The Effects of Fe, Mn and Zn Foliar Application on Yield, Ash and Protein Percentage of Forage Sorghum in Climatic Condition of Esfahan
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
Improving forage yield and quality remains a major concern of the producer. Among the various ways of supplying nutrient to the crops, the efficient utilization of nutrients by the plants is made through foliar application. Foliar application had significant effect on plant height, the number of leaf, the number of tiller, LAI, fresh forage yield, dry leaf yield, dry stem yield, total dry yield, dry leaf weight/dry stem weight ration and ash percentage. Application methods of micronutrients are very important to attain the best absorption. The results of this study demonstrated that, Fe, Zn and Mn had positive effect on yield and quality of forage sorghum. The highest plant height, LAI, Fresh forage yield, dry leaf and stem yield, total dry yield and dry leaf weight/dry stem weight was obtained in Zn+Fe+Mn application. The highest number of tiller was related to combination of Zn and Mn. The maximum ash percentage and appropriate protein percentage also was achieved in application of Zn+Fe+Mn. So, on the basis of the results, it seems that application Zn+Fe+Mn was suitable to gain high forage yield and gain to high quality.
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