Wolverines and declining snowpack: response to comments
Bibliographic record
Abstract
Abstract The critiques by DeVink et al. () and McKelvey et al. () are flawed for several reasons. We show here that, contrary to what DeVink et al. claim, the influence of annual pelt price on wolverine harvest returns is essentially negligible. DeVink et al. also suggest that our results show the influence of snowpack on trapper success, rather than on actual wolverine population dynamics. This is unlikely, since most of the snowpack terms in our models are at 1‐ or 2‐year time lags, whereas the impact of snow conditions on trapper success can only manifest in the current year. Both DeVink et al. and McKelvey et al. claim that wolverine populations across Canada are actually increasing, but provide no quantitative data to support this claim. Both sets of authors present alternative explanations for the declines in harvest returns, but none of those explanations are mutually exclusive with our own, and none can explain the significance of time‐lagged snowpack on annual harvest returns. McKelvey et al.'s claim that our results represent a spurious correlation, as well as other points that they raise, suggests either a superficial understanding or deliberate misrepresentation of our methods and can simply reflect their underlying philosophical biases.
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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 | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.028 | 0.021 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".