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Examination of marine and vegetable oil oxidation data from a multi-year, third-party database

2018· article· en· W2790730740 on OpenAlexaff
Anna A. De Boer, Adam Ismail, Keri Marshall, Gerard Bannenberg, Kevin L. Yan, William J. Rowe

Bibliographic record

VenueFood Chemistry · 2018
Typearticle
Languageen
FieldChemistry
TopicEdible Oils Quality and Analysis
Canadian institutionsNutrasource
FundersSouth Dakota Governor's Office of Economic Development
KeywordsSunflower oilPeroxide valueFood scienceFish oilOlive oilChemistrySunflowerAlgae fuelFish <Actinopterygii>BiologyFisheryHorticultureOrganic chemistryBiodiesel

Abstract

fetched live from OpenAlex

Fish oil (FO) products constitute good sources of omega-3 fats. Oxidation data from a large third-party database of 1900 + globally-sourced FO samples were assessed. In FO products, for peroxide value (PV), 13.9% exceeded 5 mEq O2/kg (2.2% >10); for acid value (AcV) 2.1% exceeded 3 mg KOH/g, while for p-anisidine value (pAV) in unflavoured oils, 6.1% exceeded 20, (3.8% >30), and 8.8% exceeded TOTOX limits (26). Additionally, we compared FO with other dietary oils. The FO median PV was similar to those of algal and sunflower oils, 4.8-fold greater than krill oil, and 5.2-fold less than extra-virgin olive oil. The median pAV differed non-significantly among oils. The FO median AcV was similar to those of algal and extra-virgin olive oils, 3.4-fold greater than sunflower oil, and 11.9-fold less than krill oil. This study has provided new insight that retail FO products predominantly meet regulatory guidelines and are comparable in oxidative status to other dietary oils.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0090.009
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.055
GPT teacher head0.281
Teacher spread0.226 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations61
Published2018
Admission routes1
Has abstractyes

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