{"id":"W4236624671","doi":"10.30955/gnj.001076","title":"Using inverse modeling to estimate parameter values for three dimensional transport of contaminants in Lake Ontario","year":2013,"lang":"en","type":"article","venue":"Global NEST Journal","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Pollutant; Calibration; Surface runoff; Shore; Inverse method; Hydrology (agriculture); Sensitivity (control systems); Sink (geography); Meteorology; Statistics; Mathematics; Ecology; Geography; Geology; Oceanography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004173156,0.0003572227,0.0001866076,0.000316487,0.0004789339,0.0005205979,0.0004957263,0.0003810097,0.0004690682],"category_scores_gemma":[0.002321471,0.0002673785,0.0003363909,0.0004733891,0.0004202935,0.0002659451,0.0003207744,0.000335654,0.00007740062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003483198,"about_ca_system_score_gemma":0.003902912,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6494339,"about_ca_topic_score_gemma":0.611315,"domain_scores_codex":[0.9998388,0.00003018014,0.000008965585,0.00003572297,0.00006587764,0.00002037527],"domain_scores_gemma":[0.999595,0.0001787495,0.00007627838,0.00002345072,0.0001177609,0.000008822676],"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":[0.00004032259,0.00002520646,0.02119544,0.00003346337,0.00002004874,0.00004659248,0.0001945219,0.9581371,0.004915643,0.0004591817,0.0001722564,0.01476014],"study_design_scores_gemma":[0.000007096572,0.00002025952,0.01069029,0.000003454203,0.000009437236,0.000009686312,0.00004941426,0.9857754,0.002817961,0.0002310483,0.0003665464,0.00001946404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9286469,0.0000576108,0.06813793,0.0001016599,0.000004942173,0.00004013931,0.000282198,0.0002102013,0.002518393],"genre_scores_gemma":[0.982111,0.00004251005,0.01667399,0.000007930589,9.182495e-7,0.00002965074,0.0002320303,0.00001701988,0.000885025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3505661,"threshold_uncertainty_score":0.7052612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06922434675632153,"score_gpt":0.3268366109391776,"score_spread":0.2576122641828561,"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."}}