The Effects of Human Age, Group Composition, and Behavior on the Likelihood of Being Injured by Attacking Pumas
Bibliographic record
Abstract
Documentation from the years 1890 to 2000 of 185 instances of pumas (Puma concolor) attacking humans in the United States and Canada has provided statistical evidence that pumas are less likely to kill or injure humans in certain circumstances. We identified incidents of fatal attacks, severe injuries, light injuries, and no injuries as a function of human age class, group size, body posture, and conspicuous action, such as noise making, running, or shooting. Ordinal multinomial regression revealed that age class (< 13 years old vs. older) was not a statistically reliable predictor of attack severity. This statistical method also revealed that there was no reliable association between the number of individuals present during the attack and attack severity. Nevertheless, examination of specific attack outcomes indicated that the likelihood of escaping injury increased when two or more people were present. The speed that individuals moved during the attack did not predict attack severity, but it was apparent that the lowest likelihood of escaping injury (26%) and greatest frequency of severe injuries (43%) occurred when individuals remained stationary. In contrast, half of the individuals who ran when they were attacked escaped injury, whereas running was associated with only a small increase in the frequency of fatal attacks (28%), compared with remaining stationary (23%). Evidence that half of the individuals who ran escaped injury suggests that pumas are assessing immobility in humans as they might with other prey, using it as an index of prey inattention or disablement and hence greater vulnerability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".