The Early Life Exposures in Mexico to ENvironmental Toxicants (ELEMENT) Project
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
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.
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 | 0.000 | 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