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Record W4311885274 · doi:10.3390/plants11243577

Integrated Biorefinery and Life Cycle Assessment of Cassava Processing Residue–From Production to Sustainable Evaluation

2022· review· en· W4311885274 on OpenAlexaff
Larissa Renata Santos Andrade, Raul José Alves Felisardo, Ianny Andrade Cruz, Muhammad Bilal, Hafiz M.N. Iqbal, Sikandar I. Mulla, Ram Naresh Bharagava, Ranyere Lucena de Souza, Lucas Carvalho Basilio Azevedo, Luiz Fernando Romanholo Ferreira

Bibliographic record

VenuePlants · 2022
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsBiorefineryLife-cycle assessmentResidue (chemistry)Production (economics)Sustainable productionEnvironmental scienceProduction cyclePulp and paper industryWaste managementEngineeringManufacturing engineeringChemistryEconomicsBiofuel

Abstract

fetched live from OpenAlex

Commonly known as a subsistence culture, cassava came to be considered a commodity and key to adding value. However, this tuber's processing for starch and flour production is responsible for generating a large amount of waste that causes serious environmental problems. This biomass of varied biochemical composition has excellent potential for producing fuels (biogas, bioethanol, butanol, biohydrogen) and non-energetic products (succinic acid, glucose syrup, lactic acid) via biorefinery. However, there are environmental challenges, leading to uncertainties related to the sustainability of biorefineries. Thus, the provision of information generated in life cycle assessment (LCA) can help reduce bottlenecks found in the productive stages, making production more competitive. Within that, this review concentrates information on the production of value-added products, the environmental impact generated, and the sustainability of biorefineries.

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)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.122
GPT teacher head0.369
Teacher spread0.247 · 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
GenreReview

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

Citations22
Published2022
Admission routes1
Has abstractyes

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