SIMULATIONS OF TRANSITIONS FROM REGULAR TO STOCHASTIC PHYLLOTACTIC PATTERNS
Bibliographic record
Abstract
The paper deals with a statistical method to analyze irregular phyllotactic patterns. To characterize the degree of order in phyllotactic systems, we determine the variation of the angle of divergence of a given leaf with regard to the preceding one. By knowing the range of uncertainty of the angle of divergence, it is possible to determine from which leaves rank a system becomes completely disorganized. We show that there is a quantitative link between the degree of uncertainty of the angle of divergence, and the number of regularly and randomly distributed leaves. To quantify this relationship, we deduced a formula from numerical simulations involving different ranges of uncertainty that can be observed in the angle of divergence in three different phyllotactic patterns: distichous (two orthostichies), opposite-decussate (four orthostichies) and spiral (137°). A χ2statistical test allows us to determine the threshold of transition between ordered and disordered phyllotactic patterns with a fixed level of confidence. By using the sho mutants described by Itoh et al.1as a case study, we show that this formula is useful mainly for analyzing the degree of order in phyllotactic mutants from two complementary points of view: the number of regularly distributed leaves and the degree of uncertainty of the divergence angle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".