Community dynamics of a mixed-bacterial culture growing on petroleum hydrocarbons in batch culture
Bibliographic record
Abstract
The effects of various hydrocarbon substrates, and a chemical surfactant capable of enhancing crude-oil biodegradation, on the community structure of a mixed-bacterial inoculum were examined in batch culture. Of 1000 TSA-culturable isolates, 68.6% were identified at the genus level or better by phospholipid fatty acid analysis over 7-day time course experiments. Cultures were exposed to 20 g/L Bow River crude oil with and without 0.625 g/L Igepal CO-630 (a nonylphenol ethoxylate surfactant), 5 g/L saturates, 5 g/L aromatics, or 125 g/L refinery sludge. A group of six genera dominated the cultures: Acinetobacter, Alcaligenes, Ochrobactrum, Pseudomonas/Flavimonas, Stenotrophomonas, and Yersinia. Species from four of the genera were shown to be capable of hydrocarbon degradation, and counts of hydrocarbon degrading and total heterotrophic bacteria over time were nearly identical. Pseudomonas/Flavimonas and Stenotrophomonas normally dominated during the early portions of cultures, although the lag phase of Stenotrophomonas appears to have been increased by surfactant addition. Acinetobacter calcoaceticus was the most frequently isolated microorganism during exposure to the saturate fraction of crude oil. Regardless of substrate, the culture medium supported a greater variety of organisms during the latter portions of cultures. Understanding the community structure and dynamics of mixed bacterial cultures involved in treatment of heterogeneous waste substrates may assist in process development and optimization studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".