{"id":"W4412330946","doi":"10.1017/s174849951700025","title":"Conditional Monte Carlo for sums, with applications to insurance and finance","year":2018,"lang":"en","type":"article","venue":"","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Monte Carlo method; Econometrics; Actuarial science; Economics; Computer science; Statistical physics; Mathematics; Statistics; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004452498,0.00005683794,0.0001018268,0.00004306822,0.0002102575,0.00006004838,0.0002073276,0.00002669495,0.00002438364],"category_scores_gemma":[0.0001370909,0.00003420825,0.00001822294,0.0002407276,0.0002104612,0.0001786939,0.00003636891,0.00002569787,0.00006455726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008178216,"about_ca_system_score_gemma":0.00004528863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003886302,"about_ca_topic_score_gemma":0.0007475218,"domain_scores_codex":[0.9991096,0.00001419083,0.0001672204,0.0003283884,0.0002652293,0.0001154307],"domain_scores_gemma":[0.9988764,0.0003438976,0.00004062668,0.0002814953,0.0004101252,0.00004745489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003592794,0.0001744117,0.03624175,0.00001100913,0.00001983184,5.634921e-7,0.001109468,0.003627735,0.0002466119,0.8042667,0.05273536,0.1012073],"study_design_scores_gemma":[0.000505129,0.0003212437,0.09673394,0.000007975878,0.000003870127,0.000007098629,0.0001132978,0.009645412,0.0008048021,0.4952468,0.3964228,0.000187652],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4210491,0.0000542634,0.5740817,0.00196665,0.00002628622,0.0005806849,0.0001732911,0.00001963753,0.002048375],"genre_scores_gemma":[0.9618779,0.000005964662,0.03406722,0.0006687777,0.0000578311,0.0002557027,0.000001433953,0.00000286067,0.003062321],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5408288,"threshold_uncertainty_score":0.1617152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09854785864549534,"score_gpt":0.3863882448993676,"score_spread":0.2878403862538722,"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."}}