Bibliographic record
Abstract
The populations of coastal British Columbia’s Roosevelt Elk (Cervus canadensis roosevelti) decreased greatly from first European settlement to the start of the 1920s. This has been credited to pressures from hunting and decreased habitat, both of which were caused by humans. The remainder of the 20th century was categorized by a period of strict hunting regulations; facilitating a, while slow, re-growth in numbers. Beginning in the latter part of the 20th century, “elk management projects” were also initiated. The goal of these projects was the translocation of elk into areas where their numbers were threatened. During the past decade these projects have intensified and transitioned into translocating elk into drainages where it has been determined that they have been extirpated from. This paper does not determine any negative or positive factors within these projects. However it does investigate potential question’s that should be asked about such projects. These questions are shaped in response to the examination of three case studies: Newfoundland’s introduction of Moose (Alces alces americana), Haida Gwaii’s introduction of Sitka black-tailed deer (Odocoileus hemionus sitkensis), and New Zealand’s introduction of Red Deer (Cervus elaphus).
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".