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LIFE CYCLE ASSESSMENT OF SOYBEAN OIL PRODUCTION

2006· article· en· W2065045885 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

VenueJournal of Food Process Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsKimberly-Clark (Canada)
Fundersnot available
KeywordsSoybean oilHexaneProcess engineeringSupercritical fluidLife-cycle assessmentExtraction (chemistry)Environmental scienceProcess (computing)Supercritical fluid extractionPulp and paper industryScale (ratio)ChemistryComputer scienceChromatographyEngineeringProduction (economics)Food scienceOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT A life cycle assessment (LCA) case study was conducted on the processing of soybeans to soybean oil. Three stages of soybean oil processing are studied in detail: preprocessing, extraction and separation, and postprocessing. For extraction, hexane (current industrial process) and supercritical CO 2 (research and development [R & D ] laboratory‐scale process) methods are compared in detail. The initial life cycle comparison found that the laboratory‐scale CO 2 system was not as good in life cycle impacts as the hexane system. However, reasonable engineering improvements typical of scale‐up practices would make the CO 2 technology better than hexane and eliminate the hexane emissions. Utilization of membrane techniques to separate the small molecular CO 2 from the soybean oil hydrocarbon appears to be a much better R & D direction for development. This article illustrates the ability to use life cycle as an aid to R & D to select more advantageous directions for process improvement.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.525

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 head0.005
GPT teacher head0.218
Teacher spread0.213 · 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