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Record W2969640192 · doi:10.1016/j.dib.2019.104347

Data set of green extraction of valuable chemicals from lignocellulosic biomass using microwave method

2019· article· en· W2969640192 on OpenAlexafffund
Carlos S. Osorio‐González, Krishnamoorthy Hegde, Satinder Kaur Brar, Azadeh Kermanshahi‐pour, Antonio Avalos Ramírez

Bibliographic record

VenueData in Brief · 2019
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsCentre National en Électrochimie et en Technologies EnvironnementalesDalhousie UniversityCollège ShawiniganYork UniversityInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSyringaldehydeVanillinFurfuralLignocellulosic biomassVanillic acidBiomass (ecology)Pulp and paper industryFerulic acidExtraction (chemistry)Raw materialChemistryLigninCellulosic ethanolBiotechnologyBiochemical engineeringEnvironmental scienceOrganic chemistryFood scienceCatalysisCelluloseAgronomyBiologyEngineering

Abstract

fetched live from OpenAlex

Lignocellulosic biomass is a promising alternative for the replacement of limited fossil resources to produce various chemical compounds, such as 5-hydroxymethylfurfural, furfural, vanillin, vanillic acid, ferulic acid, syringaldehyde, and 4-aminobenzoic acid. However, the complex biomass structure is a limitation to making effective use of this naturally found feedstock. This research presents a data set of different compounds obtained directly from forest residues, with special emphasis on achieving effective utilization of the biomass. The extraction method and the catalyst are considered as the two main factors in this valorization process.

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)0.003
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.067
GPT teacher head0.317
Teacher spread0.251 · 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
GenreDataset

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

Citations12
Published2019
Admission routes2
Has abstractyes

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