ORCA: A Tool for Radiological Consequences for Accidental Releases
Bibliographic record
Abstract
Evaluating atmospheric dispersion and radiological doses in the vicinity of buildings is required for small modular reactors (SMRs) because of the reduced size of their exclusion area boundary. The current Canadian nuclear industry tool for these calculations implements the methodology defined in CSA Standard N288.2-M91, which was written to support large Canada Deuterium Uranium (CANDU) nuclear reactors as opposed to SMRs. The ORCA (On/offsite Radiological Consequences of Accidents) code has been developed to address this technical concern in addition to evaluating atmospheric dispersion and doses in the far field. The code calculates worker and public doses following an airborne release of radioactive material into the atmosphere under postulated accident conditions at a nuclear facility. The current paper presents the key assumptions and methods utilized in ORCA and discusses qualification of the software to the requirements of CSA Standard N286.7-16. The new model is applicable to SMRs and existing reactor designs and reduces conservatisms in the near field (i.e., <1 km from the source) relative to the methods in CSA N288.2-M91.
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 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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.010 |
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".