Chemical Prioritisation for Human Biomonitoring in Ireland: A Synergy of Global Frameworks and Local Perspectives
Bibliographic record
Abstract
Human biomonitoring (HBM) is a critical scientific tool for assessing human exposure by quantifying chemicals and their metabolites in biological specimens such as blood and urine. This approach provides a comprehensive and accurate evaluation of internal exposures from diverse sources and exposure routes. In Ireland, establishing a national HBM programme requires a systematic chemical prioritisation process that aligns global frameworks with local public perceptions. This study integrates insights from international initiatives such as the European Joint Programme Human Biomonitoring for Europe (HBM4EU) and the Partnership for the Assessment of Risks from Chemicals (PARC)-along with HBM programmes from EU countries (Germany, France, Belgium, Norway, Slovenia, Czech Republic, and Sweden) and non-EU countries (US, Canada, South Korea, China, and New Zealand). In addition, a national survey was conducted to capture the perceptions of people in Ireland regarding chemicals of concern to develop a comprehensive priority list of chemicals and biomarkers. The broader chemical groups identified include heavy metals (lead, cadmium, mercury, arsenic, and chromium VI), plasticisers (phthalates), bisphenols, pesticides, flame retardants, PFASs (per- and polyfluoroalkyl substances), PAHs (polycyclic aromatic hydrocarbons), POPs (persistent organic compounds), VOCs (volatile organic compounds), and UV (ultraviolet) filters. This integrated, participatory approach provides a roadmap for a robust, adaptable chemical list that supports evidence-based policy decisions in HBM in Ireland and enhances public health outcomes.
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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 | 0.039 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| 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".