{"id":"W2732616350","doi":"10.1109/comst.2017.2721379","title":"A Survey of Enabling Technologies of Low Power and Long Range Machine-to-Machine Communications","year":2017,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":202,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Software deployment; Machine to machine; Power consumption; Bandwidth (computing); Range (aeronautics); Wireless; Telecommunications; Computer network; Power (physics); Computer security; Internet of Things; Engineering","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":[0.005585301,0.0002311014,0.0006147305,0.0002034999,0.0005163155,0.0001078535,0.003638054,0.0002005018,0.00001128357],"category_scores_gemma":[0.001528333,0.0002323849,0.000076197,0.0003245503,0.0006429279,0.0002377401,0.001062865,0.0003687293,0.000007805671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003809038,"about_ca_system_score_gemma":0.00004790242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002530181,"about_ca_topic_score_gemma":0.006273397,"domain_scores_codex":[0.9974892,0.001122677,0.0007683788,0.0001818725,0.000180112,0.0002577868],"domain_scores_gemma":[0.9897724,0.001959615,0.0003736336,0.007409211,0.000420434,0.00006465364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001771061,0.001172282,0.8182672,0.001068491,0.001262371,0.000003671084,0.003754003,0.005791785,0.02958613,0.005436865,0.003013159,0.1304669],"study_design_scores_gemma":[0.002136192,0.0002070595,0.9512032,0.00103311,0.00009657331,0.000006152788,0.0001136237,0.01919525,0.009000068,0.0008686878,0.01504045,0.001099675],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5969334,0.1514755,0.1446716,0.005519484,0.01078966,0.04892282,0.009465681,0.004628639,0.02759316],"genre_scores_gemma":[0.9932848,0.003178959,0.002662105,0.000006229776,0.00003009715,0.0006816258,0.00007665857,0.00004651441,0.00003295945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3963514,"threshold_uncertainty_score":0.9476383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06359738787176865,"score_gpt":0.3305264634287325,"score_spread":0.2669290755569638,"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."}}