Testing the atmospheric dispersion model of CSA N288.1 with site-specific data
Bibliographic record
Abstract
The atmospheric dispersion component of CSA Standard N288. 1, which provides guidelines for calculating derived release limits, has been tested. Long-term average concentrations of tritium in air were predicted using site-specific release rates and meteorological data and compared with measured concentrations at 43 monitoring sites at all CANDU stations in Canada. The predictions correlate well with the observations but were found to be conservative, overestimating by about 50% on average. The model overpredicted 84% of the time, with the highest prediction lying a factor of 5.5 above the corresponding observation. The model underpredicted the remaining 16% of the time, with the lowest prediction about one-half of the corresponding measurement. Possible explanations for this bias are discussed but no single reason appears capable of accounting for the discrepancy. Rather, the tendency to overprediction seems to result from the cumulative effects of a number of small conservatisms in the model. The model predictions were slightly better when site-specific meteorological data were used in the calculations in place of the default data of N288.1. Some large discrepancies between predictions and observations at specific monitoring sites suggest that it is the measurements rather than the model that are at fault. The testing has therefore provided a check on the observations as well as on the model. Recommendations on model use and data collection are made to improve the level of agreement between predictions and observations in the future.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".