{"id":"W1993091056","doi":"10.1109/isie.2006.296096","title":"Machine Learning-Assisted Device Selection in a Context-Sensitive Ubiquitous Multimodal Multimedia Computing System","year":2006,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Ubiquitous computing; Component (thermodynamics); Adaptation (eye); Context (archaeology); Human–computer interaction; Multimedia; User modeling; Selection (genetic algorithm); User interface; Modalities; Artificial intelligence; Operating system","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008226014,0.000335197,0.0005328674,0.0004305684,0.0002531975,0.0002731515,0.0003851707,0.0001788516,0.000009795014],"category_scores_gemma":[0.0001192521,0.0003376426,0.0001260446,0.001057976,0.00004372559,0.0006261914,0.0002143615,0.0004970467,0.0002177172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000536366,"about_ca_system_score_gemma":0.0001067556,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0198568,"about_ca_topic_score_gemma":0.01664085,"domain_scores_codex":[0.9967121,0.0007490002,0.0007269761,0.0008038354,0.0004549839,0.0005531448],"domain_scores_gemma":[0.9980559,0.0008387647,0.0003593607,0.0002366083,0.0003939607,0.0001153701],"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":[0.0001903776,0.001242973,0.1042328,0.0003984858,0.0001960376,0.0006411871,0.005791989,0.006025171,0.05482937,0.006250671,0.0004376274,0.8197633],"study_design_scores_gemma":[0.00165224,0.00008774258,0.0456882,0.0001819235,0.000008776557,0.0004163024,0.0006197953,0.9453232,0.005129324,0.000007068967,0.0004910417,0.0003943632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3164304,0.00007750629,0.6743836,0.0002023225,0.0005407523,0.0008034847,0.000006144663,0.001566396,0.00598947],"genre_scores_gemma":[0.9892908,6.409294e-7,0.009838508,0.0001170762,0.0001992161,0.00002681065,0.00001834368,0.00002784105,0.0004807276],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.939298,"threshold_uncertainty_score":0.9999076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01601107238850516,"score_gpt":0.2424722342540775,"score_spread":0.2264611618655724,"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."}}