Application and methodology of <i>in vivo</i> K x‐ray fluorescence of Pb in bone (impact of KXRF data in the epidemiology of lead toxicity, and consistency of the data generated by updated systems)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract K x‐ray fluorescence (KXRF) technology has been used to make in vivo measurements of lead in bone for more than three decades. The data obtained are beneficial to research on lead toxicity as well as, in certain circumstances, the practice of occupational and environmental medicine. This paper reviews the impact of KXRF data on epidemiologic research involving lead toxicity and demonstrates that bone lead is and will continue to be a valuable biomarker in addressing long‐term health effects related to cumulative exposure. The KXRF system has been improved and upgraded several times ever since it was first used. The consistency of the data obtained from these KXRF systems has been investigated in many studies. This paper provides an overview of the factors that will affect the data generated by the KXRF systems. A calibration problem encountered in one of the major KXRF laboratories is described, and the approach taken to solve the problem is discussed. Despite all the theoretical considerations, there are still some important practical challenges to the intercalibration of KXRF instruments both within the laboratory, and between laboratories. Copyright © 2007 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it