Quantifying Densities of Snowshoe Hares in Maine Using Pellet Plots
Bibliographic record
Abstract
Population densities are costly and logistically infeasible to measure directly across the broad geographic ranges of many wildlife species. For snowshoe hares (Lepus americanus), a keystone species in northern boreal forest, indirect approaches for estimating population densities based on fecal pellet densities have been developed for boreal forest in northwestern Canada and in conifer-dominated montane forest in Idaho. Previous authors cautioned against applying these estimates across the geographic range of hares without further testing, but no published relationships for estimating densities from pellet counts are available for the mixed conifer—deciduous forests of the southeastern portion of the hare's range in North America. Thus, we estimated pellet and hare densities in 12 forested stands, 4 sampled twice during 1981–1983 and 8 sampled once during 2000–2002. Mark—recapture estimated densities of snowshoe hares from eastern and western Maine during 1981–1983 were linearly related to pellet densities to 15,000 pellets/ha/month (1.5 hares/ha) (Adj. r2 = 0.87, n = 8, P < 0.001) and accurately predicted densities of hares ( = 7% greater) estimates than actually observed at higher pellet densities sampled in northern Maine during 2000–2002.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".