Methods for Estimating Optimum Plot Size for ‘Gigante’ Cactus Pear
Bibliographic record
Abstract
The optimum plot size for ‘Gigante’ cactus pear can be estimated by several methods; thus, ultimately aiming for efficiency, simple use and high precision, the objective of this study was to compare methods for estimating plot sizes: modified maximum curvature method, Hatheway’s convenient plot size method, linear and quadratic response plateau models, and comparison of variances method for evaluating phenotypic characteristics in experiments with ‘Gigante’ cactus pear. Plot sizes were estimated by conducting a uniformity trial. Estimated optimum plot sizes varied with the method and vegetative characteristic. The quadratic response plateau regression estimated the largest plot sizes, whereas Hatheway’s method estimated the smallest plot sizes. Comparison of variances method estimated intermediate plot sizes in comparison with the other methods for most measured characteristics. Plots sizes estimated by modified maximum curvature method are more consistent with results reported by studies on ‘Gigante’ cactus pear. 10 basic unit plot sizes estimated by the linear response plateau model can be used with high precision and practical feasibility for growing cactus pear, thereby improving the use of resources.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".