Induction of resistance in tomato against buckeye rot (Phytophthora nicotianae var. parasitica)
Bibliographic record
Abstract
Buckeye rot disease of tomato caused by Phytophthora nicotianae var. parasitica is the most destructive disease for reducing tomato yields especially in those regions where fruiting coincides with rainy season. In the present study, the pathogen was characterized by sequencing the DNA region coding for internal transcribed spacer (ITS) region and sequence was deposited in NCBI with accession no. MF398189. The phylogenetic analysis using the Maximum Composite Likelihood (MCL) approach revealed that the isolated pathogen clustered together with P. nicotianae with high bootstrap value of 99%. Incubation period of 120 h was observed in pin-prick method of pathogen inoculation compared to 168 h in surface inoculation method. Further, the disease resistance induced by nine different elicitors of induced resistance against buckeye rot disease of tomato were studied under field conditions for two consecutive years 2016 and 2017. Minimum disease incidence of 9.57% and 7.93% was observed with foliar spray of ß-aminobutyric acid (2 mM) for 2016 and 2017, respectively. It was followed by potassium chloride (100 mM) with disease incidence of 11.32% and 8.85% for year 2016 and 2017, respectively. Maximum fruit yield of 7.02 kg and 8.12 kg was found in treatment with ß-aminobutyric acid as compared to 2.61 kg and 2.55 kg in control for year 2016 and 2017, respectively.
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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 | 0.000 |
| 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".