{"id":"W7125602920","doi":"10.1109/cascon66301.2025.00030","title":"Life Event Detection in Bank Conversations: An Industry Case Study","year":2025,"lang":"","type":"article","venue":"","topic":"Mental Health via Writing","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Personalization; Ask price; Key (lock); Identification (biology); Event (particle physics)","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":[0.003030692,0.0006677964,0.0004019804,0.001784194,0.002095409,0.00156414,0.0008999637,0.002360586,0.001620183],"category_scores_gemma":[0.01343033,0.0002675642,0.0004290266,0.001650005,0.0006858191,0.001463135,0.001491003,0.001553652,0.001286053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009893572,"about_ca_system_score_gemma":0.0008722653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006714588,"about_ca_topic_score_gemma":0.0143045,"domain_scores_codex":[0.9949535,0.003233892,0.0002995395,0.0005619417,0.0006486141,0.0003025301],"domain_scores_gemma":[0.9831618,0.01314762,0.0009895195,0.0007694367,0.001198001,0.000733557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002524581,0.005206828,0.3925934,0.002644533,0.0004503888,0.02336318,0.09776559,0.01622815,0.02867885,0.005257894,0.07775624,0.3475304],"study_design_scores_gemma":[0.0004173731,0.001669554,0.3188114,0.0008909347,0.000465354,0.01745699,0.1684223,0.2533622,0.0499918,0.01190907,0.1760898,0.0005133084],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9744928,0.0008582053,0.01229438,0.00237148,0.00007537465,0.0003192868,0.00413943,0.0005525768,0.00489638],"genre_scores_gemma":[0.969662,0.0004151521,0.02243769,0.0006068079,0.000119637,0.0003019585,0.003616968,0.00007822035,0.002761563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006714588,"threshold_uncertainty_score":0.01602799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0711417688075004,"score_gpt":0.4461154908458879,"score_spread":0.3749737220383875,"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."}}