Characterizing Agricultural Residue Nutrient Properties and Removal Variation in Ontario
Bibliographic record
Abstract
Due to recent climate change and energy consumption concerns, several markets have emerged for agricultural biomass in the province of Ontario, Canada. Understanding variation of residue nutrient concentrations across the province and the causal factors is crucial for determining the feasibility of crop residue use in Ontario. The purpose of this study was to survey variation of Ontario winter wheat, soybean and corn residue nutrient concentrations and removals, as well as to determine the effect of altering cutting height and delaying harvest on the nutrient concentrations and removals of these residues. It was found that across-site nutrient concentration and removal variation were greater than within-site concentration and removal variation, and that site-scale climatological events, such as precipitation, are largely responsible. Concentration and removals differed significantly by year. Variation of nutrient concentration and removal did not correlate with crop grain yield, or soil characteristics such as organic matter, pH or texture. A leaching treatment significantly reduced residue nutrient concentrations and removals, but had no significant effect on the variation among residue samples. Finally, concentrations and removals differed significantly with cutting height and harvested corn component, highlighting the importance of harvest method in the system nutrient balances.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".