{"id":"W4401988943","doi":"10.1016/j.jwpe.2024.106067","title":"Comparison of novel physics-guided machine learning models with empirical equations for predicting longitudinal dispersion coefficient in diverse natural river systems","year":2024,"lang":"en","type":"article","venue":"Journal of Water Process Engineering","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Dispersion (optics); Natural (archaeology); Empirical modelling; Physics; Statistical physics; Computer science; Optics; Geology; Simulation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001367314,0.0006436948,0.0006079954,0.0006069859,0.0003906245,0.0007416427,0.001287139,0.001000629,0.000762976],"category_scores_gemma":[0.004059482,0.0002856682,0.0006002431,0.0005285824,0.0003481736,0.001195413,0.0004670704,0.0008080809,0.0001552212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001055051,"about_ca_system_score_gemma":0.001352037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02218201,"about_ca_topic_score_gemma":0.01600218,"domain_scores_codex":[0.9997703,0.00007863699,0.00002720204,0.00006232182,0.00004094464,0.00002051543],"domain_scores_gemma":[0.9972796,0.001898144,0.0002105201,0.0001159885,0.0004454126,0.00005034882],"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":[0.00003792227,0.00008844293,0.002783809,0.00002188872,0.00003339758,0.00001284548,0.00001830691,0.9817136,0.0003200469,0.0006423922,0.0001785728,0.01414869],"study_design_scores_gemma":[0.000002821555,0.000005339307,0.0002101552,9.57306e-7,0.000002378896,0.000001421561,0.000001600165,0.9995726,0.00006435876,0.0001177021,0.00001912171,0.000001542576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7192028,0.0005969105,0.2755315,0.0004806283,0.0001158631,0.0000918752,0.0003606961,0.001029093,0.002590639],"genre_scores_gemma":[0.9707818,0.0001488151,0.02799668,0.00004937157,0.00002784106,0.00006964261,0.0002584007,0.00003744132,0.0006299115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02218201,"threshold_uncertainty_score":0.04410583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0786413473218229,"score_gpt":0.310874714552991,"score_spread":0.2322333672311681,"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."}}