{"id":"W6979365818","doi":"","title":"A Population Sampling Framework for Claim Reserving in General Insurance","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Weighting; Sampling (signal processing); Aggregate (composite); Population; Sampling bias; Sampling design; Inverse probability weighting","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00229873,0.0001035154,0.0002512984,0.0002276753,0.0002329897,0.00009625657,0.0005573041,0.0001640339,0.00002181674],"category_scores_gemma":[0.006264396,0.00008319863,0.00009848153,0.0009781733,0.0000411468,0.000362266,0.0001498418,0.0002231507,0.00002730908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007239009,"about_ca_system_score_gemma":0.00005487985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003107249,"about_ca_topic_score_gemma":0.0007557163,"domain_scores_codex":[0.9981662,0.0001313902,0.0006029809,0.0004944674,0.0003331744,0.0002717877],"domain_scores_gemma":[0.9974869,0.001611783,0.0001185719,0.0005910102,0.0001499755,0.00004168986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004758096,0.00002815914,0.9596577,0.00001158399,0.000003254085,4.173966e-7,0.0002234925,0.005718336,0.000179707,0.02623824,0.00008070615,0.007810783],"study_design_scores_gemma":[0.0001332497,0.000007969338,0.5546606,0.00006325817,0.000001392453,1.293908e-7,0.00004197867,0.006657292,0.00009965229,0.4377223,0.0005561477,0.00005608236],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8308462,0.0002463229,0.1662742,0.001732732,0.0004292012,0.0002607469,0.000007878688,0.00003081306,0.000171951],"genre_scores_gemma":[0.9711698,0.00001761405,0.02769738,0.0005819304,0.00009696892,0.00004331887,0.000004522192,0.000006342662,0.0003821196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.411484,"threshold_uncertainty_score":0.749952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3016369360652444,"score_gpt":0.4707470434177993,"score_spread":0.1691101073525549,"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."}}