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Beyond waste in agriculture: Feedstock characterization for thermochemical conversion based on potato above-ground biomass

2024· article· en· W4405031290 on OpenAlexafffund
Javier Chávez, Jianbing Li

Bibliographic record

VenueBioresource Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Northern British Columbia
KeywordsBiomass (ecology)Raw materialWaste managementEnvironmental scienceAgricultureAgricultural wasteBioenergyCharacterization (materials science)Municipal solid wasteBiofuelAgronomyPulp and paper industryChemistryEngineeringMaterials scienceEcologyNanotechnology

Abstract

fetched live from OpenAlex

• Plant moisture content is a key determinant of suitable conversion routes. • Overall potato above-ground biomass moisture was 89%, with variations over time. • At 40 days after planting (DAP), potato (Red Pontiac variety) heights were fully developed. • The canopy of Red Pontiac potatoes reached maximum development at 85 DAP. • Growing degree days (GDD) could be used for residue recovery in agriculture. Agricultural residues represent a valuable opportunity to develop circular bioeconomic systems centered on biomass. Characterizing this type of biomass can alleviate the pressure on current biomass sources (e.g., in forests and their biodiversity), enhance agricultural waste management, and reduce crop field emissions. Thus, this study aimed to evaluate the potential of agricultural plant-based residues as feedstock for thermochemical conversion processes, focusing on potato above-ground biomass to enhance herbaceous characterization. The gravimetric characterization of this type of biomass revealed a water content of 89 % for potato above-ground biomass with differences per plant section. Biomass abundance was also measured, showing that well-developed leaves and main stems were more plentiful. After 70 days after planting (DAP), maximum plant development was achieved, differing from heights development at 40 DAP. Hence, identifying specific times for residue recovery can help develop strategies for biomass recovery, thereby reducing pretreatment costs following the selection of suitable conversion technologies.

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 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 score0.019
Threshold uncertainty score0.936

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 integrity0.0010.000
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 head0.005
GPT teacher head0.196
Teacher spread0.192 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes2
Has abstractyes

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