Estimating snowshoe hare population density from pellet plots: a further evaluation
Bibliographic record
Abstract
We counted fecal pellets of snowshoe hares (Lepus americanus) once a year in 10 areas in the southwestern Yukon from 1987 to 1996. Pellets in eighty 0.155-m 2 quadrats were counted and cleared each June on all areas, and we correlated these counts with estimates of absolute hare density obtained by intensive markrecapture methods in the same areas. There is a strong relationship between pellet counts and population density (r = 0.76), and we present a predictive loglog regression to quantify this relationship, which improves on our previously published 1987 regression, particularly at low hare densities. The precision of density estimates can be improved most easily by increasing the number of sets of quadrats in an area (one set = 80 plots), rather than increasing the number of plots counted within one set. The most important question remaining concerns the generality of this relationship for snowshoe hares living in other habitats in the eastern and southern portions of their geographic range.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".