Canadian Specific Exposure Factors for Certain Personal Care Products
Bibliographic record
Abstract
Humans use a variety of personal care products (PCPs) every day. Estimating human exposures to chemicals found in PCPs requires information on the frequency of use and amount of product used. This type of information is usually obtained from various questionnaires and surveys and is available from regulatory guidance documents and in the scientific literature; however, very little data exists for the Canadian population.The Canadian Health Measures Survey (CHMS), a national survey that collects important health information from individuals aged 3 to 79 years old, included some questions regarding the use of certain grooming products by Canadians in Cycle 1 (2007-2009) and Cycle 2 (2009-2011), which sheds some light on Canadian use patterns. This poster will highlight Canadian specific data on the frequency of use of certain PCPs by adults, teens and children as young as 3 years old. The poster will also illustrate how this data is being used by the Existing Substances Risk Assessment Bureau (ESRAB) for risk assessments conducted under Canada’s Chemicals Management Plan. Results will be presented on how the use of certain PCPs by Canadians compares with use pattern data from the US and Europe.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".