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Record W2789566936 · doi:10.1002/aocs.12020

Identification of <i>n</i>‐6 Monounsaturated Fatty Acids in <i>Acer</i> Seed Oils

2018· article· en· W2789566936 on OpenAlexafffund
Jinyue Sun, Xinkun Wang, Mark Smith

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2018
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsPlant Biotechnology Institute
FundersNational Research Council CanadaNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsErucic acidFatty acidChemistryPolyunsaturated fatty acidLinolenic acidBotanyFood scienceBiologyBiochemistryLinoleic acid

Abstract

fetched live from OpenAlex

Abstract Seed oils from Acer species are a potential source of the nutraceutical fatty acids, nervonic acid (cis‐15‐tetracosenoic acid, NA), and γ‐linolenic acid (cis‐6,9,12‐octadecatrienoic acid, GLA). To further characterize the genus, seed fatty acid content and composition were determined for 20 species of Acer. Fatty acid content ranged from 8.2% for Acer macrophyllum to over 36% for A. mono and A. negundo. The presence of very‐long‐chain fatty acids (VLCFA), with chain length of 20‐carbons or greater, and GLA were characteristic features of the seed oils. In all species, erucic acid (cis‐13‐docosenoic acid, EA) was the predominant VLCFA with the highest level of NA being only 8.6% in A. olivianum. Regioselective lipase digestion demonstrated that VLCFA are largely absent from the sn‐2 position of seed triacylglycerol, whereas GLA is primarily located at this position. Five Acer species contained low levels (<2%) of cis‐12‐octadecenoic acid and cis‐14‐eicosenoic acid, uncommon n‐6 fatty acids not previously reported from Acer.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.301
Teacher spread0.288 · 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 designBench or experimental
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

Citations10
Published2018
Admission routes2
Has abstractyes

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