Chapetr 29: Bear down: resilience and multispecies ethology
Bibliographic record
Abstract
On the morning of July 30, 2015, a black bear was discovered dead on a driveway at the north end of Sudbury, a city of 150,000 humans in northeastern Ontario, Canada. When officers from the Greater Sudbury Police, along with an official from the province’s Ministry of Natural Resources and Forestry (MNRF), appeared on the scene, they discovered that the bear had died from a bullet wound. It had been killed. According to one report, the bear’s death was a “vigilante killing” by a city resident who took matters into his or her own hands (Moodie, “Vigilante”). The “vigilante” label stems from the fact that Sudbury-though located in the heart of Ontario’s black bear country-was experiencing an unprecedented rate of bear sightings and “nuisance” behavior within city limits, and a proportion of the general public, though divided on the reasons behind recent bear behavior, blamed their unusually high presence on a lack of management and protection by qualified officials.1 Even though there were no visible signs or reports of threatening behavior by the bear, it was nevertheless shot and left to die in what the police called an “inhumane” manner.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".