MétaCan
Menu
Back to cohort
Record W3049594411 · doi:10.1021/acsomega.0c02181

Optimization of Ultrasound-Assisted Extraction of Functional Food Fiber from Canadian Horseweed (<i>Erigeron canadensis</i> L.)

2020· article· en· W3049594411 on OpenAlexaboutno aff
Sebastian Balicki, Izabela Pawlaczyk‐Graja, Roman Gancarz, Peter Capek, Kazimiera A. Wilk

Bibliographic record

VenueACS Omega · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsnot available
FundersVedecká Grantová Agentúra MŠVVaŠ SR a SAVPolitechnika Wrocławska
KeywordsExtraction (chemistry)BioproductsResponse surface methodologyFiberBiomass (ecology)Pulp and paper industryChromatographyBiofuelBiologyBiotechnologyChemistryAgronomy

Abstract

fetched live from OpenAlex

). A comparison with the conventional procedure for retrieval of horseweed biomacromolecules revealed that the optimized ultrasound-assisted extraction was more sustainable, with the cumulative energy demand being 38% lower (12.2 MJ), 6.6 times reduced water consumption (3.5 L), and 1.2 times shorter (41 h) total processing time. Moreover, the optimal ultrasound-assisted extraction process-purified food fiber turned out to be a better anticoagulant agent by 57%, compared to a conventional product, and was a more selective indirect inhibitor of the human Xa coagulation factor.

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.

How this classification was reachedexpand

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.983

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 head0.015
GPT teacher head0.214
Teacher spread0.199 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations25
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueACS OmegaSame topicProtein Hydrolysis and Bioactive PeptidesFrench-language works237,207