11. Substituting Biofuel for Coal: Determining the Optimal Perennial Grass Species and Nitrogen Fertilization Level for Bioenergy Production in Eastern Ontario
Bibliographic record
Abstract
Renewable energy production has become an increasingly important issue in recent years due to climate change and energy security issues, and bioenergy crops may be able to supply a large amount of the world’s energy needs. Bioenergy is a carbon-neutral energy source, and perennial grasses are an ideal bioenergy crop due to their high growth rates and ability to grow under a range of conditions. To enhance grass productivity, nitrogen fertilization is often applied to this crop, but this can lead to increased Lafarge Cement in Bath, Ontario, three perennial grass species (Panicum virgatum, Schizachyrium scoparium and Andropogon gerardii) are being grown in conjuction with three fertilization treatments (0, 50 and 150 lbs. per acre of nitrogen as urea) to explore the tradeoffs associated with using nitrogen fertilizer in these bioenergy systems. Soil N2O emissions were measured weekly during the growing season following fertilizer application in May 2013. At the same time, various soil properties known to influence N2O emissions (e.g. pH, soil nitrate, soil temperature, soil moisture) were measured. Crop productivity was measured at the end of the growing season to determine the species and fertilization level at which GHG emission benefits are maximized. The results of this study will demonstrate the viability of perennial grasses as a bioenergy crop for industrial purposes in Eastern Ontario by identifying the maximum GHG benefits that this bioenergy system can achieve in this region.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".