Bibliographic record
Abstract
Pregnancy is a critical period of body weight regulation for both mother and baby. Vital energy-sensing processes are established in utero that aid in nutrient storage and metabolic control later in life. Excessive weight gain during pregnancy and a surplus of maternal resources leads to preferential shuttling of nutrients and growth-promoting peptides across the placenta, resulting in fetal overgrowth — a well-established predictor of childhood obesity. Physical activity during pregnancy offers a safe and accessible way in which one can modify these intricate cellular networks across the maternal-placental-fetal interface to prevent dysregulation and optimize fetal birth weight. This commentary highlights the clinical utility of physical activity during pregnancy and provides practical recommendations as a way to ensure the best health and safety of mother, baby, and future generations. RésuméLa grossesse est une période critique de la régulation du poids corporel pour la mère et le bébé. Des processus vitaux de détection d’énergie sont établis in utero qui contribuent au stockage des nutriments et au contrôle métabolique plus tard dans la vie. Une prise de poids excessive pendant la grossesse et un surplus de ressources maternelles conduit à un transfert préférentiel des nutriments et des peptides favorisant la croissance à travers le placenta, entraînant une prolifération fœtale - un prédicteur bien établi de l’obésité infantile. L’activité physique pendant la grossesse offre un moyen sûr et accessible pour modifier ces réseaux cellulaires complexes à travers l’interface materno-placentaire-fœtal afin de prévenir le dérèglement et optimiser le poids de naissance fœtal. Ce commentaire met en évidence l’utilité clinique de l’activité physique pendant la grossesse et fournit des recommandations pratiques pour assurer la meilleure santé et sécurité de la mère, du bébé et des générations futures.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".