Bibliographic record
Abstract
Few animals evoke such a range of attitudes in people as bears do. As large iconic predators capable of competing with humans far back in time, their stature and behaviour places them as an alien culture, often in conflict with people's endeavours. The wonderfully exact drawings of bears in the Chauvet Caves of France suggest that worship and respect for bears goes back at least 30,000 years. While indigenous people in North America have co-existed with brown bears for millennia, the proprietorial mindset of trappers, settlers and colonists from Europe succeeded in almost extinguishing the species everywhere settlement occurred. Only in national parks and remote wilderness do grizzlies survive in the contiguous United States and Canada; even there, the ebb and flow of legal battles rages on. Among the many regions on earth where the diminishing numbers of the eight species struggle to survive, bear habitat is being lost due to the activities of billions of humans. Even where wild country is adequate for the survival of residual populations, those formerly wilderness areas suffer from human intrusion. When, because of habitat loss, bears move out into human-occupied zones there is always the potential to create conflicts. Negative impacts can also be indirect on bears in remote areas, as with polar bears, where toxic chemicals in ocean and air currents are sterilising bears and global warming continues to shrink their habitat and seal diet. The organisers of this volume invited authors to deliver topics that will appeal to a wide international audience. Unlike many proceedings of scientific conferences and symposia on specialised aspects of bear ecology, behaviour and management, the author-editors have mapped new avenues to advance solutions. It is a most propitious time to have scholars with broad experience contribute current results from many parts of the world. The reader cannot fail to recognise how many of the threats and conflicts in human-wildlife relations are similar around the globe. Concomitant benefits from this revelation of ideas for management applications are policies that preserve bears, such as engaging local people and integrating their needs along with those of the threatened bears. This is especially well done in the cooperative work in Peru for Andean or spectacled bears. The results are heartening, indeed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.166 | 0.097 |
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".