The Early Life Exposures in Mexico to ENvironmental Toxicants (ELEMENT) Project
Bibliographic record
Abstract
ELEMENT (Early Life Exposures in Mexico to ENvironmental Toxicants) study was founded in 1994 as a collaboration between Harvard University and the National Institute of Public Health (INSP) in Mexico. ELEMENT is now administered by researchers at the University of Michigan (Karen Peterson) where the biorepository and database reside; fieldwork is conducted by investigators at the INSP (Martha M Téllez-Rojo), and investigators are housed at Michigan, Washington, Indiana, Toronto, York Universities and INSP. Funding from US and Mexico sources has supported data collection efforts over a 26-year period, demonstrating sustained research excellence and productivity. ELEMENT is an award-winning, 26-year longitudinal study comprising 3 epidemiologic birth cohorts sequentially-enrolled over a 10-year period in Mexico City. The original goal was to investigate the influence of lead exposure on fetal and infant development. Through subsequent research, repeat exposures to metal mixtures, fluoride, phenols and phthalates have been characterized as well as cognition, behavior, sexual maturation, dental health, cardio metabolic and obesity-related outcomes, including metabolomics. ELEMENT is an international collaboration with a demonstrated long-term commitment for research excellence; it has provided the basis for many spin-off studies including an ethnographic component, has created a structure for training >50 researchers, and has informed US and international policy guidelines regarding environmental health. The rigorous design of ELEMENT, its follow-up rates, and the multidisciplinary expertise of our team have allowed us to generate more than 100 publications in the international scientific literature.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".