Comments on FAOs State of World Fisheries and Aquaculture (SOFIA 2016)
Bibliographic record
Abstract
Comments are provided on several points in the 2016 State of the World Fisheries and Aquaculture produced by the Food and Agriculture Organization of the United Nations (FAO). It is shown that data assembled by FAO from submissions by countries suggest a “stable” trend mainly because the declining catches of a number of countries with reliable statistics is compensated for by unreliable statistics from countries where reporting increasing catches may be politically expedient, e.g., China, Myanmar. Also, concerns are raised as to why FAO chose to ignore the well-documented data ‘reconstruction’ process, which fills the gaps that exist in data reported by countries to FAO. It is being ignored despite its importance for governance and resource conservation being well known. This process and its findings could be used by FAO to encourage countries to improve their data reporting, including retroactive corrections. This is important in view of successive analyses of the status of fisheries resources undertaken by FAO (published in current and past SOFIAs) and also in modified form by the Sea Around Us . This suggests a degradation of marine fisheries, and, if trends continue, a crisis by mid-century. Finally, comments are presented on the proposition that aquaculture will overtake wild capture fisheries in terms of food production, notably because current aquaculture requires huge quantities of wild-caught fish as feed. Indeed, this emphasis on aquaculture-as-substitute for fisheries raises issues of food security and malnutrition in developing countries, from which much of the fish used as feed originates.
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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.042 | 0.034 |
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".