Farming the waters: Bringing aquatic plant and animal species to agriculture
Bibliographic record
Abstract
Aquaculture has a long history, with carp culture in Asia starting before 2000 BC and oyster culture in the Roman Empire before the time of Julius Caesar. However, it is clearly the past 40 yr that have seen the most dramatic expansion of aquaculture. The world's population now exceeds 6 billion people and is still growing at an alarming rate. The world's wild fish harvest has clearly peaked at or above the maximum sustainable yield of about 90 million t. Many fish stocks are suffering from over-fishing and there is little hope of any increase in the capture fisheries production. Though modern agricultural practices have been very efficient at increasing the per acre yields, the world is experiencing an alarmingly steady decrease in the amount of agricultural land devoted to food production. In the past 20–30 yr production of fish, molluscs, crustaceans and aquatic plants (aquaculture) has become an increasingly important means of producing food, and in some countries aquaculture production accounts for more than half of the total fishery harvest and is even as high as 90% in a few countries. I have reviewed the historical growth of aquaculture, compared the product value in various countries and reviewed aquaculture practices for a number of plant, molluscan, crustacean and fish species around the world. These culture technologies were compared and contrasted with agricultural practices. Finally, some predictions for the future of aquaculture development in Canada and the world have been made. Key words: Canadian aquaculture, history, salmon, fish farming, production statistics, fish culture technology
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".