{"id":"W4294080569","doi":"10.1007/978-3-031-07113-3_6","title":"Automated in Situ Water Quality Monitoring—Characterizing System Dynamics in Urban-Impacted and Natural Environments","year":2022,"lang":"en","type":"book-chapter","venue":"Geography of the physical environment","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto and Region Conservation Authority","funders":"","keywords":"Geospatial analysis; Environmental science; Water quality; Turbidity; Water column; In situ; Software deployment; Hydrology (agriculture); Remote sensing; Environmental resource management; Geography; Meteorology; Computer science; Engineering; Ecology; Oceanography; Geology","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":[0.0001343188,0.0003747052,0.00021349,0.0003414172,0.0001324758,0.0007570931,0.0006416118,0.0004180321,0.002435463],"category_scores_gemma":[0.0002729417,0.0002587178,0.0001559871,0.0007299933,0.0002810281,0.0008886157,0.000303357,0.0002889663,0.0007401451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003546784,"about_ca_system_score_gemma":0.0002134491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003473979,"about_ca_topic_score_gemma":0.007864985,"domain_scores_codex":[0.9998955,0.00001377256,0.000002365018,0.00003097106,0.00004915397,0.00000829353],"domain_scores_gemma":[0.999871,0.00007183348,0.00001376662,0.00001942724,0.00002027877,0.000003732976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00006960596,0.00008272781,0.004327533,0.0002386929,0.00003269916,0.0001025612,0.0001988487,0.09008011,0.04816322,0.01665784,0.02317289,0.8168733],"study_design_scores_gemma":[0.00001097052,0.0001536122,0.02727997,0.00008261873,0.00003930043,0.0006102708,0.0003056085,0.6796494,0.07169951,0.04164341,0.1784838,0.00004166961],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05783436,0.004101416,0.8661485,0.00055321,0.0002525661,0.00005287644,0.0009474964,0.002769557,0.06734011],"genre_scores_gemma":[0.6466091,0.006405029,0.2297713,0.0002631463,0.0002763,0.0001045225,0.001907019,0.0006414409,0.1140221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003473979,"threshold_uncertainty_score":0.008147478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01197027126213315,"score_gpt":0.2226077551596845,"score_spread":0.2106374838975514,"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."}}