Acoustic Mechanisms of a Species-Based Discrimination of the chick-a-dee Call in Sympatric Black-Capped (Poecile atricapillus) and Mountain Chickadees (P. gambeli)
Bibliographic record
Abstract
Previous perceptual research with black-capped and mountain chickadees has demonstrated that these species treat each other's namesake chick-a-dee calls as belonging to separate, open-ended categories. Further, the terminal dee portion of the call has been implicated as the most prominent species marker. However, statistical classification using acoustic summary features suggests that all note-types contained within the chick-a-dee call should be sufficient for species classification. The current study seeks to better understand the note-type based mechanisms underlying species-based classification of the chick-a-dee call by black-capped and mountain chickadees. In two, complementary, operant discrimination experiments, both species were trained to discriminate the species of the signaler using either entire chick-a-dee calls, or individual note-types from chick-a-dee calls. In agreement with previous perceptual work we find that the D note had significant stimulus control over species-based discrimination. However, in line with statistical classifications, we find that all note-types carry species information. We discuss reasons why the most easily discriminated note-types are likely candidates to carry species-based cues.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".