Effect of browse on post-ingestive energy loss in an Arctic ruminant: implications for muskoxen (<i>Ovibos moschatus</i>) in relation to vegetation change
Bibliographic record
Abstract
Shrubs are predicted to dominate tundra with warmer temperatures at northern latitudes. We tested the null hypothesis that addition of browse to a graminoid diet would not alter post-ingestive energy loss in muskoxen ( Ovibos moschatus (Zimmermann, 1780)). Energy losses over 8 h following feeding were used to test our hypothesis. Willows ( Salix alaxensis (Anderss.) Coville, Salix planifolia ssp. pulchra (Cham.) Argus) and birch ( Betula nana L.) (twigs in winter, leaves in summer) were separately mixed at graded levels (0%, 20%, 40%, 60%, and 80%) with chopped hay ( Bromus inermis Leyss.) and fed as single meals to muskoxen. Meals containing ≥60% browse were often partially or completely rejected. Meals containing 20%–60% woody or leafy S. alaxensis or S. p. pulchra resulted in higher energy expenditure than meals of 100% hay. Meals containing 20%–60% woody B. nana tended to decrease energy expenditure relative to 100% hay, while 20%–60% leafy B. nana was similar to 100% hay. We conclude there is an energy cost associated with consuming browse. This cost varies by browse species and type. Since muskoxen tolerated up to 40% browse in the diet, this cost may be within their ecological tolerance. This tolerance has important implications under global warming scenarios.
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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) | 0.001 | 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".