Bibliographic record
Abstract
Abstract The twentieth century saw greatly expanded use and environmental dispersion of lead, a dense metal valued since antiquity because of its low melting point, pliability, and durability. Analysis of a Swiss peat bog showed that annual average lead deposition increased 1600-fold between 5000 B.C. and the maximum in 1979. Recognition of adult lead poisoning with abdominal colic can be traced to Hippocrates in about 370 B.C. (in metal workers) and Baker in 1767 (who linked Devonshire colic to consumption of lead-contaminated cider) (Table 4–1). Childhood lead poisoning was recognized as a distinct entity in 1892 and neurotoxicity in experimental animals by the 1920s. There has been substantial progress in reducing childhood lead exposure, but many children remain at risk. The objective of this chapter is to illustrate how failure to apply the precautionary principle allowed inappropriate uses of lead, massive environmental contamination, and major adverse impacts on child health. The first section focuses mainly on the susceptibility of the developing human nervous system to adverse neurobehavioral effects from relatively low-level lead exposure, as evidenced by epidemiologic and toxicologic studies. The discussion includes environmental indices and biomarkers of lead exposure and toxico-kinetics. The risk management section addresses lead sources (air, water, food, soil/dust) and interventions for preventing childhood lead 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.041 |
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".