Bibliometric Analysis of Microbiologically Influenced Corrosion (MIC) of Oil and Gas Engineering Systems
Bibliographic record
Abstract
Managing microbiologically influenced corrosion (MIC) is both an economic and technological challenge for the oil and gas industry. There are studies and data generated regarding the corrosion mechanism, microbial species involved, and chemicals that may enhance/inhibit MIC. However, these data are diffuse, sometimes having contradictory conclusions and ignoring one or more key factors that drive MIC. This paper investigates the evolution of MIC knowledge in the past decades by conducting a bibliometric analysis of the literature. The paper also identifies current knowledge gaps and proposes future research directions. Although MIC mechanisms, monitoring, and control have been active areas of research in recent years, linking microbiological activities, the chemical environment (e.g., produced water lines vs. crude lines), and the corrosion mechanisms is still an important knowledge gap. The importance of a coordinated multidisciplinary approach to develop integrated knowledge, MIC mechanistic models, and integration of these factors in effective decision-making is also discussed in this paper.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.129 | 0.172 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".