Baiting and feeding \nmammalian game species: \ncurrent practices in North \nAmerica and Europe
Bibliographic record
Abstract
It is commonly stated in the scientific literature that baiting and feeding are widespread wildlife management practices. However, it is difficult to determine exactly how widespread these management practices are, and what exactly falls under the definitions of baiting and feeding. Laws and regulations vary greatly between countries and are often changed or adapted within a country. The aim of this thesis is to obtain an overview on the current practices of baiting and feeding mammalian game species in North America and Europe. I reviewed the hunting regulations available online for all states, provinces and territories in the USA and Canada (i.e., North America), and sent an email questionnaire survey aimed at understanding regulations on baiting and feeding of wildlife to researchers and wildlife managers in all European countries. Current practices in North America and Europe range from general bans of all baiting and feeding, to baiting and feeding of selected species under certain circumstances, to generally allowing baiting and feeding of a wide selection of species, with a wide selection of baiting and feeding materials. Most ADs in North America and most European countries allow some hunting over bait. However, there is tremendous variation regarding both regulations and which species are allowed to be baited. Similar variation is also observed in relation to supplementary feeding, which is legal in one form or another in most ADs in North America and in most European countries. In comparison, diversionary feeding is generally not mentioned in the North American hunting regulations, while respondents from 16 European countries reported that diversionary feeding is practiced in their country. Baiting and feeding wild animals are widespread management practices, despite a considerable body of scientific evidence suggesting that the consequences remain poorly understood.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".