What's in a mate? Social pairing decisions and spatial cognitive ability in food-caching mountain chickadees
Bibliographic record
Abstract
While researchers have investigated mating decisions for decades, gaps remain in our understanding of how behaviour influences social mate choice. We compared spatial cognitive performance and food caching propensity within social pairs of mountain chickadees inhabiting differentially harsh winter climates to understand how these measures contribute to social mate choice. Chickadees rely on specialized spatial cognitive abilities to recover food stores and survive harsh winters, and females can discriminate among males with varying spatial cognition. Because spatial cognition and caching propensity are critical for survival and likely heritable, pairing with a mate with such enhanced traits may provide indirect benefits to offspring. Comparing the behaviour of social mates, we found that spatial cognitive performance approached a significant correlation within pairs at low, but not at high elevation. We found no correlation within pairs in spatial reversal cognitive performance at either elevation; however, females at high elevation tended to perform better than their social mates. Finally, we found that caching propensity correlated within pairs at low, while males cached significantly more food than their social mates at high elevations. These results suggest that cognition and caching propensity may influence social mating decisions, but only in certain environments and for some aspects of cognition.
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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 | 0.001 | 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".