{"id":"W2765540589","doi":"10.1109/icices.2017.8070736","title":"Using wide area monitoring WSN","year":2017,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity College","funders":"","keywords":"Wireless sensor network; Continuous monitoring; Computer science; Air monitoring; Hazardous waste; Air quality index; Sample (material); Smart city; Transport engineering; Internet of Things; Environmental science; Real-time computing; Meteorology; Engineering; Computer security; Geography; Environmental engineering; Computer network; Operations management","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":[0.0003472511,0.0006059469,0.0004057555,0.0006292828,0.0003366048,0.001004139,0.0006416985,0.0006077373,0.001495703],"category_scores_gemma":[0.0006464392,0.0002119771,0.0003269562,0.0008261014,0.0002010161,0.001693486,0.0008687012,0.0004328285,0.0008270261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001727813,"about_ca_system_score_gemma":0.0002162276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006653027,"about_ca_topic_score_gemma":0.0007587507,"domain_scores_codex":[0.999543,0.0001113826,0.00002786834,0.0001540896,0.0001373103,0.00002642918],"domain_scores_gemma":[0.9997892,0.00005920701,0.00003680902,0.00004899898,0.00005034446,0.00001545018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002268375,0.0001657683,0.008241067,0.0008424458,0.0002331432,0.0006994159,0.000235375,0.08323121,0.1490815,0.01813865,0.01214952,0.7267551],"study_design_scores_gemma":[0.00007239963,0.0007064929,0.01100238,0.0002697158,0.0003135996,0.002529794,0.0003180903,0.624511,0.09304597,0.03785747,0.2292304,0.0001427046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03059569,0.002859876,0.9450287,0.0005504767,0.0003753563,0.0001760802,0.0004325631,0.002963856,0.01701728],"genre_scores_gemma":[0.6023098,0.007826719,0.3691345,0.0005165127,0.0004793747,0.0003810945,0.001421981,0.0002072857,0.01772285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001495703,"threshold_uncertainty_score":0.005003572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09145160249349492,"score_gpt":0.3057591766606695,"score_spread":0.2143075741671746,"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."}}