{"id":"W4220828128","doi":"10.1016/j.envsoft.2022.105370","title":"A review of parallel computing applications in calibrating watershed hydrologic models","year":2022,"lang":"en","type":"review","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of Waterloo; University of Guelph","funders":"","keywords":"Speedup; Computer science; Suite; Parallel computing; Supercomputer; Watershed; Cost efficiency; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006140757,0.0005825694,0.001592914,0.000110968,0.0003297269,0.000009264961,0.0007302238,0.0001889481,0.001333182],"category_scores_gemma":[0.00001025559,0.0005071138,0.0004260346,0.0003114599,0.0002959587,0.000189939,0.001540716,0.0006365809,0.00010494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004268411,"about_ca_system_score_gemma":0.00001048724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008632049,"about_ca_topic_score_gemma":0.000002514439,"domain_scores_codex":[0.9966433,0.0003465678,0.001153907,0.0009291089,0.0003832245,0.0005438829],"domain_scores_gemma":[0.9985046,0.0002482713,0.0005899442,0.0005789481,7.710118e-7,0.00007744324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005361723,0.000324674,0.0007446961,0.02631059,0.0001293234,0.00002011033,0.0005041239,0.8329408,1.755974e-7,0.00003170484,0.0002509128,0.1387375],"study_design_scores_gemma":[0.0002698328,0.00008583898,0.000004842929,0.01070114,0.0006525973,0.00001283796,0.00008400969,0.05780989,3.529617e-7,0.001607079,0.9278009,0.0009707085],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00004679445,0.9406012,0.05695871,0.00001802397,0.00003918021,0.001774713,0.00005280719,0.00007538326,0.0004332245],"genre_scores_gemma":[0.0004320861,0.9901248,0.0076494,0.000305536,0.00001873776,0.0006025154,0.0006916802,0.00006871505,0.0001065433],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.92755,"threshold_uncertainty_score":0.999738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04831261435215807,"score_gpt":0.264457847169482,"score_spread":0.216145232817324,"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."}}