{"id":"W4211245467","doi":"10.32920/ryerson.14655732","title":"Email marketing from message filters to AR filters","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mobile marketing; Business; Digital marketing; Novelty; Augmented reality; Marketing; Marketing research; Computer science; Advertising; Human–computer interaction","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":[0.001547224,0.0003701498,0.0002609869,0.0008611193,0.0008038346,0.004456397,0.0004292236,0.001289782,0.02148666],"category_scores_gemma":[0.004239254,0.00022933,0.0003744813,0.0007262754,0.0007459074,0.00332966,0.0008324455,0.0007356524,0.00387856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009628999,"about_ca_system_score_gemma":0.0006376423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008201555,"about_ca_topic_score_gemma":0.001035764,"domain_scores_codex":[0.9989177,0.0004413542,0.00003714068,0.00013603,0.0003200565,0.000147731],"domain_scores_gemma":[0.9967667,0.002261498,0.0002205158,0.0002193732,0.0003409713,0.0001909266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001110293,0.001937711,0.008129886,0.00129678,0.00004284498,0.000547913,0.007181797,0.001470377,0.02596428,0.04936711,0.01951732,0.8834336],"study_design_scores_gemma":[0.0004881302,0.008479976,0.08303081,0.00203819,0.0002541681,0.002069092,0.01729564,0.01570797,0.07159885,0.04717678,0.7516499,0.0002104817],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6295161,0.005924083,0.03739991,0.008398359,0.0005774916,0.0009121115,0.0002780556,0.002035477,0.3149584],"genre_scores_gemma":[0.8865828,0.003485051,0.02708264,0.002222118,0.0004115911,0.0003557845,0.00017637,0.0002114797,0.07947223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02148666,"threshold_uncertainty_score":0.07188004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1232941175771789,"score_gpt":0.3829892986230403,"score_spread":0.2596951810458614,"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."}}