{"id":"W4210667077","doi":"10.3390/e24020232","title":"Associations between Longitudinal Gestational Weight Gain and Scalar Infant Birth Weight: A Bayesian Joint Modeling Approach","year":2022,"lang":"en","type":"article","venue":"Entropy","topic":"Birth, Development, and Health","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Weight gain; Linear regression; Regression analysis; Bayesian probability; Scalar (mathematics); Linear model; Birth weight; Econometrics; Regression; Statistics; Simple linear regression; Pregnancy; Computer science; Mathematics; Medicine; Body weight; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005636893,0.0001612261,0.0003645133,0.0002088653,0.0006138725,0.00002691129,0.00006836349,0.00006289299,0.0002895222],"category_scores_gemma":[0.00004947162,0.0001517988,0.00006998887,0.000245071,0.00003546544,0.00007659846,0.00009541155,0.0004672362,0.00001114583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003546146,"about_ca_system_score_gemma":0.0004140549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001140291,"about_ca_topic_score_gemma":0.000008189048,"domain_scores_codex":[0.9981323,0.0001115553,0.0004248388,0.0003619054,0.0005895715,0.0003797566],"domain_scores_gemma":[0.9993343,0.00004472921,0.0001370834,0.0001587926,0.00007292908,0.0002521602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006840251,0.0002944216,0.8373905,0.0001090181,0.0002388113,0.00003132008,0.002623685,0.000537494,0.0001531748,0.1553529,0.002451134,0.0007490927],"study_design_scores_gemma":[0.003475661,0.000274782,0.8275861,0.00004174717,0.000172308,0.00008103034,0.000474781,0.142324,0.00007338503,0.02296651,0.002125706,0.0004039836],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8541447,0.0007771077,0.1298155,0.009133813,0.0002650475,0.0009401565,0.0004198266,0.0001764344,0.004327454],"genre_scores_gemma":[0.9791249,0.000946735,0.01813942,0.0005561623,0.0003865834,0.00006856196,0.0006326617,0.00002948318,0.0001155042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1417865,"threshold_uncertainty_score":0.6190174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03566144003166329,"score_gpt":0.2761658659368379,"score_spread":0.2405044259051746,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}