Plot Size by the Variance Comparison Method for With ‘Gigante’ Cactus Pear
Bibliographic record
Abstract
Appropriate plot size is recognized as a means of maximizing experimental accuracy and contributes to efficient treatment assessment. This study aimed to estimate the optimal plot size for experiments with ‘Gigante’ cactus pears using the comparison of variances method (CVM). A uniformity trial was conducted to assess plant height (PH), number of cladodes (NC), yield (Y), cladode area index (CAI), cladode length (CL), width (CW), thickness (CT) and cladode area (CA) in a cactus pear crop. A rectangular-shaped plot consisting of 10 rows of 50 plants each was used, totaling 500 plants, with 384 basic units (BU), corresponding to the study area. A hierarchical classification approach was adopted, simulating a split-plot design in which each plant was denominated a basic unit (BU), and considering the effects of blocks (B), plots (P)/B, subplots (S)/P/B, rows (R)/S/P/B and plants (Pln)/F/S/P/B. This resulted in five plots sizes, consisting of 1, 12, 24, 48 and 96 basic units. Plots with 12, 24, 48 and 96 BU were statistically equal for the variables Y, PH, NC, CAI, CL, CW and CT, with lower variances than the plot with 1 BU. As such, 4.8 m² with 12 basic units is the optimal experimental plot size for ‘Gigante’ cactus pears.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".