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Canola Oil

2020· other· en· W4211184280 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
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsUniversity of AlbertaUniversity of Manitoba
Fundersnot available
KeywordsCanolaEdible oilOleic acidBiofuelVegetable oilEnvironmentally friendlyEnvironmental scienceBiotechnologyFood sciencePulp and paper industryChemistryBiologyEngineeringBiochemistry

Abstract

fetched live from OpenAlex

Abstract The high quality, positive nutritional aspects and heat stability, along with advancements in plant breeding enhancements of canola has resulted in canola oil becoming the third largest vegetable oil of choice for human consumption globally. Given the trend to provide shelf‐stable oils without hydrogenation and the nutritional concerns of trans fatty acid production during the hydrogenation process, canola oil, especially the high oleic canola oils, are ideally suited to be used as liquid oil and can replace hydrogenated oils in many applications. Processing techniques and processing equipment used in the processing of canola continue to evolve providing higher efficiency, environmentally friendly, reliable, and more stable canola oil products. This article covers the background, composition, processing techniques, and nutritional properties that have firmly established canola oil as a safe and healthy oil for human consumption as well as its use in biofuels and other nonfood applications.

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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.218
Threshold uncertainty score1.000

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 integrity0.0010.001
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 head0.037
GPT teacher head0.214
Teacher spread0.176 · 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