{"id":"W2607477801","doi":"10.1287/isre.2020.0921","title":"Matching Mobile Applications for Cross-Promotion","year":2020,"lang":"en","type":"article","venue":"Information Systems Research","topic":"Green IT and Sustainability","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Leverage (statistics); Mobile apps; Matching (statistics); Analytics; Promotion (chess); App store; World Wide Web; Data science; Machine learning","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":[0.008196988,0.0009891212,0.001058894,0.001506996,0.001165521,0.002834846,0.001206714,0.0013453,0.003840299],"category_scores_gemma":[0.02969782,0.00050498,0.000821847,0.001741931,0.001122536,0.004530532,0.002139784,0.001675993,0.00105158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001300664,"about_ca_system_score_gemma":0.001244296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003628716,"about_ca_topic_score_gemma":0.00376951,"domain_scores_codex":[0.994154,0.003340313,0.0002159691,0.001087955,0.0007498104,0.0004519204],"domain_scores_gemma":[0.9802138,0.01192451,0.001634686,0.004516601,0.001174419,0.0005359275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002724439,0.003721424,0.1127035,0.0004635322,0.0003564,0.0003052005,0.001142717,0.1891625,0.008353321,0.05944432,0.006622275,0.6150004],"study_design_scores_gemma":[0.0001769889,0.00154706,0.02324051,0.00006368951,0.0002339239,0.0004475499,0.000684261,0.8846835,0.01050595,0.06828088,0.01005617,0.00007956582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8123478,0.00118178,0.1655729,0.001528583,0.0001243338,0.0006368526,0.00057507,0.001050377,0.0169823],"genre_scores_gemma":[0.9762421,0.0001418818,0.02164193,0.0001202337,0.00003192418,0.0000872828,0.0001731274,0.00003758733,0.001524073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008196988,"threshold_uncertainty_score":0.0433504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07115770294658356,"score_gpt":0.3770349994372165,"score_spread":0.3058772964906329,"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."}}