The Sustainable Development of Fisheries and Aquaculture
Bibliographic record
Abstract
The majority of the 500 million people who rely on fisheries and aquaculture as a source of income do so directly or indirectly from these industries. Biologic diversity and social sustainability may conflict. If the ecology of a fishery continues to provide goods that society can utilise, then the fishery is socially sustainable. Aquaculture practices that prioritise environmental, economic, and social sustainability are referred to as sustainable aquaculture practices. This approach aims to enhance capacity building and efficiently manage land for aquaculture operations. The goal is to develop a supply of aquatic-sourced food and commercial items that will expand availability while minimising environmental damage and safeguarding various aquatic species. There are various types of aquaculture, each with varying degrees of sustainability. Pure water Aquaculture is practiced in fish ponds, fish pens, fish cages or on a smaller scale, rice paddies. Brackish water aquaculture is primarily practiced in coastal fish ponds. Fish cages or substrates for mollusks and seaweeds such as stakes, ropes, and rafts are used in marine culture.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.020 |
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".