Ability to Swim (Not Morphology or Environment) Explains Interspecific Differences in Crinoid Arm Regrowth
Bibliographic record
Abstract
Regrowth of body parts occurs in almost every phylum of the animal kingdom, but variation in this process across environmental, morphological, and behavioral gradients remains poorly understood. We examined regeneration patterns in feather stars – a group known for a wide range of morphologies and behaviors and up to a forty-fold difference in arm regeneration rates – and found that the variation in arm regeneration rates is best explained by swimming ability, not temperature, food supply, morphology (total number of arms and number of regenerating arms), or degree of injury. However, there were significant interactive effects of morphology on rates of regeneration of the main effect (swimming ability). Notably, swimmers grew up to three-fold faster than non-swimmers. The temperate feather star Florometra serratissima regenerated faster under warmer scenarios, but its rates fell within that of the tropical species suggesting temperature can account for intraspecific but not interspecific differences. We urge comparative molecular investigations of crinoid regeneration to identify the mechanisms responsible for the observed interspecific differences, and potentially address gaps in stem cell research.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".