Bibliographic record
Abstract
Both declining and increasing moose (Alces alces) populations have been reported across North America over the last decade. We surveyed all jurisdictions with extant moose populations to determine the extent of these population trends. In 2014–2015, the North American moose population was estimated at ~1,000,000 animals distributed in 30 jurisdictions, which is unchanged since the turn of the century. Populations occurred in 12 Canadian provinces or territories, and in at least 18 states. In the past 5 years, moose density is believed to be increasing in 9, relatively stable in 8, and declining in 11 jurisdictions; estimates of change were unavailable in 2 jurisdictions. In 2014–2015, an estimated 425,537 licensed moose hunters harvested 82,096 moose in 23 jurisdictions. Hunter numbers increased by 39,118, whereas total harvest remained virtually unchanged from a decade earlier. Harvests by Indigenous and subsistence users, although largely unquantified, are believed substantial and important to quantify in certain jurisdictions. A variety of active and passive harvest strategies used to manage moose are discussed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".