Effect of Liquid Municipal Biosolid Application Method on Tile and Ground Water Quality
Bibliographic record
Abstract
This study examined bacteria and nutrient quality in tile drainage and shallow ground water resulting from a fall land application of liquid municipal biosolids (LMB), at field application rates of 93,500 L ha(-1), to silt-clay loam agricultural field plots using two different land application approaches. The land application methods were a one-pass AerWay SSD approach (A), and surface spreading plus subsequent incorporation (SS). For both treatments, it took between 3 and 39 min for LMB to reach tile drains after land application. The A treatment significantly (p < 0.1) reduced application-induced LMB contamination of tile drains relative to the SS treatment, as shown by mass loads of total Kjeldahl N (TKN), NH(4)-N, Total P (TP), PO(4)-P, E. coli., and Clostridium perfringens. E. coli contamination resulting from application occurred to at least 2.0-m depth in ground water, but was more notable in ground water immediately beneath tile depth (1.2 m). Treatment ground water concentrations of selected nutrients and bacteria for the study period ( approximately 46 d) at 1.2-m depth were significantly higher in the treatment plots, relative to control plots. The TKN and TP ground water concentrations at 1.2-m depth were significantly (p < 0.1) higher for the SS treatment, relative to the A treatment, but there were no significant (p > 0.1) treatment differences for the bacteria. For the macroporous field conditions observed, pre-tillage by equipment such as the AerWay SSD, will reduce LMB-induced tile and shallow ground water contamination compared to surface spreading over non-tilled soil, followed by incorporation.
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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 | 0.002 | 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, 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".