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Record W4415395853 · doi:10.1086/738235

Mistaking Fresh for Wild: Lessons from a Classroom Blind Tasting of Wild and Farmed Salmon

2025· article· en· W4415395853 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.

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

VenueMarine Resource Economics · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWine tastingTasteFish <Actinopterygii>AquacultureQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

Four market-available (in December) fish were presented to students in a master’s course: fresh farmed Atlantic salmon, fresh farmed steelhead trout, frozen wild sockeye salmon, and wild king salmon. Tasters were asked to identify their favorite fish; which they thought was most expensive; whether they thought each was fresh; and whether they thought each was wild. When the king salmon was frozen, 79% of tasters preferred the farmed fish, largely because it is fresh. Many tasters erroneously attributed the bright, clean flavors and flaky texture they like to being wild: 39% of tasters thought the fresh steelhead was wild, though it is farmed. Still, the strongly flavored and lean sockeye was preferred by about a quarter of the tasters, despite being frozen. This mismatch between consumers’ preferred taste attributes and the production attributes on which they base choices implies an opportunity for aquaculture products to continue to expand their market.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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