Improving coastal livelihoods through sustainable aquaculture practices - a report to the collaborative APEC Grouper Research and Development Network
Bibliographic record
Abstract
Wild-harvest fisheries for live reef fish are largely over-exploited or unsustainable because of over-fishing and the widespread use of destructive fishing practices such as blast and cyanide fishing. Sustainable aquaculture – such as that of groupers – is one option for meeting thestrong demand for reef fish, as well as potentially maintaining or improving the livelihoods of coastal communities. This report from a short study by the STREAM Initiative draws on secondary literature, media sources and four diverse case studies from at-risk reef fisheries, to frame a strategy for encouraging sustainable aquaculture as an alternative to destructive fishing practices. It was undertaken as a component of the APEC-funded project Collaborative Grouper Research and Development Network (FWG/01/2001) to better understand how recent technical advances in grouper culture and other complementary work – including that of the Asia-Pacific Marine Finfish Aquaculture Network (APMFAN) hosted by NACA – could better support the livelihoods of poor coastal communities. (PDF contains 49 pages)
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".