Wolverines and declining snowpack: response to comments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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 it