Bibliographic record
Abstract
Abstract This is the first textbook to focus on environmental threats to child health. It will interest professionals and graduate students in public health, paediatrics, environmental health, epidemiology, and toxicology. The first three chapters provide overviews of key children’s environmental health issues as well as the role of environmental epidemiology and risk assessment in child health protection. Overarching themes are the susceptibility of the rapidly developing fetus and infant to environmental toxicants, the importance of modifying factors (e.g. poverty, genetic traits, nutrition), the role of health outcome and exposure monitoring, uncertainties surrounding environmental exposure limits, and the importance of timely intervention. Later chapters address the health effects of metals, PCBs, dioxins, pesticides, hormonally active agents, radiation, indoor and outdoor air pollution, and water contaminants. In analyzing potential environmental hazards, the book addresses both biologic and epidemiologic evidence, including the likelihood of causal relationships. Among the health outcomes he discusses are developmental, reproductive, and neurobehavioral effects, respiratory disease, cancer, and waterborne infectious diseases. These discussions cover environmental exposure sources/indicators, interventions, and standards, and conclude with a summary of calls for an improved science base to guide public health decisions and protect child health.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".