Predicting Acceptability of Jaguars and Pumas in the Atlantic Forest, Brazil
Bibliographic record
Abstract
Jaguars and pumas are threatened species in Brazil’s Atlantic Forest, especially at the borders of protected areas. This article assessed the influence of emotions, attitudes, existence value, and agency credibility on acceptability of big cats among rural residents living adjacent to two protected areas in this forest. Data from self-administrated questionnaires (n = 326) indicated those with positive attitudes toward big cats (β = .28, p < .001), those who valued the existence of big cats (β = .14, p < .05), those who would feel sorrow if big cats disappeared (β = .21, p < .001), and those who considered the managing agency as credible (β = .16, p = .002) were more accepting of big cats. The model provided theoretical and practical insights into large carnivore conservation. For example, given the significance of agency credibility, a positive relationship between park authorities and residents is crucial for big cat conservation.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".