{"id":"W2517794266","doi":"10.4309/jgi.2016.33.7","title":"Crimping the Croupier: Electronic and mechanical automation of table, community and novelty games in Australia","year":2016,"lang":"en","type":"article","venue":"Journal of Gambling Issues","topic":"Gambling Behavior and Treatments","field":"Psychology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Table (database); Harm; Automation; Novelty; Product (mathematics); Acronym; Marketing; Control (management); Computer science; Advertising; Business; Engineering; Psychology; Artificial intelligence; Social psychology; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001178438,0.0001030766,0.0002596597,0.0001500837,0.00007360618,0.0000295396,0.0001356045,0.00008513679,0.00007010488],"category_scores_gemma":[0.0001243967,0.00005759563,0.0000409503,0.0001210967,0.00008527688,0.0001608521,0.00003718905,0.0003194458,0.000002636738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004529707,"about_ca_system_score_gemma":0.0000201443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005293281,"about_ca_topic_score_gemma":0.0001075877,"domain_scores_codex":[0.9988855,0.0002501585,0.0004179108,0.00008415912,0.0001561862,0.0002060995],"domain_scores_gemma":[0.9991077,0.0002758442,0.0003063543,0.000167558,0.00009786845,0.000044632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009494791,0.001718384,0.77913,0.0001190676,0.0006093335,0.00008006524,0.02069417,0.00001049164,0.1123712,0.01012768,0.0009056873,0.07328448],"study_design_scores_gemma":[0.003101608,0.0009133552,0.9805745,0.0004286369,0.0001566213,0.000284297,0.001543692,0.00001943311,0.007704495,0.004554653,0.0005859804,0.0001327411],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979184,0.001067446,0.0002048429,0.0005398425,0.0001433132,0.00007523492,0.000003352851,0.000007322938,0.0000402814],"genre_scores_gemma":[0.9990667,0.0002419125,0.0003338762,0.00001723552,0.00004312256,0.000002147817,4.582524e-7,0.000008811538,0.0002857748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2014445,"threshold_uncertainty_score":0.2348682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.278892858139389,"score_gpt":0.4674952441081126,"score_spread":0.1886023859687236,"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."}}