{"id":"W2915530530","doi":"10.1109/glocom.2018.8648071","title":"Kernel Based Estimation of Domain Parameters at IoT Proxy","year":2018,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Kernel density estimation; Computer science; Parameterized complexity; Probability distribution; Kernel (algebra); Probability density function; Probability estimation; Anomaly detection; Proxy (statistics); Algorithm; Domain (mathematical analysis); Data mining; Artificial intelligence; Mathematics; Machine learning; Statistics","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":[0.000515662,0.0005483106,0.0007200265,0.0005298569,0.000269557,0.001104105,0.0008560637,0.0004711128,0.0007712521],"category_scores_gemma":[0.004603289,0.0002159861,0.0002742818,0.0006058791,0.0003331244,0.002215651,0.0008986018,0.0008534168,0.0004936556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005704698,"about_ca_system_score_gemma":0.0005848882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002283609,"about_ca_topic_score_gemma":0.001557968,"domain_scores_codex":[0.9995042,0.00007689642,0.00003397348,0.0001504293,0.000148474,0.00008601698],"domain_scores_gemma":[0.9987049,0.0004320261,0.0002154517,0.0003385321,0.0002425132,0.00006657263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004148133,0.0001589657,0.01842131,0.0001278464,0.00007955586,0.00022985,0.0001666986,0.7498248,0.03915156,0.009855014,0.001662454,0.1799071],"study_design_scores_gemma":[0.000001768988,0.000008722875,0.0008391564,0.000002161171,0.000002825825,0.00004680014,0.00001380909,0.993614,0.003896805,0.001378551,0.0001896019,0.000005791805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06347076,0.0001246547,0.9344494,0.00004561265,0.00001927804,0.00001948833,0.0001009837,0.001148494,0.000621218],"genre_scores_gemma":[0.9195741,0.0001067775,0.07941381,0.00002206977,0.00001573242,0.00002898008,0.000246514,0.00007434349,0.0005176658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002283609,"threshold_uncertainty_score":0.004540682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01593447298912943,"score_gpt":0.2473919572987537,"score_spread":0.2314574843096243,"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."}}