Statistical classification of black-capped (Poecile atricapillus) and mountain chickadee (Poecile gambeli) call notes.
Bibliographic record
Abstract
Both black-capped (Poecile atricapillus) and mountain chickadees (Poecile gambeli) produce a chick-a-dee call that consists of several distinct note types. In some regions, these 2 species live sympatrically, and it has been shown that 1 species will respond weakly to songs of the other. This suggests that chickadee song, and potentially other of their vocalizations, contains species-specific information. We tested the possibility that call notes were acoustically sufficient for species identification. Black-capped and mountain non-D notes were summarized as a set of 9 features and then analyzed by linear discriminant analysis. Linear discriminant analysis was able to use these notes to identify species with 100% accuracy. We repeated this approach, but with black-capped and mountain D notes that were summarized as a set of 4 features. Linear discriminant analysis was able to use these notes to identify species with 94% accuracy. This demonstrates that any of the note types in these chickadee calls possesses sufficient information for species classification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".