Adding sustainability to salmon farming regulations : a comparative case study of salmon farming regulations and the ASC salmon standard
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it