{"id":"W3192649581","doi":"10.1017/apr.2022.47","title":"Predicting the last zero before an exponential time of a spectrally negative Lévy process","year":2023,"lang":"en","type":"article","venue":"Advances in Applied Probability","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Brownian motion; Exponential function; Optimal stopping; Stopping time; Moment (physics); Zero (linguistics); Mathematical analysis; Lévy process; Nonlinear system; Function (biology); Time horizon; Wiener process; Applied mathematics; Mathematical optimization; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002358797,0.0005020761,0.0005998837,0.0006166738,0.0004539027,0.001182293,0.001021842,0.001278994,0.001525892],"category_scores_gemma":[0.008854504,0.0002609874,0.000366202,0.0002839573,0.002117771,0.002105704,0.0008875228,0.001482629,0.0001311375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009581654,"about_ca_system_score_gemma":0.0009345035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003290128,"about_ca_topic_score_gemma":0.001324182,"domain_scores_codex":[0.9996492,0.0001108462,0.00001400289,0.00006779851,0.00007938037,0.0000787223],"domain_scores_gemma":[0.9961959,0.002388199,0.0005366142,0.000152154,0.0003833609,0.0003437693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001689147,0.00006572744,0.005183833,0.00004429623,0.00002709947,0.0002698873,0.00009631016,0.8785263,0.003705985,0.1076632,0.0004965474,0.003751949],"study_design_scores_gemma":[0.00000578482,0.00002210616,0.0002796108,0.000008358105,0.000003582643,0.00001266066,0.0000121552,0.9824528,0.0008053763,0.01632181,0.00006541063,0.00001032582],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6835136,0.0002860291,0.3112807,0.0009481173,0.00007917787,0.00002116953,0.00005598789,0.0001890278,0.003626175],"genre_scores_gemma":[0.9919816,0.00005151168,0.007076922,0.00003637176,0.00001232199,0.000007967917,0.00002750784,0.00001671961,0.000789149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003290128,"threshold_uncertainty_score":0.01247466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04330609024094607,"score_gpt":0.3545225235757785,"score_spread":0.3112164333348324,"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."}}