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 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) | 0.001 | 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".