{"id":"W4323667315","doi":"10.1080/14459795.2023.2183974","title":"Setting a hard (versus soft) monetary limit decreases expenditure: an assessment using player account data","year":2023,"lang":"en","type":"article","venue":"International Gambling Studies","topic":"Gambling Behavior and Treatments","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Limit (mathematics); Economics; Econometrics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01044438,0.00027623,0.0004231877,0.0008418022,0.0005081456,0.001206115,0.001022167,0.0004901058,0.003903002],"category_scores_gemma":[0.02746152,0.0001871859,0.0006983108,0.001107299,0.0008953738,0.0009870224,0.0006343624,0.000643359,0.0003754372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00202211,"about_ca_system_score_gemma":0.001834956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05179668,"about_ca_topic_score_gemma":0.1031533,"domain_scores_codex":[0.9953595,0.002612517,0.0002666375,0.0002443674,0.00135376,0.0001633142],"domain_scores_gemma":[0.9832959,0.01025179,0.003089535,0.0009010519,0.001892432,0.0005693482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02014454,0.006996747,0.6588042,0.001743536,0.001142848,0.0001286061,0.003833418,0.003930341,0.003168884,0.004671678,0.004034396,0.2914008],"study_design_scores_gemma":[0.0009859463,0.01434988,0.9585653,0.0004857674,0.0009845885,0.0001165461,0.002104439,0.006609449,0.002175482,0.000866012,0.01268866,0.00006794438],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876003,0.0006134163,0.001283707,0.0005799759,0.00001397478,0.0004980975,0.001022401,0.00002487385,0.008363093],"genre_scores_gemma":[0.9941807,0.0004454431,0.002280655,0.0001418174,0.0000112384,0.0003313427,0.0008561283,0.000009978894,0.001742595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05179668,"threshold_uncertainty_score":0.1029904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4716277395787691,"score_gpt":0.5524135398435465,"score_spread":0.08078580026477733,"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."}}