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Record W1530791293 · doi:10.1002/9783527628698.hgc001

Atom Economy – Principles and Some Examples

2010· other· en· W1530791293 on OpenAlexaff
Audrey Moores

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsMcGill University
Fundersnot available
KeywordsAtom economyAtom (system on chip)Context (archaeology)Product (mathematics)Process (computing)EconomicsEconomyChemistryComputer scienceCatalysisMathematicsOrganic chemistryHistory

Abstract

fetched live from OpenAlex

Abstract The concept of atom economy was introduced in the early 1990s by Trost and Sheldon to emphasize the importance of minimizing the waste created by chemical reactions. Atom economy can be defined as the reduction, if not the elimination, of all the atoms introduced in the process that do not end up in the desired product. This idea came together with a desire to be able to measure rationally the created waste and several criteria of atom economy were thus grafted. Since then, many new chemical reactions that can be called atom economical have been developed. In this chapter, the context, the principle, the criteria and the tool box of atom economy will be presented. A mini‐review on atom economical processes involving CH activation follows.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.004
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
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 head0.007
GPT teacher head0.176
Teacher spread0.169 · 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
GenreOther

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

Citations6
Published2010
Admission routes1
Has abstractyes

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