Community algorithms reveal song type themes in Adelaide’s warbler song type sequence networks
Bibliographic record
Abstract
Some New World Warblers (Family: Parulidae) sing with immediate variety during the dawn chorus and eventual variety during daytime song. We used network analysis, including a community clustering algorithm, to further characterise song type sequences during the dawn chorus and daytime song in male Adelaide’s warblers (Setophaga adelaidae). Networks had longer path lengths than expected by chance, indicating that song type transitions were constrained. Community analysis revealed the presence of ‘themes’, or groups of song types that individuals deliver in close sequential proximity. To our knowledge, this is the first report of song type themes in Parulidae. Males did not cycle through their repertoires efficiently, as would be expected if large repertoires were attractive to females. Themes might emerge from the learning process or from interactions with neighbours. Themes may function to improve vocal performance or organise song types with similar functions. Relative to dawn chorus networks, daytime song networks had longer paths and stronger community structure. We hypothesise that song type networks are more structured during daytime song because song delivery is optimised for vocal warm-up during the dawn chorus, males frequently switch among intended receivers during the dawn chorus, or females prefer extended themes during daytime song.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".