Generic names and mislabeling conceal high species diversity in global fisheries markets
Bibliographic record
Abstract
Abstract Consumers have the power to influence conservation of marine fishes by selectively purchasing sustainably harvested species. Yet, this power is hindered by vague labeling and seafood fraud, which may mask market biodiversity and lead to inadvertent consumption of threatened species. Here, we investigate the repercussions of such labeling inaccuracies for one of the world's most highly prized families of fishes‐–the snappers (Family: Lutjanidae). By DNA barcoding 300 “snapper” samples collected from six countries, we show that the lax application of this umbrella term and widespread mislabeling (40%) conceal the identities of at least 67 species from 16 families in global marketplaces, effectively lumping taxa for sale that derive from an array of disparately managed fisheries and have markedly different conservation concerns. Bringing this trade into the open should compel a revision of international labeling and traceability policies, as well as enforcement measures, which currently allow such extensive biodiversity to be consumed unknowingly.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".