Bibliographic record
Abstract
David Warrell Animals, especially large ones, wild and domesticated, should always be treated with respect and not approached unnecessarily. Tigers, lions, leopards and other big cats, hyenas, domestic dogs, jackals, wolves, bears, elephants, rhinos, hippopotamuses, buffaloes, bison, domestic cattle, moose, elk, other large deer and antelopes, domestic and wild pigs, rams, tapirs, chimpanzees, baboons, ostriches, cassowaries, and even ferrets have killed people. Learn about the local hazards by asking the residents. Be vigilant at all times. Beware of wandering alone and unprotected between dusk and dawn when most attacks by large mammals occur. Travel in groups, do not stray from vehicles, and do not take dogs with you; they attract large predators. A look-out armed with a large caliber rifle (preferably >0.35 mm) is essential if you are working in the open in country inhabited by big game animals. All bears, even giant pandas, are potentially dangerous carnivores. Mothers with cubs are responsible for 80% of attacks on people. In North America, where backpackers and campers in national parks are victims of daytime/ evening attacks, black bears (Ursus americanus) were responsible for about 5.8 attacks and 0.3 deaths/year, while brown bears (U. arctos), including grizzlies and Kodiak bears, were responsible for about 1.65 attacks and 0.6 deaths/year in the 1990s. Brown bears also kill and injure people in Romania, Scandinavia, and other parts of Europe. Polar bears (U. maritimus) are the most predatory, aggressive, and dangerous of all, killing six people in Canada (1965–85), and attacking 50 people in Svalbard (Spitzbergen, Norway) (1973–86) ( p. 592–3). Asian sloth bears (Melursus ursinus) killed 48 people and injured 687 in Madya Pradesh, India (1989–94).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.161 | 0.078 |
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".