A New Rapid Technique for Screening the Potential of Implanted Microorganisms to Tolerate and Grow on Petroleum Oily Sludges
Bibliographic record
Abstract
Standard experimental protocols for biodegrading petroleum hydrocarbons employ microbiological and/or analytical analysis, and require advanced machines and specialized professionals to carry them out. Furthermore, the obtained results involve a high margin of error. In this paper, a new experimental technique was developed to facilitate the study of microbial capability to degrade petroleum oily sludges. The technique is based on growing microorganisms on 0.22 microm filter membranes laid over oily sludge placed in metallic cups. The microbial growth was assessed using the plate counts technique. Sludge degradation was assessed using Fourier Transform Infrared Spectrometry (FTIR), and compared to the decrease in total petroleum hydrocarbons (TPH) using solvent extraction. The protocol was tested using three types of inocula: fungal inoculum containing Paecilomyces variotii; bacterial inoculum containing Bacillus cereus; and fungal-bacterial inoculum containing both strains. Both, fungal and bacterial, strains were isolated from the oily sludge used in the present study, and were tentatively identified as oil degraders. The results of the newly developed technique helped to assess the potential of these cultures to tolerate and grow on the oily sludge.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".