Study of microbial combination and nutrients in remediation of petroleum-contaminated soil
Bibliographic record
Abstract
In this study, degradation of polluted soil (with 1, 4 and 9% petroleum contamination) was performed using different microbial combinations and nutrients. Biosurfactant production and lipase production of the isolated microbes were also tested. A5 and A14 (isolated from petroleum-contaminated soil (PCS)) and G1 and G6 (isolated from garden soil) showed a positive result in the biosurfactant production test. A1, A5, A6, A13, A14, A15, A16, G1 and G5 showed zones around agar wells; these bacterial strains produced lipase. Combinations of microbes were applied in treatment of 1% PCS; the combination of only bacteria and the combination of bacteria and fungi showed petroleum degradation of 71 and 73·17%, respectively. In 4 and 9% PCS, the degradation activities of microbes decreased; the microbes needed a longer time to activate the large amount of petroleum. Furthermore, tests with and without nutrients were conducted to determine the effect of using nutrients in bioremediation of PCS. The results showed that petroleum degradation percentages from the treatment using nutrients were about 5% higher than the degradation percentages from treatments without nutrients.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".