Soil factors influencing the efficacy of liquid swine manure added to soil to kill<i>Verticillium dahliae</i>
Bibliographic record
Abstract
Addition of liquid swine manure (SwM) to field soils killed Verticillium dahliae microsclerotia (MS) and reduced verticillium wilt of potato, but only at one (site B) of several fields tested. This study examined what factors in soil influence the capability of SwM to kill V. dahliae MS. When added to soil from site B in a microcosm assay, the SwM used in the field study killed MS within 1 day after application. Also, efficacy increased as the concentration of SwM increased, indicating that one or more components were directly toxic to MS. Toxicity of SwM to MS was reduced with increasing soil moisture, indicating that the active product(s) was (were) undergoing dilution. Adjusting the pH of site B soil from 5.0 to 6.5 eliminated the toxicity of the SwM. Conversely, when soil from a location where SwM had no effect was reduced from its initial value of 7.5 to below 6, MS mortality occurred. Adding increasing concentrations of SwM at pH 7.7 to soils commonly raised the pH of the mixture to levels where efficacy of kill became progressively less effective. In soils with high buffering capacity, the pH did not immediately rise and MS kill increased with higher rates. Liquid swine manure killed MS to the same extent in soils ranging from sand to loam when these were made equal with respect to pH and SwM concentration. It was also equally effective in soils with organic carbon contents ranging from 1.4 to 6% when soil pH and moisture levels were made equal. Increasing soil temperature slightly improved the toxicity of the SwM. The results demonstrate that the efficacy of SwM in soil to kill V. dahliae MS is most influenced by soil pH and, to a lesser extent, by soil moisture level, buffering capacity, and temperature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".