Use of Measurement Data in Evaluating Exposure of Humans and Wildlife to POPs/PBTs
Bibliographic record
Abstract
The Stockholm Convention on Persistent Organic Pollutants (POPs) recognized that POPs resist degradation, undergo long-range transport, and accumulate in remote ecosystems. The Stockholm Convention also acknowledged that indigenous communities, particularly in the Arctic, were at risk because of the biomagnification of POPs and contamination of their traditional foods. This recognition was largely based on environmental monitoring data and demonstrates the need to have adequate guidance on data collection and use. Although long-range transport, persistence, and bioaccumulation models are important for screening potential POPs and for assessing human exposure, environmental measurement data are needed to confirm predictions. Indeed the Stockholm Convention (Annex E) requires monitoring data for assessing "exposure in local areas and, in particular, as a result of long-range environmental transport". However, there is relatively little guidance available on the most appropriate environmental measurement approaches, particularly for new candidate POPs, and on how to create a weight of evidence based on such data. We provide guidance on how to assess existing data that have been generated by monitoring programs and individual studies on the exposure of top predators and humans to candidate or potential POPs, as well as considerations for collecting new additional data. Our overall recommendation for assessing exposure in humans and top predators is to use or obtain direct measurements of the compound of concern from a significantly and uniquely exposed population (indigenous populations, remote populations), as well as data demonstrating biomagnification within food webs and time trends if possible. These data must be from the appropriate sample matrix type, collected and analyzed using accepted methodologies, reviewed for quality assurance, and interpreted correctly in order to be used to assess exposure.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".