In vitro Inhibition of Soilborn Phytopathogens Treated With Swine Wastewater
Bibliographic record
Abstract
Swine wastewater (SWW) is a residue from pig farming that presents a high load of nutrients and organic matter. The appliance of organic matter in soil alters the microbial dynamic and may suppress soilborn phytopathogens. This study aimed at evaluating the inhibition on mycelial growth of Sclerotinia sclerotiorum and Sclerotium rolfsii in vitro under SWW doses. Hereupon, three kilograms of a soil classified as red dystroferric latosol was collected and sieved. Half of it was autoclaved. SWW was incorporated at doses of 0 mL, 2.5 mL, 5 mL, 10 mL and 20 mL in both soil conditions, autoclaved and not autoclaved. Afterwards, 130 grams of each soil was separately put into Petri plates above what a thin layer (≅ 5 mL) of Water-Agar (2%) medium was carefully spread over. Above this agar layer, one disk (6 mm diameter) of pure mycelium from each fungal grown in Potato Dextrose Agar medium was individually placed on the center of each plate. Daily evaluations on mycelial growth measuring were taken and ended when mycelium in control plates (without SWW addition) reached plate borders. Results indicated that in autoclaved soil condition, the inhibition was proportional to the dose, what is to say that the higher the dose the less the mycelial growth. In not autoclaved soil there was no significant difference among treatments, suggesting stimuli on suppression effect for both pathogens caused by SWW. In addition, the confirmed potential of SWW as a suppressor of S. sclerotiorum and S. rolfsii leads to promising investigations on other phytopathogens hard to control.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".