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Record W7100664339

Agricultural biomass residue inventories and conversion systems for energy production

2013· article· en· W7100664339 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCorn stoverCrop residueBioenergyStoverBiofuelRaw materialRenewable energyStrawEnergy crop
DOInot available

Abstract

fetched live from OpenAlex

Interest in renewable energy has increased in recent years due to concerns about diminishing fossil fuel supplies and global climate change. Dedicated energy crops such as corn or short rotation forestry plantations can provide feedstock for bioenergy production, but agricultural residues are typically less inexpensive because production costs are included in the cost of producing the main crop or livestock product. This report provides an overview of available crop residues and livestock manure in eastern Canada. The inventories are linked to an assessment of energy conversion systems that are either in the commercial stage or are in the late development or pilot stage. Crop Residues Cereal straws and corn stover were identified as feedstocks with high potential for bioenergy production in eastern Canada, whereas hay, soybean stover, and crop residues from oilseed production had lower potential. Approximately 1.0 million oven dry tonnes (odt) per year (y) of cereal straw and 3.0 million odt/y of corn stover are available in eastern Canada. By region, 615 000, 310,00 and 65,000 odt of straw and 1.9 million, 1.1 million and 6 000 odt/y of corn stover are available per year in Ontario, Quebec and the Atlantic Provinces, respectively. Although corn stover represents a larger feedstock pool than cereal straw, procurement systems for stover require considerable development before this residue source can be utilized. The gross energetic potential of these residues is approximately 92 million GJ/year. Assuming a combustion efficiency of 50%, 46 million GJ of heat energy could be produced with a gross energetic value of 22.2 million GJ. By

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.181

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 head0.007
GPT teacher head0.169
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2013
Admission routes1
Has abstractyes

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