Substantial gains and little downside from farming of Totoaba macdonaldi
Bibliographic record
Abstract
Abstract Illegal wildlife trade threatens species globally. Conservation farming introduces farmed substitutes to reduce poaching. Predicting if farming will succeed necessitates understanding how supply and demand interact and how markets respond. We focus on illegal trade for totoaba ( Totoaba macdonaldi ), dominated by a Mexican cartel, which has continued unabated despite long-standing prohibitions. We investigate if farmed totoaba can successfully compete with poaching and support a healthy wild totoaba population. We simulate an illegal supply chain describing the current trade: poachers sell to traders who sell to end-markets. If traders reduce the quantity supplied in response to farming, poaching could decrease by 28%, but if traders select a price that undercuts farming, poaching may increase by 6%. Under both responses, a stable wild population is maintained. Our results are sensitive to costs, demand, product substitutability, market structure, and combinations thereof, and we discuss how to quantitatively evaluate and mitigate for these issues.
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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 | 0.000 |
| 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) | 0.000 | 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".