CKow: A Dynamic Model for Chemical Transfer to Meat and Milk
Bibliographic record
Abstract
The objective of this study is to increase the understanding and transparency of chemical biotransfer modeling into meat and milk and explicitly confront the uncertainties in exposure assessments of chemicals that require such estimates. In cumulative exposure assessments that include food pathways, much of the overall uncertainty is attributable to the estimation of transfer into biota and through food webs. Currently, the most commonly used meat and milk-biotransfer models date back two decades and, in spite of their widespread use in multimedia exposure models, few attempts have been made to advance or improve the outdated and highly uncertain K(ow) regressions used in these models. Furthermore, in the range of K(ow) where meat and milk become the dominant human exposure pathways, these models often provide unrealistic rates and do not properly reflect the transfer dynamics. To address these issues, we developed a dynamic three-compartment cow model (called CKow), distinguishing lactating and nonlactating cows. For chemicals without available overall removal rates in the cow, a correlation is derived from measured values reported in the literature to predict this parameter from K(ow). Results on carry over rates (COR) and biotransfer factors (BTF) demonstrate that a steady-state ratio between animal intake and meat concentrations is almost never reached. For meat, empirical data collected on short-term experiments need to be adjusted to provide estimates of average longer term behaviors. The performance of the new model in matching measurements is improved relative to existing models-thus reducing uncertainty. The CKow model is straightforward to apply at steady state for milk and dynamically for realistic exposure durations for meat COR.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".