Fire-mediated patterns of habitat use by male moose (<i>Alces</i> <i>alces</i>) in Alaska
Bibliographic record
Abstract
Fire severity is an important control over regeneration of deciduous species and can influence the overall quality of habitat for herbivores, such as moose (Alces alces (Linnaeus, 1758)), but the relationships between availability and duration of biomass production and moose habitat use are largely unknown. We evaluate the relative influence of a regenerating burn, paying particular attention to fire severity, on winter forage production and duration, offtake, nutritional quality, and seasonal moose habitat use. We used data from 14 GPS collared male moose in the 20-year-old Hajdukovich Creek Burn (HCB) in interior Alaska, USA, to generate seasonal dynamic Brownian bridge movement models. Within HCB, moose selected for low-severity sites more than high- and moderate-severity sites during the winter. Over the past decade, willow (species of the genus Salix L.) biomass production in low-severity sites has doubled and is likely influencing winter habitat selection patterns. In summer, moose selected for high-severity sites where there is a more abundant understory layer (e.g., stem densities) providing both forage and cover. The initial pulse of biomass production in high-severity sites, as well as the delay in growth and maturation of vegetation in low-severity sites, indicate that differing distributions of wildfire severity can create a dynamic mosaic of habitat patches that may extend the value of burns over time for moose.
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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".