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Record W2773376367 · doi:10.1002/9781119135388.ch1

Nomenclature and general classification of antioxidant activity/capacity assays

2017· book-chapter· en· W2773376367 on OpenAlexaff
Yong Sun, Cheng Yang, Rong Tsao

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldChemistry
TopicFree Radicals and Antioxidants
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsAntioxidant capacityComputational biologyNomenclatureSet (abstract data type)Classification schemeBiologyBiochemistryBiochemical engineeringAntioxidantChemistryComputer scienceEngineeringMachine learningTaxonomy (biology)Ecology

Abstract

fetched live from OpenAlex

This chapter intends to find a way to reconcile the different views and provides a relatively simplified approach to the nomenclature and general classification of various antioxidant activity/capacity (AOA/TAC) assays currently in use for the assessment of AOA/TAC in diets and biological fluids. The concept of AOA/TAC may be traced back to its origin in chemistry and then its applications in food science, in biology and medicine, and in nutrition and epidemiology. Most of the current AOA/TAC assays are named based on the reactants, the reaction mechanism and/or the corresponding techniques; and the chapter summarizes these factors. An integrated approach to the existing complex classification systems is adopted to classify the existing AOA/TAC assays into the following five categories: hydrogen atom transfer (HAT)-based assays; single electron transfer (SET)-based assays; mixed-mode (HAT/SET) assays; in vivo antioxidant activity/capacity assays; and miscellaneous methods. The chapter tabulates this classification, and gives a brief explanation of these assays.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.583
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.250
Teacher spread0.210 · 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.

Study designBench or experimental
Domainnot available
GenreOther

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

Citations14
Published2017
Admission routes1
Has abstractyes

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