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Record W2262093755 · doi:10.13031/aim.20141904742

Investigating cost of delivering Switchgrass (Pnicum Virgatum) to a greenhouse using IBSAL model

2014· article· en· W2262093755 on OpenAlexfundaboutno aff
Hamid Khaleghi Hamedani, Shahab Sokhansanj, Anthony Lau, Jake DeBruyn, Mahmoud Ebadian

Bibliographic record

Venue2014 ASABE Annual International Meeting · 2014
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
FundersOntario Ministry of Food and AgricultureU.S. Department of Energy
KeywordsGreenhouseGreenhouse gasPanicum virgatumEnvironmental scienceBiomass (ecology)Dry matterAgricultural engineeringBioenergyWaste managementAgronomyBiofuelEngineeringEcology

Abstract

fetched live from OpenAlex

<abstract> <bold>Abstract.</bold> In this paper, the logistics system to deliver Switchgrass to a greenhouse in Ontario was evaluated. The daily heat demand of a greenhouse of 10000 <inline-graphic id=ID0EDGAE xlink:href=141904742_files/141904742-05.jpg/> in 2012 was calculated. The annual biomass demand of the greenhouse was 2177 Mg (with a boiler with the efficiency of 70%). The Integrated Biomass Supply Analysis and Logistics (IBSAL) model was used for simulating biomass logistics system. Three different farms in Clinton, Ontario were investigated in this study. The biomass logistics system included harvesting, raking, baling, loading, transportation, and storage. Cost, energy and carbon emission for the entire logistics system were estimated to be 64.72 $/Mg, 151.27 MJ/Mg and 10.37 kg CO2/Mg, respectively. The annual dry matter loss was 474 Mg. Two different harvest scenarios were compared, fall and winter harvests. The comparison showed that it is more viable to have winter harvest. To meet the demand of the greenhouse, there are two options of having another supplier, or decreasing the dry matter losses.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.853

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 head0.017
GPT teacher head0.254
Teacher spread0.237 · 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 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
Published2014
Admission routes2
Has abstractyes

Explore more

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