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Record W2058170661 · doi:10.1039/b501692b

Green challenges: student perspectives from the 2004 ACS-PRF Summer School on Green Chemistry

2005· article· en· W2058170661 on OpenAlexaff
Selma Bektesevic, Julie Beier, Liang Chen, Nicolas Eghbali, Stephanie King, Galit Levitin, Geeta Mehta, Richard J. Mullins, Jessica L. Reiner, Ross R. Weikel, Songwen Xie, Erica Gunn

Bibliographic record

VenueGreen Chemistry · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsMcGill University
Fundersnot available
KeywordsLibrary scienceChemistryPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Participants in the American Chemical Society-Petroleum Research Fund (ACS-PRF) Summer School on Green Chemistry discuss the topics covered and lessons learned during the week-long summer school held July 31 through August 7, 2004, at Carnegie Mellon University. An outline of the program is accompanied by a discussion of the challenges and needs of the field of green chemistry as seen by the participants. These include further education of the public as well as members of the scientific community, thorough research and rigorous publication standards, and the formation of a cooperative and collaborative group of researchers.

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)0.019
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0370.006
Scholarly communication0.0200.008
Open science0.0030.017
Research integrity0.0120.019
Insufficient payload (model declined to judge)0.0080.002

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 head0.013
GPT teacher head0.225
Teacher spread0.212 · 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 designQualitative
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

Citations4
Published2005
Admission routes1
Has abstractyes

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