The OECD Pipe Failure Data Exchange Project: Validation of Canadian Data
Bibliographic record
Abstract
Structural integrity of piping systems is important to plant safety and operability. Information on degradation and failure of piping components and systems is collected and evaluated by regulatory agencies, international organizations (e.g., OECD/NEA and IAEA) and industry organizations worldwide to establish systematic feedback to reactor regulation and research and development programs associated with non-destructive examination (NDE) technology, in-service inspection (ISI) programs, leak-before-break evaluations, risk-informed ISI, and probabilistic safety assessment (PSA) applications involving passive component reliability. In 2002, the Nuclear Energy Agency (NEA) of the Organization for Economic Co-Operation and Development (OECD) has initiated an international pipe failure data collection and exchange project. The OECD Pipe Failure Data Exchange (OPDE) Project has been established to encourage multilateral co-operation in the collection and analysis of data relating to pipe failure events in commercial nuclear power plants. At present, the database contains 3644 records to which twelve participating countries contributed. This paper presents a brief description of the ODPE project objectives and work scope, as well as the Canadian contribution on data validation with respect to development and application of the pipe failure data collection on which OPDE is based. It gives a number of tables and figures that can be obtained from these records, with selected data ranging from a very broad (i.e. level of participation in the database from each member country), to very specific (i.e. plant operational state at time of pipe failure discovery for CANDU reactors).
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 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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".