Linking Sulfur Cycling and MIC in Offshore Water Transporting Pipelines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Microbial activities in oil and gas operations cause souring, the production of sulfide by sulfate-reducing bacteria (SRB), and microbiologically-influenced corrosion (MIC). MIC may be especially severe in systems were several different types of fluids are mixed together, as this may provide a variety of nutrients for microbial growth. We have studied samples from an offshore production site and an onshore terminal for separation, crude oil storage, effluent treatment and disposal. We have investigated the samples using chemical analyses, culture-based microbial counts and molecular DNA-based techniques (pyrosequencing) to obtain whole microbial community composition. We found that (i) sulfate reduction by SRB (Desulfovibrio, Desulfobacterium, Desulfobacter) leads to the formation of sulfide, that (ii) sulfide is reoxidized to form elemental sulfur both abiotically and through the metabolism of sulfide-oxidizing bacteria (Sulfurimonas, Arcobacter) and that (iii) sulfur is converted back to sulfide by sulfur-reducing bacteria (S0RB, Desulfuromonas, Desulfuromusa), completing the sulfur cycle. Samples from these systems have significant sulfate, sulfide and sulfur (S8) concentrations and reactions (i) to (iii) can be demonstrated to occur. We find that the presence of elemental sulfur, which is increased by reactions (i) and (ii) and decreased by reaction (iii) gives rise to considerably increased corrosion risk towards steel infrastructure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 it