Patuxent’s Long-Term Research on Wolves
Bibliographic record
Abstract
The gray wolf (Canis lupus) was one of the first species placed on the Endangered Species List in 1967. The Endangered Species Act of 1973 legally protected the wolf along with other listed species. Patuxent Wildlife Research Center (Patuxent) in Laurel, MD, began its Endangered Wildlife Program in 1966, and U.S. Fish and Wildlife Service (USFWS) biologist Ray Erickson was assigned to lead it. In 1973, I was transferred to the program from Region 3 of the USFWS, having been employed there since 1969 to study wolves in Minnesota. Endangered Species Act protection of the wolf fostered its quick population response, and wolf numbers began to increase in their reservoir in northeastern Minnesota and adjacent Canada and expand throughout northern Minnesota and eventually into Wisconsin and Michigan. In 2009, the number of wolves in Minnesota was approximately 3,000, and there were at least 1,500 in Wisconsin and Michigan. This chapter describes Patuxent’s wolf research, which continued into 1993 when Congress incorporated the USFWS’s Endangered Wildlife Research Program into the National Biological Survey (NBS). Eventually the NBS merged with the U.S. Geological Survey, and the long-term wolf research program was transferred to the Northern Prairie Wildlife Research Center. Through all the administrative changes, Patuxent’s wolf research project continued through the various agencies into the present (2016). The text that follows is modified from Mech (2009).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".