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LIPASE‐CATALYZED ACIDOLYSIS OF ALGAL OILS WITH CAPRIC ACID: OPTIMIZATION OF REACTION CONDITIONS USING RESPONSE SURFACE METHODOLGY

2004· article· en· W2043490979 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 Lipids · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCapric AcidChemistryDocosapentaenoic acidDocosahexaenoic acidLipaseSubstrate (aquarium)CatalysisFatty acidAlgae fuelEnzymeCandida antarcticaResponse surface methodologyHydrolysisChromatographyOrganic chemistryPolyunsaturated fatty acidBiodieselLauric acid

Abstract

fetched live from OpenAlex

ABSTRACT Lipase‐assisted acidolysis of algal oils, arachidoinc acid single cell oil (ARASCO), docosahexaenoic acid single cell oil (DHASCO) and a single cell oil rich in both docosahexaenoic acid (DHA) and docosapentaenoic acid (DPA, n‐6) known as OMEGA‐GOLD, with a medium‐chain fatty acid (capric acid) was studied. Response surface methodology was used to obtain a maximum incorporation of CA into algal oils. The process variables studied were the amount of enzyme (2–6%), reaction temperature (35–55C) and incubation time (12–36 h). The amount of water added and mole ratio of substrate (algal oil to CA) were kept at 2% and 1:3, respectively. All experiments were conducted according to a face‐centered cube design. Under optimum conditions (12.3% of enzyme; 45C; 29.4 h), the incorporation of CA was 20.0% into ARASCO, 22.6% into DHASCO (4.2% enzyme; 43.3C; 27.I h) and 20.7% into the OMEGA‐GOLD oil (2.5% enzyme, 46.6C; 25.2 h).

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 score0.187
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.021
GPT teacher head0.283
Teacher spread0.262 · 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