Validation of a Landscape-Based Model for Whitefly Spread of the Cucurbit Yellow Stunting Disorder Virus to Fall Melons
Bibliographic record
Abstract
The cucurbit yellow stunting disorder virus (CYSDV) transmitted by Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) has caused significant reductions in fall melon (Cucumis melo L.) yields in Yuma County, Arizona. In a recent landscape-based study, we found evidence that cotton and spring melon fields increased abundance of B. tabaci and spread of CYSDV infection in fall melon fields. Here, we show that a statistical model derived from data collected in 2011-2012 and based on areas of cotton and spring melon fields located within 1,500 m from edges of fall melon fields was sufficient to retrospectively predict incidence of CYSDV infection in fall melon fields during 2007-2010. Nevertheless, the slope of the association between areas of spring melon fields and incidence of CYSDV infection was three times smaller in 2007-2010 than in 2011-2012, whereas the slope of the association between areas of cotton fields and incidence of CYSDV infection was consistent between study periods. Accordingly, predictions were more accurate when data on areas of cotton alone were used as a basis for prediction than when data on areas of cotton and spring melons were used. Validation of this statistical model confirms that crop isolation has potential for reducing incidence of CYSDV infection in fall melon fields in Yuma County, although isolation from cotton may provide more consistent benefits than isolation from spring melon.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".