{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001389803,0.0007304707,0.0006388839,0.001454579,0.00147903,0.005687366,0.001664746,0.002519304,0.02096169],"category_scores_gemma":[0.004521878,0.0003502757,0.0004379137,0.002815038,0.001763715,0.009514286,0.004649449,0.002048602,0.008097781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014073,"about_ca_system_score_gemma":0.00125218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001882537,"about_ca_topic_score_gemma":0.001900743,"domain_scores_codex":[0.9982186,0.0005351398,0.0001159992,0.0002693699,0.0006857485,0.0001750849],"domain_scores_gemma":[0.9981975,0.0005869513,0.0001138114,0.0006287664,0.000347257,0.0001257728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007548164,0.00004030366,0.0007841576,0.0005997784,0.00002819453,0.0002794669,0.001196538,0.0009976712,0.003332781,0.4715474,0.08010188,0.4410164],"study_design_scores_gemma":[0.00001727787,0.00004996711,0.0006501402,0.0006461074,0.00003125469,0.0007055892,0.0008783307,0.004494797,0.001633904,0.1803618,0.8104866,0.00004428613],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.008099695,0.06774007,0.4452751,0.02423912,0.00244502,0.0004907323,0.001310792,0.004266808,0.4461327],"genre_scores_gemma":[0.3809526,0.09094858,0.3227859,0.01028069,0.004979542,0.0013725,0.003077885,0.0008314759,0.1847708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02096169,"threshold_uncertainty_score":0.07012379,"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."}}