Climate change and caribou: effects of summer weather on forage
Bibliographic record
Abstract
In 1989, the Chisana caribou (Rangifer tarandus) herd in the northern Wrangell Mountains, Alaska, U.S.A., declined substantially in population size and productivity. Grasses, sedges, forbs, and willows (Salix spp.) are critical components of the diet of caribou in spring and summer, and the abundance and quality of forage are influenced by climate. To evaluate effects of climatic variation on caribou forage we conducted a field experiment in subarctic tundra where light, air temperature, and precipitation were manipulated. We used a plastic tarpaulin to increase air temperature and decrease precipitation. We also decreased light intensity with a shade cloth and increased precipitation by adding water to determine climatic effects on nutrient content and biomass of forage for caribou during the summers of 1994 and 1995. The most notable treatment effect on aboveground biomass was that shading resulted in higher nitrogen concentrations in all plant growth forms. In addition, shading consistently reduced biomass in forbs during mid and late season. Water treatment increased total plant biomass in the greenhouse plots during midseason in 1994 and in late spring in 1995. Water treatment also increased late-season biomass in control plots during 1994 but had no effect on biomass in shaded plots in either 1994 or 1995. A decline in nitrogen concentration in plants occurred throughout summer, a pattern that was not evident in in vitro dry matter digestibility. Climate variation and subsequent effects on forage plants have the potential to influence the population dynamics of caribou through effects on their food supply.
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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".