Individual Variation in Behavior Among Male Blue Grouse
Bibliographic record
Abstract
Individual variation in behavior among male Blue Grouse Martin K, McNlcho!! Ornithologists, and banders in particular, studying populations of birds in the wild are frequently impressed by individual variations in behavior, with certain individuals consistently acting in a manner different from other conspecifics of the same sex and age.As noted by Thomson {1964}, such variability might be expected to be even greater than morphological variation, as behavior may be determined by genetics, experience, or both.Indeed, variation to an individual level is documented well for some behavioral components, such as ca]] notes and song {e.g.Falls 1969; Beer 1970; Falls and McNicho]] in press}.Yet, few behavior studies stress such variability, except in relation to such practica] problems as trapability {e.g.Doan 1976; Hamerstrom and Hamerstrom 1977}, band removal affecting population estimates, longevity data and other information based on banding returns {Wiseman 1977}, and persistent difficulty of feeding some individuals in captivity {Berry 1975}.Studies, such as those by Kennard {1894}, Lockley {1940}, 'and Partridge {1976}, emphasizing individual variation in behavior, are relatively few.The current development of fast and efficient methods of analyzing large volumes of numerical data make the quantification of behavior both valuable and tempting.Yet behavior patterns are rarely so stereotyped as to allow ready tabulations without loss of qualitative information.Knowledge of individual variation, however, does allow quantification of behavior with considerable confidence.For example, 13 male Blue Grouse (Dendragapus obscurus) on Vancouver Island,
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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 | 0.000 |
| 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".