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Record W2945585512 · doi:10.1021/acssuschemeng.9b00811

Carbonyl Reduction and Biomass: A Case Study of Sustainable Catalysis

2019· article· en· W2945585512 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsBiomass (ecology)Context (archaeology)CatalysisBiochemical engineeringBenchmark (surveying)Green chemistryProcess engineeringEnvironmental scienceChemistryRenewable energyWaste managementNanotechnologyPulp and paper industryOrganic chemistryMaterials scienceEngineeringReaction mechanismEcology

Abstract

fetched live from OpenAlex

Catalysis plays a major role at mitigating the environmental impact of the chemical industry, drastically cutting its energy and material consumption. For this Perspective, we have chosen C═O reduction in the context of biomass as a benchmark reaction to introduce and illustrate essential aspects of green catalysis. We first covered the most used C═O hydrogenation substrates made from biomass. Then, we looked at alternative energy sources to convective heating, discussed the use of greener solvents and reductants, and listed a few precious metal-free catalytic systems. Finally, we looked at various hydrogen sources, including biosourced ones. In particular, we emphasized the use of metrics in order to quantify the actual impact of these innovations.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.003
GPT teacher head0.174
Teacher spread0.171 · 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