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
Proponents of new pipeline projects are often asked by regulators to provide estimates of risk and reliability for their proposed pipeline. On existing pipelines, the availability of operating and assessment data is generally considered to be essential to the task of performing an accurate and defendable risk or reliability assessment. For proposed or new pipelines, the absence of these data presents a significant challenge to those performing the analysis. The reliance on industry incident data presents problems, since the vast majority of loss-of-containment incidents relate to older pipelines in which the design, routing criteria, material properties, material manufacturing processes, and early operating practices differ significantly from those that are characteristic of modern pipelines. As a consequence, much of the available failure incident data does not accurately reflect the threats or the magnitudes of the threats that are associated with modern pipelines. In order to address this problem, ‘adjustment factors’ are often applied against incident data to try to account for threat differences between the source data and the intended application. The selection of these adjustment factors can often be quite subjective, however, and open to judgment; therefore, they can be difficult to justify. With the rapidly growing practice of regular in-line inspection (ILI) on transmission pipelines, an extensive repository of ILI data has been accumulated — much of it relating to modern pipelines. Through the judicious selection of source data, ILI data sets can be mined so that an analogue data set can be created that constitutes a reasonable representation of the attributes of reliability of a specific new pipeline of interest. Key reliability properties, such as tool error distribution, feature incidence rate, feature size distribution, and apparent feature growth rate distribution can be derived from such analogue data. By applying these reliability properties in an analysis along with known pipeline design and material properties and their associated distributions, and by taking consideration of planned inspection intervals, a reliability basis can be derived for estimating pipeline risk and reliability. Estimates of risk and reliability that are derived in this manner employ methodologies that are repeatable, defendable, transparent, and free of subjectivity. This paper outlines an approach for completing risk and reliability estimates on new pipelines, and presents the results of some sample calculations. The reliability estimates illustrated are based on an approach whereby corrosion feature size and growth rates are obtained from analogue ILI datasets, and treated as random variables. In that regard, they constitute the probability of exceeding a limit state that represents an approximation of the condition for failure.
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