Response of a Model Microbiologically Influenced Corrosion Community to Biocide Challenge
Bibliographic record
Abstract
A model community consisting of four species was developed to represent clades of metabolism commonly found in microbiologically influenced corrosion including a sulfate reducer (Desulfovibrio vulgaris), an iron oxidizer (Geoalkalibacter subterraneus), a nitrate reducer (Thauera aromatica), and a biofilm pioneering organism (Pseudomonas putida). This community was grown in parallel in a CDC Biofilm Reactor (CBR), and both planktonic and sessile communities (grown on carbon steel coupons) were monitored using optical density, adenosine triphosphate (ATP activity), DNA concentrations, and 16S rRNA targeted quantitative PCR (qPCR). One CBR was challenged with low concentration of a biocide (THPS or BAC) for seven days, then media was flushed, and the community was allowed to recover. The other CBR was used as a biocide-free control and monitored identically. A set of custom primers targeting multidrug resistance efflux pump (MDREP) genes which provide resistance to the biocide(s) were developed and used to track the level of these genes in the two sessile communities to determine if any of these MDREP targets changes in abundance in the community because of the biocide challenge. MDREP abundance was determined by comparing raw copy numbers of the MDREP genes to 16S rRNA genes. As the MDREP genes are frequently located on mobile genetic elements, they can move and/or replicate within and between genomes of neighboring cells. An increase in the MDREP/16S rRNA gene ratios suggests a genetic response of the community in response to the biocide. These genetic targets can then serve as markers in environmental samples for potential predisposition toward increased tolerance toward different biocides, thus improving biocide treatment programs before application.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".