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Record W4382196193 · doi:10.1016/j.wmb.2023.06.005

Saccharification of agricultural residues by Streptomyces sp. and ethanol production from agro-waste mixture hydrolysate

2023· article· en· W4382196193 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

VenueWaste Management Bulletin · 2023
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrolysateFermentationEthanol fuelHydrolysisRaw materialSugarChemistryFood sciencePulp and paper industryReducing sugarYeastPulp (tooth)Orange (colour)BiotechnologyBiologyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The present work demonstrated the potential of different agro-waste mixtures to produce ethanol. The forest soil bacterium (Streptomyces sp.) was exploited for the saccharification of the agro-waste mixture formulated by extreme vertices mixture design and the hydrolysate produced by saccharification was used for fermentation. The best formulation contained 43.33% orange peel, 33.33 % pumpkin pulp+seeds, and 23.33% pomegranate peel which exhibited significantly high reducing sugar (22.36±0.54 mg/g dry weight) among all other mixtures. The hydrolysate of this mixture when supplemented with 2% w/v fructose produced a maximum of 7.86±0.08% v/v ethanol by the yeast isolated from the brewer’s spent grains. Thus, easily available waste could be a promising source for yeast isolation and feedstock for ethanol production. Further, this study aids to reduce the risk of health, and environmental pollution, and developing the economy.

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 score0.317
Threshold uncertainty score0.548

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 head0.007
GPT teacher head0.181
Teacher spread0.175 · 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