Variability and Experimental Desing for Trials with Cucurbita pepo and Capsicum annuum
Bibliographic record
Abstract
The variability within rows of cultivation may reduce the accuracy of vegetables trails; however, little is known about this variability in protected environments. This study aimed at to assess the variability in greenhouses cultivated with Cucurbita pepo and Capsicum annuum and to verify the effect of borders use and plot size in minimizing this variability. Data from two uniformity trials each crop were used. For statistical analysis, the total of productivity by plant was used, considering the plants arranged in parallel crop rows the lateral openings of the greenhouse and the same plants arranged in columns perpendicular to these openings. Different scenarios were designed by excluding rows and columns to generate the borders in different plot sizes. For each scenario, a variance homogeneity test was performed among the remaining rows and columns and the variance and coefficient of variation were calculated. There is variability among rows and columns in trials with C. pepo and C. annuum in greenhouses and the use of borders does not bring benefits in terms of reduction of the coefficient of variation or reduction of cases of variances heterogeneity among rows or columns. The use of a plot size greater than or equal to or two plants for trials with C. pepo and ten plants for trials with C. annuum provides homogeneity of variances among rows and columns enabling the use of the completely randomized design.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".