Sample Size for Morphological Traits of Sunn Hemp
Bibliographic record
Abstract
Sunn hemp (Crotalaria juncea L.) is an annual leguminous plant used for crop rotation, biomass formation, biological nitrogen fixation, and nematode control. To this end, sampling is an important tool owing to its advantages, such as low cost and fast data acquisition. This study aimed to determine the sample size required to estimate the mean morphological traits of sunn hemp and to assess sample size variability among traits, crop development stages, and sowing seasons. Two uniformity trials were performed in the field during the 2014/2015 harvest. Crops were sown in October and December on an experimental area of the Department of Crop Science at the Federal University of Santa Maria, Brazil. An area of 1,200 m2 was allocated for each trial. Each trial was divided into 2 m × 2 m grids, which formed 25 rows and 12 columns. One plant was marked per plot, totaling 300 plants in each trial. Leaf number and plant height were assessed weekly. Stem diameter and root length were measured at flowering. Normality and randomness tests were performed on each trait. The sample sizes (plants number) were calculated for the confidence interval half-widths (estimation errors) of the 2, 4, …, 20% mean estimates. There is variability of the sample size (plants number) between morphological traits, crop development stages, and sowing seasons. When choosing a single sample size to evaluate the morphological traits of sunn hemp, at least 70 plants should be sampled for an accuracy of 10% and a 95% confidence level.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".