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Record W4283330430 · doi:10.1021/acscentsci.2c00325

An International Study Evaluating Elemental Analysis

2022· article· en· W4283330430 on OpenAlexafffund
Rupert E. H. Kuveke, Lachlan Barwise, Yara van Ingen, Kanika Vashisth, Nicholas J. Roberts, Saurabh S. Chitnis, Jason L. Dutton, Caleb D. Martin, Rebecca L. Melen

Bibliographic record

VenueACS Central Science · 2022
Typearticle
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsDalhousie University
FundersAustralian Research CouncilEngineering and Physical Sciences Research CouncilNatural Sciences and Engineering Research Council of CanadaDepartment of Education and TrainingNational Science FoundationGovernment of CanadaResearch Councils UKWelch Foundation
KeywordsStatistical analysisCombustionElemental analysisChemistryComputer scienceData scienceStatisticsMathematicsOrganic chemistry

Abstract

fetched live from OpenAlex

A statistical study on elemental analysis for 5 small organic compounds at 18 independent service providers across multiple countries demonstrates variation in the returned results that is outside journal guidelines (0.4%) in greater than 10% of measurements. The results indicate that a deviation of 0.4% is not a realistic journal requirement with the variability attributed to random error.

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)0.034
metaresearch head score (Gemma)0.069
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.069
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.012
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.028
GPT teacher head0.367
Teacher spread0.340 · 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 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

Citations94
Published2022
Admission routes2
Has abstractyes

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