MétaCan
Menu
Back to cohort

Flax Oil and High Linolenic Oils

2020· other· en· W3007622378 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

VenueBailey's Industrial Oil and Fat Products · 2020
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid metabolism and biosynthesis
Canadian institutionsUniversity of SaskatchewanSaskatchewan Ministry of Agriculture
Fundersnot available
KeywordsPolyunsaturated fatty acidFood scienceChemistryLinoleic acidLinolenic acidCamelina sativaComposition (language)CamelinaEdible oilFatty acidBiologyBiochemistryAgronomyCrop

Abstract

fetched live from OpenAlex

Abstract The fatty acid composition of plant oils varies greatly among plant species. Plant sources with high omega‐3 fatty acid content, including those rich in α‐linolenic acid (ALA), have recently received special attention due to the beneficial effects of these oils on human health. Many of the commercially available plant oils and fats are good sources of oleic and linoleic acids, but fewer offer substantial amounts of ALA. This article discusses oils high in ALA from the seeds of flax, perilla, camelina, and chia. All of these seed oils contain above 70% polyunsaturated fatty acids (PUFAs), and the ALA contents are above 50% of the total oil content. Oils from these crops are produced on an industrial scale and the processes are outlined in this article, along with pretreatment and refining techniques to acquire food and industrial products from seed. Chemical composition, physical properties, and typical oil parameters for these oils are reviewed here.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.512
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.020
GPT teacher head0.215
Teacher spread0.194 · 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