Identification and Characterization of Sulfate-Reducing Bacteria Involved in Microbially Influenced Corrosion in Oil Fields
Bibliographic record
Abstract
Abstract Sulfate-reducing bacteria (SRB) are thought to be involved in microbially influenced corrosion (MIC) of oil field pipelines and equipment. Analysis of microbial communities on corrosion coupons installed in production facilities at Wainwright and Wildmere, two oil fields in Alberta, was done by reverse sample genome probing (RSGP) a DNA hybridization assay in which multiple bacteria can be tracked simultaneously. RSGP indicated dominance of SRB in the microbial communities present on 10 of 15 coupon samples. Of these Desulfovibrio spp. Lac6 and Eth3 were found to be resistant to cocodiamine biocides used in these fields, suggesting that biocide addition could be of limited use for corrosion prevention. Desulfovibrio sp. Lac6 was inhibited by nitrite, and by nitrate if a nitrate-reducing, sulfide-oxidizing bacterium was also added. Addition of nitrite or nitrate thus offer alternative ways to contain SRB, although their effect on corrosion rates have not been extensively studied.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".