Predicting Morphological Disparities in Sea Urchin Skeleton Growth and Form
Bibliographic record
Abstract
Abstract Sea urchins exhibit among their many species remarkable diversity in skeleton form (e.g., from spheroid to discoid shapes). However, we still do not understand how some related species show distinct morphologies despite inherent similarities at the genetic level. For this, we use theoretical morphology to disentangle the ontogenic processes that play a role in skeletal growth and form. We developed a model that simulates these processes involved and predicted trajectory obtaining 94% and 77% accuracies. We then use the model to understand how morphologies evolved by exploring the individual effects of three structures (ambulacral column, plate number, and polar regions). These structures have changed over evolutionary time and trends indicate they may influence skeleton shape, specifically height–to-diameter ratio, h:d. Our simulations confirm the trend observed but also show how changes in the attributes affect shape; we show that widening the ambulacral column or increasing plate number in columns produces a decrease in h:d (flattening); whereas increasing apical system radius to column length ratio produces an increase in h:d (gloublar shape). Computer simulated h:d matched h:d measured from real specimens. Our findings provide the first explanation of how small changes in these structures can create the diversity in skeletal morphologies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".