Who are the <i>rehaleros</i>? A first analysis of socio-demographics, habits and perceptions of the hunting dog-pack owners in Spain
Bibliographic record
Abstract
Big game-driven hunting with dogs is a traditional and popular type of hunting in Spain, in which dog-pack owners are a key but understudied stakeholder. The authors aimed to evaluate the socio-demographics, hunting habits, and perceptions of dog-pack owners through a survey (n = 134). The majority of respondents (n = 76, 57%) were under 46 years old, male (97%), had an education higher than secondary level (63%), and were employees (62%), hence hunting was mainly a form of leisure. The average owner had 1.27 dog packs with 31 dogs per pack, and hunted in two different regions 40 days per season. Dog-pack owners from southern regions had a significantly higher number of dogs per pack compared with northern owners. Above half of respondents (52%) hunted only in private hunting grounds. Dog-pack owners were in favor of improving education and training activities for them and conducting a public “pro-hunting campaign.” Policy makers and dog-pack owners should work in close collaboration to ensure the key role of the latter in wildlife management, either for leisure or control purposes.AU: The abstract is currently too long. Please edit the abstract down to no more than 150 words.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".