Gray Wolf (<i>Canis lupus</i>) recolonization failure: a Minnesota case study
Bibliographic record
Abstract
During the past few decades, Gray Wolves (Canis lupus) have recolonized many areas in the United States and Europe. In many other cases, however, although dispersing wolves reached areas with adequate prey, a population failed to recolonize. Herein, we provide a case study detailing how a wolf pack attempted for three years to recolonize an area 55 km from a long-established population and within 25 km of Minneapolis and St. Paul, Minnesota, but failed. The pack produced three litters of pups and at one time included 11–19 members, but it preyed on livestock and dogs and, consequently, was lethally removed. The history of this pack’s attempt to recolonize an area long devoid of wolves exemplifies the issues that have prevented earlier recolonizations in non-wild lands in Minnesota and elsewhere and that promise to do so well into the future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".