Red Lake Wolverine Project Field Report 2022
Bibliographic record
Abstract
WCS) Canada began the Red Lake wolverine project in spring 2018.This report provides a summary of data we collected through five field seasons and highlights from our 2022 field season.Our reports from previous field seasons are available on the WCS Canada website (https://www.wcscanada.org/Publications/Conservation-Reports.aspx).The Ontario government listed wolverines as threatened under the Endangered Species Act, 2007.Scientists drafted a Wolverine Recovery Strategy (2013) in response to their listing and the Ministry of Natural Resources and Forestry (MNRF) created a Government Response Statement (2016) that prioritized research and conservation actions for wolverines.We designed our field project to address six action items in the Government Response Statement: Produce data that quantifies wolverine abundance in Red Lake and across the Ontario shield (Action #1). Determine wolverine habitat use and den-site selection in response to industrial disturbance (Actions #2 and #4). Develop best-management practices for human activities in wolverine habitats (Actions #7 and #13). Promote public awareness of wolverines through targeted communication products (Action #14).The focus of our fieldwork has been deploying GPS collars on wolverines and tracking them to document den-site use, habitat use, foraging, and mortality sources.We used a grid of live traps and run poles to estimate wolverine abundance.For additional detail on our methods, please see our 2020-2021 report.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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".