Particle fractionation controls Escherichia coli release from solid manure
Bibliographic record
Abstract
Bacteria transport through soil is a complex process particularly when the cells are released from solid manures and co-transported with particles. This study focuses on understanding of the Escherichia coli release from different particle fractions (0.25-, 0.5-, 1-, and 2-mm) of solid manure and evaluating different influent boundary conditions during cell release from manure and when a solid manure is applied to the soil. The 0.25-mm and 2-mm particle sizes resulted a greater cell release compared to 0.5-mm and 1-mm fractions (p < 0.05). The shape and magnitude of the cell release curves (CRCs) from the original manure bulk were mainly influenced by the two 0.25-mm and 2-mm fractions, respectively. The arithmetic mean for normalizing the CRCs and the time variablebased normalized CRCs for the manure-treated soil were the robust variables in evaluation of the experimental data. However, a single maximum bacteria concentration could provide the realistic dataset for the modeling process. Evaluation of the root-mean-squared-error and Akaike criterion showed that the two-and threeparametric models are recommended for simulating the cell release from solid manure in comparison with one parametric models. This study also suggests considering separate microbial release evaluations, with regards to influent concentration, for manure and manure-treated soils to propose best management practices for controlling bacteria pollution. Further research will reveal the key roles of different woody components and soluble material ratios for the various solid manures in bacteria release.
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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 | 0.000 | 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".