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Record W1563413398 · doi:10.1002/047167849x.bio065

Novel Separation Techniques for Isolation and Purification of Fatty Acids and Oil By‐Products

2005· other· en· W1563413398 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 · 2005
Typeother
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsMemorial University of NewfoundlandAgriculture and Agri-Food Canada
Fundersnot available
KeywordsDegree of unsaturationChemistryDistillationMelting pointFatty acidOrganic chemistryPolyunsaturated fatty acidVapor pressureChromatographyDegree (music)

Abstract

fetched live from OpenAlex

Abstract Although TAGs are the predominant molecular form of edible fats and oils, it might be necessary to subject them to separation according to their chemical composition or to modify them in different ways. Principles of separating fatty acids are based on specific properties of each acid or acid group. Two major properties (vapor pressure and melting point difference) are used in developing separation techniques. The vapor pressure of a mixture of fatty acids varies significantly with the chain length of fatty acids involved, which is used in fractional distillation as a means of separating short‐ and long‐chain fatty acids. However, vapor pressure does not change much with the degree of unsaturation. In the other method, the melting point of fatty acids changes considerably with the degree of unsaturation, which could be used to separate a mixture of fatty acids into saturated and unsaturated components. By changing the temperature of the mixture, fatty acids can be separated according to the degree of unsaturation at their respective crystallization temperature.

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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.715
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 integrity0.0010.000
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 head0.025
GPT teacher head0.247
Teacher spread0.222 · 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