Vocal Communication in Androgynous Territorial Defense by Migratory Birds
Bibliographic record
Abstract
Many temperate zone breeding birds spend their non-breeding period in the tropics where they defend individual territories. Unlike tropical birds that use song for breeding and non-breeding territorial defense, vocal defense differs strikingly between breeding and non-breeding territories in migrants. Song, restricted to males, is used during defense of breeding territories but callnotes are used to defend non-breeding territories. To explain why callnotes and not songs predominate in the non-breeding context, we present an empirical model based upon predictions from motivational/structural rules, ranging theory and latitudinal differences in extra-pair mating systems. Due to sex role divergence during breeding that favors singing in males, but not females, females may be unable to range male song. Ranging requires a signal to be in both the sender and receiver’s repertoire to allow the distance between them to be assessed (ranged). Non-breeding territories of migrants are defended by both males and females as exclusive individual (androgynous) territories. Ranging Theory predicts callnotes, being shared by both males and females can, in turn, be ranged by both so are effective in androgynous territoriality. Where songs are used for non-breeding territorial defense both sexes sing, supporting the evolutionary significance of shared vocalizations in androgynous territorial defense.
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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 | 0.000 | 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".