Relative Efficiency of Zinc-Coated Urea and Soil and Foliar Application of Zinc Sulphate on Yield, Nitrogen, Phosphorus, Potassium, Zinc and Iron Biofortification in Grains and Uptake by Basmati Rice (Oryza sativa L.)
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
Two on-farm trials conducted one each in Aligarh and Meerut districts of the state of Uttar Pradesh, India on zinc (Zn) deficient soils during the rainy season (July-October) showed that Zn application increased not only Zn concentration and uptake by rice but also increased protein content of rice kernels and concentrations of Fe, N, P and K due to the overall improvement in crop growth. Foliar application of Zn was better from the viewpoint of Zn biofortification of rice kernels; nevertheless much of the foliar applied Zn was retained in husk. Since, foliar application of Zn is made at a late stage of crop growth, hence it was not as effective as soil application in increasing yield attributes, yield and concentration and uptake of Fe, N, P and K in rice. This study brought out that adequate soil application of Zn sulphate followed by its foliar application is the best approach. Zn coated urea applying less than half the amount of Zn as applied through soil + foliar application was very close to it and is quite promising.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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