Bio-Saline Agriculture Modeling, Using Saline Water for Irrigation Purposes
Bibliographic record
Abstract
Climate change effects increase the scarcity of irrigation water and deterioration of its quality, which affects the crop water requirements. Researchers were studying the water recycling technique and finding about other possible renewable water resources for irrigation, they conclude that saline water can be used to meet part of the irrigation water needs for many crops under special field management, because there are many crops have a high tolerance to the salinity without decreasing in the yield. The current study aims to evaluate the economic yield of saline drainage water in irrigation. A computer program (Fıuat Ujaj) using Visual Basic language was constructed to use the largest possible amount of drainage water for irrigation after removing the toxic effects and then calculates the relative yield of the selected crops. The Main Outflow Drain (MOD) in Iraq was selected as a saline water resource which has 4.63 dS m-1 Electrical Conductivity (EC). This saline water contained high concentrations of chlorine and sodium ions. MFUP results showed that toxic effect of these ions can be removed by diluting with 35% of the nearby river water. MFUP results showed that the crops with high and medium tolerance to salinity give an acceptable yield ratio when they were irrigated with diluted water (35%) to remove toxicity effects only, while the dilution increases for crops of medium sensitivity, but the acceptable yield of sensitive crops is not achieved except with fresh river water. If the crop production is lower than the economically acceptable limit, another 5% of the river water will be added to mitigate, and the dilution process continues until the percentage of the river water become 80% of the irrigation water. If the required product is not achieved, then the program instructs to irrigate this crop with the river water only.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".