Grid search: an innovative method for the estimation of the rates of lead exchange between body compartments
Bibliographic record
Abstract
This paper describes a new metabolic model for lead in humans and a numerical method to solve the differential equations governing the transfer of lead between body compartments. The model includes 3 compartments-cortical bone, trabecular bone and blood-and accounts for absorption from external sources and release through excreta. Estimation of the lead kinetics parameters was performed using the grid search method. Grid search is a simple procedure that allows the fit of an arbitrary function to data. When applied to data from occupationally exposed populations, the method demonstrated the exposure dependence of the rate of lead uptake and release by the compartments in the model. The results confirm and refine previous observations of the significant decrease of the transfer rate of lead from cortical bone to blood with increasing exposure, as expressed by half-lives of (in years): 6.5 +/- 0.7, 13.6 +/- 1.0 and 47.5 +/- 2.3, in subgroups of low, intermediate and high long-term lead exposure. A similar trend was observed for the transfer rate from trabecular bone, which could be statistically supported for the first time. Reduction by a factor of 7 to 10 in the default values assigned to the fractional removal of lead from cortical bone to plasma in existing metabolic models was also predicted. These results can be used in the review of current metabolic models for lead, which are still based on the assumption of a constant rate of lead removal from bone, independently of the level of exposure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".