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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 OpenAlex
Carlos S. Osorio‐González, Krishnamoorthy Hegde, Satinder Kaur Brar, Azadeh Kermanshahi‐pour, Antonio Avalos Ramírez

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

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.

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)0.000
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 score0.228
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.067
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