Apocalypse in world fisheries? The reports of their death are greatly exaggerated
Bibliographic record
Abstract
Abstract Daan, N., Gislason, H., Pope, J. G., and Rice, J. C. 2011. Apocalypse in world fisheries? The reports of their death are greatly exaggerated. – ICES Journal of Marine Science, 68: 1375–1378. The catch-based methods underlying the forecast that by 2048 all commercially exploited stocks will have collapsed have been severely criticized, and a recent and more-elaborate analysis by a group of scientists that included the lead author of the original article has led to a quite different interpretation. Nonetheless, the 2006 forecast of a forthcoming apocalypse in the oceans is still uncritically referred to by critics of current management and fisheries science. In the title, the quote by Mark Twain is paraphrased to underline the fact that this prediction is both technically and conceptually flawed: (i) any series of random numbers subjected to the algorithm underlying the prediction will show a pattern similar to that observed in catch statistics; (ii) this pattern should be accounted for in making predictions; and (iii) interpreting the period of maximum harvest in a time-series as generally reflecting a period during which a stock was fully exploited is incorrect, because history often has shown that these maximum yields were taken during a period of overexploitation and could not have been sustainable.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".