Variation in pika ( <i>Ochotona collaris, O. princeps</i> ) vocalizations within and between populations
Bibliographic record
Abstract
Understanding geographic call variation can resolve evolutionary and behavioural questions, yet the factors influencing divergent acoustic signals remain poorly understood in mammals. We explored call variation between collared pikas in Yukon and Alaska and American pikas in Alberta, and between individuals within a population of collared pikas. Classification trees were used to determine the extent of call divergence between populations and the elements of calls driving these differences. Pika populations had significant differences in call structure, and individual pikas were classified to their correct populations with up to 94% accuracy. To investigate possible mechanisms responsible for interspecific variability, we tested the acoustic adaptation hypothesis by using a playback experiment to explore whether American and collared pika calls transmit with less degradation across their own species' habitat than the habitat of their congener. We found no support for the acoustic adaptation hypothesis. Geographic call variation in these two species of pikas likely reflects genetic divergence, and may be a result of separate evolutionary histories. We calculated the potential for individual coding for both time and frequency measurements of calls. High frequency harmonics showed greater between‐ than within‐individual variation, and may act as sources of information regarding individual identity.
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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".