Bibliographic record
Abstract
Purpose The world’s population is expected to increase by 30 percent to 10bn people by 2050 and with 70 percent of the earth’s surface covered by water aquaculture will play an important role in producing food for the future. The paper aims to discuss this issue. Design/methodology/approach While Canada has the longest coastline in the world by far (202,080 km) with 80,000 km of marine coastline capable of supporting aquaculture and fisheries, it ranks only 25th in terms of world aquaculture production. The reasons are many and varied, and this review examines statistical reports and publications to trace the beginnings of the aquaculture sector in Canada, and highlights some areas of strength and potential, and the challenges for future growth and expansion. Findings Currently, less than 1 percent of the 3.8m hectares of freshwater and marine areas that are considered suitable for seafood (i.e. finfish, shellfish and aquatic plants) production are being farmed so Canada has an ocean of opportunity to be a leader in world aquaculture production in the future. Originality/value The review highlights the need for a national strategic plan to increase aquaculture production in Canada and the need to simplify the current complex regulatory framework that has resulted in significant uncertainties and delays that have limited growth in this sector. The review highlights the potential and interest to triple current production while fostering greater involvement of First Nation 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".