{"id":"W33152642","doi":"10.1016/j.placenta.2020.10.014","title":"New Perspectives and Methods in Loss Reserving Using Generalized Linear Models","year":2014,"lang":"en","type":"dissertation","venue":"Placenta","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development; National Institute on Alcohol Abuse and Alcoholism; Concordia University; Raymond and Beverly Sackler Foundation","keywords":"Generalized linear model; Econometrics; Estimator; Variance (accounting); Linear model; Credibility; Mathematics; Statistics; Computer science; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03468409,0.002847069,0.003521929,0.003606153,0.001144474,0.00443741,0.005240641,0.002776962,0.005589469],"category_scores_gemma":[0.06207094,0.001480041,0.004323782,0.004060155,0.003485887,0.003914076,0.004076082,0.006464742,0.001356523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002851477,"about_ca_system_score_gemma":0.004427535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01911671,"about_ca_topic_score_gemma":0.01314179,"domain_scores_codex":[0.9835,0.01341484,0.0004323109,0.001483855,0.0007522959,0.0004166315],"domain_scores_gemma":[0.9139181,0.07817677,0.002260648,0.002824698,0.002137592,0.0006821525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002020325,0.0002641394,0.009546404,0.0006344537,0.001348583,0.000460959,0.0007577993,0.3849179,0.0003259231,0.482197,0.007833061,0.1115117],"study_design_scores_gemma":[0.00003768273,0.00007291235,0.0005525789,0.0001158942,0.0000885456,0.0000640014,0.0001145017,0.6078517,0.000107208,0.3841433,0.006787237,0.00006443021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002567864,0.001831154,0.9911617,0.002695575,0.0001582641,0.00006975149,0.0002024787,0.0001680709,0.001145164],"genre_scores_gemma":[0.1927424,0.009705966,0.7767477,0.002577813,0.00271268,0.00154624,0.00124704,0.0006351426,0.01208501],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03468409,"threshold_uncertainty_score":0.1834292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1976364076508767,"score_gpt":0.4956713247287344,"score_spread":0.2980349170778577,"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."}}