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Record W3189076416 · doi:10.5650/jos.ess21065

Soybean Oil Treatment Using the Dissolving Curve Equation of Hydrogen

2021· article· en· W3189076416 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 Oleo Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsScience North
Fundersnot available
KeywordsSolubilityHydrogenDissolutionSoybean oilCatalysisChemistryHexaneOrganic chemistryvan der Waals forceThermodynamicsChemical engineeringBiochemistryMolecule

Abstract

fetched live from OpenAlex

The solubility of hydrogen in n-hexane was determined using a homemade reactor. The solubility of hydrogen in soybean oil was established using the Peng-Robinson (PR) equation of state and the van der Waals mixing rule. The curve equation established a linear relationship between the solubility of hydrogen in oil and the number of moles of hydrogen in the reactor. Under the optimal temperature and catalyst, the relationship between the hydrogen consumption of the hydrogenation of oil and fat and the TFAs formed in the oil was determined. When the reaction pressure exceeded 3.0 MPa, the hydrogenation of oil was consumed. The amount of hydrogen, the rate of hydrogenation, and the change in the TFAs all stabilized. Therefore, the pressure of the general hydrogenation reaction should not exceed 3.0 MPa. This result provides a quick and simple method for controlling TFAs in oils and fats for industrial 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)0.001
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 score0.029
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.090
GPT teacher head0.291
Teacher spread0.201 · 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