rule, not drawn to look right
Fig. 6 The four, on one logarithmic axis. They differ by two orders of magnitude at a single exponent and none of them crosses another.

Why “how many organs” is a question with no answer in the rule

The rule contains no number of organs. It contains an exponent, and a loop bound that has been shown to change nothing.

That is worth separating, because the two are constantly confused. The loop bound is how far back the implementation actually sums; it is a parameter of the program. The neighbourhood is how far back the terms matter; it is a property of the exponent. Sweeping the first from one local spacing to six moves the divergence’s wander by a factor of 1.30, against a factor of 3.28 between random seeds at one setting — which is the shape a parameter has when it is not in the answer.

A sixfold neighbourhood, and nothing to diluteThe prediction was that a rule with fewer neighbours would convert a jostle into divergence scatter more efficiently. Across a sixfold widening the ratio sits between 0.89 and 0.89, and the one point that differs is the narrowest, at 0.88 — smaller, where the prediction wanted larger. The row underneath is why: past four spacings the rule builds the identical lattice, internode for internode, so there is no neighbourhood left to widen.00.5001124how far the rule looks, in units of the local spacingscatter a jostle adds, over the scatter the same displacement adds after the choiceequal damage0.88 — the wrong wayinternodes that differ between one neighbourhood and the next501→2442→43 runs per point · window 16–64 nodeseach disagreement is one grid sample
Fig. 7 The loop bound swept, and the null it gives. Whatever the rule is sensitive to, it is not how far the sum is taken.
The same rule at p = 1, cut off at two distancesThe top 220 nodes of two stems grown by an identical rule whose energy does not converge. Allowed to see 2/√h neighbours it produces 8/13 at 137.64° with 0.61° of scatter — a lattice no test on this site would question. Allowed 8/√h it produces 29° of scatter and no pattern. The truncation was doing the work.cut at 2/√h8/13 at 137.64°0.61° of scattercut at 8/√hno divergence angle29.19° of scatterexponent 1 · identical but for the neighbourhood0.61° against 29.2°
Fig. 8 And the reason the loop bound cannot simply be set to the neighbourhood: cutting the sum off is a change to the rule, and a truncated rule makes patterns the full one never produces.

So the only knob that reaches the neighbourhood is the exponent — and what a band of exponents can be made to say is its own caution — while the neighbourhood is a summary of a sum with no edge. Any number of organs attached to it is a summary somebody chose, and the four here are four such choices.

Where the lattice ends, for two falloff shapes at p = 1Both shapes are read in the same unit — the distance at which the weight has halved — and they still disagree, by 50%: the exponential holds a lattice out to about 3.75 spacings and the gaussian only to about 2.25. So the range is not what decides whether there is a pattern.00.2500.5000.750112345range at which the interaction has halved, in local spacingsshare of runs that still have a latticeexponentialgaussian4 runs per point, separated by 0.2° of noiseboundaries 50% apart
Fig. 9 The general form of the difficulty: a range attached to a rule with no edge is a statement about the summary as much as about the rule.

They disagree about the size by a factor of ten

Read down the exponent-3 row: the rule’s neighbourhood is 3 organs, 30 organs, 149 organs or 21 organs, depending on which summary is asked. That is a range of fifty to one at a single setting of the rule, and every one of the four is a defensible answer to “how many organs is it looking at”.

Four ways to count the rule's neighbourhood, and one orderingHow many organs the placement rule is looking at, as its falloff exponent is swept from 1.5 to 6, by two different definitions. Counting the organs that carry half the profile gives 33, 12, 3, 2, 1. Counting the organs that carry nine tenths gives 182, 120, 30, 8, 3. They disagree about the size of the neighbourhood by a factor of 10 — the widest moves by 61 times across the sweep and the narrowest by 6.1 — and every one of them falls as the exponent rises. So no choice among them changes which rule is the deeper, and no redefinition can turn a result about the depth around.131030100half the profilenine tenths1.52346falloff exponent of the placement ruleorgans in the neighbourhoodrise 0.005 · 5 exponents · top lags 13, 8, 5, 21, 26 at every onegenerated from a stated rule, not drawn to look right
Fig. 10 Two of the four on their own, at the widest separation. Half the profile is carried by three organs at this exponent and nine tenths by thirty.

They also disagree about how much the neighbourhood changes across the sweep, which matters more for what the depth was being used for. Across exponents 1.5 to 6, the nine-tenths count moves by a factor of 60.7; the half count by 33; the ninety-nine count by 31.6; and the weighted mean by 6.1.

Four ways to count the rule's neighbourhood, and one orderingHow many organs the placement rule is looking at, as its falloff exponent is swept from 1.5 to 6, by two different definitions. Counting the organs that carry nine tenths gives 182, 120, 30, 8, 3. Counting the organs that carry the weighted mean lag gives 68, 45, 21, 14, 11. They disagree about the size of the neighbourhood by a factor of 10 — the widest moves by 61 times across the sweep and the narrowest by 6.1 — and every one of them falls as the exponent rises. So no choice among them changes which rule is the deeper, and no redefinition can turn a result about the depth around.31030100nine tenthsweighted mean lag1.52346falloff exponent of the placement ruleorgans in the neighbourhoodrise 0.005 · 5 exponents · top lags 13, 8, 5, 21, 26 at every onegenerated from a stated rule, not drawn to look right
Fig. 11 The widest and the narrowest. One says the sweep changes the neighbourhood sixtyfold and the other says sixfold, and both are computed from the same runs.

So a claim of the form “the rule’s neighbourhood spans a factor of sixty across this sweep” was carrying a chosen constant that nobody had varied, and varying it moves the factor to six.

One exponent fitted to an organ that has 7 of themEach dot is one step between consecutive rings, reporting 2 ln φ / ln(s′/s) — the exponent that step would have if the organ had one. They run from 0.997 to 1.002. The line is what a single fit returns, 1.000, which is their harmonic mean of 1.000 and sits below their plain average of 1.000.0.99811.000123456which step of the ladderexponent reportedfitted 1.000plain average 1.000harmonic mean 1.000residual 0.001of one stepa cone · 7 stepsfitted 1.000 against a harmonic mean of 1.000
Fig. 12 And the same hazard in the neighbouring subject: a single fitted exponent turns out to be a particular average of the things it summarises rather than a property of any of them.

Where each definition puts its emphasis

The four disagree in a structured way rather than randomly, and reading the structure is most of what makes the table useful.

The half count is a statement about the very top of the profile: at an exponent of 6 it is one organ, which says that a single term is more than half of everything. The ninety-nine hundredths count is a statement about the tail: at an exponent of 1.5 it is 284 organs, which is nearly the whole window the profile was computed over, and says that the tail there is heavy enough to matter out to the edge of what was measured.

Four ways to count the rule's neighbourhood, and one orderingHow many organs the placement rule is looking at, as its falloff exponent is swept from 1.5 to 6, by two different definitions. Counting the organs that carry half the profile gives 33, 12, 3, 2, 1. Counting the organs that carry ninety-nine hundredths gives 284, 263, 149, 54, 9. They disagree about the size of the neighbourhood by a factor of 10 — the widest moves by 61 times across the sweep and the narrowest by 6.1 — and every one of them falls as the exponent rises. So no choice among them changes which rule is the deeper, and no redefinition can turn a result about the depth around.131030100300half the profileninety-nine hundredths1.52346falloff exponent of the placement ruleorgans in the neighbourhoodrise 0.005 · 5 exponents · top lags 13, 8, 5, 21, 26 at every onegenerated from a stated rule, not drawn to look right
Fig. 13 The two extremes of emphasis. One reports how concentrated the top is and the other how far the tail runs, and they are two different questions about one distribution.

The nine tenths count sits between them and inherits both sensitivities, which is why it moves furthest across the sweep: at a steep exponent it is reporting the top and at a shallow one it is reporting the tail, so it is measuring different things at the two ends.

14 specimens separate 14.7% from 50%The exact binomial power against sample size, for a one-sided test at 5 per cent. It is a staircase rather than a curve because the decision rule is a whole number of specimens: at 14 the cut sits at 5 and the power is 91.0 per cent. A normal approximation smooths that staircase away and reports a different answer.00.2500.5000.75015101520specimenschance of detecting it14 specimens90%exact binomial · one-sided at α = 0.0514 specimens, cut at 5
Fig. 14 The general shape of the trouble: a statistic that changes what it is sensitive to as a parameter moves will report a larger swing than any of its components.

The weighted mean is the least sensitive because it never sorts. Every organ contributes its own lag weighted by its own term, so a heavy top pulls it down and a heavy tail pulls it up, and at these profiles the two partly cancel.

Four ways to count the rule's neighbourhood, and one orderingHow many organs the placement rule is looking at, as its falloff exponent is swept from 1.5 to 6, by two different definitions. Counting the organs that carry half the profile gives 33, 12, 3, 2, 1. Counting the organs that carry the weighted mean lag gives 68, 45, 21, 14, 11. They disagree about the size of the neighbourhood by a factor of 10 — the widest moves by 61 times across the sweep and the narrowest by 6.1 — and every one of them falls as the exponent rises. So no choice among them changes which rule is the deeper, and no redefinition can turn a result about the depth around.131030half the profileweighted mean lag1.52346falloff exponent of the placement ruleorgans in the neighbourhoodrise 0.005 · 5 exponents · top lags 13, 8, 5, 21, 26 at every onegenerated from a stated rule, not drawn to look right
Fig. 15 The most and least sensitive of the four, on one axis. Thirty-three against six, from the same array of terms.

And they agree about the order, exactly

Every one of the four falls as the exponent rises. Not approximately, not with a crossing somewhere in the middle: at every adjacent pair of exponents, each of the four definitions puts the same rule deeper than the other.

Four ways to count the rule's neighbourhood, and one orderingHow many organs the placement rule is looking at, as its falloff exponent is swept from 1.5 to 6, by four different definitions. Counting the organs that carry half the profile gives 33, 12, 3, 2, 1. Counting the organs that carry nine tenths gives 182, 120, 30, 8, 3. Counting the organs that carry ninety-nine hundredths gives 284, 263, 149, 54, 9. Counting the organs that carry the weighted mean lag gives 68, 45, 21, 14, 11. They disagree about the size of the neighbourhood by a factor of 10 — the widest moves by 61 times across the sweep and the narrowest by 6.1 — and every one of them falls as the exponent rises. So no choice among them changes which rule is the deeper, and no redefinition can turn a result about the depth around.131030100300half the profilenine tenthsninety-nine hundredthsweighted mean lag1.52346falloff exponent of the placement ruleorgans in the neighbourhoodrise 0.005 · 5 exponents · top lags 13, 8, 5, 21, 26 at every onegenerated from a stated rule, not drawn to look right
Fig. 16 The four lines, and the fact that none of them crosses another. That is the whole content of this section.

That is the finding, and it is a negative one. No redefinition of the depth can change the sign of a result about the depth. The earlier sweep found the deep rule passing more drift than the shallow one and no crossover between them; if “deep” and “shallow” mean the same ordering under every available definition, then that result cannot be an artefact of how the neighbourhood was counted.

The wander climbs because its denominator fallsThree quantities across the same sweep of the rule's depth, each divided by its own value for the shallowest rule so that they share an axis. The wander rises by a factor of 2.8 as the neighbourhood goes from 3 organs to 182. The scatter between neighbouring divergences falls by 1.43, because that is the part of a disturbance a placement rule corrects and a deeper rule corrects it better. What is left — how many degrees of slow drift actually reach the divergences — changes by 1.18, from 1.09 to 1.29 degrees. The rule barely filters a drift at any depth.01232.261.480.477organs in the neighbourhoodagainst the shallowest rulewanderscatterdrift througha drift correlated over 9 organs · 3 seeds a pointdrift through: 1.09° to 1.29°
Fig. 17 The result the negative protects. Whatever the neighbourhood is called, the exponents are ranked the same way, so the ordering of the outcome cannot be repaired by renaming.

That closes off one repair and leaves the result standing. It does not explain it, and it makes the situation stranger rather than less strange: a quantity that varies by a factor of sixty by one measure and six by another is producing an outcome whose ordering does not depend on which.

The weighted mean is the one worth keeping

Of the four, the weighted mean is the one this collection should report from here on, and there are two reasons that are not “it gives a different answer”.

The first is that it has no chosen constant. The other three all require somebody to say how much of the profile counts as the neighbourhood, and there is no principled answer — nine tenths and ninety-nine hundredths are equally defensible and give 30 organs and 149 at the same exponent. The weighted mean asks a different question, “where is the centre of the profile”, and the answer needs no threshold.

Four ways to count the rule's neighbourhood, and one orderingHow many organs the placement rule is looking at, as its falloff exponent is swept from 1.5 to 6, by two different definitions. Counting the organs that carry ninety-nine hundredths gives 284, 263, 149, 54, 9. Counting the organs that carry the weighted mean lag gives 68, 45, 21, 14, 11. They disagree about the size of the neighbourhood by a factor of 10 — the widest moves by 61 times across the sweep and the narrowest by 6.1 — and every one of them falls as the exponent rises. So no choice among them changes which rule is the deeper, and no redefinition can turn a result about the depth around.1030100300ninety-nine hundredthsweighted mean lag1.52346falloff exponent of the placement ruleorgans in the neighbourhoodrise 0.005 · 5 exponents · top lags 13, 8, 5, 21, 26 at every onegenerated from a stated rule, not drawn to look right
Fig. 18 The one with no chosen constant beside the one whose constant is largest. They disagree by a factor of seven at the deepest exponent and agree about the order.

The second is that a count of organs is a count of the largest terms, wherever they are, and it therefore mixes two different facts: how heavy the top of the profile is, and how quickly the tail dies away. The weighted mean mixes them too, but it weights each organ by its own contribution rather than sorting and counting, so a handful of very large terms at short lags cannot be counted as “three organs” while a hundred small ones are ignored.

A stem gathers neighbours linearly; a growing disc barely gathers them at allOn a cylinder of circumference 1 with a rise of 0.005, the nodes within distance d number 2d/0.005 once d exceeds one turn — a fitted exponent of 1.011 and 400 per unit against the 400 the geometry fixes. In the disc model an element of age k sits at radius e^(0.3k), so each doubling of the distance adds the same two or three neighbours rather than twice as many.1234-0.50000.50011.50distance from the node, log₁₀neighbours, log₁₀a stemslope 1a disca logarithmrise 0.005 · 24000 nodes · meristem growth 0.3slope 1.011 against slope 1
Fig. 19 The distribution being summarised, at a slightly different growth setting. A few large terms, a long tail, and no natural place to cut.
The neighbourhood of 13/21, and where its background was taken fromμ₂ across nine tenths of a degree either side of 13/21, on a head of 315 organs — 15 in each of its 21 rows. The deep notch at the centre is the dip. The shaded columns are the offsets this site now takes a background from: those at least 0.03° from every rational with a denominator of 60 or less, of which there are 60 here. The marked sample at 0.2° is the one the earlier work used, and it lands 0.512° from 34/55. It reads 0.533 against a floor of 0.074. The clear offsets give 0.445.0.2000.400-0.400-0.300-0.200-0.10000.1000.2000.3000.400degrees from 13/21, at 222.8571°μ₂, the second moment of the side-count distribution34/55the background from the clear offsets: 0.445the old single sample: 0.53313/21 · head of 315generated from a stated rule, not drawn to look right
Fig. 20 And the same object at a finer arrangement, where the near neighbours are more numerous and the tail is longer.

None of that changes the ordering, which is the finding. It changes what a number attached to the neighbourhood is worth, and the number the earlier work carried was the one that moves furthest.

Why a second definition was worth the trouble

Because the alternative was to leave a number in a negative result that nobody had tested.

The nine-tenths share was chosen when the depth was first computed, and the note attached to it said the claim did not turn on it — that at eight tenths the exponents separate by thirty-eight and at ninety-five hundredths by fifty. That is a check on the robustness of the number and it is not the same as a check on the choice of statistic. Both eight tenths and ninety-five hundredths are the same summary with the dial moved. The weighted mean is a different summary, and it is the one that disagrees.

Every open question here needs under 28 specimensThe sample size at which each comparison reaches 80 per cent power at a 5 per cent false-positive rate, from the exact binomial rather than a normal approximation. The census question — do plants show consecutive Fibonacci pairs far more often than the geometry does — needs 4: 14.7% is the share of divergence angles giving a consecutive Fibonacci pair at a fine rise; 90% is what a grown history gives.plants show consecutive Fibonacci pairs far more…4and more often even than a coin weighted to a half10a conifer cone's rings are spaced as a cone rather…1multijugate patterns are a real minority rather than…28against 14.7%, if the truth is 90%needs: the pair, at a stated rungagainst 14.7%, if the truth is 50%needs: the pair, at a stated rungagainst φ² = 2.62, if the truth is φ^(2/1.88) = 1.67needs: three ring positions, to ±3%against 2%, if the truth is 15%needs: the pair; the whorl's symmetryspecimens neededexact binomial · α = 0.05 · power 0.81 to 28 specimens
Fig. 21 The general form of that distinction: moving a parameter inside a method tests the parameter, and it does not test the method.
Both vary; only one of them varies enough to findEach organ's step exponents, divided by its own mean so the two are comparable. The ogive's run over 15 per cent of their mean across 5 rings. The convex head's run over 1.15 per cent across 5 — inside the band a 10 per cent error on each ring position leaves, so no ruler separates it from a flat disc.0.9000.95011.050123which step of the ladderexponent ÷ its meanan ogive — 15%a convex head — 1.15%what 10% per ring allowsogive 5 rings · head 515% against 1.15%
Fig. 22 And the reason to bother: a quantity that appears in a claim ought to be one somebody has tried to compute a different way, particularly when the claim is a refusal.

The trouble was also cheap. The profile is computed on an undisturbed stem, once per exponent, and every summary of it comes out of the same array of terms. All four definitions are four lines of arithmetic over one measurement, which is the usual state of affairs when a chosen constant has gone unexamined for a while.

A rule too long-ranged makes no pattern; every shorter one makes the same patternEach dot is 4 runs from a coarse start at one exponent, separated by 0.2° of placement noise. Below p ≈ 1.1 the divergence scatters by tens of degrees, which is what an arbitrary sequence gives. From p = 1.25 to p = 8 — a sixfold range — every run ends on 8/13 with the scatter between 0.75° and 1.06°.00.50011.50-0.25000.2500.5000.750falloff exponent, log₁₀scatter, log₁₀ degrees00.50011.50-0.25000.2500.5000.750falloff exponent, log₁₀scatter, log₁₀ degreesno latticethe same lattice, whatever pevery runsome runsno runneighbourhood 12/√h · 4 runs per exponenta lattice from p ≈ 1.25 upward
Fig. 23 The object all four summarise, from the other end: what the rule actually produces as its falloff is swept, which is the same lattice over most of the range.

And there is one more definition worth mentioning and not using: the deepest organ in the top share, rather than how many organs are in it. Those are different numbers, because the top share is assembled by size and not by lag — the thirty organs carrying nine tenths of the profile at an exponent of 3 include lags well past thirty. That distinction is recorded in the machinery and it is not a fifth definition; it is a reminder that “thirty organs” already means “thirty terms” and never meant “the last thirty”.

What the earlier sweep was entitled to say

With four definitions in hand it is possible to be precise about which of the earlier statements survive and which were carrying the chosen constant.

“The falloff is the neighbourhood, and the loop bound is not.” Survives, and is strengthened. Every definition moves with the exponent and none moves with the loop bound.

The wander climbs because its denominator fallsThree quantities across the same sweep of the rule's depth, each divided by its own value for the shallowest rule so that they share an axis. The wander rises by a factor of 2.8 as the neighbourhood goes from 3 organs to 182. The scatter between neighbouring divergences falls by 1.43, because that is the part of a disturbance a placement rule corrects and a deeper rule corrects it better. What is left — how many degrees of slow drift actually reach the divergences — changes by 1.18, from 1.09 to 1.29 degrees. The rule barely filters a drift at any depth.01232.262.081.480.9030.477organs in the neighbourhoodagainst the shallowest rulewanderscatterdrift througha drift correlated over 9 organs · 3 seeds a pointdrift through: 1.09° to 1.29°
Fig. 24 The sweep the statements were made on, at a shorter correlation than the one usually quoted.

“The neighbourhood spans a factor of sixty-one across the sweep.” Does not survive as stated. It spans sixty-one by one summary, thirty-three by another, thirty-two by a third and six by the fourth, and the figure quoted was the largest of the four.

“The deep rule passes more drift than the shallow one.” Survives, because “deep” and “shallow” are the same ordering under all four.

“There is no crossover at any depth.” Survives this essay and does not survive the next two, for a reason that has nothing to do with the definition of depth.

The disturbance with the largest wander leaves none in the sequenceHow much of a divergence sequence's variance survives being averaged over blocks, on kinematic lattices. The vertical quantity is B² times the variance of the block means divided by the variance of the sequence, which is one at every block size for independent errors — the arithmetic is normalised for a *differenced* stream, since a divergence is the difference of two organs' errors. A line that climbs is a sequence with power at frequencies below one per block. The disturbances that remember the last error climb to 32 at a block of 128. The ones inherited between touching organs do not climb at all — 1.06 and 0.83 at the same block — although their own deviates carry ×5 and ×49 an independent stream's variance in exactly this statistic. What they do instead is dig a hole: at block sizes of 8 and 13, which are the offsets they couple at, the statistic falls to 0.22 and 0.21.10.11024813163264128block size, in organsvariance of the block means, against independent errorsindependenta memory, ρ = 0.5a memory, ρ = 0.9a memory, ρ = 0.97inherited, a = 0.5inherited, a = 0.7shared once, a = 0.76000 organs · 6 runs eachgenerated from a stated rule, not drawn to look right
Fig. 25 The statistic the crossover was looked for in, which is where the trouble turns out to be rather than in the definition of the neighbourhood.

That is a useful sorting to have. Two of the four claims are strengthened by having a second measurement, one is qualified, and the one that eventually falls falls for an unrelated reason — which is roughly the distribution a careful re-measurement of an old quantity ought to produce.

Both vary; only one of them varies enough to findEach organ's step exponents, divided by its own mean so the two are comparable. The ogive's run over 15 per cent of their mean across 5 rings. The convex head's run over 1.15 per cent across 5 — inside the band a 15 per cent error on each ring position leaves, so no ruler separates it from a flat disc.0.9000.95011.050123which step of the ladderexponent ÷ its meanan ogive — 15%a convex head — 1.15%what 15% per ring allowsogive 5 rings · head 515% against 1.15%
Fig. 26 The habit behind the sorting: separating what a measurement establishes from what was assumed in order to make it, before anything is built on top.

The thing the table is quietly saying

Look at the exponent-6 row. Half the profile is carried by one organ; nine tenths by three; ninety-nine hundredths by nine. Now look at exponent 1.5: half by thirty-three, nine tenths by a hundred and eighty-two.

The shape of the profile is changing enormously and the ordering is not. Which suggests that whatever is at the top of the profile might be the same at both — and if it is, then counting the neighbourhood in organs was never going to distinguish the two rules in the way the sweep needed.

Four ways to count the rule's neighbourhood, and one orderingHow many organs the placement rule is looking at, as its falloff exponent is swept from 1.5 to 6, by three different definitions. Counting the organs that carry half the profile gives 33, 12, 3, 2, 1. Counting the organs that carry ninety-nine hundredths gives 284, 263, 149, 54, 9. Counting the organs that carry the weighted mean lag gives 68, 45, 21, 14, 11. They disagree about the size of the neighbourhood by a factor of 10 — the widest moves by 61 times across the sweep and the narrowest by 6.1 — and every one of them falls as the exponent rises. So no choice among them changes which rule is the deeper, and no redefinition can turn a result about the depth around.131030100300half the profileninety-nine hundredthsweighted mean lag1.52346falloff exponent of the placement ruleorgans in the neighbourhoodrise 0.005 · 5 exponents · top lags 13, 8, 5, 21, 26 at every onegenerated from a stated rule, not drawn to look right
Fig. 27 The definition that barely moves, against two that do: weight the lags by their own terms and the centre of the neighbourhood slides by a factor of six, where both share-based counts slide by about thirty.

That is checkable in one line, and the answer is the subject of the next essay.

What links here

Computed from the collection, not written here: the essays that point at this one.

Shares its objects with

Essays that name at least two of the same things, and that neither author linked.

  • What the ratio was hiding — both name falsifiability, honest limits, measurement, negative result, neighbourhood depth, noise, the placement rule, summary statistic
  • What the rule does to a drift — both name honest limits, measurement, negative result, neighbourhood, noise, the placement rule, summary statistic, tolerance
  • Not the shorter of the two — both name falsifiability, honest limits, measurement, negative result, neighbourhood, the placement rule, tolerance
  • One offset, two answers — both name falsifiability, honest limits, measurement, negative result, the placement rule, underdetermination
  • The boundary belongs to the pattern — both name measure, measurement, noise, the placement rule, summary statistic, tolerance
  • The fragility belonged to the window — both name the range of the interaction, negative result, neighbourhood, noise, the placement rule, tolerance

Named objects

A flat tag is an object no other essay names yet.

Exponent fittingFalsifiabilityHonest limitsThe range of the interactionThe local exponentMeasureMeasurementNegative resultNeighbourhoodNeighbourhood depthNoiseThe placement ruleSummary statisticToleranceUnderdetermination