Snow Tracking and Trapping Harvest as Reliable Sources for Inferring Abundance: A 9-year Comparison
Bibliographic record
Abstract
Trapping harvest and snow tracking are frequently used to infer population dynamics, yet there have been few evaluations of these indices. We developed population indices for Martes americana (American Marten), Mustela spp. (weasels), and Tamiasciurus hudsonicus (American Red Squirrel) from 9 years of snow-tracking data in eastern Canada. We employed mean track counts per unit effort as population indices derived from a generalized linear model (GLM) of track counts as a function of year and covariates including forest age. Mean track counts were significantly correlated with American Marten and weasel pelt sales and year effects in GLM were correlated with American Red Squirrel and weasel pelt sales. The results of both methods are in agreement; therefore they are likely valid sources to infer population dynamics for these species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".