{"id":"W4283756298","doi":"10.3390/su14138046","title":"A New Clustering Method to Generate Training Samples for Supervised Monitoring of Long-Term Water Surface Dynamics Using Landsat Data through Google Earth Engine","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Principal component analysis; Support vector machine; Cluster analysis; Environmental science; Random forest; Surface water; Training (meteorology); Remote sensing; Computer science; Feature (linguistics); Pattern recognition (psychology); Artificial intelligence; Meteorology; Geography; Environmental engineering","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":[0.0007342457,0.001126966,0.0006868591,0.002009323,0.0007456523,0.0004764398,0.001285111,0.0006700417,0.001122517],"category_scores_gemma":[0.001483721,0.000447246,0.001101362,0.001548447,0.0003211107,0.0008124127,0.0005945329,0.0006712666,0.00105333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003666121,"about_ca_system_score_gemma":0.0009122117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01294479,"about_ca_topic_score_gemma":0.02061406,"domain_scores_codex":[0.9992837,0.00007520677,0.00005498311,0.0002733754,0.0002380033,0.00007462123],"domain_scores_gemma":[0.9993379,0.0000896332,0.0000545802,0.00007926534,0.0004147745,0.00002379587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001887405,0.0001958548,0.006031991,0.0002024797,0.0001513668,0.0001472762,0.0003728599,0.06627937,0.04809743,0.001511547,0.007053606,0.8697674],"study_design_scores_gemma":[0.00003405633,0.00009657381,0.008324389,0.00002026213,0.00005684367,0.0001760981,0.000156669,0.9537883,0.02984832,0.001227445,0.006212049,0.00005899173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02847528,0.0001209258,0.9661167,0.00005253686,0.00004804049,0.0001677936,0.0003671284,0.003889423,0.0007621096],"genre_scores_gemma":[0.1767177,0.0001116759,0.8183673,0.00005473213,0.00002783218,0.0003269357,0.002343951,0.0003399243,0.001710014],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01294479,"threshold_uncertainty_score":0.02573889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08053572540468008,"score_gpt":0.3514448174023495,"score_spread":0.2709090919976694,"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."}}