{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003982653,0.00010673,0.000108105,0.0000429891,0.00009500055,0.00003222904,0.0005598376,0.00002596167,0.0001398893],"category_scores_gemma":[0.000004561358,0.0001208468,0.00001131247,0.00009360327,0.00001125441,0.0002510281,0.0006208606,0.0002325589,0.00002757512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001773338,"about_ca_system_score_gemma":0.000008292465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001743463,"about_ca_topic_score_gemma":0.00002554779,"domain_scores_codex":[0.9991142,0.00007522849,0.0001287604,0.0002194119,0.0002309903,0.0002314793],"domain_scores_gemma":[0.9992875,0.00001601699,0.00001699576,0.0006252059,0.000001031537,0.00005329174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001234438,0.0006074781,0.7731806,0.0003412954,0.0001178216,0.0003691637,0.003300069,0.09561051,0.01925155,0.00005909192,0.05425481,0.05278415],"study_design_scores_gemma":[0.004299211,0.0005245341,0.07329244,0.0002063315,0.00004350051,0.00003109645,0.0002503608,0.4247096,0.002826917,0.0002049698,0.4921717,0.00143926],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865208,0.0008722789,0.001215762,0.00003395964,0.001072479,0.007746144,0.001050673,0.0003059395,0.001182021],"genre_scores_gemma":[0.9975274,0.000004326831,0.0001974457,0.00005496906,0.0001190267,0.0006276779,0.001427295,0.00002905314,0.00001280429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6998882,"threshold_uncertainty_score":0.4927988,"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."}}