Methods for the analysis of developmental sequence data
Bibliographic record
Abstract
Heterochrony, evolutionary changes in developmental timing, can be studied either by examining changes in growth or changes in the sequence of developmental events. Developmental sequence data has the potential to address many important questions in the field of developmental evolution, but methodological challenges remain due to the biological and logical dependence of events in a ranked sequence. In the past 10 years, the study of sequence heterochrony has undergone a rebirth, with the creation of several new methods for the analysis of this type of data. These methods can be divided into two broad categories: phenetic comparisons between terminal taxa that strive to uncover integrations within the developmental sequences and putative shared sequence heterochronies, and phylogeny-based methods that derive ancestor-descendent sequence heterochronies and establish statements of sequence evolution. In this review, we will discuss the strengths and weaknesses of the methodologies that have been proposed to quantitatively examine developmental sequence data, and studies that have attempted to implement them in an evolutionary context.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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".