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
Defect assessment is essential for the determination of the FFS of pipelines. While the corrosion defect information and relevant data are usually obtained from ILI tools and other sources, models and standards have been developed to assess the defects, predicting failure pressure of the pipelines containing corrosion defects. The defect assessment techniques have experienced evolution in terms of definition of the corrosion defect geometry, assessment methods, computational complexity, and inclusion of multiple affecting factors such as the interaction of adjacent corrosion defects. Levels I and II defect assessment techniques have been used for pipeline failure prediction and FFS determination for several decades. They differentiate each other mainly based on the defect geometrical definition and inclusion of multiple corrosion defects in consideration. While the Level I defect assessment methods have been commonly used for several decades, they tend to provide conservative results for prediction of failure pressure of corroded pipelines. Level II assessment techniques can enhance the prediction accuracy of pipeline FFS by improving the definition of the geometrical shape and dimensions of corrosion defect and considering the mutual interaction of multiple corrosion defects in adjacency.
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) | 0.003 | 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