Bibliographic record
Abstract
SS4-02 Abstract: The intake fraction—the fraction of a pollutant's emissions that is taken in by a population—is an interesting and useful concept that has been defined within a multidisciplinary collaborative effort, bringing together scientists from multimedia modeling, risk assessment, outdoor and indoor air pollution, and life cycle impact assessment. As mentioned by Nazaroff, “intake fraction is a lens through which we can view the problem and bring important aspects into focus that have never been seen before.” So far, most of the research and evaluation emphasis has been set on the fate component of the evaluation, with, eg, the OECD comparison exercise by Fenner et al for multimedia models. This comparison has shown that improving the quality of input data is of the highest priority with models providing rather comparable results using the same quality input data. Significant progress has also been achieved in the modeling of intake fraction for primary and secondary pollutants (iF from 10−5 to 10−7) and in the estimation of indoor intake fractions that lead to higher values of 10−2 to 10−4. Recently, a stronger emphasis is being placed on estimating exposure, which has improved the assessment of the food chain in which intake fractions of 10−2 to 10−3 are reported. In the case of bioaccumulation, this is occurring with 1) improved correlation or better modeling of the bioconcentration factors in vegetation, meat, milk, and eggs; 2) the modeling of the full food web within the aquatic ecosystem; and 3) the better description of coastal marine environments in which 80% of the fish catch takes place. Further developments are emerging to link intake with absorption, uptake, and metabolism in the body, coupling multimedia fate and pharmacokinetic models to cover the whole cause–effect chain from source to body burden.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".