{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009875705,0.000192953,0.0002904611,0.00005665713,0.0002515122,0.000006133273,0.0002361495,0.00005375242,0.000005478995],"category_scores_gemma":[0.00002990568,0.00007598306,0.00003682656,0.00005972764,0.0003401561,0.0003380325,0.0004920074,0.00007546351,3.251343e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000915058,"about_ca_system_score_gemma":0.00000640217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002809208,"about_ca_topic_score_gemma":0.003128646,"domain_scores_codex":[0.9985293,0.0000706933,0.0006585514,0.0002576505,0.0003405155,0.0001432881],"domain_scores_gemma":[0.9994003,0.00009885974,0.0003138967,0.0001490479,0.00002299837,0.00001489131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0002843186,0.001228295,0.3046954,0.0002622028,0.0003097382,0.000001129513,0.6400258,0.003709998,0.03578632,0.00007155714,0.00002655271,0.0135987],"study_design_scores_gemma":[0.002499489,0.0005357189,0.5236905,0.0003436187,0.0002067218,0.00001636018,0.1040261,0.01352089,0.34973,0.004928632,0.00002402201,0.0004778719],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943888,0.0001141216,0.00387604,0.0006361082,0.00001694006,0.0008963709,0.000003850285,0.000004046649,0.00006371734],"genre_scores_gemma":[0.9982544,0.0001981045,0.001371214,0.00001955576,0.000004531769,0.0001150707,0.000003799401,0.000007601865,0.00002572969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5359997,"threshold_uncertainty_score":0.3098499,"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."}}