{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01550725,0.0008909646,0.001391012,0.002609327,0.0007440137,0.001691855,0.002307052,0.001364582,0.002688223],"category_scores_gemma":[0.02673002,0.0009040474,0.001832753,0.002272006,0.0008880672,0.001169927,0.002054924,0.001968141,0.0004595014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001285364,"about_ca_system_score_gemma":0.002016681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0302141,"about_ca_topic_score_gemma":0.03092069,"domain_scores_codex":[0.9950486,0.00366526,0.0001603052,0.0006754221,0.0002908031,0.0001596309],"domain_scores_gemma":[0.9850107,0.01242166,0.00102089,0.0008017948,0.0005009204,0.0002440493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006416123,0.0003302245,0.08679849,0.0002317626,0.001410651,0.0005011379,0.0008352765,0.6233086,0.0008390761,0.1255177,0.005846262,0.1537391],"study_design_scores_gemma":[0.00005676987,0.00006541354,0.006617082,0.00005893856,0.0001494548,0.00008900569,0.00005467049,0.9349395,0.00009919724,0.05550236,0.002329687,0.00003803756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08176875,0.001339059,0.9106237,0.002274906,0.00006186905,0.0001518002,0.001552511,0.0004406292,0.001786692],"genre_scores_gemma":[0.7303192,0.001580649,0.2594112,0.0003834613,0.0002740733,0.0005862978,0.002984538,0.0001309368,0.004329549],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0302141,"threshold_uncertainty_score":0.08201116,"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."}}