Environmental impact of repeated applications of combined paper mill biosolids in silage corn production
Bibliographic record
Abstract
Paper mill biosolids (PB) may provide benefits for crop yields and soil nutrients. However, few data are available on metal accumulation and pathogenic populations resulting from applications in silage corn production. A study was initiated to determine the effect of annual spring application of combined PB during 3 consecutive years, with or without calcitic lime, on yield and on the environmental risk posed by N, P, heavy metals (Cu, Zn, Cd, Mo) and Escherichia coli to a silage corn cropped in a loamy sand in eastern Ontario, Canada. Treatments consisted of complete mineral NPK fertilization (PB0), 30 Mg wet weight ha -1 with supplemental N and K, and 60 and 120 Mg wet ha -1 supplemented with K, either with or without 2.5 Mg ha -1 calcitic lime. The PB at 30 Mg wet ha -1 with reduced mineral N and PB at 60 Mg wet ha -1 provided comparable yields to the mineral N fertilization in all years, whereas application of PB at 120 Mg wet ha -1 increased corn yield by 6.0 Mg ha -1 in the third year. After 3 yr, contents of soil NO 3 -N and the P saturation index (P/Al) were increased, indicating a possible risk of nitrate and P leaching. Lime increased soil pH by 0.8 unit, which in turn caused a large increase in the tissue Mo concentration and Mo uptake by silage corn. The PB, particularly at 120 Mg wet ha -1 , produced significant accumulations of Cd and Zn in soil, plant tissue and uptake. The PB showed small counts of E. coli, and consequently very little contamination was observed in the soil and on the harvested crop. Based on these results, the PB used are a good source of nutrients that benefits silage corn yields and represent as well a low risk for human and animal health and for the environment when the application does not exceed 60 Mg wet ha -1 yr -1 .Key words: Zea mays L., paper mill biosolids, lime, heavy metals, cadmium, molybdenum, Escherichia coli
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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 | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".