{"id":"W3014496091","doi":"10.1061/(asce)he.1943-5584.0001904","title":"Comparing Discharge Computation Methods in Great Lakes Connecting Channels","year":2020,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Computation; Water discharge; Discharge; Structural basin; Hydrology (agriculture); Environmental science; Drainage basin; Meteorology; Geology; Computer science; Geography; Algorithm; Geotechnical engineering; Geomorphology; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006257641,0.000105163,0.000264708,0.00007923089,0.00004933703,0.00001326539,0.0001354221,0.00003915161,0.00007102281],"category_scores_gemma":[0.0001843525,0.00008571975,0.00004685149,0.000234305,0.00002413986,0.0001956865,0.000135099,0.000240363,0.00001530811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004020074,"about_ca_system_score_gemma":0.000001089409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005040694,"about_ca_topic_score_gemma":0.000001709603,"domain_scores_codex":[0.9991845,0.00006164208,0.0003413318,0.00011811,0.00009967911,0.000194661],"domain_scores_gemma":[0.9996617,0.0001033615,0.0001464537,0.0000345761,0.000004867007,0.00004907288],"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":[0.00001595562,0.00001175206,0.04688463,0.00001414782,0.00002546993,0.00004364647,0.0009023285,0.9455668,0.005903307,0.00003528218,0.00007340527,0.0005233317],"study_design_scores_gemma":[0.0006133681,0.0002118589,0.01067964,0.00002745449,0.00002634248,0.00003670311,0.0001208167,0.9848124,0.001456519,0.0003485106,0.001506671,0.0001596764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8716668,0.0001000349,0.1259256,0.001292038,0.0001898427,0.00005929606,1.272253e-7,0.00002667277,0.0007396224],"genre_scores_gemma":[0.9863711,0.00001681901,0.01328631,0.0002433035,0.00006779828,0.000001446288,4.461386e-7,0.000006987802,0.000005771802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1147043,"threshold_uncertainty_score":0.349555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03441734647726453,"score_gpt":0.277226194262397,"score_spread":0.2428088477851325,"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."}}