Experimental Approach for Treatment of Contaminated Soil with Crude Oil
Bibliographic record
Abstract
Oil is considered one of the most dangerous sources of environmental pollution, especially for the soil, as it turns it into sterile soil that is not suitable for animal, plant or any living thing. Therefore, in this research, we will use four methods to treat soil contaminated with crude oil, where animal and plant fertilizers were used at rates of 20% and 40% in each method, with both types active and inactive, on samples of contaminated soil, after preparing these samples they were compared with each other by analyzing the data and the results of scientific experiments held on the samples. Where the results of the treatment of contaminated soil, and by using some of these methods, showed an increase in the ratio of bacteria beneficial to the soil and at the same time an increase in the ratio of organic materials feeding the plant, because of the existence of types of bacteria in these fertilizers that eliminate or feed on the toxic oil compounds that exists in the soil, in addition to this activating and providing the soil again with bacteria that are beneficial to it. After we applied the treatment processes and scientific experiments, it became clear that the method (B1) using animal fertilizer (sheep manure) after adding a quantity equal to 40% of the sample gives the most positive result when treating soil contaminated with crude oil.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".