Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT We present an analysis of human–bear ( Ursus spp.) conflicts that occurred in Alaska, USA, from 1880 to 2015. We collected 682 human–bear conflicts, consisting of 61,226 data entries, from various sources available to us. We found that human–bear attacks are rare events, averaging 2.6/year across the study period, though increasing to 7.6/year in the current decade. Grizzly bears ( U. arctos ) dominated conflicts (88%), followed by black bears ( U. americanus; 11%), and lastly polar bears ( U. maritimus ; 1%). Although grizzly bear family groups are often involved in conflicts (32% of all attacks), single grizzlies are involved more than any other cohort (45%). Human–bear conflicts occurred during every month of the year and the majority occurred during daytime when people were most active (82%). Human group size was a significant factor in bear conflicts: the larger the group (≥2 persons), the less likely to be involved in a confrontation. Habitat visibility also contributed to conflict, the poorer the visibility the more likely bears were to engage with people, presumably because of an inability to detect them until very close. When domestic dogs intervened in attacks, they terminated them nearly half of the time (47.5%). However, in 12.5% of cases, dogs appeared to have initiated the conflict. When involved, rescuers terminated maulings in 90.3% of cases, but were themselves mauled 9.7% of the time. We offer these, and other, insights derived from this work that will inform wildlife biologists’ bear safety training and public outreach. © 2018 The Wildlife Society.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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 it