{"id":"W2911972325","doi":"10.1145/2930238","title":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","year":2016,"lang":"en","type":"paratext","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Personalization; Computer science; Adaptation (eye); User modeling; Human–computer interaction; World Wide Web; User interface; Psychology; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.006961962,0.001763981,0.002042836,0.001432021,0.001472694,0.007106844,0.002428269,0.002414912,0.07844229],"category_scores_gemma":[0.01276808,0.000660281,0.001518927,0.001786634,0.00132092,0.007720879,0.00490525,0.004872913,0.03537092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001832606,"about_ca_system_score_gemma":0.003345742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006473968,"about_ca_topic_score_gemma":0.008242223,"domain_scores_codex":[0.9954453,0.001533054,0.0003736629,0.0009910876,0.001358166,0.000298826],"domain_scores_gemma":[0.9931509,0.001894112,0.0001585873,0.001568591,0.00250447,0.0007231892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002581361,0.0002142846,0.001131004,0.0005219338,0.0001000332,0.0001330193,0.0009400445,0.001255653,0.001969104,0.02247383,0.5924512,0.3785518],"study_design_scores_gemma":[0.00002046303,0.00005381126,0.0008721129,0.0002695129,0.00004126375,0.0001599287,0.000366397,0.005866922,0.0008379348,0.01149821,0.9799718,0.00004169879],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01523688,0.08593625,0.5138139,0.04776178,0.06764075,0.001884543,0.00711146,0.01044544,0.250169],"genre_scores_gemma":[0.09391358,0.05917091,0.2036691,0.009155822,0.01444614,0.002043349,0.03362309,0.003972392,0.5800056],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.07844229,"threshold_uncertainty_score":0.2624156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06329878078319169,"score_gpt":0.2634530597239036,"score_spread":0.200154278940712,"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."}}