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Record W4382725065 · doi:10.1039/d3sc90103c

Outstanding Reviewers for <i>Chemical Science</i> in 2022

2023· editorial· en· W4382725065 on OpenAlexfundno aff

Bibliographic record

VenueChemical Science · 2023
Typeeditorial
Languageen
FieldMaterials Science
TopicAdvanced Nanomaterials in Catalysis
Canadian institutionsnot available
FundersUniversity of California, IrvineUniversity of Colorado BoulderUniversity of California, Los AngelesUniversity of ManchesterUniversité de GenèveIndian Institute of Technology GuwahatiUniversity of Cape TownRheinische Friedrich-Wilhelms-Universität BonnUniversità della CalabriaUniversidade de VigoUniversité de StrasbourgIndian Institute of Science Education and Research MohaliDanmarks Tekniske UniversitetNational and Kapodistrian University of AthensUniversitetet i BergenNanjing UniversityQueensland University of TechnologyPhilipps-Universität MarburgUniversità di BolognaUniversity of South FloridaIndian Institute of ScienceUniversity of TorontoUniversidad Autónoma del Estado de MéxicoImperial College LondonDartmouth CollegeCity University of Hong Kong
KeywordsPsychologyData scienceComputer science

Abstract

fetched live from OpenAlex

We would like to take this opportunity to highlight the Outstanding Reviewers for Chemical Science in 2022, as selected by the editorial team for their significant contribution to the journal.

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)0.009
metaresearch head score (Gemma)0.044
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.079
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0000.006
Scholarly communication0.0010.001
Open science0.0050.002
Research integrity0.0010.001
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 head0.017
GPT teacher head0.333
Teacher spread0.316 · 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
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

Citations0
Published2023
Admission routes1
Has abstractyes

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