The effect of multiple applications of a de-inked papermill biosolid on soil properties and crop growth
Bibliographic record
Abstract
An approach was tested, using the biochemical composition of a de-inked papermill biosolid PB, to determine optimum rates of supplemental nitrogen fertilizer needed to meet the requirements to decompose the PB and grow crops. In this study, increasing rates of the PB were applied annually for three years on four different agricultural soils in southwestern Ontario. Supplemental nitrogen fertilizer treatments >= 1 kg N Mg-1 PB were generally sufficient to maintain or increase corn yields on the PB amended soils. Soybean yields were also maintained or increased, on PB amended soils, without needing additional supplemental nitrogen fertilizer treatments. Greater concentrations of soil mineral nitrogen were measured over the growing season at supplemental nitrogen fertilizer rates >= 1 kg N Mg -1 PB. In addition, residual soil mineral nitrogen concentrations, measured at post harvest, were typically below 10 ppm in soils amended with PB, even at the highest rate of supplemental nitrogen fertilizer. Soil physical and chemical properties at the study sites, including bulk density, infiltration, and total carbon, were improved by additions of PB. Other soil chemical properties, such as pH and electrical conductivity, were unchanged in all soils receiving PB. No consistent trends toward increasing the concentrations of heavy metal were observed after two years of de-inked papermill biosolid applications. Earthworm density and biomass, a biological measure of soil health, were increased throughout the study on PB amended soils. This study demonstrated that the nitrogen requirements for decomposition of papermill biosolids can be successfully managed with crop production. A number of benefits attributed to papermill biosolid treatments were also discovered on physical, chemical, and biological properties of agricultural soils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".