{"id":"W2135257499","doi":"10.1145/1217935.1217969","title":"URICA","year":2006,"lang":"en","type":"article","venue":"","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Adaptation (eye); Content adaptation; Mobile device; Multimedia; Bandwidth (computing); Fidelity; Human–computer interaction; World Wide Web; Ubiquitous computing; Computer network; Telecommunications","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":[0.002045106,0.001508804,0.001320931,0.002068997,0.001300684,0.004574813,0.003393634,0.001830491,0.1081175],"category_scores_gemma":[0.008318799,0.001006224,0.001182596,0.001735686,0.0007050991,0.004581491,0.004716692,0.001884776,0.0907838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001069729,"about_ca_system_score_gemma":0.001802239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003366061,"about_ca_topic_score_gemma":0.003071558,"domain_scores_codex":[0.997403,0.0004741935,0.0001735387,0.0005789919,0.001075991,0.0002941915],"domain_scores_gemma":[0.9970934,0.0004709389,0.0001618491,0.001094478,0.000853709,0.0003255699],"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":[0.001703825,0.0003651901,0.002591292,0.001208122,0.0001953336,0.0005654223,0.0008956982,0.003190398,0.01913561,0.04783713,0.4962115,0.4261006],"study_design_scores_gemma":[0.0001534103,0.0001678825,0.001360509,0.0001813554,0.00006899193,0.0004964483,0.000110579,0.01782047,0.01066806,0.01455159,0.9543169,0.0001038281],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"other","genre_scores_codex":[0.01044948,0.003638066,0.2621373,0.001422773,0.001159996,0.001432267,0.01627895,0.4179047,0.2855765],"genre_scores_gemma":[0.1442715,0.0036982,0.3203419,0.002634465,0.0006590447,0.002887123,0.08427672,0.07902437,0.3622067],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1081175,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01516964511449863,"score_gpt":0.3216469950067069,"score_spread":0.3064773498922083,"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."}}