Bibliographic record
Abstract
Song variation has been studied extensively over the past 50 yr but almost entirely in oscine passerines. Although learning is an essential component of song development in most, if not all, oscines, there is no definitive evidence for song learning in suboscine passerines. This suggests that the patterns and extent of individual and geographic variation may differ between these groups as well. We examined individual variation in the “fee-bee-o” song in a population of Alder Flycatchers (Empidonax alnorum) in southwestern Alberta. Songs of individual males were recorded during the breeding season in 2001. We measured temporal and frequency variables of songs and conducted univariate and multivariate statistical analyses to characterize variation within and among individuals. There was little variation among the songs of an individual male during single recording sessions and across recordings made over the breeding season. All measured variables varied significantly more among than within individuals. Discriminant function analysis assigned 91–100% of songs to the correct individual. Therefore, there was sufficient variation among individual males to identify them statistically and, potentially, to permit individual discrimination by the birds.
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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 | 0.000 |
| 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 | 0.001 |
| 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".