Are lemming winter nest counts a good index of population density?
Bibliographic record
Abstract
Lemmings construct nests of grass and moss under the snow during winter, and counting these nests in spring is 1 method of obtaining an index of winter density and habitat use. We counted winter nests after snow melt on fixed grids on 5 areas scattered across the Canadian Arctic and compared these nest counts to population density estimated by mark-recapture on the same areas in spring and during the previous autumn. Collared lemmings were a common species in most areas, some sites had an abundance of brown lemmings, and only 2 sites had tundra voles. Winter nest counts were correlated with lemming densities estimated in the following spring (rs = 0.80, P < 0.001), but less well correlated with densities the previous autumn (rs = 0.55, P < 0.001). Winter nest counts can be used to predict spring lemming densities with a log-log regression that explains 64% of the observed variation. Winter nest counts are best treated as an approximate index and should not be used when precise, quantitative lemming density estimates are required. Nest counts also can be used to provide general information about habitat-use in winter, predation rates by weasels, and the extent of winter breeding.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 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".