Assessing the impact of climate changes on the distribution of two corn diseases: corn stunt and corn reddening
Bibliographic record
Abstract
Corn stunt (CS) and corn reddening (CR) are considered the main phytosanitary problems of corn crops in the Neotropical region, caused by Spiroplasma kunkelii and Candidatus Phytoplasma ssp., respectively. Models that evaluate the potential geographic distribution of CS and CR are important to know which regions and areas are suitable for formulating appropriate policies and preventive measures. This study aimed to identify highly suitable areas and assess the impact of climate change on the distribution of CS and CR. To do this, we developed two spatial distribution models for CS and CR. We found 193 points of occurrence for CS and 158 points for CR. Considering its biology and ecology, we used R-based analysis version 4.4.0 ‘Puppy Cup’ to predict potential global distribution of CS and CR using bioclimatic variables. We found that the most critical abiotic variables driving the global distribution of CS were: mean diurnal range, maximum temperature of the warmest month, and temperature seasonality. For the global distribution of CR, the most important variables were: isothermality, mean diurnal range, precipitation of the warmest quarter, and precipitation of the driest quarter. With regard to the validation of the forecast (2041–2060), the SSP2–4.5 models showed greater adaptability in the world’s main corn-producing countries: the United States, China and Brazil. On the other hand, for SSP5–8.5, Maxent predicted that suitable CS and CR habitat will decrease by 2060 in the United States, China and Brazil. These countries showed a significant reduction in the occurrence of CS and CR. Our modelling results will provide helpful information to determine the spatial distribution of CS and CR and outline implications for monitoring through the risks of these diseases based on climatic conditions worldwide, especially in SSP2–4.5 senarios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".