Bibliographic record
Abstract
Aquaculture is likely to supply most of the projected increased need for seafood over the next few decades. With available land and freshwater becoming scarce, marine aquaculture (finfish, shellfish, and seaweeds) will be an increasingly important contributor to the world’s future food supply. Aquaculture is well established in many countries and continues to grow worldwide. Aquaculture creates jobs and supports working waterfronts. Aquaculture helps to maintain working waterfronts by relying on common infrastructure (docks, boats, processing facilities) and supports industries throughout the seafood supply chain such as equipment, supplies, feeds, processing, wholesaling, retailing, and food services. Aquaculture is one of the most resource-efficient ways to produce protein. Marine aquaculture is a sustainable method to produce food (relative to beef and pork production) from the standpoint of resource efficiency (e.g., use of water, land, feed, energy consumption, and greenhouse gas emissions) and in terms of environmental effects. Research and data show that aquaculture is sustainable if properly managed. U.S. and Canadian marine aquaculture is characterized by smart design, evolved management practices, effective monitoring capabilities, and strict regulatory requirements. Much environmental concern expressed about marine aquaculture stems from outdated domestic practices that have since been improved or practices in other countries, which may have varying environmental protection standards. Growing seafood locally makes sense to maintain regulatory oversight and economic benefits, as well as reducing carbon footprint from storage and transport, and generating healthy high quality protein for our communities.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".