0311 Development of a predictive model for estimating gamma radiation exposures among Ontario uranium miners0311 Development of a predictive model for estimating gamma radiation exposures among Ontario uranium miners
Bibliographic record
Abstract
<h3>Objectives</h3> The objective of this study is to develop and validate a predictive model for estimating gamma radiation exposure for miners working in uranium mines between 1981 and 1985. <h3>Method</h3> The dose prediction model was developed and validated using multiple linear regression. To aid in model development, 70% random sample of workers were used in the model development (i.e., Training Sample) while the remainder 30% (i.e., Test Sample) was used to determine model performance. A stepwise approach was used to select variables into the model. Criteria for retaining the variables in the model was based on a p-value of <=0.15. Model fit was assessed using adjusted R-square. Co-linearity was determined by the magnitude of the variance inflation factor (VIF). Variables with VIF greater than 3.0 were removed from the model. In addition, SAS procedure ROBUSTREG was used to minimise the effects of outliers and high leverage in order to provide resistant (stable) results in the presence of outliers and high leverage. <h3>Results</h3> Based on 8949 employments records, dosimetric measurements of gamma radiation were significantly correlated with radon exposure (r = 0.499), duration of employment (r = 0.429), year of exposure (r = 0.239), and ore production (r = 0.230). Age was inversely related to gamma dose. Regression analysis showed that individual dosimetric readings can be modestly predicted by individual work history and geological characteristics of Ontario uranium mines (p < 0.001, R2 = 0.374). Additional sources of variation are likely related to individual variability that could not be accounted for in this ecological assessment. <h3>Conclusions</h3> Reconstructed gamma dose provides modest agreement with individual dosimetric readings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".