Modelling of Corrosion under Insulation in Oil Sands
Bibliographic record
Abstract
Abstract Corrosion Under Insulation (CUI) in Alberta’s oil sands industry has been observed in above ground assets in thermal operations carrying emulsion, steam, hot water and/or warm water that are externally insulated to ensure safe and energy efficient operations. CUI has also been observed in oil sands mining operations in various piping systems, tanks and/or vessels, and structural supports including insulated support rings, which are frequently in contact with soil or standing groundwater. Furthermore, CUI can go easily unnoticed over prolonged periods of time, only detected after an insulation or pressure containment failure occurs. Most of the CUI occurrences are usually found at the 6 o’clock position or lowest collecting point. Therefore, it is important to identify suitable methodologies to predict CUI for Alberta’s oil sands industry. This work, carried out within the Materials and Reliability in Oil Sands (MARIOS) consortium at InnoTech Alberta, explores existing methodologies for CUI prediction reported in the public domain, and provides a preliminary validation of findings using field data collected by the producer members of the MARIOS consortium.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".