Bibliographic record
Abstract
Climate change and energy security issues have made renewable energy production an important global issue. Bioenergy crops may be able to provide a large amount of the world’s energy needs; therefore it is important to determine their potential for viable and sustainable use. Perennial grasses are an ideal bioenergy crop because they grow quickly across a wide range of climatic and soil conditions. Nitrogen based fertilizers are often used to increase productivity but overuse can lead to excessive nitrous oxide (N2O) emissions, a greenhouse gas (GHG) 300 times more potent that carbon dioxide (CO2). Fertilizing becomes counterproductive when N2O emissions outweigh the benefit gained from reduction in CO2 emissions. Three perennial grass species (Panicumr virgatum, Schizachyrium scoparium and Andropogon gerardii) were grown in collaboration with Lafarge Cement in Bath, Ontario. Each species was grown under three different fertilization regimes: 0, 50 and 150 lbs/acre of nitrogen as urea. Results of one study indicate that low levels of fertilizer addition enhance the GHG benefits of the grass, but results from another site suggest no benefit. Our current research is exploring the importance of various soil processes to production of GHG’s in these perennial grass bioenergy systems, and whether the grasses alter soil conditions to favor certain soil processes. The information gained will help to further predict the feasibility of using nitrogen fertilizers to enhance production in these bioenergy systems and the overall viability of bioenergy crops for cement manufacturing and other applications.
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 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 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.005 | 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".