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
Nuclear utilities have many kilometres of piping buried in a relatively small physical area resulting in what has been called a “spaghetti bowl”. Until recently, much of this piping has been neglected and considered “out of sight / out of mind” therefore given a low operational impact. However, current failures have raised the profile of buried piping maintenance with both utilities and regulators. Buried piping programs face many of the challenges familiar to well run maintenance programs, but these challenges are compounded for a number of reasons. This paper will discuss how Atomic Energy of Canada Limited (AECL) Nuclear Laboratories have partnered with utilities, service providers, CANDU Owners Group (COG), and the Electric Power Research Institute (EPRI) to provide support to the development and implementation of maintenance programs for buried piping. Initially, AECL developed station strategy manuals to establish a mechanism to ensure a proficient ongoing program. As part of this program, extensive data on the systems was collected using station records. This data was then used to produce risk informed assessments, with the help of EPRI’s BPWORKS™ software, and ultimately the selection of inspection locations. Lessons learned from this work have not only been integrated into the station’s buried piping program, but also incorporated into improvements to the EPRI BPWORKS software.
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 | 0.001 | 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