Sample Sufficiency for Mean Estimation of Productive Traits of Sunn Hemp
Bibliographic record
Abstract
Sunn hemp (Crotalaria juncea L.) is an annual cycle legume with high potential for biological nitrogen fixation, being widely used in crop rotation for biomass formation and control of nematodes. The objectives of this study were to determine the sample size for the mean estimation of productive traits of sunn hemp and verify the sample size variability between traits and sowing dates. Two uniformity trials were performed in the agricultural year 2014/2015, with sowing in October (trial 1) and December (trial 2). At the crop flowering stage, 300 plants of each trial were harvested and fresh and dry matter of leaves, stem, root, aerial part, and total weight were evaluated. The normality and randomness tests were performed for each trait and the sample size was calculated for the semi-amplitudes of the confidence interval (estimation errors) of 2, 4, 6, 8, 10, 12, 14, 16, 18 and 20% of the mean estimate. There is sample size variability between productive traits and between sowing dates. The assessment of at least 101 plants is required for mean estimation of productive traits with maximum estimation error of 20% of the mean and 95% confidence level.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".