{"id":"W3094484681","doi":"10.2166/hydro.2020.066","title":"Advancing model calibration and uncertainty analysis of SWAT models using cloud computing infrastructure: LCC-SWAT","year":2020,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Guelph; St. Michael's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Calibration; Cloud computing; SWAT model; Asynchronous communication; Node (physics); Uncertainty analysis; Distributed computing; Database; Data mining; Real-time computing; Simulation; Operating system; Engineering; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001262111,0.0009639325,0.0006009314,0.0007438542,0.000778063,0.0009203426,0.001233767,0.0005337282,0.001655903],"category_scores_gemma":[0.002830233,0.0004158474,0.0007556306,0.0008337938,0.0005737167,0.001083629,0.001157983,0.00117029,0.0003951238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001322537,"about_ca_system_score_gemma":0.00309439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03710891,"about_ca_topic_score_gemma":0.0312352,"domain_scores_codex":[0.9993747,0.0001165787,0.00003266064,0.0001496001,0.0002583185,0.0000682236],"domain_scores_gemma":[0.9984068,0.0003682509,0.0001640071,0.0003657858,0.000574694,0.0001203525],"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":[0.0002799231,0.0002395526,0.01162235,0.00006992799,0.0001420051,0.000154902,0.0001061067,0.9048787,0.01381357,0.00302008,0.004993984,0.06067885],"study_design_scores_gemma":[0.00001284583,0.00001047293,0.0006313031,0.000001770135,0.000005005985,0.000004871752,0.000009326101,0.9962663,0.002240883,0.0003559784,0.0004554866,0.000005730912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4227403,0.0002248374,0.5408792,0.0007379259,0.0001624572,0.000343132,0.001541349,0.02330287,0.01006792],"genre_scores_gemma":[0.8404897,0.00006493709,0.1564487,0.000120789,0.00002309724,0.00009416269,0.001375155,0.0006532325,0.0007302053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03710891,"threshold_uncertainty_score":0.07378578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01437988504032934,"score_gpt":0.2315968050220361,"score_spread":0.2172169199817068,"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."}}