Alberta Pregnancy Outcomes and Nutrition (APrON)
Bibliographic record
Abstract
Numerous studies have found that nutrient levels in our bodies relate to our physical and mental health. Scientists know much less, however, about the relationship between prenatal nutrition and health (for the mother and child). The Alberta Pregnancy Outcomes and Nutrition (APrON) study is a longitudinal, prospective cohort in Calgary and Edmonton, Alberta, Canada designed to investigate the relationship between maternal nutrient intake/status before, during and after gestation and a) maternal mood, b) birth and obstetric outcomes, and c) infant neurodevelopment. The data for approximately 5000 participants (2200 pregnant women, their offspring and many of their partners) includes information about maternal nutrition, anthropometric, biological, and mental health data at multiple points in pregnancy and the postpartum period, as well as obstetric, birth, health and neurodevelopment outcomes of these pregnancies and infants. Data from the APrON cohort can be used to answer questions related to prenatal nutrition, health and well-being and how they can impact neurodevelopmental and behavioural outcomes from infancy to childhood. Anchor participants were women over 16 years who can understand and answer questions in English, are pregnant for less than 27 weeks and are living in or near Calgary or Edmonton were eligible for recruitment into this study. Associated participants are the biological fathers and offspring resulting from study pregnancies. At this date this deposit of APrON data includes only the anthropometric measurements and questionnaires of survey mothers at the time of pregnancy and immediately post-partum. These metadata can also be found on SAGE's searchable metadata website: http://sagemetadata.policywise.com/nada/index.php/catalog/10
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".