{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005503421,0.0005936279,0.0007817116,0.000197057,0.0001379802,0.00002509704,0.0008833437,0.0002562418,0.00005451919],"category_scores_gemma":[0.00001095771,0.0004457949,0.0003080548,0.00009415731,0.0008488029,0.0001822888,0.002678955,0.001077748,0.00003879966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002138794,"about_ca_system_score_gemma":0.000003814288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007237566,"about_ca_topic_score_gemma":0.00001674163,"domain_scores_codex":[0.9965366,0.0002304743,0.0007640503,0.0008422776,0.001019873,0.0006066844],"domain_scores_gemma":[0.99836,0.00009910869,0.0004076498,0.001048971,0.000001302343,0.0000829167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003043279,0.001391856,0.6749805,0.0005652472,0.0004520501,0.00009085373,0.003228833,0.00389781,0.3064938,0.003576065,0.00005611641,0.004962606],"study_design_scores_gemma":[0.0008042089,0.0001365438,0.9661414,0.0003183435,0.00009447087,0.000005349408,0.0002520695,0.001047144,0.02580217,0.001916714,0.002489977,0.0009915957],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961841,0.00008946781,0.000001737763,0.0003203968,0.0003134605,0.0007177476,0.00008826585,0.0001682792,0.002116611],"genre_scores_gemma":[0.9982482,0.00008820534,0.0001004636,0.00001082008,0.00004297804,0.00007073589,0.00004919078,0.00006284789,0.001326578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.291161,"threshold_uncertainty_score":0.9997994,"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."}}