Data from: Population genetic structure and its implications for adaptive variation in memory and the hippocampus on a continental scale in food-caching black-capped chickadees
Bibliographic record
Abstract
Abstract Food-caching birds rely on stored food to survive the winter and spatial memory has been shown to be critical in successful cache recovery. Both spatial memory and the hippocampus, an area of the brain involved in spatial memory, exhibit significant geographic variation linked to climate-based environmental harshness and the potential reliance on food caches for survival. Such geographic variation has been suggested to have a heritable basis associated with differential selection. Here, we ask whether population genetic differentiation and potential isolation among multiple populations of food-caching black-capped chickadees is associated with differences in memory and hippocampal morphology by exploring population genetic structure within and among groups of populations that are divergent to different degrees in hippocampal morphology. Using mitochondrial DNA and 583 AFLP loci, we found that population divergence in hippocampal morphology is not significantly associated with neutral genetic divergence or geographic distance, but instead is significantly associated with differences in winter climate. These results are consistent with variation in a history of natural selection on memory and hippocampal morphology that creates and maintains differences in these traits regardless of population genetic structure and likely associated gene flow.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".