Relationship Between Sudden Death Syndrome caused by <i>Fusarium virguliforme</i> and Soybean Yield: A Meta-Analysis
Bibliographic record
Abstract
In total, 52 uniform field experiments were conducted in Illinois, Indiana, Iowa, Michigan, South Dakota, and Wisconsin in the United States and Ontario, Canada from 2013 to 2017 comparing crop protection products against sudden death syndrome (SDS) of soybean. Data were analyzed using meta-analytic models to summarize the relationship between foliar disease index (FDX) and yield. For each study, correlation and regression analyses were performed separately to determine three effect sizes: Fisher’s transformation of correlation coefficients (Z r ), intercept (β 0 ), and slope (β 1 ). Random- and mixed-effect meta-analyses were used to summarize the effect sizes. Study- and location-specific moderator variables FDX (low < 10% and high ≥ 10%), date of planting (early = prior to 7 May, conventional = 7 to 21 May, and late = after 21 May) cultivar (susceptible and partially resistant to SDS), study location, and growing season were used as fixed effects. The overall mean effect sizes of transformed correlation coefficient [Formula: see text] r was −0.41 and different from zero (P < 0.001), indicating that yield was negatively correlated with FDX. The [Formula: see text] r was affected by disease level (P < 0.01) and cultivar (P = 0.02), with a greater effect at higher disease levels and with susceptible cultivars. The mean [Formula: see text] 0 was 4,121 kg/ha and mean [Formula: see text] 1 was −21 kg/ha/% FDX and were different from zero (P < 0.01). Results from these data indicate that, for every unit of FDX increase, yield was decreased by 0.5%. Study locations and year affected the [Formula: see text] 0 , whereas none of the moderator variables significantly affected [Formula: see text] 1.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.041 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".