MIC under Conditions of Oxygen or Nitrate Ingress
Bibliographic record
Abstract
The majority of microbiologically influenced corrosion (MIC) scenarios occur under anoxic conditions and often implicate sulfate-reducing bacteria (SRB). These bacteria catalyze sulfate respiration and form sulfide as a metabolic end product. While SRB may be one of the most aggressive players in biocorrosion and are by far the most often studied, MIC can involve other organisms as well. The ingress of electron acceptors other than sulfate, such as oxygen or nitrate, into an SRB-dominated environment can lead to changes in the microbial community and in the corrosion threat. For instance, the partial respiration of nitrate by nitrate-reducing bacteria forms nitrite, a corrosive metabolite. The chemical reaction of oxygen or nitrite (a by-product of incomplete nitrate respiration) with SRB-produced sulfide forms polysulfides or elemental sulfur. Sulfur and polysulfides are highly corrosive, and evidence of sulfur-mediated corrosion can be seen when examining the metal surface in the form of severe 310 pitting. Furthermore, changes in microbial community composition may also serve as evidence of sulfur- and polysulfide-mediated corrosion. DNA pyrotag sequencing was used to determine the microbial community compositions of field samples from a North Sea oil production site and from an Alberta oilfield. In both cases, microbial communities associated with sulfur metabolism were uncovered. An abundance of microorganisms able to oxidize aqueous sulfide using oxygen as an electron acceptor (genera Arcobacter , Sulfurospirillum , Sulfurimonas ) were discovered. The potential for sulfur formation by these organisms greatly enhances corrosion threat. The fact that other bacteria capable of using elemental sulfur as an electron acceptor (genera Desulfuromonas , Desulfuromusa ) were also found in these samples serves as evidence that sulfur is often produced at these sites. The presence of these different sulfur cycle microorganisms and the subsequent activity shown under oxygen or nitrate ingress is evidence for their participation in MIC.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".