Bibliographic record
Abstract
The last three bi-annual State of World Fisheries and Aquaculture (SOFIA) reports by the Food and Agriculture Organization of the United Nations (FAO) gave the impression that they downplayed the stark reality of declining trends in global marine fisheries catches. In contrast, the most recent SOFIA 2018 deserves praise for seemingly striking a different tone, and for more directly and clearly identifying the key issues faced by marine fisheries. This includes the acknowledgment of globally declining catches and several data deficiencies, such as the ‘presentist’ bias in official data reported by countries to FAO, and the utility of catch data reconstructions in informing such data deficiencies, as advocated by the Sea Around Us for nearly two decades. FAO also acknowledges its personnel limitations and hence the need to collaborate with non-governmental entities. Further, we congratulate FAO on explicitly addressing in SOFIA 2018 two major challenges in global marine fisheries, namely the effects of climate change and the problems related to subsidies for the enormous Chinese fishing fleets. We applaud FAO for this different, more open tone in SOFIA 2018, which even includes animal welfare consideration, and we hope that it signals a new period of increased FAO engagement with Civil Society and academia, to address the important fisheries and sustainability challenges facing our world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.029 | 0.028 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".