Analysis of Genetic Diversity Using Simple Sequence Repeat (SSR) Markers and Growth Regulator Response in Biofield Treated Cotton (Gossypium hirsutum L.)
Bibliographic record
Abstract
Cotton is the most important crop for the production of fiber that plays a key role in economic and social affairs. The aim of the study was to evaluate the impact of biofield energy treatment on cotton seeds regarding its growth, germination of seedling, glutathione (GSH) concentration, indole acetic acid (IAA) content and DNA fingerprinting using simple sequence repeat (SSR) markers for polymorphism analysis. The seeds of cotton cv. Stoneville-2 (Gossypium hirsutum L.) was obtained from DNA Land Marks Inc., Canada and divided into two groups. One group was remained as untreated, while the other was subjected to Mr. Trivedi biofield energy and referred as treated sample. The growth-germination of cotton seedling data showed higher germination (82%) in biofield treated seeds as compared to the control (68%). The alterations in length of shoot and root of cotton seedling was reported in the treated sample with respect to untreated seeds. However, the endogenous level of GSH in the leaves of treated cotton was increased by 27.68% as compared to the untreated sample, which may suggest an improved immunity of cotton plant. Further, the plant growth regulatory constituent i.e. IAA concentration was increased by 7.39%, as compared with the control. Besides, the DNA fingerprinting data, showed polymorphism (4%) between treated and untreated samples of cotton. The overall results suggest that the biofield energy treatment on cotton seeds, results in improved overall growth of plant, increase germination rate, GSH and IAA concentration were increased. The study assumed that biofield energy treatment on cotton seeds would be more useful for the production of cotton fiber.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".