Spatial variation in mink and muskrat interactions in Canada
Bibliographic record
Abstract
We investigated the spatial attributes of mink ( Mustela vison ) and muskrat ( Ondatra zibethicus ) interactions in Canada using 160 geographically paired historic time series of mink ( n =80) and muskrat ( n =80) harvest data obtained from Hudson's Bay Co. Archives. All series were 25 years in length (1925–1949) and were distributed primarily throughout five ecozones. We used autoregressive models and cross‐correlation analysis to characterize the interactions between mink and muskrat. Model selection results did not differ among ecozones, and indicated that a predator‐prey autoregressive model incorporating a delayed density‐dependent term best described both the mink and muskrat harvest time series. Subsequent analysis of autoregressive coefficients and estimated lags indicated that mink and muskrat interactions vary throughout Canada. In western Canada, the trophic interactions appear to be strong, and mink population cycles lag behind muskrats 2–3 years. In central Canada, mink harvests lagged behind muskrats 1 year, and mink and muskrat interactions in central Canada, with the exception of the Hudson Plains ecozone, were intermediate. In eastern Canada, the trophic interactions appeared weakest, and there were no distinct time lags between mink and muskrat. Stronger interactions in western Canada may be a result of decreased prey diversity, forcing mink to specialize more on muskrats, whereas comparatively stronger perturbations stemming from other trophic interactions may alter the estimated interaction between mink and muskrat in eastern Canada.
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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) | 0.001 | 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 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".