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Record W2259237421 · doi:10.1021/acssuschemeng.5b01555

Critical Evaluation of Published Algorithms for Determining Environmental and Hazard Impact Green Metrics of Chemical Reactions and Synthesis Plans

2016· article· en· W2259237421 on OpenAlexaff
John Andraos, Melanie L. Mastronardi, Laura B. Hoch, Andrei Hent

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAlgorithmComputer scienceAnilineHazardChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In this paper, we analyze six published algorithms that evaluate environmental and hazard impact green metrics. The methods are compared and contrasted on a common set of chemical reactions and synthesis plans. The relative greenness of four reaction procedures to prepare iron(II)oxalate dihydrate and three industrial preparations of aniline are examined. We also examine the organic syntheses preparations of 2,2-diethoxy-1-isocyanoethane, thiete 1,1-dioxide, and ethyl phenylcyanopyruvate that we previously evaluated by material efficiency. We discuss the merits and limitations of all algorithms with respect to quality of calculation outputs, visualization, and ease of use.

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)0.024
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.098
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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.

Study designNot applicable
DomainEvaluation
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

Citations29
Published2016
Admission routes1
Has abstractyes

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