{"id":"W4390974155","doi":"10.2166/hydro.2024.041","title":"Improving incomplete mixing modeling for junctions of water distribution networks","year":2024,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Systems and Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mixing (physics); Mechanics; Flow (mathematics); Volumetric flow rate; Distribution (mathematics); Pipe network analysis; Software; Materials science; Mathematics; Simulation; Computer science; Physics; Mathematical analysis","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":[0.001955805,0.0008415338,0.001344018,0.0007675264,0.0005066794,0.001258929,0.001508033,0.001235243,0.000787129],"category_scores_gemma":[0.004744887,0.0006780489,0.001283416,0.000562426,0.0007500498,0.001450467,0.001245185,0.001275703,0.0001263885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001289948,"about_ca_system_score_gemma":0.001017199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01842643,"about_ca_topic_score_gemma":0.007038909,"domain_scores_codex":[0.999196,0.0003217262,0.00003888274,0.0002093135,0.0001305864,0.0001035659],"domain_scores_gemma":[0.9981006,0.001129477,0.0003441183,0.0001066999,0.0002243733,0.00009473854],"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":[0.0000141521,0.000007399785,0.0006246616,0.000005432101,0.000007662304,0.00001391426,0.000009163085,0.9970363,0.0003310885,0.0008874858,0.00002253257,0.00104024],"study_design_scores_gemma":[8.608376e-7,0.000003116171,0.00004683584,5.676641e-7,0.000001232164,8.720296e-7,0.000001226579,0.9996251,0.0001076953,0.0001872405,0.00002417095,0.000001046708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.247867,0.0002721739,0.7490382,0.0001281813,0.00003465457,0.00004864471,0.0001912947,0.0004121663,0.002007647],"genre_scores_gemma":[0.95981,0.0001002367,0.03884229,0.00002207409,0.00001387032,0.00004714167,0.0002025077,0.00004789582,0.0009139623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01842643,"threshold_uncertainty_score":0.03663832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008826252977722401,"score_gpt":0.1913206031568814,"score_spread":0.182494350179159,"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."}}