Knowledge about big cats matters: Insights for conservationists and managers
Bibliographic record
Abstract
ABSTRACT Jaguars ( Panthera onca ) and pumas ( Puma concolor ) are declining in the Brazilian Atlantic Forest because of anthropogenic threats (e.g., habitat loss, depletion of prey, human persecution). We assessed the influence of local people's factual knowledge about jaguars and pumas on fear of these big cats, attitudes toward big cats, and the acceptability of big cats. We also examined the influence of demographics (i.e., age, gender) on knowledge. We collected data from 326 rural residents adjacent to 2 protected areas located in a pristine fragment of the Atlantic Forest: Alto do Ribeira State Park and Intervales State Park. Although factual knowledge did not influence attitudes, knowledge was related to fear of big cats and acceptability of these species in the wild. Individuals that were more knowledgeable about big cats were less afraid and more tolerant of jaguars and pumas. Males and adults were more knowledgeable about big cats than were females and younger individuals. Our findings provide evidence that local knowledge can affect tolerance for big cats in the region and potentially reduce people–big cat conflict. Our findings also suggest that conservation efforts should focus on women and youth. © 2017 The Wildlife Society.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".