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Record W1550812733 · doi:10.1002/047167849x.bio010

Flax Oil and High Linolenic Oils

2005· other· en· W1550812733 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 · 2005
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid metabolism and biosynthesis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFood sciencePolyunsaturated fatty acidLinseed oilLinolenic acidChemistryCamelinaLinoleic acidComposition (language)Camelina sativaalpha-Linolenic acidFatty acidOrganic chemistryBiologyCropAgronomyDocosahexaenoic acid

Abstract

fetched live from OpenAlex

Abstract Oils with different composition of fatty acids are available from plants. Sources of oils with high content of linolenic acid have recently received special attention because of the abnormal ratio of omega‐3 to omega‐6 fatty acids present in consumed oils and fats. Available commercial oils and fats mainly contain linoleic acid, and not many oils offer linolenic acid as a source of omega‐3 fatty acids. In this chapter, oils high in linolenic acid, namely, flax, perilla, camelina, and chia oils, are discussed. Content of linolenic acid in the oils reported is above 50%, and contribution of polyunsaturated fatty acids for all of them is above 70%. Oilseeds and oils from these crops are produced on an industrial scale, and they are available as health food products. Chemical composition, physical properties, and typical oil parameters for these oils are also discussed.

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), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.684
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 head0.017
GPT teacher head0.218
Teacher spread0.201 · 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