Effect of Elevated CO2 Concentration on the Disease Severity of Compatible and Incompatible Interactions of Brassica napus–Leptosphaeria maculans Pathosystem
Bibliographic record
Abstract
Global warming by increased atmospheric CO2 concentration has been widely accepted. Yet, there has not been any consistent conclusion on the doubled CO2 concentration that in the future will affect plant disease incidence and severity. Blackleg disease, mainly caused by Leptosphaeria maculans, is a major disease on canola production globally. Brassica napus and L. maculans have a gene-for-gene interaction, which causes an incompatible reaction between canola plants carrying resistance genes and L. maculans isolates carrying corresponding avirulence genes. In this study, B. napus varieties and lines inoculated with different Leptosphaeria isolates were subjected to simulated growth conditions, namely, growth chambers with normal environments and with controlled CO2 concentrations of 400, 600, and 800 ppm. The results indicated that the elevated CO2 concentrations have no noticeable effect on the inferred phenotypes of the canola–blackleg interactions. However, the disease severity decreased in most of the B. napus–L. maculans interactions at extremely high CO2 concentration (800 ppm). The varied pathogenicity changes of the B. napus–L. maculans pathosystem under elevated CO2 concentrations at 400 or 600 ppm may be due to the genetic background or physiological differences in plants and pathogenicity differences in L. maculans isolates having different Avr gene profiles. The mechanisms by which elevated CO2 concentrations affect the B. napus–L. maculans pathosystem will help us understand how climate change will impact crops and diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".