Petroleum oil biodegradation by Corynebacterium aquaticum and Pseudomonas aeruginosa strains isolated from the industrial rejection of the refinery of Arzew-Algeria
Bibliographic record
Abstract
Abstract: Petroleum hydrocarbons including polycyclic aromatic hydrocarbons have been categorized as priority pollutants by US Environmental Protection Agency (USEPA), Quebec Ministry of Environment (QMENV) and many other environment and health organizations in the world. In the present study, fifteen organisms were isolated from soil and water samples from the industrial rejection of the Refinery of ARZEW (Oran-Algeria). The bacterial strains were selected due to their capacity of growing in the presence of hydrocarbon. The isolates were identified as belonging the majority to genera Corynebacterium (5 stains), Corynebacterium aquaticum (3 strains). Besides, three strains were identified as Pseudomonas aeruginosa, one as Pseudomonas spp, one as Pseudomonas fluorescence, one as Brevibacterium spp and one strain as Stenotrophomonas maltophilia. Growth of the mentioned strains was realized in mineral liquid media supplemented with petroleum oil as sole carbon source. Optimized conditions to improve the biodegrading activity of the isolated strains were studied using different concentration of petroleum oil and surfactant. A consortium of the bacteria having the best biodegradation activity was also tested in addition to the estimation of DBO and DCO in the biological treatment pond of the purifying station of ARZEW Refinery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".