Bibliographic record
Abstract
Our lab has been studying songbird communication from an integrative perspective for over 13 years. A significant part of this program involves conducting bioacoustic analyses of vocalizations that are critical to survival. Initially, black-capped chickadees and their chick-a-deecall were our main research focus. In more recent years, we have turned our attention back to the fee-bee song, intensely studied by Weisman, Ratcliffe, and colleagues. Our re-examination of the fee-bee song revealed several, previously unreported features of this seemingly acoustically simple vocalization. We showed that songs contain regionalized cues for dominance status, and that females respond differentially to dominant song playback irrespective of their geographic origin. Moreover, females themselves produce a fee-bee song not previously reported in the literature. Female song is acoustically distinct from male song, and we have used operant conditioning experiments to identify features that can be used to identify sex of the singer. Finally, we have begun to explore neural response to different vocalizations, including song, and found that responses vary by singer, listener, and vocalization type. Our current efforts are aimed at unraveling the acoustic basis of communication in this nearly ubiquitous North American species.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".