Geographical Analysis of the Distribution of Publications Describing Spatial Associations among Outdoor Environmental Variables and Really Small Newborns in the USA and Canada
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
Newborns defined as being of “low birth weight” (LBW) or “small for gestational age” (SGA) are global health issues of concern because they are vulnerable to mortality and morbidity. Prenatal exposures may contribute to LBW/SGA. In this review, we searched peer-reviewed scientific literature to determine what location-based hazards have been linked with LBW/SGA in the industrialized nations of Canada and the USA. After selecting studies based on inclusion/exclusion criteria, we entered relevant details in to an evidence table. We classified and summarized 159 articles based on type of environment (built = 108, natural = 10, and social = 41) and general category of environmental variables studied (e.g., air pollution, chemical, water contamination, waste site, agriculture, vegetation, race, SES, etc.). We linked the geographic study areas by province/state to political boundaries in a GIS to map the distributions and frequencies of the studies. We compared them to maps of LBW percentages and ubiquitous environmental hazards, including land use, industrial activity and air pollution. More studies had been completed in USA states than Canadian provinces, but the number has been increasing in both countries from 1992 to 2018. Our geographic inquiry demonstrated a novel, spatially-focused review framework to promote understanding of the human ‘habitat’ of shared environmental exposures that have been associated with LBW/SGA.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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