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Record W2069060721 · doi:10.1021/ef901202b

Biodiesel Production from Greenseed Canola Oil

2010· article· en· W2069060721 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnergy & Fuels · 2010
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCanolaTransesterificationBiodieselMethanolChemistryBiodiesel productionPulp and paper industryOrganic chemistryFood scienceCatalysis

Abstract

fetched live from OpenAlex

Greenseed canola oil is low-grade oil with a green color. Because of the high level of chlorophyll, this oil is considered as a “waste product” and cannot be used for edible purposes. In this research, biodiesel was produced from canola oil and greenseed canola oil via KOH-catalyzed transesterification with methanol, ethanol, and a mixture of methanol and ethanol. The reaction was conducted at 60 °C and a stirring speed of 600 rpm for 90 min. Prior to transesterification, greenseed canola oil was bleached to remove pigments using various adsorbents at different conditions. The optimum bleaching material was found to be montmorillonite K10. The pigment content was reduced from 94 to 0.5 ppm with using 7.5 wt % of this material at 60 °C and a stirring speed of 600 rpm for 30 min. Biodiesel derived from the treated greenseed canola oil showed an improvement in oxidative stability (induction time of 0.7 h) as compared to that derived from crude greenseed canola oil (induction time of 0.5 h). In addition, it was found that the amounts of unsaturated compounds as well as pigments contained in oil had an adverse effect on the oxidative stability of biodiesel.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.537

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 head0.010
GPT teacher head0.196
Teacher spread0.186 · 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