Evaluation of the Growth and the Yield of Eggplant Crop Under Different Irrigation Depths and Magnetic Treatment of Water
Bibliographic record
Abstract
The use of magnetizers for the treatment of irrigation water can be used in agriculture as an alternative to increase the growth and yield of several crops. The objective of this study was to evaluate the effect of different irrigation depths and magnetic treatment of water on eggplant crop cultivated in protected environment. The study was carried out in two experiments, in the first one, the design was completely randomized with four replications and two factors: water depths (50, 75 and 100% ETc) for two water qualities (water treated by magnetizers and water without treatment). In the second one, the design was completely randomized with five replicates and two factors: water depths (75 and 100% ETc) for two irrigation water treatment (water treated by magnetizers and water without treatment). In the second experiment was ignored the treatment of 50% of ETc in order to increase the number of repetitions to check if there are differences between water treated to water without treatment. There were no significant differences in eggplant yield and growth as function of the magnetic treatment of water. The water depth that provided the highest yield, number of fruits per plant and stem dry matter in the two experiments was 100% ETc regardless of water quality.
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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".