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Record W2921939909 · doi:10.30958/ajte.3-2-1

Forest Biomass and Paper Industry, a Pathway to Green Biofuels

2016· article· en· W2921939909 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.

Bibliographic record

VenueAthens Journal of Τechnology & Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsBiofuelBiomass (ecology)BioenergyEnvironmental scienceNatural resource economicsAgroforestryBusinessEconomicsWaste managementEngineeringAgronomyBiology

Abstract

fetched live from OpenAlex

The components of wood, celluloses, hemicelluloses and lignin, can be separated and converted into new non paper products. This paper treats the specific case of lignin and hemicellulose extraction from a Kraft pulping process. Two processes have been investigated for the extraction of lignin from black liquor. One is an enhanced version of precipitation of lignin under low pH conditions achieved by carbonation of Kraft black liquor. The second process focuses on the acidification of the black liquor by electrodialysis with a bipolar membrane. This process produces clean lignin and valuable caustic soda. The extraction of hemicelluloses from wood chips and their partitioning into a mixture of C5 and C6 sugars can be accomplished by a two-step hydrolysis. The sugars can then be converted into a large number of derivatives. The case of fermentation into butanol is presented. The energy intensification of the site eliminates the requirement for fossil fuel thus enhancing the feasibility of the green integrated forest biorefinery.

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 score0.365
Threshold uncertainty score0.368

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 integrity0.0000.000
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 head0.006
GPT teacher head0.186
Teacher spread0.180 · 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