Estimating the impact of the illegal trade of primates in Mexico: a potential threat to wildlife
Bibliographic record
Abstract
The primates of Mexico, Ateles geoffroyi, Alouatta palliata, and Alouatta pigra, are seriously threatened by habitat loss, fragmentation, and illegal hunting and trade. Very little is known about the extent of illegal trade and its impacts on declining primate populations. Our study proposes a potential method based on estimating the number of individuals that die in the trade before being detected and those that probably cannot be detected. This facilitates estimating the number of animals extracted and allows an assessment of how trafficking impacts their populations. We derive estimates from seizure data of primates in Mexico between 2010 and 2019. To do this, we created wildlife detection rates and mortality rates from the existing literature (scientific articles, journalistic articles, and notes) to estimate the number of primates that die during capture, transport, and sale and the number of trafficked primates that were not detected by Mexican authorities. We estimate that 946 primates were removed from the wild for the pet trade each year (spider monkey Ateles geoffroyi = 854; black howler monkeys Alouatta pigra = 38, mantled howler monkey Alouatta palliata = 54). The annual reduction in population size caused by trafficking was greatest for Ateles geoffroyi (2.2%), followed by Alouatta pigra (1.3%), and Alouatta palliata (0.4%). Our estimates show the percentage of impacts that trafficking has on Mexican primate populations. Nevertheless, trade has the potential to impact declining populations and still must be addressed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".