{"id":"W2031397432","doi":"10.1016/j.insmatheco.2003.08.004","title":"The classical risk model with a constant dividend barrier: analysis of the Gerber–Shiu discounted penalty function","year":2003,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":225,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Laplace transform; Exponential function; Integro-differential equation; Mathematics; Penalty method; Risk model; Dividend; Exponential distribution; Poisson distribution; Constant (computer programming); Ruin theory; Distribution (mathematics); Function (biology); Applied mathematics; Differential equation; Mathematical analysis; Mathematical optimization; First-order partial differential equation; Statistics; Computer 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002755426,0.0001437282,0.0004315774,0.00007353671,0.0005128945,0.0002404831,0.0003986995,0.00006504419,0.00001473548],"category_scores_gemma":[0.0008630956,0.00006217738,0.0001936547,0.000426023,0.0007416906,0.0002063443,0.0000742726,0.0001648848,0.000003683763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003219035,"about_ca_system_score_gemma":0.0001194391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001444204,"about_ca_topic_score_gemma":0.001243779,"domain_scores_codex":[0.9983523,0.0001122628,0.0007334541,0.0003119147,0.0002973879,0.0001926488],"domain_scores_gemma":[0.9968927,0.001454054,0.0006114195,0.0008260621,0.0001442244,0.00007155923],"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":[0.0001523966,0.0002006198,0.1167837,0.00002756893,0.0008758446,3.655356e-7,0.003478373,0.3464088,0.000034916,0.5268077,0.0001451447,0.005084613],"study_design_scores_gemma":[0.0001824453,0.00002099238,0.007051341,0.00001101672,0.0001946608,0.000002550842,0.0005609373,0.6231671,0.00003382734,0.3683775,0.0003111398,0.00008646378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9360801,0.0001890118,0.06136191,0.0003154603,0.00007827001,0.0001879459,0.0001673607,0.000006325482,0.001613601],"genre_scores_gemma":[0.9975786,0.0003809574,0.001697884,0.00004644317,0.000006665084,0.00001226766,5.857867e-7,0.000007303515,0.0002692508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2767583,"threshold_uncertainty_score":0.3944821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0456581980335037,"score_gpt":0.2796974940073377,"score_spread":0.234039295973834,"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."}}