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Record W2102879186 · doi:10.3390/en8064827

Ash Content and Calorific Energy of Corn Stover Components in Eastern Canada

2015· article· en· W2102879186 on OpenAlexafffundabout
Pierre-Luc Lizotte, Philippe Savoie, Alain De Champlain

Bibliographic record

VenueEnergies · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsAgriculture and Agri-Food CanadaUniversité Laval
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsStoverCorn stoverCrop residueAgronomyHeat of combustionHuskBiomass (ecology)CropEnvironmental scienceWater contentChemistryAnimal scienceCombustionBotanyAgricultureBiologyFood scienceEcology

Abstract

fetched live from OpenAlex

Corn stover is an abundant agricultural residue that could be used on the farm for heating and crop drying. Ash content and calorific energy of corn grain and six stover components were measured from standing plants during the grain maturing period, between mid-September and mid-November. Ash of stover in standing corn averaged 4.8% in a cool crop heat unit zone (2300–2500 crop heat units (CHU)) and 7.3% in a warmer zone (2900–3100 CHU). The corn cob had the lowest ash content (average of 2.2%) while leaves had the highest content (from 7.7% to 12.6%). In the fall, ash content of mowed and raked stover varied between 5.5% and 11.7%. In the following spring, ash content of stover mowed, raked and baled in May averaged 3.6%. The cob and stalk located below the first ear contained the highest calorific energy with 17.72 MJ·kg−1. Leaves and grain had the lowest energy with an average of 16.99 MJ·kg−1. The stover heat of combustion was estimated at 17.47 MJ·kg−1 in the cool zone and 17.26 MJ·kg−1 in the warm zone. Based on presented results, a partial “cob and husk” harvest system would collect less energy per unit area than total stover harvest (44 vs. 156 GJ·ha−1) and less biomass (2.51 vs. 9.13 t·dry matter (DM)·ha−1) but the fuel quality would be considerably higher with a low ash-to-energy ratio (1.45 vs. 4.27 g·MJ−1).

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.312

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 head0.081
GPT teacher head0.194
Teacher spread0.113 · 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 designObservational
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

Citations65
Published2015
Admission routes3
Has abstractyes

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