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Record W2462848888 · doi:10.1021/bk-2009-1000.ch012

The Twelve Principles of Green Chemistry

2008· book-chapter· en· W2462848888 on OpenAlexaff
Philip G. Jessop, Sofia Trakhtenberg, John C. Warner

Bibliographic record

VenueACS symposium series · 2008
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsQueen's University
Fundersnot available
KeywordsProduct (mathematics)Set (abstract data type)DozenField (mathematics)Engineering ethicsEngineeringPath (computing)Management scienceBiochemical engineeringNanotechnologyChemistryComputer scienceEngineering managementMaterials scienceMathematics

Abstract

fetched live from OpenAlex

In this, the centenary year of the Industrial and Engineering Chemistry Division, we present a dozen short essays, one for each of the 12 principles of green chemistry. These principles were first articulated in 1998 as a set of tools to help the design scientist to anticipate downstream issues at the earliest stage of an R&D effort. Recognizing that most materials scientists lack the formal training necessary to deal with issues related to human health, the environment, and regulatory implications, the twelve principles serve to provide a path forward in designing products and processes that would be less environmentally damaging while maintaining or enhancing product performance and economic cost. Designing processes to minimize environmental impact has become, in recent years, essential to industrial and engineering chemistry, and is likely to shape the field for the next 100 years.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.008
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0130.009

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 head0.007
GPT teacher head0.167
Teacher spread0.160 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations62
Published2008
Admission routes1
Has abstractyes

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