Using an artificial neural network to classify black-capped chickadee (<i>Poecile atricapillus</i>) call note types
Bibliographic record
Abstract
The "chick-a-dee" call of the black-capped chickadee (Poecile atricapillus) contains four note types, A, B, C, and D that have important functional roles. This provides strong motivation for studying the classification of acoustic components of the call into different note types. In this paper, the spectrograms from a sample of A, B, and C notes (370 in total) were each described as a set of 9 summary features. An artificial neural network was trained to identify note type on the basis of these features, and was able to obtain better than 98% accuracy. An internal analysis of this network revealed a distributed code in which different hidden units generated high activities to different subsets of notes. By combining these different sensitivities, the network could discriminate all three types of notes. The performance of this network was compared to a discriminant analysis of the same data. This analysis also achieved a high level of performance (95%). A comparison between the two approaches revealed some striking similarities, but also some intriguing differences. These results are discussed in terms of two related issues: developing a research tool for note classification, and developing a theory of how birds themselves might classify notes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".