{"id":"W2182446056","doi":"10.1109/ipin.2015.7346947","title":"Automated detection of burned-out luminaries using indoor positioning","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Leverage (statistics); Computer science; Real-time computing; Dynamic time warping; Mobile device; Embedded system; Internet of Things; Participatory sensing; Artificial intelligence; Data science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003844991,0.000742683,0.0008289042,0.0008199149,0.0003718042,0.0005924332,0.0008671634,0.0005586391,0.0007176959],"category_scores_gemma":[0.001616535,0.0002688317,0.0003460079,0.0007434333,0.0003090326,0.0005692363,0.001289168,0.0005296203,0.0006002631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001848767,"about_ca_system_score_gemma":0.0003000011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006987855,"about_ca_topic_score_gemma":0.00176878,"domain_scores_codex":[0.9993945,0.0001314935,0.00001957728,0.0001574745,0.0002140665,0.0000828889],"domain_scores_gemma":[0.9990914,0.0002680232,0.0001798145,0.0001846758,0.0002122567,0.00006384776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007449792,0.0002420266,0.04385158,0.0005311511,0.0001881012,0.0009583529,0.001136237,0.06985009,0.27686,0.002174353,0.003248246,0.600215],"study_design_scores_gemma":[0.00005953297,0.0006316552,0.04662243,0.00006622263,0.0001238338,0.001196083,0.0007581243,0.7918929,0.146526,0.003340279,0.008664284,0.0001186916],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3843907,0.0004665976,0.6076871,0.0001362078,0.0001693209,0.00008074134,0.000222526,0.001989436,0.004857309],"genre_scores_gemma":[0.8965749,0.0001584923,0.1012138,0.00005780392,0.00004310663,0.00005175514,0.0001957407,0.00007777136,0.001626654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008671634,"threshold_uncertainty_score":0.002400935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02517603045272453,"score_gpt":0.2475415584557597,"score_spread":0.2223655280030352,"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."}}