Bibliographic record
Abstract
It has long been clear that fisheries have already had major impacts on target species and their supporting ecosystems (Jackson et al. 2001).However, the lack of reliable data on fishery catches, has not only impaired the ability to predict future stock size and the capacity to provide yields, but also seriously masked relevant trends and the scale of these impacts.Despite that, until now there has been only one source of data on global fishery catches since 1950: catch statistics assembled and disseminated annually by the Food and Agriculture Organisation of the United Nations (FAO).It is, however, widely known that this global data set, which relies on data reported by member countries, is not accurate mainly because most of its members underreport bycatch, discarded fish, and the catch of small-scale fisheries (Pauly & Zeller 2016).Therefore, the desperate need for accurate catch statistics and the move toward sustainable catches nicely set out both the context and purpose of The Global Atlas of Marine Fisheries.As a kind of expansion of the previous book In a Perfect Ocean: The State of Fisheries and Ecosystems in the North Atlantic Ocean (Pauly & Maclean 2003), this book is the first to provide detailed fishery catch data covering literally the entire world's oceans.It contains a collection of key results of The Sea Around Us Project, a project intended to document fisheries' impacts on marine ecosystems and to propose policies to mitigate those impacts.Structured in two main parts, part I exploring the global accounts and part II focusing on country and territory accounts, the book has its main emphasis on a well-structured system of catch reconstructions covering 61 years .Despite the debate over how reliable these techniques are (e.g., FAO 2016), in the lack of proper data, catch reconstructions are definitely a valuable attempt to assess the overall impact of global fisheries.In the first part, the first 5 of the 14 chapters are somewhat interconnected.In the first 3, the authors provide an effective introduction to the scientific rationale and technical features adopted to quantify the total marine fisheries through catch reconstructions.They wisely refer to the highly criticized but very influential paper by Worm et al. (2006), "Impacts of 1478
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.180 | 0.051 |
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".