Growing Wheat and Vegetable Crops on Medium Composed of Olive Mill Wastewater, Pomace, and Limestone
Bibliographic record
Abstract
The main objective of this project is to develop sustainable method of innovative agriculture practices that relies on reducing the liquid and solid waste generated from olive mill and limestone slurry by-product from factories in Palestine. The second aim is to use these waste by-products in a proper ratio mixture and their feasibility in the agricultural use for optimal and best conditions. The overall output is the implementation of applied research on wheat crop and solanaceous vegetables including tomato and pepper which proved their tolerance to grow well in such natural medium. It was observed that the best mixing ratio of the two parameter of limestone slurry and OMW was at 90:10 respectively. Crop growth, plant length and leaf area were measured. The best ratio of lime to OMW in wheat and pepper was 90:10, while tomato 80:20. It appears that the best result including plant height and leaf surface area were obtained at 90:10 mixing ratio of limestone and OMW as medium of limestone, pomace and OMW was suitable for cultivating the different types of studied crops. In general, the best results of plant growth were achieved when the percentage of limestone was high in the medium. The research has shown that it is possible to prepare an alternative media for plant growth from three major environmental by-products that were considered pollutants. In general, as the percentage of limestone increased in the medium, the plants grow proportionally. Selection of project site was within the AAUP Biology department and arboretum.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".