{"id":"W3177299994","doi":"10.5194/isprs-annals-v-3-2021-279-2021","title":"WATER QUALITY MONITORING OVER FINGER LAKES REGION USING SENTINEL-2 IMAGERY ON GOOGLE EARTH ENGINE CLOUD COMPUTING PLATFORM","year":2021,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Environmental science; Water quality; Sediment; Surface runoff; Hydrology (agriculture); Turbidity; Cloud computing; Satellite imagery; Sampling (signal processing); Surface water; Remote sensing; Physical geography; Geology; Oceanography; Environmental engineering; Geography; Computer science; Ecology","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":[0.0001087843,0.0002117959,0.0001333002,0.0005559808,0.0002044248,0.0002384153,0.0001541053,0.0001300471,0.0009615211],"category_scores_gemma":[0.0001739767,0.00008289271,0.0001529669,0.0008638313,0.0001132096,0.0003654179,0.0002656165,0.0001354608,0.0001862934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003235065,"about_ca_system_score_gemma":0.0004260452,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08762427,"about_ca_topic_score_gemma":0.1883013,"domain_scores_codex":[0.9999143,0.000009872676,0.000005859277,0.00002250333,0.00003322451,0.00001419204],"domain_scores_gemma":[0.9998661,0.00001357522,0.00002351857,0.00001474364,0.00006598281,0.00001596452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006385652,0.0003919393,0.7187574,0.0003047078,0.0002212181,0.0009936792,0.0008769798,0.03130605,0.1192137,0.0007093602,0.02159333,0.1049931],"study_design_scores_gemma":[0.0000349106,0.0001078932,0.8408319,0.00002246686,0.0000542757,0.0001057786,0.0009000343,0.1393513,0.01383412,0.0001579538,0.004565153,0.00003416521],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890127,0.00006612074,0.002263058,0.00009202439,0.00001299774,0.0000442887,0.00585675,0.0004252169,0.002226795],"genre_scores_gemma":[0.9868997,0.00008339473,0.005867684,0.00002226324,0.000006454114,0.00003056613,0.006107586,0.00002295476,0.0009594589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9123757,"threshold_uncertainty_score":0.1742284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06873127427371288,"score_gpt":0.3250317228213355,"score_spread":0.2563004485476226,"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."}}