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Record W2892122378 · doi:10.1515/psr-2018-0074

Recent advances in the application of carbohydrates as renewable feedstocks for the synthesis of nitrogen-containing compounds

2018· article· en· W2892122378 on OpenAlexafffund
Seyed Iraj Sadraei, Brent St Onge, John F. Trant

Bibliographic record

VenuePhysical Sciences Reviews · 2018
Typearticle
Languageen
FieldChemistry
TopicCarbohydrate Chemistry and Synthesis
Canadian institutionsUniversity of Windsor
FundersUniversity of SussexUniversity of Windsor
KeywordsBiomass (ecology)ChemistryRenewable energyBiofuelFossil fuelOrganic chemistryCelluloseNitrogenRenewable fuelsBiochemical engineeringEnvironmental sciencePulp and paper industryWaste managementEngineeringEcologyBiology

Abstract

fetched live from OpenAlex

Abstract Carbohydrates, in the form of chitin, chitosan and cellulose, are one of the most available, renewable, and sustainable chemical feedstocks. Their conversion to biofuels, fine chemicals, and industrially-relevant monomers is becoming increasingly viable and promising as innovation decreases the price of this technology, and climate change and the price of fossil fuels increases the social and economic costs of using traditional feedstocks. In recent years, carbohydrates have been increasingly used as sources for nitrogen-containing fine chemicals. This chapter, with 86 references, provides a brief overview of the conversion of carbohydrate biomass to the standard hydrocarbon and oxygen-containing derivatives, and then provides a survey of recent progress in converting the biopolymers, and the derived mono and di-saccharides, into nitrogen-containing molecules with a special focus on N-heterocycle synthesis for medicinal applications.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.270
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.322
Teacher spread0.287 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2018
Admission routes2
Has abstractyes

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