Optimum Sample Size in the Germination of Atemoya Seeds (Annona× atemoya Mabb.)
Bibliographic record
Abstract
Atemoya currently has its seeds studied in several aspects, from the technological and physiological point of view. However, for the performance of the germination test, there is no standardization in relation to the number of seeds and replicates. Thus, this work aims to determine the optimal sample size for germination tests with atemoya seeds. A germination test was carried out with 5 treatments, considering 10, 20, 30 40 and 50 seeds per sampling unit with 50 replicates of each. Data were analyzed using logistic regression, non-linear Gompertz regression models, bootstrap simulation and graphs in the form of contour lines in order to be able to infer the best binomial of the number of seeds per sampling unit and the number of replicates. The expected germination percentage, maximum germination speed and times for the beginning of the germination process, maximum germination speed and interruption of the germination process were determined. The treatment of 20 seeds per sampling unit statistically differs from that of 40 seeds by the Tukey-Kramer test with 5% significance. It could be concluded that for germination tests with atemoya seeds to have statistical validity, the smallest sample size is 10 seeds per unit, regardless of number of replicates.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".