Optimal experimental plot size for papaya cultivation
Bibliographic record
Abstract
Abstract: The objective of this work was to determine the optimal size of experimental plots for the evaluation of agronomic characteristics and fruit quality of papaya, by the linear model of plateau response, under soil and climatic conditions of the Recôncavo Baiano region, in the state of Bahia, Brazil. The experiment consisted of a uniformity test, with the papaya lineage L78, at 3×2 m spacing, in 16 rows with 22 plants, totaling 352 plants and 2,112 m2 useful area. Each plant was considered as a basic unit, and 11 forms of pre-established plots, with rectangular and row formats, were obtained. The agronomic characteristics and fruit quality were evaluated in the plots. Optimal plot size varied greatly among the variables related to agronomic characteristics, with a greater participation of the variable number of marketable fruit per plant at 14 months (16 basic units). The optimal plot size for the evaluation of the agronomic characteristics and fruit quality in papaya is eight experimental units, with 48 m2 area, at a spacing of 3 m between rows and 2 m between papaya plants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.004 | 0.001 |
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 teacher head, 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".