{"id":"W4312237231","doi":"10.1109/tii.2022.3205368","title":"A Machine Learning Assisted Method for Coverage Optimization in a Network of Mobile Sensors","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Concordia University","funders":"Defence Research and Development Canada","keywords":"Computer science","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":[0.0003871391,0.0001233703,0.0002333652,0.000232437,0.0001727099,0.0000171177,0.00011186,0.0001533263,0.00009791013],"category_scores_gemma":[0.00002330629,0.0001380675,0.00007927177,0.0007418931,0.00001659521,0.0001256765,0.000002378462,0.0005716772,9.634933e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001737665,"about_ca_system_score_gemma":0.00003500514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002445834,"about_ca_topic_score_gemma":0.000007450503,"domain_scores_codex":[0.9989387,0.00006033454,0.0005909657,0.00005805723,0.0001608763,0.00019111],"domain_scores_gemma":[0.9994955,0.0002072983,0.0001144138,0.0001218966,0.00003912541,0.00002176894],"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":[0.00006729366,0.00003179529,0.000009300322,0.00002949729,0.00003068889,3.26786e-7,0.0005679609,0.97705,0.00002423732,0.00004627906,0.0001665653,0.02197603],"study_design_scores_gemma":[0.001333852,0.0002262086,6.789947e-7,0.00001836736,0.00002369398,0.000004500964,0.0007939924,0.9885344,0.005722982,0.00002700361,0.003182638,0.0001316633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00509619,0.00001407389,0.9929862,0.0000106123,0.0005725811,0.0005729138,0.0001520272,0.0002687792,0.000326619],"genre_scores_gemma":[0.9670752,0.00005976326,0.03220467,0.00003044234,0.00003204041,0.0003956834,0.00007417471,0.00003462914,0.00009334431],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9619791,"threshold_uncertainty_score":0.5630229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02542030347254687,"score_gpt":0.2507858312828514,"score_spread":0.2253655278103046,"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."}}