{"id":"W2992215474","doi":"10.2175/193864716819707346","title":"Challenges of Model Development &amp; Calibration in the Development of Multiple Basin-Level Models and their Integration into the City’s Sanitary Long-Range Plan: A Case Study of City of Calgary Sanitary District Studies","year":2016,"lang":"en","type":"article","venue":"Proceedings of the Water Environment Federation","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Range (aeronautics); Plan (archaeology); Calibration; Geography; Environmental planning; Water resource management; Environmental science; Engineering; Archaeology; Statistics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04058736,0.0008156993,0.00100374,0.001119992,0.001577373,0.007464892,0.004464983,0.003068378,0.003566286],"category_scores_gemma":[0.08420211,0.001366964,0.001468292,0.002027484,0.002573399,0.007314069,0.003045475,0.005185318,0.001085138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005449296,"about_ca_system_score_gemma":0.01172362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07107043,"about_ca_topic_score_gemma":0.05844701,"domain_scores_codex":[0.9841956,0.01142949,0.0007422815,0.0009966746,0.002136763,0.0004992607],"domain_scores_gemma":[0.9371164,0.04204657,0.002461925,0.007185586,0.01037723,0.0008121977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001363216,0.0004057497,0.0178907,0.0004646981,0.0003511575,0.0003356219,0.001753173,0.7582435,0.001722703,0.0501082,0.008894474,0.1596938],"study_design_scores_gemma":[0.00009457486,0.0001194149,0.005354241,0.0003505391,0.0001011574,0.000160563,0.002753806,0.9179826,0.002663867,0.04636796,0.02391759,0.0001336722],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.275304,0.002968339,0.6226335,0.05434116,0.0007289217,0.00062118,0.002004194,0.003070024,0.03832881],"genre_scores_gemma":[0.7221469,0.001091789,0.2719492,0.001493706,0.0001013135,0.0003130425,0.0005856313,0.0004993293,0.001819084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07107043,"threshold_uncertainty_score":0.214649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08937565483885626,"score_gpt":0.2443864734924112,"score_spread":0.155010818653555,"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."}}