Implementing recovery of the red wolf-integrating research scientists and managers
Bibliographic record
Abstract
The United States Fish and Wildlife Service (USFWS) developed guidelines for the composition and role of endangered species recovery implementation teams, but few teams have been established and their success has not been evaluated. Using the recovery program of the red wolf (Canis rufus) as a model, we describe the genesis, function, and success of the Red Wolf Recovery Implementation Team (RWRIT) in helping guide the establishment of a viable red wolf population in eastern North Carolina. In operation since 1999, the RWRIT meets bi-annually to review USFWS progress and provide recommendations aimed at maximizing success of species recovery. The team is comprised of 8 research scientists from disciplines including population genetics, canid ecology, population ecology, veterinary medicine, and captive management. Representation from each of these disciplines is deemed necessary for proper evaluation of recovery progress and assessment of future needs. Meeting attendance by the USFWS field management team ensures both proper reporting of past progress and future implementation of management recommendations. Over time, RWRIT members have assumed specific assignments for data analyses, further contributing to the recovery effort. Through the combined efforts of the USFWS field team and the RWRIT, the threat of introgression of coyote (Canis latrans) genes into the red wolf population has been substantially curtailed within the recovery area, and red wolf numbers and range have increased. The RWRIT serves as an example of a recovery implementation team that is successfully incorporating the principles of adaptive management and whose template could be adapted to other endangered species.
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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 | 0.028 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".