{"id":"W2898661956","doi":"10.5194/hess-22-5639-2018","title":"HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community","year":2018,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":348,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy; State Key Laboratory of Hydraulics and Mountain River Engineering; Sichuan University; National Science Foundation","keywords":"Grassroots; Computer science; Field (mathematics); Data science; Process (computing); Baseline (sea); Citizen science; Artificial intelligence; Nature versus nurture; Data sharing; Sociology; Political science; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006757303,0.0005904608,0.0004033306,0.0009520384,0.003341074,0.01086124,0.002134489,0.01171933,0.09101586],"category_scores_gemma":[0.03445809,0.0002728462,0.0005356052,0.001040119,0.00404545,0.01147814,0.007609801,0.01168554,0.02012625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00484483,"about_ca_system_score_gemma":0.01088169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004756685,"about_ca_topic_score_gemma":0.007159799,"domain_scores_codex":[0.9952992,0.001199386,0.0001927972,0.0007627225,0.00178154,0.0007642814],"domain_scores_gemma":[0.9645457,0.007051273,0.001985218,0.002154955,0.01154221,0.01272064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005059142,0.00002135844,0.0005097526,0.000103972,0.00001379629,0.0001909938,0.0002480685,0.0002961105,0.0003912071,0.06095055,0.9088854,0.02833809],"study_design_scores_gemma":[0.00001758381,0.0000134254,0.000220181,0.0001333215,0.00000527478,0.00005117611,0.0004793197,0.0007399025,0.0004639469,0.02379046,0.9740638,0.000021636],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0013538,0.002151435,0.002982028,0.9180558,0.03769737,0.00003313726,0.0005064987,0.0002084238,0.03701165],"genre_scores_gemma":[0.1466035,0.01021455,0.008132618,0.4560892,0.05388984,0.0001894378,0.001174435,0.00074997,0.3229565],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.09101586,"threshold_uncertainty_score":0.3044784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02148488182457135,"score_gpt":0.2763075689827972,"score_spread":0.2548226871582259,"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."}}