Evaluating Multi-Media Fate and Transport Models on a Regional and Global Scale
Bibliographic record
Abstract
This is not a comprehensive presentation of all of the findings of this project, but only a brief introduction to project highlights that we feel may be of most interest to those involved in organic chemical exposure assesssment. The project results are, or will be in the near future, documented in more than ten papers in peer-reviewed scientific journals, and more detail can be found in these publications (see pages 11 to 12 for a list). Finally, on the basis of the project results, we have identified five areas, where we think future efforts in bioaccumulation and multimedia fate and transport modelling should be directed (see pages 10 to 11). Achievements The project set out to expand existing multimedia fate and transport models (MFTMs) to include food chain bioaccumulation up to and including humans, and to evaluate their performance. In particular, we wanted to identify what chemical fate situations MFTMs are capable of addressing and for what situations they may not be applicable. We assembled a dynamic human bioaccumulation model (ACC-Human) which includes both the aquatic and agricultural foodchains, and parameterized it for two distinct human populations (Southern Swedes, Arctic Inuit) for which evaluation data could be compiled. In both cases good agreement between
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".