Bibliographic record
Abstract
Grieg Seafood ASA is a Norwegian salmon aquaculture company with current production in Norway, Shetland, and British Columbia.In addition, it is starting up production in Newfoundland, and a large share of Green Bond proceeds are expected to be used here.The carbon footprint of farmed salmon is considerably lower than that of beef, but higher than that of chicken and wild-caught fish.The majority of its footprint (at harvest) is due to feed production, with a disproportional contribution from soy, which has been linked to deforestation in Brazil.Around 20% of Green Bond proceeds are expected to finance procurement of feed that meets the company's sustainability criteria.Soy used in this feed will be certified as not originating from recently deforested land.However, certification of soy does not fully solve the deforestation problem.The company therefore seeks to use its market power to influence the soy industry towards reducing deforestation.This framework excludes one feed supplier because its mother company has been accused of contributing to deforestation. Airfreight can more than double farmed salomn's carbon footprint.An increasing share of Grieg Seafood's produce is tranported by airfreight, currently one fifth.However, production in Newfoundland can be transported to the fastgrowing North American market without airfreight.Aquaculture causes a range of other environmental problems.Parts of proceeds will be used to finance fish farms certified, or in preparation to become certified, by the Aquaculture Stewardship Council (ASC).The ASC is regarded as the strictest voluntary certification scheme on environmental criteria.Grieg Seafood has put forward science-based targets covering scope 1,2, and 3 GHG emissions.Its climate related reporting is rated A by the CDP and is in accordance with the TCFD and the GRI.However, reported emissions (scope 1 and 2) have increased the last two years.Under this framework, the company commits to report on several indicators, with external verification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.468 | 0.313 |
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".