Characterization of <i>Microbacterium</i> sp. F10a and its role in polycyclic aromatic hydrocarbon removal in low-temperature soil
Bibliographic record
Abstract
A polycyclic aromatic hydrocarbon degrading bacterium isolated from oil-polluted soil was identified as Microbacterium sp. F10a based on 16S rDNA gene sequence analysis. Plant growth promoting characteristics of the strain, degradation rate of phenanthrene and pyrene, and cell surface hydrophobicity characteristics of the strain were further characterized. The strain was also evaluated for promoting the growth of wheat and phenanthrene and pyrene removal from soil artificially contaminated with a mixture of phenanthrene (200 mg.kg-1) and pyrene (150 mg.kg-1) in pot experiments. The strain had the plant growth promoting characteristics of producing indole acetic acid, siderophore, and 1-aminocyclopropane-1-carboxylate deaminase activity and solubilizing inorganic phosphate. The strain also has a cell surface hydrophobicity that could increase the aqueous polycyclic aromatic hydrocarbon solubility. High-performance liquid chromatographic analysis showed that the degradation rates of phenanthrene (50 mg.L-1) and pyrene (20 mg.L-1) were 98% and 65%, respectively, under 28 degrees C after 7 days. Inoculation with the strain was found to significantly increase (p < 0.05) the growth of wheat and phenanthrene and pyrene removal in the unplanted or planted soils in a low-temperature environment. There were no significant differences in culturable bacterial numbers between live bacterial inoculation and dead bacterial inoculation controls in the unplanted and planted soils. However, the numbers of polycyclic aromatic hydrocarbon degrading bacteria were significantly greater in the inoculated planted or unplanted soils compared with the dead bacterial inoculation controls.
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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".