The best catch data that can possibly be? Rejoinder to Ye et al. “FAO's statistic data and sustainability of fisheries and aquaculture”
Bibliographic record
Abstract
Here we reply to a commentary by Ye et al. (Mar. Policy 2017; Ye et al.) on our article (Pauly and Zeller, 2017 [2]) commenting on FAO's interpretation of current fisheries trends in SOFIA 2016 (The State of World Fisheries and Aquaculture). We show how arguments such as FAO's catch statistics being “the best they can possibly be”, and other manifestations of FAO's difficulties in constructively engaging with comments compromises FAO's stated goal to engage with academia and civil society. This is particularly serious in an age where the value of an open scientific discourse is increasingly under threat, as is the food security of many poor countries in which fish supplied by domestic fisheries constitutes a strong component of local diets.
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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 | 0.037 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.077 | 0.092 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".