Effect of Sodium Bisulfite Injection on the Microbial Community Composition in a Brackish-Water-Transporting Pipeline
Bibliographic record
Abstract
Pipelines transporting brackish subsurface water, used in the production of bitumen by steam-assisted gravity drainage, are subject to frequent corrosion failures despite the addition of the oxygen scavenger sodium bisulfite (SBS). Pyrosequencing of 16S rRNA genes was used to determine the microbial community composition for planktonic samples of transported water and for sessile samples of pipe-associated solids (PAS) scraped from pipeline cutouts representing corrosion failures. These were obtained from upstream (PAS-616P) and downstream (PAS-821TP and PAS-821LP, collected under rapid-flow and stagnant conditions, respectively) of the SBS injection point. Most transported water samples had a large fraction (1.8% to 97% of pyrosequencing reads) of Pseudomonas not found in sessile pipe samples. The sessile population of PAS-616P had methanogens (Methanobacteriaceae) as the main (56%) community component, whereas Deltaproteobacteria of the genera Desulfomicrobium and Desulfocapsa were not detected. In contrast, PAS-821TP and PAS-821LP had lower fractions (41% and 0.6%) of Methanobacteriaceae archaea but increased fractions of sulfate-reducing Desulfomicrobium (18% and 48%) and of bisulfite-disproportionating Desulfocapsa (35% and 22%) bacteria. Hence, SBS injection strongly changed the sessile microbial community populations. X-ray diffraction analysis of pipeline scale indicated that iron carbonate was present both upstream and downstream, whereas iron sulfide and sulfur were found only downstream of the SBS injection point, suggesting a contribution of the bisulfite-disproportionating and sulfate-reducing bacteria in the scale to iron corrosion. Incubation of iron coupons with pipeline waters indicated iron corrosion coupled to the formation of methane. Hence, both methanogenic and sulfidogenic microbial communities contributed to corrosion of pipelines transporting these brackish waters.
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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) | 0.000 | 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) | 0.001 | 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".