Kinetic growth model and metabolic effect of a bacterial consortia from a petrochemical processing plant
Bibliographic record
Abstract
Abstract This study focused on presenting the newly developed growth model for bacterial species present in a petrochemical processing plant in South Africa. The findings of the study serve as a theoretical basis for future experiments aimed at understanding the formation of bacterial metabolites as the bacteria develops. An unstructured kinetic model using AQUASIM 2.3, together with experimental spectrophotometric results, were used to evaluate the growth of Gram‐negative bacteria in a batch reactor system. Spectrophotometer results showed the absence of a stationary phase. The exponential bacterial growth phase supported the total organic carbon (TOC) results, showing that bacterial growth occurred on days 6 and 13; this is rarely reported in literature, as the growth in this system was much slower than the growth of single‐strain studies. The TOC concentration values indicated that carbon sources did not deplete in the death phase, suggesting the presence of a long‐term stationary phase and the production of acetate. The presence of Pseudomonas sp. and sulphate‐reducing bacteria (SRB) are commonly reported in industrial systems as they play a role in equipment failure in industry. However, in this multispecies study, methods using third generation sequencing together with high‐performance liquid chromatography (HPLC) have shown that the selective attachment and production of acetate by abundant Clostridium sp. has ascertained their role in equipment failures in the petrochemical environment.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".