Data from: The relationship between plumage colouration, problem-solving and learning performance in great tits Parus major
Bibliographic record
Abstract
Recent studies suggest that individuals with better problem-solving and/or learning performance have greater reproductive success, and that individuals may thus benefit from choosing mates based on these performances. However, directly assessing these performances in candidate mates could be difficult. Instead, the use of indirect cues related to problem-solving and/or learning performance, such as condition-dependent phenotypic traits, might be favored. We investigated whether problem-solving and learning performance on a novel non-foraging task correlated with sexually selected plumage colouration in a natural population of great tits Parus major. We found that males successful in solving the task had darker blue-black crowns than non-solvers, and that males solving the task more rapidly over multiple attempts (i.e. learners) exhibited blue-black crowns with higher UV chroma and shorter-wavelength hues than non-learners. In contrast, we found no link between behavioural performance on the task and the yellow breast colouration in either sex. Our findings suggest that blue-black crown colouration could serve as a signal of problem-solving and learning performance in wild great tit males. Further research remains necessary to determine whether different sexually selected traits are used to signal cognitive performance for mate choice, either directly (i.e. cognitive performance influencing individual's health and ornamentation through diet for example) or indirectly (i.e. due to a correlation with a third factor such as individual quality or condition).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.041 | 0.014 |
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".