Mistaking Fresh for Wild: Lessons from a Classroom Blind Tasting of Wild and Farmed Salmon
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".