NUTRIENTS IN MARGINAL LAND SOILS AND THEIR POTENTIAL EFFECT ON THE ENVIRONMENT
Bibliographic record
Abstract
Increasing global population leads to an increase in demand for foods and cleaner energy such as biofuel and bioenergy that are produced from feedstocks. Utilizing marginal land for production of these feedstocks alleviates the competition of fuel versus food that comes with use of prime agricultural land. Canada has a large area of marginal land. Sorghum is an important plant for food, fodder, and forage production. It is regarded as a nature-cared plant with low input requirements and is recommended as a top crop for removing carbon from the atmosphere. As a part of a collaborative project to develop a system for producing biomass (sorghum) on marginal land in Canada, this research focuses on the species and their distribution, mobility and availability (to plants) of nitrogen (N) and phosphorous (P) in marginal land soils from selected locations in Canada. US EPA method 1312 was followed to simulate the leaching process of nutrients from soils in the natural environment. Colorimetry and ICP-OES were used for the determination of the nutrient species. Preliminary results show that the predominant leachable and plant-usable form of nitrogen is nitrate (NO3 -) while the majority of phosphorus in the soil is not water leachable; depth variation of leachable nitrogen and phosphorus species in the soils is indicated; the concentrations of nitrate in the soils increased shortly after N-fertilizer application but the level decreased to that observed before planting, suggesting that atmospheric precipitate/deposition can move nitrogen from marginal land soils to surface water.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".