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Record W1994251749 · doi:10.1021/es9010114

Not All Salmon Are Created Equal: Life Cycle Assessment (LCA) of Global Salmon Farming Systems

2009· article· en· W1994251749 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnvironmental Science & Technology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
FundersKillam TrustsNatural Sciences and Engineering Research Council of CanadaEsmée Fairbairn FoundationPew Charitable Trusts
KeywordsLife-cycle assessmentSustainabilityGreenhouse gasAquacultureEnvironmental impact assessmentAgricultureProduction (economics)Resource (disambiguation)FisheryEnvironmental scienceEnvironmental resource managementBusinessNatural resource economicsEcologyEconomicsFish <Actinopterygii>Biology

Abstract

fetched live from OpenAlex

We present a global-scale life cycle assessment of a major food commodity, farmed salmon. Specifically, we report the cumulative energy use, biotic resource use, and greenhouse gas, acidifying, and eutrophying emissions associated with producing farmed salmon in Norway, the UK, British Columbia (Canada), and Chile, as well as a production-weighted global average. We found marked differences in the nature and quantity of material/energy resource use and associated emissions per unit production across regions. This suggests significant scope for improved environmental performance in the industry as a whole. We identify key leverage points for improving performance, most notably the critical importance of least-environmental cost feed sourcing patterns and continued improvements in feed conversion efficiency. Overall, impacts were lowest for Norwegian production in most impact categories, and highest for UK farmed salmon. Our results are of direct relevance to industry, policy makers, eco-labeling programs, and consumers seeking to further sustainability objectives in salmon aquaculture.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.253
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it