Differential temperature preferences of vertebral phenotypes in <i>Gasterosteus</i>
Bibliographic record
Abstract
Across a broad diversity of freshwater and marine fish that have wide latitudinal distributions, intraspecific variability in vertebral number shows a robust trend for higher vertebral counts in colder latitudes (Jordan’s Rule). Variability in this meristic trait is determined during early larval development by a combination of heritable and temperature-mediated influences. We experimentally evaluate for the first time whether different vertebral phenotypes from a population actively segregate across a temperature gradient, and if so, whether the segregation is consistent with broad geographical trends across taxa of greater vertebral counts that are associated with cooler waters. Using threespine stickleback (Gasterosteus aculeatus L., 1758) from two populations, we conducted 22 trials with ∼50 fish per trial and a temperature gradient ranging from 2 to 15 °C among trials. Results show that six trials yielded statistically significant or near-significant results, of which five trials were in the predicted direction of more vertebrae in cooler waters. The effects were more expressed in fish with small bodies (35–55 mm) than in fish with larger bodies (60–85 mm) and in longer duration trials. We believe that these data are novel and are consistent with recent studies on swimming efficacy among vertebral phenotypes; the data also hint at much greater ecological functionality than is currently assumed by intrapopulation variation in this meristic trait.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".