The Influence of Lunar Phase on the Germination of Creole Corn in a Controlled Environment
Bibliographic record
Abstract
Although the influence of lunar phase on plant growth and development has been disregarded by most researchers, there is still little data in the literature regarding the influence of lunar phase within a controlled environment. Objective evaluates the influence of lunar phase on the germination of creole corn and on seedling development in a controlled environment. The experimental design was completely randomized with the following treatments: four lunar phases (full, waxing, waning and new), with four replications totaling 50 seeds per treatment. The treatments started on the first day of the waxing phase and extended to the full phase within the same month, respecting the germination period (nine days) and the length of each moon phase (roughly seven days). The following parameters were evaluated: germination rate, seedling length, fresh and dry weight of shoots and roots. Seedlings germinated during the waning lunar phase were longer and had greater fresh root weight and greater fresh and dry shoot weights than did seedlings germinated during the other phases. However, some limiting factors (long term experiments and in different places in the world) must be considered in future research to determine whether moon phase affects germination. Thus, longer-term, controlled, and modeled studies are needed to analyze external factors, which may influence the germination of cultivar seeds.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".