Bibliographic record
Abstract
INTRODUCTION Interactions between wildlife and humans come in many forms, from indirect observation, like the remote detection of smells or signs, or sightings (mutual and singular), to direct contact. In this chapter, we address the latter form, direct contact. Specifically, we deal with perhaps the most negative and dramatic of these interactions (from the human perspective), attacks on humans by wildlife. And, from such actions may follow perhaps the most unacceptable result, serious human injury or the loss of human life. Outside of rare or unwitting contact, most direct contacts between wildlife and humans can be viewed as negative for the individual wildlife involved. Attacks on humans, with the animal intending to repel or even kill, fall in the extreme end of the direct contact category (Thirgood et al ., Chapter 2). Attacks on humans by wildlife are not new. It is important to note that humans have been preyed upon from the earliest forms of our genus Homo and even earlier forms of hominids (Kruuk 2002). Although we have become increasingly accomplished in our ability to prey upon and defend ourselves against other animals, early hominids were highly vulnerable to a wide variety of predators and competitors (Kruuk 2002; Miller 2002). Even today, some of our primate relatives have some of the most intricate and developed forms of predator avoidance known (Miller 2002). However, the early and increasingly more elaborate development of tools separated us from other primates.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".