{"id":"W4213219791","doi":"10.32920/ryerson.14654106.v1","title":"Location and sensing network for industrial automation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Wireless sensor network; Latency (audio); Computer science; Efficient energy use; Computer network; Protocol (science); Automation; Real-time computing; Throughput; Energy (signal processing); Wireless; Embedded system; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002606244,0.000405063,0.0003340887,0.000377386,0.0004310877,0.0008946328,0.0005016642,0.0007295892,0.01437116],"category_scores_gemma":[0.0007607464,0.0001575955,0.0002517668,0.00067626,0.0003118021,0.001028548,0.0008625544,0.0006805161,0.006831498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005237006,"about_ca_system_score_gemma":0.0006681615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00114086,"about_ca_topic_score_gemma":0.001023867,"domain_scores_codex":[0.9996544,0.00006172615,0.00001672002,0.000110341,0.0001277436,0.00002905117],"domain_scores_gemma":[0.9997755,0.00004817072,0.0000274918,0.00006981227,0.00006497545,0.00001412546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002281364,0.00008774087,0.001051918,0.0004081879,0.00003791769,0.0003857937,0.0001353757,0.03161364,0.02326256,0.1369073,0.04759734,0.758284],"study_design_scores_gemma":[0.00007414405,0.0003747023,0.003368624,0.0002435947,0.00007141409,0.001418015,0.0001571649,0.2107454,0.02330461,0.1159252,0.6442553,0.00006177829],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02279447,0.01269848,0.8669439,0.002887067,0.002116679,0.0002013931,0.0006155345,0.004929236,0.08681323],"genre_scores_gemma":[0.5340644,0.01146675,0.2708005,0.0008308381,0.001137691,0.0003589067,0.001996267,0.0002755215,0.1790691],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01437116,"threshold_uncertainty_score":0.04807633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02596843939380738,"score_gpt":0.2298051350424273,"score_spread":0.2038366956486199,"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."}}