Bibliographic record
Abstract
Abstract This section describes the burden of child health conditions and summarizes their known and suspected environmental risk factors as discussed in previous chapters. The purpose is to provide an overview of progress to date in identifying environmental threats to the fetus and child and to highlight areas where research and monitoring on environmental exposures and child health outcomes are needed. The summary tables (Tables 13–1 to 13–5) have important limitations: Behavioral (tobacco, alcohol), pharmaceutical, and microbial factors are important risk factors for some adverse developmental outcomes but the causes of these conditions remain poorly defined. Based on sheer numbers, fetal deaths, low birth weight, and birth defects are major child health burdens (Table 13–1). Although about 1 million recognized fetal deaths (excluding therapeutic abortions) occur annually in the United States, about the same number of fetal deaths occur very soon after conception but are not clinically recognized. There are few proven environmental causes of fetal death in humans, at least at widely prevalent exposure levels; there is limited evidence implicating prenatal parental (usually maternal) exposure to lead, PCBs/dioxin-like compounds, pesticides, ETS, ionizing radiation, and THMs and suggestive but inadequate evidence for ambient air pollutants. Suspected environmental causes of IUGR or preterm birth include prenatal maternal exposure to lead, ETS (independent of prenatal maternal smoking), ambient air pollution, and THMs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.451 | 0.263 |
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".