{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009517669,0.001121221,0.001183244,0.0009093506,0.0004123134,0.000796602,0.00155198,0.00137085,0.002473295],"category_scores_gemma":[0.003013472,0.0005323209,0.0009749366,0.001090214,0.0006161497,0.001086138,0.001133684,0.001283109,0.0006581424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009059828,"about_ca_system_score_gemma":0.001191665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003924866,"about_ca_topic_score_gemma":0.003076567,"domain_scores_codex":[0.9993415,0.0001991562,0.00002669172,0.0001547729,0.0002129124,0.00006495474],"domain_scores_gemma":[0.9988487,0.0007195087,0.0001044363,0.00007923107,0.0002128667,0.00003523497],"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":[0.00005159151,0.0000318442,0.0003636522,0.0001122721,0.00003756997,0.00005098286,0.00004007576,0.8911512,0.002154427,0.007697228,0.001502582,0.09680661],"study_design_scores_gemma":[0.000002933909,0.00000851842,0.00002816613,0.000003622891,0.000002579064,0.000009777181,0.000002378435,0.9981937,0.0002218897,0.001159946,0.0003644411,0.000002070784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00159108,0.0001725075,0.9974104,0.00005260054,0.00002289637,0.00001792328,0.00001721689,0.0001309184,0.0005844931],"genre_scores_gemma":[0.241744,0.0006654776,0.7511383,0.0002360587,0.0001947205,0.0004638482,0.0002668973,0.0001867501,0.005103871],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003924866,"threshold_uncertainty_score":0.008274019,"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."}}