MétaCan
Menu
Back to cohort
Record W4405089303 · doi:10.1007/s00216-024-05680-4

Green analytical chemistry: integrating sustainability into undergraduate education

2024· article· en· W4405089303 on OpenAlexfundno aff
Saša M. Miladinović

Bibliographic record

VenueAnalytical and Bioanalytical Chemistry · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsnot available
FundersHaute école Spécialisée de Suisse OccidentaleUniversity of Alberta
KeywordsSustainabilityAnalytical Chemistry (journal)Medical laboratoryEnvironmental chemistryEngineeringChemistryManagement scienceEngineering ethicsMedicineEcology

Abstract

fetched live from OpenAlex

Green chemistry has become a fundamental aspect of modern scientific research and industrial practice, promoting the development of sustainable technologies that minimize environmental impact and improve safety for both humans and ecosystems. Integrating green chemistry principles into various branches of chemistry, including analytical chemistry, underscores the importance of sustainability in all chemical processes [ 1 ].

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)0.005
metaresearch head score (Gemma)0.006
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: Methods · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0050.005
Open science0.0010.008
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0130.004

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 head0.004
GPT teacher head0.237
Teacher spread0.233 · 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
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

Citations35
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAnalytical and Bioanalytical ChemistrySame topicChemistry and Chemical EngineeringFrench-language works237,207