Temperature and development drive variation in oral morphology among tailed frog ( <i>Ascaphus</i> spp.) populations
Bibliographic record
Abstract
Morphological variation is often maintained by complex and interrelated factors, complicating the identification of underlying drivers. Tadpole oral morphology is one such trait that may be driven by the independent and interacting effects of the environment and variation in developmental processes. Although many studies have investigated tadpole oral morphological diversity among species, few have sought to understand the drivers that underlie intraspecific variation. In this study, we investigated potential drivers of labial tooth number variation among populations of two species of tailed frogs, the Rocky Mountain tailed frog ( Ascaphus montanus ) and the Coastal tailed frog ( A. truei ). We counted labial teeth from 240 tadpoles collected across elevation (both species) and latitudinal ( A. truei ) gradients, providing a natural temperature gradient. We tested the effects of developmental stage and local temperature conditions on labial tooth number. We found that labial tooth number variation was independently affected by both developmental stage and local temperature, as well as the interacting effects of these two variables (pseudo-R 2 = 67-77%). Our results also uncovered consistent patterns in labial tooth row formula across the ranges of both species; however, A. truei tadpoles from northern British Columbia had a unique bifurcation of a posterior tooth row. This study highlights the diversity in intraspecific tadpole oral morphology and the interacting processes that drive it.
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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 | 0.000 |
| 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 | 0.000 |
| 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".