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Record W4245805022 · doi:10.1002/cctc.201800009

Hydrogen Peroxide Assisted Selective Oxidation of 5‐Hydroxymethylfurfural in Water under Mild Conditions

2018· article· en· W4245805022 on OpenAlexfundno aff
Ching‐Tien Chen, Zheng‐Yen Wang, Yoshio Bando, Yusuke Yamauchi, Manar Tareq Saleh Bazziz, Amanullah Fatehmulla, Wajiha Farooq, Takuya Yoshikawa, Takao Masuda, Kevin C.‐W. Wu

Bibliographic record

VenueChemCatChem · 2018
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsnot available
FundersInstitute of Population and Public HealthKing Saud University
KeywordsHydrogen peroxide5-hydroxymethylfurfuralChemistryBiomass (ecology)PeroxideCatalysisCover (algebra)Front coverOrganic chemistryEnvironmental chemistryGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract The front cover artwork for issue 2/2018 is provided by Prof. Kevin Wu's group from National Taiwan University, Taiwan. The image shows that the biomass‐derived compound 5‐hydroxymethylfurfural (HMF), can be selectively converted to 5‐formyl‐2‐furoic acid (FFCA) and 2,5‐furandicarboxylic acid (FDCA) with the assistance of hydrogen peroxide, and the large lake indicates that the reaction can be conducted in water. See the Communication itself at https://doi.org/10.1002/cctc.201701302 .

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 score0.009
Threshold uncertainty score0.557

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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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