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Record W4210766546 · doi:10.1002/ceat.202100517

Extraction of Sugars and Cellulose Fibers from <i>Cannabis</i> Stems by Hydrolysis, Pulping, and Bleaching

2022· article· en· W4210766546 on OpenAlex
Falguni Pattnaik, Sonil Nanda, Vivek Kumar, S.N. Naik, Ajay K. Dalai, M. Mohanty

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

VenueChemical Engineering & Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsHemicelluloseCelluloseLigninChemistryHydrolysisCrystallinityExtraction (chemistry)Yield (engineering)Thermal stabilityNuclear chemistryChromatographyOrganic chemistryMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Abstract Cannabis indica stems were hydrolyzed with subcritical water at various temperatures, reaction times, and feed concentrations. The highest total yield of reducing sugars of 16.4 wt % was obtained at 190 °C in 37.5 min with a feed concentration of 3.5 wt %. Solid residues from the optimized process were treated with 0.5 M NaOH (pulping) and 0.5–3 % H 2 O 2 (bleaching) to isolate cellulose fibers. The maximum yield of cellulose was 34.8 wt % with lowest lignin content of 0.5 wt %. With the removal of hemicellulose and lignin through the integrated hydrothermal processes, the crystallinity index and thermal stability of the cellulose fibers increased.

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 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 score0.027
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.002
GPT teacher head0.158
Teacher spread0.156 · 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