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Biomass waste-to-energy supply chain optimization with mobile production modules

2021· article· en· W3152745634 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

VenueComputers & Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBiomass (ecology)Supply chainProduction (economics)Work (physics)Supply chain optimizationEnvironmental scienceSustainabilityModular designWaste managementAgricultural engineeringEnvironmental economicsEngineeringEnvironmental engineeringComputer scienceBusinessSupply chain managementEconomics

Abstract

fetched live from OpenAlex

Biomass waste is a naturally occurring agricultural byproduct. It is estimated that about 60 million tons per year can be extracted sustainably without altering land use patterns or competing with existing demands. Utilizing this waste is logistically challenging due to the inherent low density and distributed availability of biomass. This work proposes a supply chain optimization problem which decides where to locate and relocate mobile and modular production units to convert biomass waste to energy . Both deterministic and two-stage stochastic formulations are presented, accounting for the inherent uncertainty of where and how much biomass is produced. The framework is applied to case studies analyzing the states of Minnesota and North Carolina. Results from both states show that mobile production modules lead to supply chain cost savings of 1–4%, or millions of dollars per year. Additionally, this work demonstrates the benefit of mobile modules as a means of protecting against uncertainty.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.160
Teacher spread0.157 · 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