Mapping potential core areas for lynx (<i>Lynx canadensis</i>) using pellet counts from snowshoe hares (<i>Lepus americanus</i>) and satellite imagery
Bibliographic record
Abstract
We used location data from radio-collared Canada lynx ( Lynx canadensis Kerr, 1792), pellet-count data from snowshoe hares ( Lepus americanus Erxleben, 1777), and cover-type data from satellite imagery to evaluate the relationship between the scale of habitat measurement and the potential for persistence of lynx in northeastern Minnesota, USA, at the southern extent of their range. We counted hare pellets at transects throughout northeastern Minnesota to index hare abundance in cover types. Pellet counts were highest in coniferous forest, regenerating–young forest, and shrubby grassland, and these cover types were greater inside lynx use areas than outside of them. Proportions of regenerating–young forest were greater at scales ≥5 km2. We used these results and satellite imagery to map potential lynx core areas. We predicted that 7%–20% of the study area was suitable for lynx. Areas that we predicted to be suitable for lynx corresponded with known core areas, including those withheld from analyses. To maintain habitat for lynx persistence, forest management should retain current levels of 10- to 30-year-old coniferous forest and include ≥5 km2 areas containing 40% of 10- to 30-year-old coniferous forest. Mapping of potential core areas would be improved if cover-type data from satellite imagery identified conifer regeneration.
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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 | 0.001 | 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".