{"id":"W4312116644","doi":"10.3390/data8010004","title":"LoRaWAN Path Loss Measurements in an Urban Scenario including Environmental Effects","year":2022,"lang":"en","type":"article","venue":"Data","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Path loss; Computer science; Environmental data; Path (computing); Linear regression; Environmental science; Path analysis (statistics); Set (abstract data type); Regression analysis; Telecommunications; Machine learning; Wireless; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002717861,0.0004593236,0.0002783551,0.0007512673,0.0002975491,0.0003057157,0.0004242933,0.0003070775,0.0006178395],"category_scores_gemma":[0.0008643611,0.00009458809,0.0002136532,0.001170812,0.0002229206,0.0003852008,0.0003219702,0.0004387608,0.0004260178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005176054,"about_ca_system_score_gemma":0.0002889192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01382976,"about_ca_topic_score_gemma":0.01971976,"domain_scores_codex":[0.999643,0.00006789072,0.00002161755,0.00007804822,0.0001262392,0.00006334271],"domain_scores_gemma":[0.9994825,0.0001106833,0.00006247583,0.0000828167,0.0002216907,0.00003974695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001416201,0.0008042036,0.2527938,0.001063022,0.000290437,0.001896859,0.000969991,0.4570015,0.03802633,0.003424098,0.04509485,0.1972186],"study_design_scores_gemma":[0.00009552072,0.0005672765,0.4205339,0.0001323257,0.0001245944,0.001225888,0.00161692,0.504017,0.03394804,0.001996867,0.03554259,0.0001991305],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9596343,0.0003556009,0.01317632,0.0002646488,0.000104179,0.00008733619,0.01494521,0.001430526,0.01000183],"genre_scores_gemma":[0.9852327,0.0001782878,0.004787312,0.00003998334,0.0000131217,0.00004961682,0.008689151,0.0000345527,0.0009752932],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01382976,"threshold_uncertainty_score":0.02749854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06952734318477873,"score_gpt":0.2728361150308636,"score_spread":0.2033087718460849,"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."}}