Effect of weather during development on cranial morphometrics of American marten (Martes americana)
Bibliographic record
Abstract
Globally, climate change is affecting species in a myriad of ways. Rapid morphological change has been proposed to be a consequence of climate change, but evidence is minimal. Furthermore, any such rapid morphological change is expected to be a phenotypic response rather than evolutionary. Adaptive, neutral, and non-adaptive phenotypic plasticity in the form of reaction norms and developmental noise such as fluctuating asymmetry can provide insights into a population’s ability to adapt to increased variability and extremes in weather, which are more common due to climate change. Using geometric morphometrics, I explored patterns of covariation between morphological variation in a population of American marten (Martes americana) near Nordegg, Alberta, and variation in weather metrics during periods when young are growing during prenatal (February–April) and postnatal (May–July) development. Analysis of variation in cranial morphology revealed significant covariation between the symmetric component of morphological variation and weather metrics during early postnatal development. I did not find significant covariation between the asymmetric component of morphological variation (fluctuating asymmetry) and weather during development. My findings are congruent with other studies, and point to both direct and indirect effects of “climate-induced” weather variation, including the potential of a feeding ecology mechanism as an explanation for the covariation.
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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 | 0.000 | 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".