Relationship between <i>Verticillium dahliae</i> inoculum and sunflower wilt in Argentina
Bibliographic record
Abstract
Leaf mottle and wilt (LM) of sunflower caused by Verticillium dahliae is the most important disease in Argentina. LM is a monocyclic disease where microsclerotia in the soil are the primary inoculum source and have an important impact on symptom expression (incidence and severity) and yield. The inoculum (microsclerotia/g of soil) and disease relationship is affected by several factors, such as temperature and soil moisture. The objective of this work was to determine the regression model that best fits the relationship between V. dahliae microsclerotia in soil and sunflower LM, over a wide environmental range. Sunflower plants were exposed to different microsclerotia densities in pasteurized and non-pasteurized soils in pots under greenhouse and field conditions. The microsclerotia in soil and LM relationship regression models were also tested in the field with natural soil borne microsclerotia infestation, in different sunflower cultivars and locations. After the flowering stage (± 8 days), disease severity was affected by inoculum density at every evaluation time (P-value≤0.0009). A 45% increase in the area under the disease progress curve was shown by soil pasteurization. Power regression model showed the highest concordance and Pearson correlation coefficients, parameter significance and no difference with the lowest Akaike index criterion value among all tested models in all experiments. This study provides a model (LM = 11.6*inoculum density^0.57) which describes the inoculum density and LM relationship. The use of this model might help farmers to predict an LM risk before sowing and to choose the appropriate field and the resistance level of the sunflower hybrid to be used.
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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) | 0.000 | 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".