Appraisal Of Plant Activators And Chemicals Against Brown Leaf Spot Of Citrus In Relation To Epidemiological Factors
Bibliographic record
Abstract
Citrus is one of most important fruit crop of the world that belongs to family Rutaceae. It is a rich source of vitamins specially, vitamin C. It is consumed as a fresh fruit and also in processed form. Its production is affected due to various biotic (fungi, bacteria, virus and nematodes) and abiotic factors like extreme temperature, high humidity and rainfall. Among fungal diseases, Brown leaf spot caused by Alternaria citri is one of the most destructive diseases of citrus. Alternaria is a fungus that is present everywhere and associated with plants, soil and animals and many of them are plant pathogens, causing several diseases including fruit rots and immature fruit drops. This disease mainly attacks the twigs of plants, leaves and citrus fruit. Disease samples of typical symptoms of necrotic spots followed by yellow halo were collected from different citrus orchard. After collecting the samples these were brought in the Plant Disease Diagnostic Laboratory for further isolation, purification and identification of different pathogens associated with Alternaria brown spot. In vitro evaluation of different chemicals and plant activators were done by using different concentrations (200, 600 and 900ppm) against isolated pathogen by using poisoned food technique. Mancozeb and salicylic acid was highly effective against Alternaria alternata.
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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 | 0.001 | 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) | 0.002 | 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".