Seasonal variation in the foraging activity of desert argali (<i>Ovis ammon</i>) in Mongolia
Bibliographic record
Abstract
Debate remains whether energy maximization or time minimization strategies best explain foraging in ungulates. It has also been hypothesized that the capacity of an animal to dissipate body heat regulates animal activity. We investigated these hypotheses while measuring the daily activity of desert argali (Ovis ammon (Linnaeus,1758)) for 12 months and relating the activity pattern to environmental seasonality. We found significant seasonal cycles in argali activity, with the greatest proportion of daytime in winter spent foraging and the greatest proportion of daytime in summer spent bedding. Consistent with an energy maximization strategy, argali reduced all behaviors during the winter in favor of foraging, compensating for the increased energy demands of winter at a time of low forage quality. Consistent with a time minimization strategy, argali in summer significantly reduced foraging and spent more time bedding in shaded areas to avoid hyperthermia due to high ambient temperatures. Both optimal foraging and heat dissipation can be used to explain the observed foraging pattern. Foraging behavior in argali is best described by the extent to which the animals schedule their activities to meet their physiological demands, the way these demands are affected by environmental variables, and the time that is available to accomplish them.
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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 | 0.000 |
| 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 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".