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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".