Improper garbage management attracts vertebrates in a Thai national park
Bibliographic record
Abstract
This research presents the issue of wildlife access to garbage at dumpsites and suggests appropriate management in Kaeng Krachan National Park in Thailand. I set camera traps at three dumpsites from May 2018 to January 2019 (601 trap nights). I detected 38 wild species and three domesticated species. There were five, 20, and 13 species of reptiles, birds, and mammals, respectively, including the globally vulnerable Malayan sun bear (Helarctos malayanus) and long-tailed macaque (Macaca fascicularis). The most prevalent species were diurnal, followed by nocturnal and then crepuscular. Nine species fed on food waste. Highly abundant species visited the dumpsites more frequently than did less abundant ones. Food waste quantities were correlated with the number of tourists, the species number, total individual animals, and species abundance. The likelihood of animals using dumpsites was dependent on the time of day, the location, the tourist season, and the group of animals. Feeding at dumpsites may change the ecological roles and foraging behaviour of wildlife, which leads to increasing populations and human-wildlife conflict. Proper management is required so that increasing waste from tourism will not negatively affect threatened species.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".