{"id":"W4387802262","doi":"10.1109/igarss52108.2023.10283059","title":"Quantification and Mapping of Water Clarity for Freshwater Lakes Using Sentinel-2 Data and Random Forest Regression Model: Application on Finger Lakes, New York","year":2023,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Radiance; Random forest; Urbanization; Environmental science; Remote sensing; Water quality; Mean squared error; CLARITY; Regression; Regression analysis; Hydrology (agriculture); Physical geography; Computer science; Ecology; Statistics; Geography; Mathematics; Machine learning; Geology; Biology","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":[0.0004415378,0.0003688721,0.0002029146,0.0004520691,0.0002962589,0.0003283854,0.0002802837,0.000205672,0.000529176],"category_scores_gemma":[0.0007524313,0.0001907159,0.0003299344,0.0006309066,0.00012145,0.0003497267,0.0002255486,0.0001552268,0.000099542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006356463,"about_ca_system_score_gemma":0.0005920564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1353429,"about_ca_topic_score_gemma":0.2304129,"domain_scores_codex":[0.9999305,0.00001673373,0.000004100667,0.0000240253,0.00001522682,0.000009482922],"domain_scores_gemma":[0.9998288,0.00006955097,0.00001806099,0.00001115314,0.00006403132,0.000008349607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003239143,0.0003322855,0.3268412,0.0001889637,0.0002110991,0.0005953967,0.0005272541,0.486967,0.03642133,0.001081591,0.002589836,0.1439201],"study_design_scores_gemma":[0.00001091143,0.00002303581,0.05332368,0.000004069895,0.00001623182,0.00002100091,0.00008874192,0.9440334,0.002061995,0.0001107065,0.0002926606,0.00001355971],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9731376,0.00008287487,0.02497097,0.00006836829,0.0000061894,0.00003349789,0.0005307053,0.0003463232,0.0008233623],"genre_scores_gemma":[0.9646668,0.00007893468,0.03352765,0.000006569036,0.000003591548,0.00002831669,0.0007819921,0.00003366598,0.0008724185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1353429,"threshold_uncertainty_score":0.2691103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0835296429832132,"score_gpt":0.2924486670545672,"score_spread":0.208919024071354,"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."}}