Comparison of Methodologies to Determine the Optimum Plot Size for Okra Seedlings Evaluation
Bibliographic record
Abstract
The production of okra using seedlings is a practice increasingly used by farmers. However, this system still lacks further research involving substrates, tray types, cell volume, pest control and disease. For this it is important to determine the optimum size of the plots, in order to reduce the experimental errors and the expenses with the experiment. The objective of this work was to determine the optimum plot size for experiments involving okra seedlings produced in Styrofoam trays of 128 cells using different methods. The methods were the maximum curvature, the maximum curvature with bootstrap simulation and the maximum curvature of the coefficient of variation. The evaluated characteristics were aerial part height, stem diameter, aerial dry matter, root dry matter, total dry matter and quality of seedlings as measured by Dickson quality index. The results showed that the optimum plot size is different between the evaluated characteristics and for characteristics there is no significant difference in the optimum plot size between the three different methods. The optimum size for evaluating okra seedlings produced in Styrofoam trays of 128 cells is 10 seedlings per plot and is indicated the use of the maximum curvature method using a bootstrap simulation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".