AECL, Chalk River Laboratories, Canada;
Bibliographic record
Abstract
Abstract: The primary heat transport system of a typical CANDU®-6 nuclear power reactor contains 760 feeder pipes. These feeders carry the coolant between the inlet or outlet headers and the individual fuel channels. Inspection requirements that developed in feeders in the 1990’s led to rapid development of NDE technology to evaluate feeder integrity. The NDE technology had to deal with the complex and variable geometry of feeders and access constraints inherent in feeder inspections. The first feeder inspection requirement was ability to detect Flow-Accelerated Corrosion (FAC). This led to an evolution in wall thickness measurement techniques for feeders that is continuing. The leading edge technique for wall thickness measurement is the METAR Crawler that scans feeder bends with 14 wall thickness probes. This system uses motors to push the bracelet along the feeder bend. Second, cracking was detected in a feeder in 1997. This led to development of a specialized, manual inspection technique to deal with access problems. This technique has been extended to use a Hydro Quebec developed drive system to move the probes over a raster scan. The presentation will explain feeder degradation modes and the NDE developed to evaluate feeder integrity. Introduction: The primary heat transport system of a CANDU-6 nuclear power reactor contains 760 feeder pipes. Feeders are made of A106B steel. These feeders carry the coolant, heavy water, between
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.524 | 0.258 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".