The illegal hunting and exploitation of porcupines for meat and medicine in Indonesia
Bibliographic record
Abstract
Indonesia is home to five species of porcupines, three of which are island endemics. While all five species are currently assessed as Least Concern by the IUCN Red List of Threatened Species, impacts of harvest and trade have not been factored in. To gain a fuller understanding of the porcupine trade in Indonesia, this study examines seizure data of porcupines, their parts and derivatives from January 2013 to June 2020. A total of 39 incidents were obtained amounting to an estimated 452 porcupines. Various confiscated commodities revealed porcupines are traded for consumption, traditional medicine, trophies/charms as well as for privately run wildlife/recreational parks. Targeted hunting of porcupines for commercial international trade was also evident. Porcupines are also persecuted as agricultural pests and wildlife traffickers take advantage of such situations to procure animals for trade. What clearly emerges from this study is that porcupines are being illegally hunted and exploited throughout their range in Indonesia facilitated by poor enforcement and legislative weakness. Porcupines are in decline due to habitat loss, retaliatory killings and uncontrolled poaching. It is therefore crucial that effective conservation measures are taken sooner rather than later to prevent further depletion of these species. Including all porcupines as protected species under Indonesian wildlife laws and listing them in Appendix II of CITES to improve regulation, enforcement and monitoring of domestic and international trade trends involving porcupines in Indonesia would contribute significantly towards this end.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".