Effect of humic and fulvic substances and Moringa leaf extract on Sudan grass plants grown under saline conditions
Bibliographic record
Abstract
Salinity is the major stress factor that limits crop cultivation, especially in developing countries. A randomized complete block, factorial (3-factor) experiment was conducted on Sudan grass grown on nonsaline and saline soils to assess humic substances with or without foliar spraying Moringa leaf extract (MLE). Factors were (i) soil (three different levels of salinity), i.e., S1: nonsaline [electrical conductivity (EC) = 3.01 dS m−1], S2: medium saline (6.12 dS m−1), and S3: highly saline (12.33 dS m−1); (ii) humic substances, i.e., B0: no addition, B1: humic acid (HA) as potassium (K) humate, B2: fulvic acid (FA) as K fulvate, and B3: HA and FA; (iii) foliar spray with MLE, i.e., C0: nontreated and C1: foliar spray. Results indicated that total chlorophyll, nutrient uptake, available nitrogen (N), phosphorus (P), and K significantly decreased within each humic substances application, and MLE with increasing salinity concentration. The highest values of fresh, dry weight, total chlorophyll, and NPK uptake under different salinity levels were observed with application of substances and MLE. Spraying of MLE increased cumulative yield and nutrient uptake by Sudan grass compared with the untreated ones. The treatment of HA and FA with or without spraying MLE gave the highest values of available NPK under the salinity levels.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".