Bibliographic record
Abstract
Moose (Alces alces) have become one of the popular big game species in Russia, whereas only decades ago, low moose numbers precluded hunting. The rapid increase in moose numbers is primarily the result of forest harvest practices and intensive moose management policies. At present, according to the Russia Statistical Committee, the moose population is stable at around 700,000 animals. Use of intensive biotechnical moose management measures such as ashtree cutting, feeding of wood waste, and rock salt, combined with large scale protective measures have also favored this population increase. However, data collected by the All–Union Research Institute show that moose density in some regions has exceeded the carrying capacity of game preserves for many years. This is the result of poor moose population estimates and low harvest rates. As a result of low harvest intensity, and in the absence of management actions aimed at increasing the carrying capacity on moose preserves, forest resources and habitat quality have been damaged in some economic regions and severely degraded in areas of the ASSR. The author suggests a winter feeding strategy for moose on hunting preserves that would use wood waste that is left after logging. This strategy would allow a more effective means of supplementing winter forage, but may be difficult to implement.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".