The estimation of the rates of lead exchange between body compartments of smelter employees
Bibliographic record
Abstract
The overwhelming proportion of the mass of lead (Pb) is stored in bone and the residence time of Pb in bone is much longer than that in other tissues. Hence, in a metabolic model that we used to solve the differential equations governing the transfer of lead between body compartments, three main compartments are involved: blood (as a transfer compartment), cortical bone (tibia), and trabecular bone (calcaneus). There is a bidirectional connection between blood and the other two compartments. A grid search chi-squared minimization method was used to estimate the initial values of lead transfer rate values from tibia (λTB) and calcaneus (λCB) to blood of 209 smelter employees whose bone lead measurements are available from 1994, 1999, and 2008, and their blood lead level from 1967 onwards (depending on exposure history from once per month to once per year), and then the initial values of kinematic parameters were used to develop multivariate models in order to express λTB and λCB as a function of employment time, age, body lead contents and their interaction. We observed a significant decrease in the transfer rate of lead from bone to blood with increasing body lead contents. The model was tested by calculating the bone lead concentration in 1999 and 2008, and by comparing those values with the measured ones. A good agreement was found between the calculated and measured tibia/calcaneus lead values. Also, we found that the transfer rate of lead from tibia to blood can be expressed solely as a function of cumulative blood lead index.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".