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Record W4365505135 · doi:10.1016/j.eti.2023.103146

A novel clean bio-pulping process for rice straw based on aerobic fermentation coupled with mechanical refining

2023· article· en· W4365505135 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEnvironmental Technology & Innovation · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsRefining (metallurgy)Pulp and paper industryRice strawFermentationStrawWaste managementChemistryProcess (computing)Environmental scienceBiotechnologyFood scienceEngineeringBiologyComputer science

Abstract

fetched live from OpenAlex

In this study, the effect of aerobic fermentation pretreatment on bio-mechanical pulping of rice straw was investigated with compound bacterial agents (2 geobacillus sp, 1 parageobacillus sp and 4 thermos sp). The microbial community, chemical composition, surface morphology, crystal index and other parameters were monitored at various time intervals during the aerobic fermentation process. The chemical constituents of rice straw (mainly cellulose, hemicellulose and lignin) were partly degraded and dissolved in the fermentation process, leading to increased surface roughness and crystallinity index. The bio-mechanical pulping process was optimized in terms of Canadian freeness standard (CSF), specific energy consumption (SEC), water retention value (WRV), pulping yield, and physical properties of the resultant pulp. Under the optimal process conditions, the fermentation pre-treatment resulted in about 54% energy saving, 13% WRV improvement, and 81% increase in tensile strength at a given pulp freeness.

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 score0.047
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.034
GPT teacher head0.309
Teacher spread0.275 · 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