{"id":"W4360989187","doi":"10.18280/ria.370102","title":"Life Span Improvement of Bio Sensors Using Unsupervised Machine Learning for Wireless Body Area Sensor Network","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Life span; Wireless sensor network; Span (engineering); Computer science; Wireless; Unsupervised learning; Artificial intelligence; Machine learning; Telecommunications; Engineering; Computer network; Medicine; Gerontology; Structural engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005586817,0.0003500655,0.0005238994,0.0002052837,0.0002308252,0.00004899783,0.0003027078,0.0001798052,0.0001008205],"category_scores_gemma":[0.0001591227,0.0003826039,0.0002183876,0.001101367,0.000088166,0.0001082761,0.0001044338,0.0003328469,0.00008369995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009010612,"about_ca_system_score_gemma":0.00003724604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005233406,"about_ca_topic_score_gemma":0.00001628002,"domain_scores_codex":[0.9976367,0.00004436996,0.0008224669,0.0004376982,0.0002174138,0.0008413373],"domain_scores_gemma":[0.9986055,0.0003974714,0.000149531,0.0004877371,0.0001252208,0.0002345533],"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":[0.00003286569,0.00002926618,0.001413311,0.0003167156,0.00008492069,0.000009291396,0.0004712974,0.927542,0.06215756,0.0001204707,0.0005029615,0.007319394],"study_design_scores_gemma":[0.0000920706,0.0001002115,0.00004691109,0.000232723,0.00003856234,0.000004566236,0.0007302292,0.8939848,0.1025484,0.00009029121,0.001778131,0.0003530906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8262636,0.000355089,0.1709038,0.00009638605,0.0007531026,0.0006995539,0.00002956137,0.0006441483,0.0002546891],"genre_scores_gemma":[0.9974216,0.0003669326,0.001050508,0.00002846015,0.0004124688,0.00005430877,0.00008723243,0.0001361443,0.000442338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.171158,"threshold_uncertainty_score":0.9998626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03973090228514323,"score_gpt":0.2505405612993871,"score_spread":0.2108096590142438,"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."}}