Impact of Ultraviolet-C Exposure and Mechanical Stress on Conidium Production in Corynespora cassiicola Isolates From Cotton and Soybean
Bibliographic record
Abstract
The procurement of inoculum for bioassays with target spot, a significant disease in cotton and soybeans, caused by Corynespora cassiicola can be hindered by the low production of conidia on artificial culture media. The study aimed to determine whether mechanical stress on the mycelium and exposure to ultraviolet-C (UV-C) radiation for varying durations could enhance conidial production in C. cassiicola isolates. Eight isolates from cotton and soy were used, grown in V8 juice medium. After five days of incubation in a climate-controlled chamber, each isolate either underwent mycelium scraping or remained unscraped and was subsequently exposed to UV-C radiation for either 1.0, 1.5 or 2.0 min, in comparison to a control group with zero UV exposure time. Following these procedures, conidial suspensions from each isolate were obtained and quantified (conidia per mL) using a Neubauer chamber The ISO 3S and ISO 4S isolates were found to produce more conidia than the other isolates, regardless of whether they were subjected to mycelium scraping or exposure to UV-C radiation. For most isolates, exposure to UV-C radiation for 1.0-1.5 min led to increased conidium production. Generally, it was not feasible to discern differences in conidial production with respect to the mycelium scraping process. Nevertheless, exposure to UV-C radiation for 1.0 min can be used to induce conidium production in C. cassiicola isolates.
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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".