Adding sustainability to salmon farming regulations : a comparative case study of salmon farming regulations and the ASC salmon standard
Bibliographic record
Abstract
Food scarcity is one of the main challenges related to our planet’s growing population and changing environment. Furthermore, our current food production is aggravating and accelerating climate change, as almost 24% of global greenhouse gases derive from agriculture (Troell, Jonell, & Henriksson, 2017). Seafood is likely to become an even more important resource for animal protein than it already is, as the population grows, and the environment becomes less predictable which potentially could result in depleted yields. Aquaculture volumes have increased substantially during the last three decades, with increased production numbers from five million tons in 1980 to more than 106 million tons in 2017 (FishStat, 2013; Zhou, 2017). One species that have seen a rapid growth in production numbers is Atlantic salmon. The increased production in aquaculture has resulted in an increased environmental concern about the consequences of intensive farming. Consequentially, this has resulted in an influx of eco-certification schemes. One of which is the Aquaculture Stewardship Council (ASC). This study has compared the national/provincial legislation on aquaculture in the four biggest salmon producing regions; Norway, Chile, Scotland (UK), British Columbia (Canada) and the ASC’s standard, to compare how different the legislations are from the guidelines set up by this eco-certification scheme. The study found that the ASC standard has stricter standards than the aforementioned regions. Furthermore, this study has compared the potential sustainability effects of using national standards versus international standards for salmon farming and found that international standards have an important role to play as they have the potential to make everyone abide by the same minimum requirement. However, in order for them to have a real effect they need to be legally binding and not just be voluntary guidelines.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".