Efficacy and ethics of intensive predator management to save endangered caribou
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Lethal population control has a history of application to wildlife management and conservation. There is debate about the efficacy of the practice, but more controversial is the ethical justification and methods of killing one species in favor of another. This is the situation facing the conservation of woodland caribou ( Rangifer tarandus caribou ) in Canada. Across multiple jurisdictions, large numbers of wolves ( Canis lupus ), and to a lesser extent bears ( Ursus americanus ) and coyotes ( C. latrans ), are killed through trapping, poisoning or aerial shooting to halt or reverse continued declines of woodland caribou. While there is evidence to support the effectiveness of predator management as a stop‐gap solution, questions remain about the extent to which this activity can make a meaningful contribution to long‐term recovery. Also, there are myriad ethical objections to the lethal removal of predators, even if that activity is in the name of conservation. Debates about predator management, just one of numerous invasive actions for maintaining caribou, are made even more complex by the conflation of ethics and efficacy. Ultimately, long‐term solutions for the recovery of caribou require governments to stop delaying difficult decisions that address the real causes of population decline, habitat change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it