Transport of bacteria on sloping soil surfaces by runoff
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pathogenic bacteria exist at soil surfaces as a result of practices as spreading of liquid manure on agricultural lands or use of treated wastewater for irrigation. Rainfall is a major factor affecting vertical and horizontal movement of bacteria in soil. Surface runoff carries bacteria significant distances downstream causing serious threats to ground and surface waters. This study uses a nalidixic acid-resistant Escherichia coli strain as a biotracer monitoring extent of bacterial migration on sloping soil surfaces by runoff action. Two 10×10-m plots in two sites having different slopes were sprayed with water containing biotracer. Soil texture at sites was clay loam. Sixteen days after spraying, two heavy rainfalls that caused runoffs were recorded. First rainfall occurred 2 days after spraying plots. Samples were collected from soil and runoff at different distances downstream of the plots. Biotracer was found in soil and runoff samples some 20 m downstream from center point of plot having the milder slope. Biotracer was found in soil and runoff samples further downstream of the second plot with the steeper slope reaching a 35- and 30-m distance respectively. Most soil and runoff samples collected after the second rainfall, occurring 15 days after inoculation, contained no biotracer except small numbers found in soil samples taken from center point of each plot 5 m downstream. Results confirm the important role of runoff in bacterial transport on soil surfaces. They show E. coli survives in semiarid areas for a long time and increases potential of contamination. © 2000 John Wiley & Sons, Inc. Environ Toxicol 15: 149–153, 2000
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.072 | 0.001 |
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 it