Is the ocean large enough for both current fish farming and 30% marine protection?
Bibliographic record
Abstract
As a response to the global degradation of marine life, the Norwegian government committed to the Kunming-Montreal agreement to protect 30% of marine areas by 2030. This involves a significant increase in protected areas, up from the current level of around 5%. Although as much as 70% of revenue from exports and over 225.000 jobs are tied up in the marine economy, little research has been conducted on how setting aside large areas for protection will affect specific industries or Norway as a whole. The main objective of this thesis is to investigate the trade-off between the fish farming industry and marine protected areas in Norway. I do so by answering the following two research questions: (1) To what extent has the fish farming industry adapted to the current marine protection? (2) To what extent will an increase to 30% marine protected areas affect the fish farming industry?\n\nThe thesis uses mixed methods in two sequential stages: I use geodata to investigate the spatial relationship between fish farms and marine protected areas, followed by a thematic document analysis to further investigate the fish farms located inside marine protected areas.\n\nThe results show that current marine protected areas (MPAs) have minimally impacted the aquaculture industry. Nine fish farms are located inside of MPAs, potentially bringing in a combined income of around one billion NOK. The fish farming industry has on some occasions also influenced MPA borders, suggesting that aquaculture is prioritized over the protection of nature. Over half of Norway’s farmed fish production capacity is located within ecologically or biologically significant areas, which means that increased conservation efforts will necessitate changes for the industry. The economic impact of increasing protection from 5% to 30% of the Norwegian coast is highly dependent upon the compromise between conservation and use.\n\nThis thesis informs the ongoing debate on marine spatial conflicts in Norway, which regards the prioritization of different objectives and how to practically balance conservation and use. While the Norwegian Government Ocean Strategy expects growth in the aquaculture sector, this thesis shows that this is difficult to reconcile with an increase to 30% marine protected areas
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".