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Record W4413403649 · doi:10.1039/d5su00554j

Waving the green flag: incorporating sustainable and green chemistry practices into research and education

2025· article· en· W4413403649 on OpenAlexafffund
Marissa L. Clapson, Greg Bannard, Gagan Daliaho, Jasmine Hong, Emma C. Davy, Julia Pitsiaeli, Connor S. Durfy, Shauna Schechtel

Bibliographic record

VenueRSC Sustainability · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsUniversity of WindsorQueen's UniversityUniversity of British ColumbiaMcGill UniversityUniversity of Prince Edward Island
FundersUniversity of British ColumbiaUniversity of Prince Edward IslandGilead SciencesMcGill UniversityWestern UniversityUniversity of Windsor
KeywordsFlag (linear algebra)Green chemistryChemistryMathematicsOrganic chemistryAlgebra over a field

Abstract

fetched live from OpenAlex

The interdisciplinary nature of green chemistry requires innovative approaches for engaging broad audiences, making active learning and gamified learning of interest for exploring green chemistry in classrooms and conference settings.

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)0.015
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0040.013
Scholarly communication0.0110.020
Open science0.0020.020
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0080.004

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 head0.010
GPT teacher head0.304
Teacher spread0.295 · 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
GenreCommentary

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

Citations7
Published2025
Admission routes2
Has abstractyes

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