{"id":"W4392863568","doi":"10.1017/asb.2024.9","title":"A representation-learning approach for insurance pricing with images","year":2024,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Representation (politics); Computer science; Actuarial science; Business; Artificial intelligence; Political science; Law","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001560054,0.0004708015,0.0007728335,0.001289489,0.0003337201,0.001659706,0.001801589,0.001531552,0.002093148],"category_scores_gemma":[0.0059133,0.0004830555,0.0009998588,0.001424303,0.0009990019,0.002229579,0.001206255,0.002223844,0.0004297929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001118736,"about_ca_system_score_gemma":0.0005993302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005784868,"about_ca_topic_score_gemma":0.003944699,"domain_scores_codex":[0.9993371,0.000298488,0.00003214804,0.0001416147,0.0001155767,0.00007498863],"domain_scores_gemma":[0.9981278,0.001137729,0.0002107282,0.0002398886,0.0002156599,0.00006833539],"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":[0.0001925189,0.0003521155,0.003412594,0.0001211667,0.0001065789,0.0002046859,0.0003036432,0.6620049,0.003857781,0.04742553,0.00400241,0.2780161],"study_design_scores_gemma":[0.000003269948,0.00001045293,0.0001404157,0.000004330878,0.000002804646,0.000008920699,0.00001229028,0.9905463,0.0002003502,0.008866193,0.0002011029,0.00000363603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04575774,0.0003980907,0.9505394,0.00122574,0.00004728453,0.00005844871,0.0002010067,0.00049544,0.001276834],"genre_scores_gemma":[0.7475055,0.000308367,0.2482256,0.0002661968,0.000175447,0.0001295983,0.0005874644,0.00008479899,0.002717008],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005784868,"threshold_uncertainty_score":0.01150239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620933116187491,"score_gpt":0.2844249396172205,"score_spread":0.2682156084553456,"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."}}