Bibliographic record
Abstract
Pythium root rot caused by Pythium aphanidermatum is one of the most important diseases of greenhouse tomatoes. Hydroponic culture exacerbates the problem. Both nutrient film technique (NFT) and recirculating growing systems pose a challenge in the control of this disease, because the pathogen, especially the zoospores, can spread easily in the recirculating solution to the whole growing system. Fortunately, hydroponically grown plants are easier to manipulate than soil grown plants, proper manipulation of root environments can lead to excellent disease control. This paper reports the development of an effective integrated control measure for pythium root rot of tomato by integrating pH, bioagent, and ultra-violet irradiation in a specific manner. This integrated control consists of three operations: a) before transplanting, the UV system is connected to sterilize the recirculating solution using 100 mJcm-2; b) after transplanting, the nutrient solution is delivered at pH 5.0 regime for five weeks followed by adjusting pH to 5.8 to 6.2 regime for one week; and c) bacterial bioagent, such as Pseudomonas is introduced into the root zone at 100 mL per plant at 10(8) bacteria mL-1 or added to the nutrient solution to arrive at 10(6) bacteria mL-1 in the solution. This report also discusses the advantages and limitations of this measure in the control of pythium root rot.
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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) | 0.000 | 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 | 0.001 |
| 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".