Prediction of Corrosion Defect Failure Pressure for Finite Length Defects
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
Corrosion defects commonly occur on operating pipelines due to a loss of protection in a corrosive environment. These defects require practical and accurate assessment, particularly for older pipeline systems, to determine the need for remediation or allow for continued operation. Previous research has shown that appropriate application of full three-dimensional finite element analysis, and newly developed analytical approaches, can provide very accurate predictions of failure pressure but require detailed material and geometric data. Although this is important, a simpler method that allows for efficient evaluation of large amounts of data is also desirable. A method has been developed from an existing analytical solution by assuming a defect can be characterized in terms of the total defect length, and a constant defect depth equal to the maximum defect depth. In general this produces a conservative estimate of the material loss. This finite-length defect solution is in good agreement with experimental data for idealized defects, and provides reasonable predictions of burst pressure, with a minimum amount of data, when applied to real corrosion defects.
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