Comparison of Estimates From a Growth Model 5 Years After the Previous Inspection
Bibliographic record
Abstract
A corrosion growth modelling procedure using repeated inline inspection data has been employed as part of the maintenance program planning for a pipeline in the Alberta portion of the TransCanada system. The methodology of matching corrosion features between the different in-line inspections, and estimating their severity at a future date, is shown to be an excellent proactive cost saving methodology. Throughout this paper estimated 80% confidence intervals for tool measurement error, total prediction error and growth methodology error are given. In this abstract the values have been rounded. For maximum penetration, for the features reported on three inspections, the confidence interval for total prediction error varies from ±12% to ±17%, and for the growth methodology from ±8% to ±10% of the wall thickness (for the 1998 and 1999 dig programs respectively). For features reported on two inspections the confidence interval varies from ±19% to ±22% for total prediction error (1998 and 1999 digs respectively), and is about ±17% for the growth methodology (for both dig programs). The estimated confidence interval for prediction error in failure pressure is about ±560 kPa for the 1998 dig program. For the 1999 dig program a good estimate of the confidence interval for total prediction error could not be obtained. Assuming the failure pressure data obtained from field measurements were perfect, the estimate of the maximum confidence interval was ±850 kPa. For the laser profile measurement field tool, compared to an ultrasonic pencil probe, the confidence interval for penetration is less than ±2% of the wall thickness. The true confidence interval values in some cases are expected to be smaller than reported above for several reasons discussed in this paper.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".