Blue mussel farming :\na comparison of the Norwegian and the Canadian industries
Bibliographic record
Abstract
Since the 1970s, many attempts have been made in Norway to turn blue mussel farming into a growth industry. Total production has increased during the last ten years, but prices have decreased and the value of the Norwegian production has fluctuated greatly. Many blue mussel farming companies have failed. Hence, the results have not been as expected. The Canadian story is different. In contrast to what has happened in Norway, some Canadian provinces – notably Prince Edward Island - have had a great success in blue mussel farming. During the same period they have developed this activity into a viable industry.\nThis thesis compares the development of blue mussel farming in Norway and Canada. Why has the Canadian industry fared better than its Norwegian counterpart? In order to highlight the issue, the thesis focuses on the bottlenecks and barriers for the development of blue mussel production in the two countries and how these challenges have been dealt with. The study is based on interviews with eleven different companies and five different governmental and membership organizations in selected regions in Canada and Norway. In addition, a wide range of secondary sources have been used.\nThe main findings are that the two industries are facing rather similar natural challenges. Toxicity is a common threat and at the moment invasive species is becoming a growing problem in Canada. What differentiates the two industries is that blue mussel farming in Canada was initiated as a response to declining fisheries. This may partly explain why the Canadian industry has been more successful. The infrastructure for industrial support also seems to be better co-ordinated in Canada than in Norway, and the Canadian producers have the benefit of a large domestic market and proximity to the US market, while the Norwegian producers have a small domestic market and greater difficulties gaining access to the well-established European market. However, these conclusions must be regarded as provisional considering the limited amount of data on which this thesis is built.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".