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Record W4392376908 · doi:10.1080/00295639.2024.2315905

ORCA: A Tool for Radiological Consequences for Accidental Releases

2024· article· en· W4392376908 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueNuclear Science and Engineering · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsKinectrics (Canada)
Fundersnot available
KeywordsRadiological weaponAccidentalNuclear engineeringComputer scienceMedicinePhysicsEngineeringRadiology

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.342
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it