A Data Driven Approach to Improving Suitability of External Corrosion Risk Algorithm for Pipelines with Unique Operating Conditions - a Case Study of Hot Bitumen Pipelines
Bibliographic record
Abstract
Abstract Bitumen is a solid or semi-solid high-viscosity liquid petroleum product at room temperature. The hot bitumen line discussed in this paper was uniquely designed to transport product at temperatures typically ranging from 140 to 149 degrees Celsius (284-300°F), preventing the application of an anti-corrosion external coating, which is ineffective at such temperatures. In this case, polyurethane foam insulation was used and an integrated moisture detection surveillance system for external moisture infiltration was installed for the long-term integrity of the bitumen line. Inline inspections are used to identify external corrosion where insulation degradation may occur. Due to the unique properties of the pipeline in the study, the conventional method to assess the risk of external corrosion required further consideration. This paper will provide an example of how the conventional method of assessing external corrosion risk was modified to better suit a buried insulated pipeline through a series of additional environmental data inputs, validated with ILI results, to improve the predictive capability of the inferential external corrosion threat model.
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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".