Stand-level Attributes of Snowshoe Hare (<em>Lepus americanus</em>) Habitat in a Post-Fire Trembling Aspen (<em>Populus tremuloides</em>) Chronosequence in Central Yukon
Bibliographic record
Abstract
Large-scale fires are anticipated to increase in frequency in the boreal forest under global climate warming scenarios. To understand concomitant responses by wildlife to fire-induced habitat changes, fecal pellet counts were used to assess Snowshoe Hare (Lepus americanus) use of four age-classes of boreal forest after fire in central Yukon, Canada. Use of stands across a chronosequence of 8–177 years was bimodal. Post-fire Trembling Aspen (Populus tremuloides) stands ≤20 years old had greater densities of Snowshoe Hare pellets (median 156 pellets/dam2) than Trembling Aspen stands 21–70 years old, mixedwood stands 71–120 years old (<17 pellets/dam2), or Western White Spruce (Picea albertiana) -dominated stands 121–170 years old (71 pellets/dam2). Forty stand-level compositional and structural variables were assessed as possible predictors of Snowshoe Hare pellet densities. Multidimensional scaling was used to identify variables (n = 10) that were most strongly related to pellet densities and was followed by multiple regression. Canopy cover of Trembling Aspen <50 cm tall and Western White Spruce ≤1 m tall, and deadfall depth, in combination, were the best estimators of Snowshoe Hare pellet densities among stands in the chronosequence (P <0.001, 64.5% variance explained). Although Trembling Aspen <50 cm tall explained the most variance, its canopy cover did not exceed 10%. More Trembling Aspen cover <50 cm tall and greater deadfall depths within the chronosequence were associated with stands ≤20 years old. Peak Snowshoe Hare use occurred in early (≤20 years old) rather than mid-successional (21–120 years old) stands, contrary to use patterns reported elsewhere. The lack of tall understory shrubs likely limited the use of mid-successional stands.
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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".