Time spent grooming is a labile trait in waterfowl that varies with foraging ecology but not sociality
Bibliographic record
Abstract
Grooming serves many purposes in animals including removing ectoparasites and thermoregulation. Time spent grooming varies to a large extent among species and often represents a large component of the time budget. Nevertheless, few comparative analyses have been carried to determine the ecological correlates of grooming in animals. Time spent grooming was analysed in a sample of 78 species of waterfowl using a phylogenetic framework and the raw data. The phylogenetic analysis revealed no relationship between time spent grooming and ecological factors such as body mass, latitude and foraging group size. The raw data analysis indicated that time spent grooming varied mostly within rather than among species, suggesting that the phylogenetic signal may be weak for this trait in waterfowl. In a sample of 153 time budgets, time spent grooming increased with body mass, decreased with latitude, was smaller in browsing species, in migratory species and in species with no access to agricultural food. However, time spent grooming was not related to sociality in the breeding and non-breeding seasons. The effect of latitude, agricultural food and foraging technique is probably related to time constraints, which are relaxed at lower latitudes, when extra food is available and when animals do not browse allowing more time to grooming. However, ectoparasite burden is also known to vary in relation to these ecological factors and disentangling the relative contribution of ectoparasite burden and time constraints remains a challenge.
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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 | 0.001 | 0.001 |
| 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".