ECONOMIC ANALYSIS OF NETCAGE VERSUS SEA-BAG PRODUCTION SYSTEMS FOR SALMON AQUACULTURE IN BRITISH COLUMBIA
Bibliographic record
Abstract
Conventional open netcage systems for salmon aquaculture are under scrutiny and criticism partially because they are believed to generate adverse environmental impacts on other resource users and the surrounding environment. One alternative to preventing or miniming these impacts is to use enclosed systems. Experience indicates that these enclosed systems are technically feasible and environmentally promising, but they are economically demanding because of high capital and operating costs. Therefore, an economic analysis of open netcage and sea-bag systems for salmon aquaculture was conducted to examine the profitability of salmon aquaculture operations between these two systems. The study shows that netcage systems are more financially profitable than sea-bag systems when environmental costs are either not or only partially considered. Sea-bag systems can be financially profitable only when they produce fish that achieve a price premium. Sensitivity analyses reveal that market price has the most important impact on the profitability of both systems; changes in discount rates, fish density, feed costs, and environmental costs also have major impacts on the profitability of netcage systems. Changes in the length of the growth cycle, survival rate, and feed conversion ratio have minor impacts on the profitability of sea-bag systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".