Foraging behaviour of snowshoe hares (<i>Lepus americanus</i>) in conifer forests regenerating after fire
Bibliographic record
Abstract
Wildfires in conifer forests create patchy, heterogeneous landscapes. For many animal species, this post-fire variability means having to navigate quite different habitat patches to locate adequate cover and food. For snowshoe hares (Lepus americanus Erxleben, 1777), post-fire landscapes could include risky open patches, as well as dense regenerating stands rich in food and cover. We analyzed snowshoe hare tortuosity, speed of movement, and amount of browse along winter foraging pathways in unburned mature forest and in dense regenerating stands or open areas with sparse regeneration 12–13 years after the Okanagan Mountain Park fire (>25 000 ha near Kelowna, British Columbia, Canada) to determine whether hares change foraging behaviour in relation to cover type. Hares moved the fastest and browsed the least in open habitats. Hares browsed most often in areas where sapling regeneration was dense; their main forage was lodgepole pine (Pinus contorta Douglas ex Loudon). No differences were found in pathway tortuosity in relation to cover type (open, regenerating, or mature patches). When hares moved slower along foraging pathways, they also moved slightly more tortuously and ate more. These results suggest that hares prefer post-fire areas with dense tree regeneration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".