{"id":"W1980801846","doi":"10.1007/s11469-007-9066-8","title":"Slot Machine Structural Characteristics: Creating Near Misses Using High Award Symbol Ratios","year":2007,"lang":"en","type":"article","venue":"International Journal of Mental Health and Addiction","topic":"Gambling Behavior and Treatments","field":"Psychology","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Near miss; Symbol (formal); Computer science; Feature (linguistics); Software; Virtual machine; Engineering; Operating system; Programming language; Reliability engineering","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":[0.001037006,0.0005323772,0.0006660523,0.001001221,0.0007128355,0.001057452,0.001137246,0.0008382525,0.005796341],"category_scores_gemma":[0.01142838,0.0003420165,0.0003568682,0.001022253,0.0003218416,0.001970867,0.001172698,0.000784423,0.001121037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002630817,"about_ca_system_score_gemma":0.0006638615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006139392,"about_ca_topic_score_gemma":0.001363243,"domain_scores_codex":[0.9991164,0.000194252,0.00006835098,0.0001804178,0.0003380482,0.0001026222],"domain_scores_gemma":[0.9934351,0.003621466,0.000661237,0.001051481,0.0008480133,0.0003825722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003993433,0.001333207,0.05467844,0.0003196814,0.0001090605,0.001412551,0.001248017,0.04118103,0.04737177,0.01416009,0.009661352,0.8245314],"study_design_scores_gemma":[0.0002393644,0.002900134,0.02491899,0.00007809605,0.0003248524,0.003276802,0.001712334,0.8425874,0.08020911,0.02506154,0.01848121,0.0002101443],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7302073,0.0001008348,0.2583594,0.0003190308,0.0001965409,0.0001208209,0.0003187639,0.005161204,0.005216088],"genre_scores_gemma":[0.9013093,0.00003453923,0.09567823,0.00005046579,0.00003398825,0.00004592167,0.0003110933,0.0002962632,0.002240164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005796341,"threshold_uncertainty_score":0.0193907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04569527137469896,"score_gpt":0.4183278916312663,"score_spread":0.3726326202565673,"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."}}