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Record W2007406600 · doi:10.3109/02652048.2014.932028

Microencapsulation of krill oil using complex coacervation

2014· article· en· W2007406600 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

VenueJournal of Microencapsulation · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMicroencapsulation and Drying Processes
Canadian institutionsQueen's UniversityMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsCoacervateKrillMaterials scienceChemical engineeringChromatographyNanotechnologyPolymer scienceChemistryFisheryBiologyEngineering

Abstract

fetched live from OpenAlex

The research work was aimed at the development of a process to yield gelatin-gum Arabic multinuclear microcapsules of krill oil (KO), via complex coacervation. On the basis of the experimental results of the screening trials, a three-level-by-three-factor Box-Behnken design was used to evaluate the effects of the ratio of the core material to the wall (RCW; x1), the stirring speed (SP; x2) and the pH (x3) on the encapsulation efficiency (EE). The experimental findings indicated that x3 has the most significant linear and quadratic effects on the EE of KO and a bilinear effect with x1, whereas x2 did not have any significant effect. The optimal conditions for a 92% of EE were: 1.75:1 for RCW, 3.8 for pH and 3 for SP. The microcapsules, formed by complex coacervation and without any cross-linking agent, were multinucleated, circular in shape and had sufficient stability to maintain their structure.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.386

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 head0.047
GPT teacher head0.248
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