{"id":"W2956318230","doi":"10.1016/j.inffus.2019.07.002","title":"Multi-sensor fusion based intelligent sensor relocation for health and safety monitoring in BSNs","year":2019,"lang":"en","type":"article","venue":"Information Fusion","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Francis Xavier University","funders":"University of South China; China Scholarship Council; National Natural Science Foundation of China; Education Department of Hunan Province","keywords":"Wireless sensor network; Computer science; Relocation; Energy consumption; Real-time computing; Sensor fusion; Key distribution in wireless sensor networks; Wireless; Embedded system; Computer network; Telecommunications; Artificial intelligence; Wireless network; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0005662428,0.0001584969,0.0001875877,0.0003364538,0.0001726802,0.0001319941,0.0002304012,0.0001128016,0.000004664882],"category_scores_gemma":[0.00007749094,0.0001533744,0.00004269009,0.0004075739,0.00001515509,0.001059946,0.0001265035,0.0001416025,0.00005222235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002211862,"about_ca_system_score_gemma":0.00008564331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001130765,"about_ca_topic_score_gemma":0.00001734936,"domain_scores_codex":[0.9984556,0.00007083811,0.0006247371,0.0002422584,0.0002964567,0.0003100946],"domain_scores_gemma":[0.9988784,0.0001692578,0.0002952736,0.0003805492,0.0001553432,0.0001212192],"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":[0.0001689687,0.000100181,0.007426482,0.0002458723,0.000004139664,5.688601e-7,0.006006189,0.724974,0.001614325,0.001394352,0.00009947673,0.2579655],"study_design_scores_gemma":[0.001101845,0.0001279345,0.006376824,0.0002277471,9.704781e-7,0.000002221339,0.0004400781,0.9796297,0.002190348,0.000005813414,0.009723661,0.0001728832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2882388,0.00006652791,0.7084709,0.001175976,0.000855761,0.0008709251,0.000003786592,0.0001508209,0.0001665481],"genre_scores_gemma":[0.8257301,0.0002272249,0.1729354,0.0007915367,0.00006129702,0.00003571627,0.00008745767,0.00001326689,0.0001180555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5374913,"threshold_uncertainty_score":0.6254426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01854163128801578,"score_gpt":0.2669810306378649,"score_spread":0.2484393993498491,"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."}}