Installing and Commissioning a New Radioactive Waste Tracking System - Lessons Learned
Bibliographic record
Abstract
Ontario Power Generation (OPG) recognizes the importance of information management particularly with regards to its low and intermediate level waste program. Various computer based waste tracking systems have been used in OPG since the 1980s. These systems tracked the physical receipt, processing, storage, and inventory of the waste. As OPG moved towards long-term management (e.g. disposal), it was recognized that tracking of more detailed waste characterization information was important. This required either substantial modification of the existing system to include a waste characterization module or replacing it entirely with a new system. After a detailed review of available options, it was decided that the existing waste tracking application would be replaced with the Idaho National Laboratory’s (INL) Integrated Waste Tracking System (IWTS). Installing and commissioning a system which must receive historical operational waste management information (data) and provide new features, required much more attention than was originally considered. The operational readiness of IWTS required extensive vetting and preparation of historic data (which itself had been created from multiple databases in varied formats) to ensure a consistent format for import of some 30,000-container records, and merging and linking these container records to a waste stream based characterization database. This paper will discuss some of the strengths and weaknesses contributing to project success or hindrance so that others can understand and minimize the difficulties inherent in a project of this magnitude.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".