{"id":"W2973947394","doi":"10.4095/315176","title":"Automated surface water extraction from RapidEye imagery including cloud and cloud shadow detection","year":2019,"lang":"en","type":"report","venue":"","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Shadow (psychology); Cloud computing; Remote sensing; Extraction (chemistry); Computer science; Environmental science; Geology; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002752022,0.0007302309,0.0003399278,0.003001802,0.0005640341,0.0009264924,0.000850526,0.0003373319,0.009677092],"category_scores_gemma":[0.0009037189,0.0003463111,0.000518384,0.001790274,0.000257703,0.0007988962,0.0006896757,0.0003551196,0.0054829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001011522,"about_ca_system_score_gemma":0.002526785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1973228,"about_ca_topic_score_gemma":0.4036738,"domain_scores_codex":[0.9996796,0.00000714944,0.00001139517,0.00004719693,0.0001970231,0.00005767048],"domain_scores_gemma":[0.9994794,0.00004255952,0.00002590004,0.00004036397,0.000395371,0.000016577],"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":[0.000293805,0.0001250121,0.02095607,0.000741902,0.00008025964,0.0007028442,0.0005228497,0.01695246,0.173513,0.001685095,0.1005551,0.6838717],"study_design_scores_gemma":[0.0001208531,0.0001311017,0.1850136,0.000307503,0.0001097627,0.0007163504,0.001803776,0.268849,0.3126188,0.003458942,0.2265373,0.0003329632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3183787,0.0006822969,0.3912887,0.0008366966,0.0003208439,0.001644611,0.1529162,0.05215314,0.08177874],"genre_scores_gemma":[0.3300396,0.0007461958,0.5198354,0.000221617,0.00004776477,0.0005962897,0.1069675,0.00357416,0.03797146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1973228,"threshold_uncertainty_score":0.3923485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02393535356775898,"score_gpt":0.2890579483448372,"score_spread":0.2651225947770782,"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."}}