{"id":"W2091411607","doi":"10.2166/hydro.2009.036","title":"Recent advances in data-driven modeling of remote sensing applications in hydrology","year":2009,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Manitoba Hydro","funders":"","keywords":"Remote sensing; Artificial neural network; Computer science; Precipitation; Water cycle; Environmental science; Remote sensing application; Data mining; Meteorology; High resolution; Machine learning; Geography","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":[0.001192885,0.0009624313,0.001117043,0.0006946811,0.0002317107,0.001378415,0.001429303,0.00108442,0.001464468],"category_scores_gemma":[0.0034134,0.0005303028,0.000873463,0.002008971,0.0004964799,0.001582039,0.0008987593,0.001434903,0.0005755035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006476899,"about_ca_system_score_gemma":0.0008290057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005220878,"about_ca_topic_score_gemma":0.003968779,"domain_scores_codex":[0.9995035,0.0001437869,0.00005124807,0.00009163529,0.0001884485,0.00002131827],"domain_scores_gemma":[0.9984061,0.0009319488,0.000139191,0.0001149844,0.000371405,0.00003643763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003525994,0.0000409017,0.001690088,0.0004236888,0.00009285683,0.00008074591,0.00004180048,0.8662625,0.001595284,0.01395161,0.001908289,0.113877],"study_design_scores_gemma":[0.000003151949,0.000009056753,0.0002949945,0.0000251428,0.00001432397,0.00001683288,0.000005842774,0.9853771,0.0003816468,0.006895671,0.006966155,0.00001013121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01445707,0.02500689,0.9507014,0.002199746,0.0004080184,0.00004663474,0.0004881837,0.000530254,0.00616169],"genre_scores_gemma":[0.4785237,0.1078884,0.3989352,0.0008266864,0.00182281,0.0003727529,0.002497938,0.0003619594,0.008770695],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005220878,"threshold_uncertainty_score":0.01038098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0373594254210455,"score_gpt":0.2726929095504284,"score_spread":0.2353334841293828,"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."}}