Author Correction: Patterns and processes of pathogen exposure in gray wolves across North America
Bibliographic record
Abstract
“We thank the wildlife professionals that contributed wolf sera, serology data, and metadata to this project: Erin Stahler, L. David Mech, Dean Beyer, Erin Largent, Susan Dicks, John Oakleaf, the Mexican Wolf Project, Lindsey Dreese, Brent Patterson, Emily Almberg, Montana Fish Wildlife and Parks, Shari Willmott, and the countless unlisted biologists that collected these samples and data through the decades. Financial support includes: E.E.B. and P.J.H. endowment from Verne Willaman; E.E.B. and P.C.C. U.S. Geological Survey (Grant G17AC00427); D.W.S., D.R.S., and D.R.M. NSF LTREB grant DEB–1245373 and many donors to Yellowstone Forever, especially Annie and Bob Graham and Valerie Gates; M.H. Parks Canada and NSF LTREB award 1556248; G.R. Pittman-Robertson Federal Aid in Wildlife Restoration Program and the State of Alaska general funds; K.B. Federal Aid in Support of Wildlife Restoration and Alaska Dept. of Fish and Game; M.A. and H.S. British Columbia Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Habitat Conservation Trust Foundation, Forest Enhancement Society of British Columbia; D.R.M and M.A. Polar Continental Shelf Project and National Geographic Society; M.L.J.G. the Office of the Director, National Institutes of Health under award number NIH T32OD010993; T.W. Ontario Ministry of Natural Resources and Forestry; B.L.B. National Park Service; A.K. GNWT Environmental Stewardship Fund. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.”
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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 | 0.004 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.032 |
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".