Feature weighting in “chick-a-dee” call notes of<i>Poecile atricapillus</i>
Bibliographic record
Abstract
Artificial neural networks were trained to discriminate between different note types from the black-capped chickadee (Poecile atricapillus) "chick-a-dee" call. Each individual note was represented as a vector of summary features taken from note spectrograms and networks were trained to respond to exemplar notes of one type and to fail to respond to exemplar notes of another type. Following initial network training, the network was presented novel notes in which individual acoustic features had been modified. The strength of the response of the network to each novel and shifted note was recorded. When network responses were plotted as a function of the degree of acoustic feature modification and training context, it became clear that modifications of some acoustic features had significant effects on network responses, while others did not. Moreover, the training context of the network also played a role in the responses of networks to manipulated test notes. The implications of using artificial neural networks to generate testable hypotheses for animal research and the role of context are discussed.
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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 | 0.000 | 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) | 0.000 | 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".