Bioremediation of Polluted Soil Sites with Crude Oil Hydrocarbons Using Carrot Peel Waste
Bibliographic record
Abstract
The biostimulation potentials of carrot peel waste and carob kibbles for bioremediation of crude petroleum-oil polluted soil were investigated. Temperature, pH, moisture, total petroleum hydrocarbon (TPH), and changes in microbial counts during 45 days were monitored when 4 mL of carrot peel waste or carob kibbles media were added to 200 g of crude oil polluted soil samples. Gas chromatography-flame ionization detection (GC-FID) was used to compare hydrocarbon present in the crude oil polluted soil and in pure fuel, composition of crude oil polluted soil was analyzed by X-ray diffraction (XRD), and the TPH was measured by distillation using distiller mud. The results showed that, at the end of experiments, the concentration of TPH decreased in crude oil polluted soil containing carrot peel waste with a percentage of 27 ± 1.90% followed by crude oil polluted soil containing carob kibbles (34 ± 1.80%) and in the unamended control soil (36 ± 1.27%), respectively. The log [Colony Forming Unit (CFU)/g] of total heterotrophic bacteria in the crude oil polluted soil increased from 10.46 ± 0.91 to 13.26 ± 0.84 for carrot peel waste, from 11.01 ± 0.56 to 11.99 ± 0.77 for carob kibbles and from 8.18 ± 0.39 to 8.84 ± 0.84 for control, respectively. Such results demonstrated that carrot peel could be used to enhance activities of the microbial hydrocarbon-degrading bacteria during bioremediation of crude petroleum-oil polluted soil.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".