Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The trade in primates as pets is a global enterprise and as access to the Internet has increased, so too has the trade of live primates online. While quantifying primate trade in physical markets is relatively straightforward, limited insights have been made into trade via the Internet. Here we followed a three-pronged approach to estimate the prevalence and ease of purchasing primates online in countries with different socioeconomic characteristics. We first conducted a literature review, in which we found that Malaysia, Thailand, the USA, Ukraine, South Africa, and Russia stood out in terms of the number of primate individuals being offered for sale as pets in the online trade. Then, we assessed the perceived ease of purchasing pet primates online in 77 countries, for which we found a positive relationship with the Internet Penetration Rate, total human population and Human Development Index, but not to Gross Domestic Product per capita or corruption levels of the countries. Using these results, we then predicted the levels of online primate trade in countries for which we did not have first-hand data. From this we created a global map of prevalence of primate trade online. Finally, we analysed price data of the two primate taxa most consistently offered for sale, marmosets and capuchins. We found that prices increased with the ease of purchasing primates online and the Gross Domestic Product per capita. This overview provides insight into the nature and intricacies of the online primate pet trade and advocates for increased trade regulation and monitoring in both primate range and non-range countries where trade has been substantially reported.
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.
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 | 0.000 |
| 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) | 0.001 | 0.001 |
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 it