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Antioxidant activity, total phenolics and flavonoids contents: Should we ban in vitro screening methods?

2018· article· en· W2797657329 on OpenAlexaff
Daniel Granato, Fereidoon Shahidi, Ronald E. Wrolstad, Paul A. Kilmartin, Laurence D. Melton, Francisco J. Hidalgo, Kazuo Miyashita, John Van Camp, Cesarettin Alasalvar, Amin Ismail, Stephen Elmore, G. G. Birch, Dimitris Charalampopoulos, Siân Astley, Ronald B. Pegg, Peng Zhou, Paul Finglas

Bibliographic record

VenueFood Chemistry · 2018
Typearticle
Languageen
FieldChemistry
TopicFree Radicals and Antioxidants
Canadian institutionsMemorial University of Newfoundland
FundersBiotechnology and Biological Sciences Research Council
KeywordsAntioxidantChemistryIn vitroChelationIn vivoHealth benefitsSinglet oxygenBiochemistryTraditional medicineOrganic chemistryBiotechnologyBiologyMedicineOxygen

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.002

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 head0.046
GPT teacher head0.308
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainMethods
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

Citations539
Published2018
Admission routes1
Has abstractno

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