{"id":"W2079321933","doi":"10.1016/j.jher.2014.06.003","title":"A hybrid inverse-modeling technique to estimate circulation in a steady wind driven open channel flow","year":2014,"lang":"en","type":"article","venue":"Journal of Hydro-environment Research","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; Memorial University of Newfoundland","funders":"","keywords":"Inverse; Channel (broadcasting); Pollutant; Flow (mathematics); Environmental science; Artificial neural network; Scale (ratio); Open-channel flow; Meteorology; Computer science; Mechanics; Mathematics; Physics; Geometry; Artificial intelligence; Chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.005396835,0.0001743786,0.0003305192,0.0003708259,0.0002742115,0.00007276438,0.0008699883,0.00007062883,0.0003459802],"category_scores_gemma":[0.0001295048,0.0001516987,0.00006434237,0.0002317244,0.0001812382,0.0006657376,0.001608296,0.0005854256,0.0004422822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005762198,"about_ca_system_score_gemma":0.00001206373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000211361,"about_ca_topic_score_gemma":0.0000617137,"domain_scores_codex":[0.9971415,0.0004756217,0.0005157538,0.0003833933,0.0008977029,0.0005860082],"domain_scores_gemma":[0.9992512,0.00008169276,0.000133629,0.0003216794,0.00001254066,0.0001992975],"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":[0.0001176973,0.000191321,0.02788964,0.00001126467,0.000028019,0.00007689468,0.0006162858,0.9625478,0.006597747,0.00001733636,0.0009217715,0.0009841955],"study_design_scores_gemma":[0.001527699,0.0009542368,0.02281287,0.0001510341,0.0000277103,0.00005469822,0.0002170837,0.9578221,0.001062384,0.009943677,0.005071669,0.0003548263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9642935,0.00001338701,0.02742944,0.003455399,0.00005104861,0.001029788,0.000001278187,0.000008243179,0.003717947],"genre_scores_gemma":[0.9952413,0.00004787926,0.004172764,0.0001808227,0.00005357487,0.00005414445,0.000002192446,0.00002075121,0.0002265486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03094785,"threshold_uncertainty_score":0.6186095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04869274503785722,"score_gpt":0.3359051530516617,"score_spread":0.2872124080138045,"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."}}