{"id":"W4383200176","doi":"10.1109/jiot.2023.3292319","title":"Transferability of Machine Learning Algorithm for IoT Device Profiling and Identification","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research and Productivity Council; University of New Brunswick","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Profiling (computer programming); Transferability; Algorithm; Machine learning; Identification (biology); Artificial intelligence; Internet of Things; Data mining; Embedded system; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001894357,0.00007269855,0.0001485346,0.00004771183,0.00008391713,0.00002646712,0.0001429459,0.00004227068,0.00002444365],"category_scores_gemma":[0.0001775242,0.00006599005,0.0000686015,0.000109617,0.00009177146,0.0001646478,0.00003349214,0.0002246469,0.00000395367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004342627,"about_ca_system_score_gemma":0.000006365616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001677202,"about_ca_topic_score_gemma":0.000002376949,"domain_scores_codex":[0.9990171,0.00006185721,0.0004260553,0.0001393318,0.0002168389,0.0001388474],"domain_scores_gemma":[0.9994636,0.0001525419,0.0002423515,0.0000602431,0.00003173882,0.00004950492],"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":[0.00007312917,0.00007790538,0.09689533,0.0002265293,0.00006372763,0.000002992715,0.01224189,0.003143972,0.3043211,0.00002574304,0.0001213063,0.5828064],"study_design_scores_gemma":[0.0004060386,0.0002419331,0.010857,0.0001667555,0.00003447264,0.00004742107,0.0005755628,0.6399545,0.3459918,0.001366264,0.0002247237,0.0001335797],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8883141,0.00002934153,0.1111477,0.00008807096,0.0002634616,0.00008596281,0.000004455118,0.00002121473,0.00004564434],"genre_scores_gemma":[0.980661,0.000014356,0.01894013,0.000007162738,0.00005851473,0.000003066239,0.000002347442,0.000009604667,0.0003037915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6368105,"threshold_uncertainty_score":0.2690996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03590139028415922,"score_gpt":0.2922806529478848,"score_spread":0.2563792626637256,"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."}}