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Record W4387965545 · doi:10.14573/altex.2307041

Report of the 3rd and 4th Mystery of Reactive Oxygen Species Conference

2023· editorial· en· W4387965545 on OpenAlexfundno aff
Shihori Tanabe

Bibliographic record

VenueALTEX · 2023
Typeeditorial
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsnot available
FundersRECETOX Přírodovědecké Fakulty Masarykovy UniverzityBundesinstitut für RisikobewertungEuropean Food Safety AuthorityHELSUS Kestävyystieteen InstituuttiL'Oreal USANorsk Institutt for VannforskningMasarykova UniverzitaJoint Research CentreJulius-Maximilians-Universität WürzburgJapan Society for the Promotion of ScienceUniversity of OttawaMinistry of Health, Labour and WelfareNorges Miljø- og Biovitenskapelige UniversitetCanadian Nuclear LaboratoriesOffice of the Secretary of DefenseKorea Institute of Science and TechnologyPhilip Morris InternationalHelsingin YliopistoEuropean CommissionJapan Agency for Medical Research and DevelopmentNorges ForskningsrådLeibniz-GemeinschaftMinistry of Education, Culture, Sports, Science and TechnologyHealth Canada
KeywordsEnvironmental science

Abstract

fetched live from OpenAlex

The main MoR discussion led to further suggestions on KE terminology, including ensuring coherence to directionality in terms of the KE descriptions (e.g., specifying increase, decrease, altered, no direction, etc.) and clarifying differences in ROS and reactive oxygen and nitrogen species (RONS), and enzymatic and non-enzymatic events. The consortium highlighted the importance of the role of ROS as a KE and an associative event in the AOP framework. Additionally, participants highlighted modification to macromolecules from the resultant RONS generation (e.g., lipid peroxidation) as a relevant endpoint to include in the KE. The possibility of grouping ROS-related KEs in the AOP framework needs to be discussed further.

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 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)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.019
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.001
Science and technology studies0.0040.003
Scholarly communication0.0080.005
Open science0.0030.002
Research integrity0.0190.020
Insufficient payload (model declined to judge)0.0160.010

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 head0.033
GPT teacher head0.284
Teacher spread0.251 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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

Citations8
Published2023
Admission routes1
Has abstractyes

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