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Record W2248998255 · doi:10.24102/ijes.v3i1.446

Biofuel Research in Canada: Some Results from Eastern Ontario

2014· article· en· W2248998255 on OpenAlexaffvenueabout
A. Tubeileh, Tim Rennie, Shamel M. Alam‐Eldein

Bibliographic record

VenueInternational Journal of Environment and Sustainability · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBiofuelGeographyBiologyEcology

Abstract

fetched live from OpenAlex

Depleting non renewable fuel resources and environmental concerns have renewed interest in sustainable environment-friendly biofuel resources. While one of the oldest sources of energy, crops occupy a large piece in the puzzle for future energy supplies. Several bioenergy projects have been launched at Kemptville since 2008 to study production potential and challenges with respect to varietal effects, nutrient requirements, mineral concentration in tissues, weed management and final product quality. The crops studied could be categorized into 3 different groups; biomass crops (perennial biomass grasses and woody species), biodiesel crops and ethanol crops. The objective of this paper it to report the effects of nitrogen (N) levels on biomass production from switchgrass ( Panicum virgatum L.), big bluestem ( Andropogon gerardii Vit.) indiangrass ( Sorghastrum nutans Nash.) and Miscanthus ( Miscanthus sinensis x M. sacchariflorus ). Our results on prairie perennial grasses indicate that it is possible to produce up to 10 Mg dry biomass/ha/yr with minimal nitrogen input (up to 50 kg/ha). At these levels, a sustainable and efficient production can be achieved. For Miscanthus, assuming a 100% establishment and winter survival rates, Miscanthus stands will be able to produce >40 Mg dry biomass/ha/yr at 120 kg N/ha. Our research helps producers make informed decisions with regard to species selection, seeding rates, fertilizer rates, expected yields and energy values of little-known bioenergy crops.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.236
Teacher spread0.211 · 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

Citations3
Published2014
Admission routes3
Has abstractyes

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