Effect of Spring Application of a Paper Mill Soil Conditioner on Corn Yield
Bibliographic record
Abstract
Use of paper mill residuals as soil amendment on farmland is believed to have a beneficial impact on crop yields and soil quality. The objective of our study was to evaluate the effect of spring applying Domtar Soil Conditioner (SC) (pulp and paper mill waste water treatment residue) to a sandy soil in eastern Ontario, Canada. The effects of SC on corn ( Zea mays L.) yields, N concentrations in plants, and post‐harvest levels in soil of NO 3 , P, K, Mg, organic matter (OM), and pH were investigated. The experimental design was a randomized complete block with five treatments, replicated four times. The treatments included two SC rates (15 and 25 Mg ha −1 dry matter: SC15 and SC25), 150 kg ha −1 NH 4 NO 3 –N (N150), a composite SC and mineral fertilizer treatment (15 Mg ha −1 dry matter SC and 75 kg ha −1 NH 4 NO 3 –N: SC15N75), and a control. The experiment was repeated annually from 1997 to 2001. Addition of SC the spring before planting increased grain yield by 2360 kg ha −1 for SC15 and by 2908 kg ha −1 for SC25 vs. the control. When N was also added (SC15N75), the average increase vs. the control was 3406 kg ha −1 . More total N was measured in the corn plants from the plots amended with SC than the control. The SC amendments temporarily increased soil OM but did not increase NO 3 –N leaching risk. Annual spring application of SC improved corn yield but had little impact on soil nutrient levels, OM, and pH.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.000 | 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) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".