THE USE OF THE ABOAV-WEAIRE LAW TO ESTIMATE A BIOLOGICAL CONSTRAINT
Bibliographic record
Abstract
Developmental processes may impose limitations and directionality in the mode of development of a particular structure. The main problem is to determine the nature and the respective effects of physical and biological constraints in the development of organisms. The Aboav-Weaire law is a semi-empirical law developed to explain the topological structure of physical materials. In the present paper, we make a formal analysis of the quantitative relationships between physical and biological constraints in biological structures by using the inflorescence of the Araceae as a case study. The Aboav-Weaire law permits to obtain a quantitative estimate of the biological constraint acting on the inflorescences of this family. In the case of the Araceae, the empirical curve presents a constant deviation with respect to the Aboav-Weaire law. This deviation is due to the presence of a biological constraint as opposed to a physical constraint. The biological constraint tends to decrease the variance of the number of sides while it is the inverse situation for the physical constraint. The results obtained using the Araceae model can be used to study the interrelationships between biological and physical constraints in any organism or biological structure.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".