{"id":"W2802701747","doi":"10.4309/jgi.2018.38.4","title":"Exploring the Effectiveness of an Intelligent Messages Framework for Developing Warning Messages to Reduce Gambling Intensity","year":2018,"lang":"en","type":"article","venue":"Journal of Gambling Issues","topic":"Gambling Behavior and Treatments","field":"Psychology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Harm; Psychology; Advertising; Social psychology; Frame (networking); Internet privacy; Applied psychology; Computer science; Business; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007399577,0.0006904862,0.0003531456,0.001140779,0.0005522836,0.00152651,0.001207424,0.0009547322,0.005572142],"category_scores_gemma":[0.02794274,0.000304218,0.000633593,0.000292201,0.0006483319,0.00165404,0.001162385,0.001130162,0.0006664866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009550019,"about_ca_system_score_gemma":0.001637233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001581544,"about_ca_topic_score_gemma":0.002181289,"domain_scores_codex":[0.9970092,0.002053745,0.0001653119,0.0001870455,0.0004203501,0.0001642851],"domain_scores_gemma":[0.982167,0.01491444,0.001340331,0.0003948953,0.0005959984,0.0005873577],"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":[0.005033407,0.03196661,0.03699244,0.003938614,0.0003669289,0.0002444142,0.01652468,0.004980438,0.01357618,0.006996675,0.002457273,0.8769223],"study_design_scores_gemma":[0.02394,0.2135038,0.3550628,0.01053837,0.01100531,0.000928141,0.03640724,0.1526029,0.07285868,0.03315018,0.089112,0.0008906709],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9653539,0.0005012272,0.01490804,0.00141508,0.0001045891,0.00262999,0.00008896385,0.000291847,0.01470627],"genre_scores_gemma":[0.9062948,0.0007643267,0.08610239,0.0006699064,0.00005322758,0.002526667,0.00010755,0.00002984328,0.003451151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007399577,"threshold_uncertainty_score":0.03913313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5048307007176556,"score_gpt":0.5051838861300186,"score_spread":0.0003531854123630662,"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."}}