Frequency-range discriminations and absolute pitch in black-capped chickadees (Poecile atricapillus), mountain chickadees (Poecile gambeli), and zebra finches (Taeniopygia guttata).
Bibliographic record
Abstract
The acoustic frequency ranges in birdsongs provide important absolute pitch cues for the recognition of conspecifics. Black-capped chickadees (Poecile atricapillus), mountain chickadees (Poecile gambeli), and zebra finches (Taeniopygia guttata) were trained to sort tones contiguous in frequency into 8 ranges on the basis of associations between response to the tones in each range and reward. All 3 species acquired accurate frequency-range discriminations, but zebra finches acquired the discrimination in fewer trials and to a higher standard than black-capped or mountain chickadees, which did not differ appreciably in the discrimination. Chickadees' relatively poorer accuracy was traced to poorer discrimination of tones in the higher frequency ranges. During transfer tests, the discrimination generalized to novel tones when the training tones were included, but not when they were omitted.
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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".