{"id":"W1977151644","doi":"10.1016/j.jglr.2013.04.001","title":"Retrospection and introspection on remote sensing of inland water quality: “Like Déjà Vu All Over Again”","year":2013,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Environmental science; Water quality; Chlorophyll a; Nutrient; Turbidity; Estuary; Remote sensing; Hydrology (agriculture); Oceanography; Ecology; Geography; Geology; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.01954419,0.0005923418,0.0003863926,0.001319116,0.003968229,0.006984806,0.001584857,0.00623112,0.001491105],"category_scores_gemma":[0.09525869,0.0004750628,0.0004452271,0.001282003,0.01615392,0.0137481,0.004400951,0.01712414,0.000662644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002095417,"about_ca_system_score_gemma":0.002483545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008350604,"about_ca_topic_score_gemma":0.009077725,"domain_scores_codex":[0.9906584,0.005657155,0.0006574635,0.001030435,0.001542013,0.0004546401],"domain_scores_gemma":[0.9134524,0.05621225,0.004781232,0.004771017,0.01762977,0.003153351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001528366,0.00006366863,0.01531498,0.0004486356,0.0001260004,0.001301148,0.210797,0.0003113989,0.002721047,0.0793706,0.6071494,0.08224329],"study_design_scores_gemma":[0.00001352725,0.00004883955,0.00767624,0.001141349,0.0000345294,0.001292526,0.08605589,0.0003987267,0.001309334,0.02673679,0.8751047,0.0001875836],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02729874,0.02086806,0.00602675,0.8958914,0.03609586,0.00001896026,0.0001568456,0.0001041252,0.0135393],"genre_scores_gemma":[0.3459419,0.01134494,0.004339255,0.6005703,0.02761948,0.00004951258,0.0001324145,0.0002859043,0.009716311],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01954419,"threshold_uncertainty_score":0.1033608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04067632229083123,"score_gpt":0.3459046588039584,"score_spread":0.3052283365131271,"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."}}