Discrimination of intra- and inter-specific forage quality by collared pikas (Ochotona collaris)
Bibliographic record
Abstract
The specific nutritional characteristics by which herbivores evaluate their foraging options are complex. We experimentally manipulated the crude protein and water content of two forage species ( Carex consimilis Holm. (= Carex bigelowii Torr. ex Schwein.) and Polygonum bistorta L.) commonly cached by collared pikas ( Ochotona collaris (Nelson, 1893)) to determine their influence on inter- and intra-specific forage selection. Preference data were collected for 27 pikas using cafeteria-style feeding trials in a randomized block design. A three-way interaction (species × protein × water) suggested that pikas made conditional forage selection decisions while caching these plants. The interaction was driven by greater selection for fresh rather than dried C. consimilis when both were not fertilized. Water content had no effect on the selection of either fertilized C. consimilis or fertilized P. bistorta. Overall, our results indicate that pikas made subtle decisions about their selection of vegetation during caching, based on variation in nitrogen and water content in addition to species-specific selection criteria. Further, our results imply that tests of foraging theory may need to consider intra-specific variation in forage characteristics, as well as inter-specific ranking of forage species.
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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 | 0.001 |
| 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".