Diet and Injection, Important Recommendations to Characterize <i>Clavibacter michiganensis</i>–Tomato Interactions
Bibliographic record
Abstract
Tomato ( Solanum lycopersicum L.) is one of the most important vegetables in the world. Its extensive cultivation has made this plant the target of many viral, fungal, and bacterial diseases. Among them, bacterial canker of tomato caused by Clavibacter michiganensis ( Cm) has been named one of the most devastating diseases affecting the tomato industry worldwide. It can significantly reduce the yields and profitability of this crop. One of the big challenges we found when working with Cm and trying to characterize the virulence of different isolates was the lack of a consensus methodology to inoculate tomato plants, fertilize them, and characterize Cm virulence. The aim of this research was to identify an artificial inoculation method to induce bacterial canker on tomato plants in greenhouse conditions to homogenize the results of different studies with Cm. We compared two inoculation methods, the scalpel and syringe methods, with two levels of fertilization, low and high fertilization. After evaluating several variables, such as the percentage of necrotic leaves and the height of the plants, the results showed that syringe inoculation with low fertilization was the most effective inoculation method, allowing for the development of a multilevel scale that can be used to study the interaction between tomato plants and Cm isolates. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.015 |
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".