Does seasonal variation in forage quality influence the potential for resource competition between muskoxen and Peary caribou on Banks Island?
Bibliographic record
Abstract
Inter- and intra-annual variation in forage quality may influence population dynamics of Peary caribou and muskoxen on Banks Island. From 1993 to 1998 we collected 300 composite samples of sedge (Carex aquatilis and Carex spp.), willow (Salix arctica), legume (Oxytropis spp. and Astragalus spp.), and avens (Dryas integrifolia). Samples were collected in mid-June (start of the growing season), mid-July (peak of the growing season), mid-late August (senescence), and early (November), mid- (February), and late- (April/May) winter. We analysed forages for percent digestibility (in vitro acid-pepsin dry matter digestibility), crude protein (CP), fibre, lignin, and energy content. There was significant inter-annual variation in levels of lignin, fibre, and energy, and significant intra-annual (seasonal) variation for all quality measures and forages, which reflected the strong difference in quality between summer and winter. We discuss the relationship between forage quality and seasonal diet composition of Peary caribou and muskoxen, and the potential implications for the reduced Peary caribou and high muskoxen populations.
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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".