{"id":"W3097641778","doi":"10.1109/tensymp50017.2020.9230959","title":"IoT enabled Low power and Wide range WSN platform for environment monitoring application","year":2020,"lang":"en","type":"article","venue":"2020 IEEE Region 10 Symposium (TENSYMP)","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Wireless sensor network; Computer science; Internet of Things; Sensor node; Real-time computing; Node (physics); Range (aeronautics); Energy consumption; Wireless; Embedded system; Key distribution in wireless sensor networks; Computer network; Wireless network; Telecommunications; Engineering; Electrical engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001136386,0.0003081379,0.000327081,0.0000381703,0.0001293675,0.00007293784,0.00017458,0.0002018558,0.00002380137],"category_scores_gemma":[0.000008641392,0.0003099946,0.0001136355,0.0001310416,0.00003585006,0.0001386987,0.0000386034,0.000180657,0.00008983709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001040728,"about_ca_system_score_gemma":0.00001221293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004443493,"about_ca_topic_score_gemma":5.812056e-7,"domain_scores_codex":[0.9986051,0.00001559208,0.0003380117,0.0004209137,0.0001969836,0.0004234334],"domain_scores_gemma":[0.9992427,0.0001070221,0.00007965965,0.0002937195,0.000030785,0.000246146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001698336,0.0002279791,0.007541795,0.003502443,0.0007245025,0.0001213645,0.005474024,0.4064563,0.3701831,0.000613403,0.1758312,0.02762552],"study_design_scores_gemma":[0.003979762,0.0006022191,0.001037836,0.0002640361,0.0001468899,0.00003948906,0.0001552073,0.3362224,0.05703962,0.0002729439,0.5988537,0.001385914],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2998118,0.004261737,0.6040263,0.01479311,0.003553944,0.05891157,0.0001036007,0.003358123,0.01117982],"genre_scores_gemma":[0.9894843,0.0008998813,0.001208788,0.0003811675,0.001460886,0.005887388,0.00002506622,0.0001583246,0.0004941985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6896725,"threshold_uncertainty_score":0.9999352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01402456964792473,"score_gpt":0.2080058712135391,"score_spread":0.1939813015656143,"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."}}