Bibliographic record
Abstract
The Science of Low-Level Lead ToxicityAbstract Number:2909 Bruce Lanphear* Bruce Lanphear* Simon Fraser University, Canada, E-mail Address: [email protected] AbstractLow levels of exposure to lead, the prototypical toxicant, are associated with an increased risk of disability and disease. In children, low-level lead exposure impacts the developing brain and is linked with diminished intelligence, ADHD, and antisocial behaviors. There is no apparent threshold for lead toxicity; indeed, for a given exposure, the decrements in intellectual function are greater at blood lead levels < 100 µg/L (i.e., <10 µg/dL), the level still considered 'acceptable' in many countries. Lead exposure is also a major risk factor for psychopathology, including ADHD and criminal behaviors. In the United States, 1 in 5 cases of ADHD have been attributed to lead exposure and the decline in blood lead levels was cited as the major reason for the dramatic decline in violent crimes over the past 5 decades. In adults, lead exposure is an established risk factor for hypertension and chronic renal failure. Lead is also a potential risk factor for some of the most prevalent causes of deaths and disabilities in industrialized societies, such as cardiovascular disease mortality and accelerated cognitive decline. Evidence is emerging that these health impacts occur at blood lead levels well below current health standards and guidelines. Despite the dramatic declines in blood lead concentrations that have been achieved in many countries, low-level lead toxicity remains a major public health problem worldwide; a large fraction of children in many industrializing countries have blood lead levels in excess of 100 µg/L and the cost of lead toxicity exceeds $1 trillion annually.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".