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Record W2035174500 · doi:10.1007/s11746-003-0829-3

Genotype and growing location effects on phytosterols in canola oil

2003· article· en· W2035174500 on OpenAlexaboutno aff
Anwar A. Hamama, Harbans L. Bhardwaj, D. E. Starner

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2003
Typearticle
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsCanolaStigmasterolCampesterolUnsaponifiablePhytosterolChemistryBotanyFood scienceBiologySterolCholesterol

Abstract

fetched live from OpenAlex

Abstract There is little information available about phytosterols in canola ( Brassica napa L.) oil and the effects of genotype and growing locations from Virginia and the mid‐Atlantic region of the United States, a potential area for the establishment of domestic production to provide edible oil. Our objectives were to characterize the phytosterols, phospholipids, unsaponifiable matter, and FA in oil from Virginia‐grown canola. Among 11 canola genotypes grown at two locations during 1995–1996 significant variations existed for oil content and FA profiles, but not for contents of phospholipids, unsaponifiable matter, total phytosterols, campesterol, stigmasterol, and β‐sitosterol, Total phytosterol content in the oil of Virginia‐grown canola varied from 0.7 to 0.9% with a mean of 0.8%. This concentration compared favorably with oil from Canadian canola, which typically contains 0.5 to 1.1% total phytosterols. The mean contents of brassicasterol, campesterol, stigmasterol, β‐sitosterol, Δ 5 ‐avenasterol, and Δ 7 ‐stigmatenol as percentages of total phytosterols in Virginia‐grown canola were: 9.7, 32.0, 0.6, 49.3, 4.99, and 3.5%, respectively. Growing location did not affect phytosterols in Virginia‐grown canola oil but had significant effects on contents of phospholipids, and saturated (myristic, stearic, and arachidic) and unsaturated (palmitoleic, linoleic, linolenic, eicosenoic, and erucic) FA.

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 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 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 score0.306
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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 teacher head, 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

Citations69
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Oil Chemists SocietySame topicCholesterol and Lipid MetabolismFrench-language works237,207