{"id":"W3002633637","doi":"10.3390/fi12020019","title":"MCCM: An Approach for Connectivity and Coverage Maximization","year":2020,"lang":"en","type":"article","venue":"Future Internet","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Maximization; Distributed computing; Quality of service; Wireless sensor network; Internet of Things; Cover (algebra); Wireless; Object (grammar); Computer network; Real-time computing; Telecommunications; Computer security; Artificial intelligence","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":[0.00009148158,0.0001205098,0.0001364047,0.00002579247,0.00004270786,0.0001572627,0.0003887335,0.00008539397,0.000005433081],"category_scores_gemma":[0.00002445187,0.0001110344,0.0000332044,0.00013903,0.00002149153,0.0003014039,0.0001305979,0.0001074279,0.000002137172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001488314,"about_ca_system_score_gemma":0.00001018555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007916029,"about_ca_topic_score_gemma":0.000004181388,"domain_scores_codex":[0.9991057,0.00005534697,0.0001170048,0.0004414297,0.0001117245,0.0001687982],"domain_scores_gemma":[0.9995041,0.00004743289,0.00006101518,0.0002181514,0.00004851841,0.0001207727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003100226,0.0004892854,0.004076809,0.0002241795,0.0001203654,0.00002658752,0.01200637,0.3216198,0.002132768,0.5225118,0.02827285,0.1082092],"study_design_scores_gemma":[0.0003504371,0.000162181,0.0008283018,0.000004202486,0.000004145884,0.000008247215,0.00002774682,0.9865748,0.0006579609,0.0001129087,0.01112436,0.0001446784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05745973,0.00008094077,0.9400535,0.0009523841,0.0003497375,0.0001685296,0.00000509519,0.0001783542,0.0007517872],"genre_scores_gemma":[0.9144586,0.00000915539,0.08324268,0.001434586,0.0006924972,0.00001540331,0.00003939285,0.00001299762,0.00009470399],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8569989,"threshold_uncertainty_score":0.4527853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01609739819753067,"score_gpt":0.2163228459384299,"score_spread":0.2002254477408993,"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."}}