Calibration of<sup>109</sup>Cd KXRF systems for<i>in vivo</i>bone lead measurements: the guiding role of the assumptions for least-squares regression in practical problem solving
Bibliographic record
Abstract
The use of least-squares regression to probe the level of lead contamination of plaster of Paris standards in the calibration of (109)Cd KXRF systems for bone lead measurement, as well as the use of iteratively reweighted least-squares (IRLS) in the case of violation of the assumptions for ordinary least-squares (OLS), is discussed here. One common violation is non-uniform residual variance, which makes the use of OLS inappropriate due to strong influence of points with large variance on the calibration line and variance of the slope and intercept. Comparison between OLS and IRLS in that case showed that IRLS estimates of the intercept are significantly smaller and more precise than OLS estimates, while a less marked increase in the calibration slope is observed when IRLS is used. Moreover, OLS underestimates bone lead concentrations at low levels of lead exposure and overestimates those concentrations at higher levels. These discrepancies are smaller in magnitude than the measurement uncertainty of conventional systems, except for high concentrations. For the newly developed cloverleaf systems, the suggested differences at bone lead concentrations below 17 ppm are comparable to the minimum detection limit, but are larger than the measurement uncertainty for bone lead concentrations above 60 ppm.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".