Bioremediation of Agriculture Soil Contaminated by Organic Pollutants
Bibliographic record
Abstract
Pipeline spills and pollution of the environment by crude oil pose a threat to natural resources, especially soil and water. One such incident occurred on 25 September 2018 in the area of Budrovac (Croatia; 46°00′14.6″ N 17°04′16.8″ E) on agricultural land as a pipeline spill. Bioremediation of the contaminated soil was carried out with organic pollutants using an environmentally safe absorbent Spill-Sorb (Canadian Sphagnum Peat Moss) and a mineral fertilizer—nitrogen. The experiment was conducted in the greenhouse of the Faculty of Agriculture, Croatia, during a six-month (October 2018–April 2019) study. Samples of agricultural soils contaminated with total petroleum hydrocarbons (TPHs) and polycyclic aromatic hydrocarbons (PAHs) were taken after the rupture of the local gas condensate pipeline. The experiment was conducted in five treatments in triplicate: I-control (clean soil); II-100% contaminated soil + organic absorbent + nitrogen; III-100% contaminated soil + organic absorbent; IV-50% clean soil + 50% contaminated soil + organic absorbent + nitrogen; and V-50% clean soil + 50% contaminated soil + organic absorbent. The soil properties studied were pH, organic matter content, carbon and nitrogen content and ratio, and changes in the concentration of potential organic contaminants—TPHs and individual PAHs. The results demonstrated that the mixture of organic absorbent and nitrogen efficiently removed organic pollutants from the contaminated soil within six months. However, the application of Spill-Sorb alone was more effective for the degradation of hydrocarbons. The effectiveness of the absorbent studied was dependent on the concentration of organic pollutants and nitrogen application.
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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 | 0.001 | 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 | 0.000 |
| 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".