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Record W4283777948 · doi:10.3390/bioengineering9070296

Agriculture Waste Biomass Repurposed into Natural Fibers: A Circular Bioeconomy Perspective

2022· article· en· W4283777948 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBioengineering · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsRepurposingBiorefineryBiomass (ecology)AgricultureCircular economyEnvironmental scienceEnvironmental pollutionBusinessWaste managementNatural resource economicsAgricultural engineeringBiochemical engineeringBiofuelEngineeringEnvironmental protectionEconomicsAgronomyEcology

Abstract

fetched live from OpenAlex

Fibers come from natural and fossil resources and are an essential commodity widely used by textile industries. Considering current supply and future demands, the repurposing of agricultural residues into fibers is an eco-friendly, attractive option that might mitigate environmental pollution. In this review, we have summarized multiple alternate secondary sources for fiber production, with a case study using banana plant residual biomass, a common agricultural waste in many developing countries. Specifically, in this review we have compared the different processing methods, e.g., chemical, mechanical, or biological methods, for repurposing agricultural residual biomass (including banana waste) into fibers. The development and analysis of an integrated biorefinery approach is needed to promote the fiber production from various agro-residual biomasses within the framework of circular bioeconomic concepts.

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 score0.014
Threshold uncertainty score0.950

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 science0.0010.000
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 head0.206
Teacher spread0.200 · 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