Effects of Perennial Biomass Grasses on Soil Quality in Bath, Ontario
Bibliographic record
Abstract
Perennial grasses have the potential to be important bioenergy crops, as they are fast-growing and produce large amounts of biomass. They can also help improve soil quality (e.g. soil organic matter) when established on marginal lands or degraded soils. In collaboration with Lafarge Cement, who are interested in replacing coal with biomass energy, I tested the impact of perennial grasses on soil quality in Bath, ON. I studied soil samples collected in 2015 from three replicates of perennial grass species and one replicate of native vegetation (as a control) established by Queen’s faculty and students in 2009. The three perennial grass species used were switchgrass (Panicum virgatum), little bluestem (Schizachyrium scoparium), and big bluestem (Andropogon gerardii). Soil carbon and nitrogen content were higher under switchgrass in the 0-10 cm layer, but otherwise did not differ by species. Both carbon and nitrogen levels declined with depth. Isotopic data suggests a replacement of original soil carbon with carbon derived from the C4 grasses. These data will be compared with data collected prior to grass establishment to see whether the grasses have altered soil quality and/or enhanced soil carbon. My research will show how management of perennial grasses can be used to enhance the climate benefits of replacing fossil fuels with perennial grass biofuel crops and provide insights into the comprehensive climate and soil quality benefits of using these crops to replace fossil fuels in the manufacturing of cement.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".