Effect of irrigation with magnetized and ionized water on yield, nutrient uptake and water-use efficiency of winter wheat in Xinjiang, China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Magnetization can alter hydrogen bonds among water molecules, leading to changes in water physicochemical properties, and ionization can use frequencies to break down polymers for better solubility. Consequently, irrigating with magnetized and ionized (MI) water might affect crop growth, yield, and water-use efficiency. This study investigated the effects and mechanisms of irrigation with MI water on water physicochemical properties, soil nutrient availability, winter wheat growth, nutrient uptake, yield, and water-use efficiency in Xinjiang, an arid region of northwest China. Our results showed that magnetization and ionization significantly increased water-dissolved oxygen and reduced water surface tension. Irrigation with MI water significantly increased soil available nitrogen and phosphorus concentrations at the heading and ripening stages. Additionally, irrigation with MI water significantly increased plant height, stem diameter, head length, and wheat biomass at the filling stage. Irrigation with MI water also enhanced wheat phosphorus uptake and grain yield, resulting in a 23 % improvement in water-use efficiency. In conclusion, irrigation with MI water can improve soil nutrient availability, stimulate wheat growth, and increase both grain yield and water-use efficiency, which could be applied in the fields of arid regions to enhance water use efficiency and sustainable agricultural water management. • Magnetization and ionization increased water-dissolved oxygen and decreased surface tension. • Magnetized and ionized water increased soil N and P availability. • Magnetized and ionized water promoted wheat growth, N uptake and yield. • Magnetized and ionized water enhanced irrigation water use efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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 it