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Record W7015283520

Sensory testing of seafood - fresh versus frozen - and development of frozen seafood recipes
\nSensory testing – sub-component

2019· book· en· W7015283520 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueQueensland Department of Agriculture and Fisheries archive of scientific and research publications (Queensland Department of Agriculture and Fisheries) · 2019
Typebook
Languageen
FieldAgricultural and Biological Sciences
TopicSensory Analysis and Statistical Methods
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsFish <Actinopterygii>Product (mathematics)Significant differenceQuality (philosophy)Fisheries ResearchSensory analysisTable (database)Congelation
DOInot available

Abstract

fetched live from OpenAlex

There is a strong negative perception of frozen fish amongst consumers, with many considering that frozen product is of inferior quality compared to ‘fresh’ (chilled) fish. The resistance to purchase frozen fish continues, despite modern freezing technology and practices resulting in frozen product that remains as premium quality for longer than chilled fish.&#13;\nThe research described in this report was driven by the Fisheries Research and Development Corporation in an endeavour to establish whether there was a discernible difference between fresh and frozen product of the same species. Two evaluation methods were used: a Chefs Table focus group method and an experienced seafood panel assessment by difference testing. Both methods were used to evaluate sashimi and cooked formats of the fish samples.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.086
GPT teacher head0.281
Teacher spread0.195 · 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