{"id":"W2740908962","doi":"10.1002/ep.12716","title":"A case study of an autonomous wireless sensor network system for environmental data collection","year":2017,"lang":"en","type":"article","venue":"Environmental Progress & Sustainable Energy","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Wireless sensor network; Data collection; Environmental monitoring; Environmental data; Agency (philosophy); Computer science; Water quality; Resource (disambiguation); Data quality; Production (economics); Hazardous waste; Environmental resource management; Environmental science; Engineering; Environmental engineering; Operations management; 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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0007499369,0.0004311137,0.0004751162,0.00006600558,0.002026729,0.0002063292,0.001827828,0.000208985,0.00005133142],"category_scores_gemma":[0.00002166276,0.0004343099,0.00007403856,0.00008516814,0.0009750174,0.001222506,0.003569696,0.0001719901,0.000009319006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001289029,"about_ca_system_score_gemma":0.00001650269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005198607,"about_ca_topic_score_gemma":0.0001930473,"domain_scores_codex":[0.9965723,0.0001960882,0.0005880255,0.001181875,0.0005395775,0.0009221459],"domain_scores_gemma":[0.9958288,0.00005938009,0.0006457334,0.003286163,0.000003875138,0.00017604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.001152485,0.01201775,0.754449,0.0004415676,0.0006686248,0.01860849,0.004162439,0.01610602,0.004841924,0.0004308094,0.001344731,0.1857762],"study_design_scores_gemma":[0.01808214,0.01547814,0.1571901,0.0001755686,0.001324588,0.007971239,0.5607017,0.1250258,0.07144677,0.0009797239,0.03582771,0.005796568],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974136,0.00008311663,0.0003489749,0.00003838301,0.0002726145,0.001407817,0.00008059401,0.0002194021,0.0001354377],"genre_scores_gemma":[0.9950779,0.00001189227,0.002300959,0.000005734682,0.000169475,0.000537106,0.0001240996,0.00008461541,0.00168819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5972589,"threshold_uncertainty_score":0.9998109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02714303817331846,"score_gpt":0.2664199596676893,"score_spread":0.2392769214943709,"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."}}