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Record W2360392601 · doi:10.7451/cbe.2016.58.8.1

Simulation of logistics to supply Corn Stover to the Ontario Power Generation (OPG) Plant in Lambton, Ontario.

2016· article· en· W2360392601 on OpenAlexaffvenueabout
Hamid Khaleghi Hamedani, Anthony Lau, Jake DeBruyn, Mahmud Ebadian, S. Sokhansanj

Bibliographic record

VenueCanadian Biosystems Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsMinistry of Agriculture, Food and Rural AffairsUniversity of British Columbia
Fundersnot available
KeywordsCorn stoverStoverPower stationAgricultural economicsEnvironmental sciencePower (physics)BusinessAgricultural engineeringEconomicsWaste managementEngineeringGeographyElectrical engineeringForestryBiofuel

Abstract

fetched live from OpenAlex

The overall goal of this research is to investigate the logistics of agricultural biomass in Ontario, Canada using the Integrated Biomass Supply Analysis and Logistics Model (IBSAL). The supply of corn stover to the Ontario Power Generation (OPG) power plant in Lambton is simulated. This coal-fired power plant is currently not operating and there are no active plans by OPG to fuel it with biomass. Rather, this scenario is considered only to demonstrate the application of the IBSAL Model to this type of scenario. Here, five scenarios of delivering corn stover to the Lambton Generating Station (GS) power plant in Lambton Ontario are modeled: (1) truck transport from field edge to OPG (base scenario); (2) farm to central storage located on the highway, then truck transport bales to OPG; (3) direct truck transport from farm (no-stacking) to OPG; (4) farm to a loading port on Lake Huron and from there on a barge to OPG; and (5) farm to a railhead and then to OPG by rail.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.015
GPT teacher head0.177
Teacher spread0.162 · 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 designSimulation or modeling
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

Citations1
Published2016
Admission routes3
Has abstractyes

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