{"id":"W6979747567","doi":"","title":"Adaptive User-Controlled Personalization for Virtual Journey Applications","year":2023,"lang":"en","type":"other","venue":"LUTPub (LUT University)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of North Carolina at Chapel Hill; Chinese University of Hong Kong; University of Toronto; Eidgenössische Technische Hochschule Zürich; Universiteit van Amsterdam; University of Queensland; Imperial College London; University of Chicago; University of Wisconsin-Madison; McGill University; Johns Hopkins University; York University; University of Pennsylvania; University of Southern California; Universiteit Leiden; Yale University","keywords":"Personalization; Digital transformation; Mobile device; Variety (cybernetics); Higher education; Information system; Information technology; Software","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001888657,0.0004914968,0.0007421431,0.002979196,0.0003125602,0.00006003634,0.0006693166,0.0005609436,0.0009474141],"category_scores_gemma":[0.00009880173,0.0005821384,0.0004694198,0.002166026,0.0001569294,0.0002040295,0.0001200262,0.0002767447,0.003482063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008326811,"about_ca_system_score_gemma":0.0003950008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001359311,"about_ca_topic_score_gemma":0.00232019,"domain_scores_codex":[0.9979271,0.0001479115,0.0002278476,0.0007642169,0.0004030546,0.0005298236],"domain_scores_gemma":[0.9981454,0.0002541218,0.000604332,0.0005079362,0.0002594601,0.0002287668],"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":[0.0008147362,0.000135137,0.00002747358,0.0000403632,0.0009949262,0.00001958057,0.0001965799,0.0001067844,0.00007301694,0.2428072,0.7542545,0.0005296298],"study_design_scores_gemma":[0.007519636,0.0001154947,0.00001944328,0.0001272,0.0007342746,0.000002050696,0.001003064,0.0008399769,0.000005810979,0.0001046281,0.9889469,0.0005815264],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00000355204,0.0001508289,0.1811742,0.00009059105,0.0003640991,0.004343044,0.002805498,0.002868928,0.8081992],"genre_scores_gemma":[0.000244478,0.000120466,0.001049952,0.0000407013,0.0006326326,0.00008846413,0.0004041403,0.002274208,0.995145],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2427026,"threshold_uncertainty_score":0.9999658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02347553537661197,"score_gpt":0.2317498118500059,"score_spread":0.208274276473394,"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."}}