How will snow retention and shading from Arctic shrub expansion affect caribou food resources?
Bibliographic record
Abstract
Increased snow cover and attenuation of light due to shrub expansion can lead to changes in the quantity and nutritional quality of food resources for migratory caribou (Rangifer tarandus). To determine how vegetation is affected by snow accumulation and shade, we conducted an experiment that simulated the light and snow conditions created by increased shrub cover at Deception Bay (Nunavik, Canada), within the summer range of the Rivière-aux-Feuilles caribou herd. We estimated the foliar biomass of two major components of the caribou diet (Betula glandulosa and Carex spp.). We also harvested foliar tissue to conduct chemical composition analyses (nitrogen, fibre, total phenolics). Experimental light attenuation was found to increase the nitrogen concentrations in B. glandulosa and Carex spp. throughout the growing season. Phenolic concentration in B. glandulosa decreased in early summer but was otherwise consistent in response to light attenuation and increased snow cover. Increased snow cover combined with ambient light had a positive effect on the foliar biomass of B. glandulosa. Increased snow cover and shade caused by shrub densification may therefore increase forage quantity and quality for caribou. We suggest investigating the effects of shrub expansion on other components of the caribou diet, such as lichens and forbs.
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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 | 0.001 | 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 | 0.001 | 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".