{"id":"W2138116293","doi":"10.1007/s11269-011-9792-3","title":"Monitoring Lake Simcoe Water Clarity Using Landsat-5 TM Images","year":2011,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada; University of Waterloo","funders":"","keywords":"Secchi disk; Environmental science; Thematic Mapper; Water quality; Remote sensing; Hydrology (agriculture); Eutrophication; CLARITY; Scale (ratio); Satellite imagery; Geology; Geography; Cartography; 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":[0.0002922113,0.0002242838,0.0001772744,0.001588369,0.0004551653,0.0004149076,0.0001991017,0.0002553015,0.001018209],"category_scores_gemma":[0.0004823784,0.0001350148,0.0001160728,0.001438752,0.0001273166,0.000579633,0.0003540613,0.0001524768,0.0002295684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007150839,"about_ca_system_score_gemma":0.0007172638,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1193713,"about_ca_topic_score_gemma":0.3329878,"domain_scores_codex":[0.9998277,0.00001349804,0.000008897599,0.00003674633,0.00008027643,0.00003273433],"domain_scores_gemma":[0.9995756,0.00002508984,0.00006290334,0.00001878949,0.0002692275,0.00004849089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004405967,0.0001440269,0.8781341,0.00008435876,0.000100243,0.0001400795,0.0005866625,0.003951177,0.057303,0.0001687451,0.004733858,0.05421317],"study_design_scores_gemma":[0.00002935191,0.00003860801,0.9729455,0.000008707359,0.00002676092,0.00002386198,0.0002253146,0.01621385,0.00777525,0.00002351344,0.002672322,0.00001698186],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923331,0.00005188047,0.001035472,0.00005894941,0.00000439795,0.00003747467,0.002375421,0.0001658156,0.003937558],"genre_scores_gemma":[0.9903057,0.00005536525,0.004420371,0.00002989373,0.000006041709,0.00002750991,0.003032305,0.00002495379,0.002097853],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8806286,"threshold_uncertainty_score":0.2373531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02894244912727184,"score_gpt":0.1968916659872239,"score_spread":0.1679492168599521,"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."}}