Towards New Estimated Daily Intakes for the Canadian Population
Bibliographic record
Abstract
O-01A2-2 Background/Aims: Health Canada's Contaminated Sites Division is involved in the revision and development of new human health soil quality guidelines (HHSQGs). Part of the HHSQG development process relies on Estimated Daily Intakes (EDIs) which estimate the typical concurrent background exposure to chemicals from all known or suspected sources (air, water, soil, dust, food and consumer products) via all known or suspected routes of exposure (inhalation, ingestion, dermal contact) for the average Canadian. Instead of using a deterministic approach to derive EDIs, a probabilistic one has been developed and will be presented as well as its strengths, limitations and recommended future improvements. Methods: For each chemical under HHSQG revision or update, an extensive review of all the available Canadian databases covering air, water, soil, dust, and food was performed through grey and scientific literature searches. The scientific validity of all available papers, grey reports, and databases was assessed using a quality score tool developed for this purpose. Then, after final selection of key data, all the environmental concentration distribution parameters and all the physiological distribution parameters involved in the EDI equations were estimated. The Crystal Ball add-in software for excel was used for the Monte-Carlo simulations and probabilistic EDI distributions were obtained for each of the 5 Health Canada human receptor age groups. Results: Instead of deriving deterministic EDIs, EDI multimedia probabilistic distributions are obtained through a fully transparent and systematic approach. This has already been done for 9 chemicals or species (Barium, Beryllium, Cadmium, Total and Hexavalent Chromium, Lead, Nickel, Vinyl chloride, Zinc). Conclusion: This is a first step to derive new Canadian EDIs integrating all the pertinent information available. However, through this systematic and transparent process a lot of limitations can be identified (data gaps, methodological limitations, no available correlations between media of exposure, etc). This allows prioritization of future research projects to improve Health Canada EDIs.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".