{"id":"W2095059834","doi":"10.1061/9780784413548.057","title":"Sensor Placement Optimization for Water Quality Model Calibration","year":2014,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2014","topic":"Water Systems and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bentley (Canada)","funders":"","keywords":"Calibration; Intrusion; Water quality; Engineering; Computer science; Field (mathematics); Data quality; Data modeling; Wireless sensor network; Data mining; Remote sensing; Real-time computing; Database","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":[],"consensus_categories":[],"category_scores_codex":[0.0001881065,0.0001843378,0.0001784596,0.00006652317,0.000127614,0.00008782711,0.00006446353,0.00005901631,0.0001290801],"category_scores_gemma":[7.972259e-7,0.0001209495,0.00004044168,0.0000102234,0.00004599554,0.0001773009,0.00004713247,0.00004958496,0.00002344718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003543614,"about_ca_system_score_gemma":3.603282e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001028691,"about_ca_topic_score_gemma":0.00002799205,"domain_scores_codex":[0.9990621,0.00004331721,0.0002809254,0.0002269773,0.0001267522,0.0002599746],"domain_scores_gemma":[0.999724,0.00001347446,0.00002457007,0.0001566722,0.000003820712,0.00007748556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002635878,0.00001316078,0.0007646024,0.00007283194,0.00001894137,1.488633e-7,0.0007283844,0.9909223,0.006405823,0.00001952968,0.0008296979,0.0001981602],"study_design_scores_gemma":[0.0005725987,0.00002462128,0.00007805997,0.00001126131,0.00001664432,0.00000111421,0.00003542392,0.9434778,0.0355088,0.00004146544,0.02000949,0.0002227331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6734067,0.00006762629,0.324347,0.0001875738,0.0003014424,0.0005290902,0.00004567376,0.0001621158,0.0009528126],"genre_scores_gemma":[0.9908807,0.00002187689,0.002361048,0.0000770398,0.0001154568,0.00007568083,0.0003486077,0.00004202944,0.006077579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3219859,"threshold_uncertainty_score":0.4932178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008249839022229867,"score_gpt":0.1845287391199775,"score_spread":0.1762789000977477,"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."}}