{"id":"W4378233118","doi":"10.1017/s1357321723000041","title":"Some observations on the temporal patterns in the surplus process of an insurer","year":2023,"lang":"en","type":"article","venue":"British Actuarial Journal","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Process (computing); Poisson process; Econometrics; Economics; Actuarial science; Inflow; Cash; Economic surplus; Set (abstract data type); Computer science; Poisson distribution; Statistics; Mathematics; Geography; Finance","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":[0.008082672,0.00009139039,0.0002103546,0.0001491006,0.0004026744,0.0007345818,0.001512558,0.00008378058,0.00009289356],"category_scores_gemma":[0.002680629,0.00005085456,0.0001239668,0.0008926924,0.0001184407,0.0008346231,0.00006294894,0.0005126206,0.00002837878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002307625,"about_ca_system_score_gemma":0.0002492459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005972952,"about_ca_topic_score_gemma":0.002377825,"domain_scores_codex":[0.996465,0.0007596758,0.000722828,0.0002290592,0.001561628,0.0002617843],"domain_scores_gemma":[0.9977297,0.001281496,0.0002702488,0.000386235,0.0002603956,0.00007194011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008155548,0.003208548,0.4739306,0.0000500476,0.0001477008,0.001541964,0.05341799,0.02787684,0.0005395189,0.02597929,0.09586392,0.316628],"study_design_scores_gemma":[0.0005301709,0.000133122,0.6213345,0.00006467685,0.000005449571,0.0001845812,0.002805916,0.002006175,0.00004682727,0.3721818,0.0006108319,0.00009597786],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926329,0.00002905561,0.00009795921,0.006143849,0.0006667089,0.0002302117,0.00006511046,0.00001537891,0.0001188803],"genre_scores_gemma":[0.9984223,0.00005721733,0.00002943721,0.0006937618,0.0006970035,0.00001312692,0.000005917063,0.000007420303,0.00007381342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3462025,"threshold_uncertainty_score":0.7083589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.200586914983435,"score_gpt":0.3826896660294285,"score_spread":0.1821027510459935,"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."}}