{"id":"W4297970693","doi":"10.1561/1100000014","title":"Ubiquitous Computing for Capture and Access","year":2009,"lang":"en","type":"article","venue":"Foundations and Trends® in Human–Computer Interaction","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Ubiquitous computing; 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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002023759,0.0001878903,0.0002013983,0.0005428594,0.0004839409,0.001085871,0.0003138777,0.00007408996,0.000003697225],"category_scores_gemma":[0.000007980627,0.0001901425,0.00005576925,0.0003629336,0.00003158974,0.001311212,0.0002044009,0.0002044774,0.000001878914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006277599,"about_ca_system_score_gemma":0.00001133369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008064135,"about_ca_topic_score_gemma":0.00003398531,"domain_scores_codex":[0.9987167,0.00004269117,0.0003478915,0.0005016032,0.0001051967,0.0002858983],"domain_scores_gemma":[0.9993435,0.0001394896,0.0001441876,0.0002233771,0.00008121857,0.00006819805],"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":[0.00001007754,0.0001257124,0.001278871,0.00002262206,0.00001956271,0.000007063641,0.002317349,0.0005590424,0.0001311676,0.0135923,0.003930022,0.9780062],"study_design_scores_gemma":[0.001378966,0.0003876866,0.1701092,0.0001753178,0.00002101972,0.0001587249,0.00003977883,0.7952651,0.0001497194,0.01264832,0.01910128,0.0005648334],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2805766,0.00006562123,0.7123016,0.001270284,0.004311383,0.0001741291,5.761252e-7,0.0001696631,0.001130207],"genre_scores_gemma":[0.9754176,0.000004399682,0.02250612,0.0003782245,0.001514435,0.000007088335,0.00004045696,0.000008985352,0.0001226815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9774414,"threshold_uncertainty_score":0.9999511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04709262605109459,"score_gpt":0.3683073684730833,"score_spread":0.3212147424219887,"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."}}