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Record W2010990056 · doi:10.1002/lite.201000054

Monitoring fish oil volatiles to assess the quality of fish oil

2010· article· en· W2010990056 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

VenueLipid Technology · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFish oilFish <Actinopterygii>Solid-phase microextractionSensory systemChemistrySensory analysisEnvironmental scienceFood scienceGas chromatography–mass spectrometryFisheryBiologyChromatography

Abstract

fetched live from OpenAlex

Abstract The fish oil industry is continuously growing; however there is a lack of analytical methods to assess fish oil quality that correlate with the results obtained through sensory testing. Solid phase microextraction (SPME) provides a means to monitor the concentration of oxidative volatiles in fish oil. Because volatile oxidation products are responsible for the off‐flavours found in oxidized fish oil, this technique may be used as a substitute for sensory panels. Principal component analysis (PCA), combined with sensory panels, can be used to determine the oxidation products that are most correlated with degradation of the sensory properties of the oil. This creates the potential for development of methods that can determine when the sensory qualities of oil have deteriorated beyond an acceptable level.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.026
GPT teacher head0.288
Teacher spread0.262 · 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