{"id":"W2981335692","doi":"10.1088/1757-899x/609/6/062028","title":"Techno-economic feasibility of Sewage Wastewater Heat Recovery (WWHR) based Community Energy Network (CEN) in a cold climate - a case study of Ryerson University, Toronto, Canada","year":2019,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Geothermal Energy Systems and Applications","field":"Energy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wastewater; Heat pump; Environmental science; Cold climate; Waste management; Waste heat; Environmental engineering; Sewage; Sewage treatment; Thermal energy; Engineering; Meteorology; Geography; Mechanical engineering; Heat exchanger","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":[0.0008411202,0.0004604803,0.0002373475,0.0007675863,0.00262995,0.001923397,0.0009530754,0.0006959544,0.002882688],"category_scores_gemma":[0.001208146,0.0002395694,0.0004624713,0.0009458218,0.001030456,0.0005090398,0.0007239877,0.0005576711,0.0001557014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03731647,"about_ca_system_score_gemma":0.0167072,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9303585,"about_ca_topic_score_gemma":0.9698493,"domain_scores_codex":[0.9991154,0.0001913221,0.00001747649,0.00007262669,0.0001921063,0.0004110566],"domain_scores_gemma":[0.9992365,0.0001921613,0.0000378749,0.00002554579,0.0002582892,0.0002496782],"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":[0.003371251,0.002699009,0.2402807,0.0008221561,0.0003256774,0.01244968,0.003220969,0.5979144,0.03416474,0.02128223,0.01040214,0.07306711],"study_design_scores_gemma":[0.0007942726,0.003404081,0.4104215,0.0002306295,0.00037797,0.0006592278,0.06280223,0.47444,0.02124406,0.002701816,0.0226441,0.0002800551],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915007,0.00008449689,0.0005155781,0.0001828982,0.000005693467,0.0001649505,0.000400332,0.00002593498,0.007119394],"genre_scores_gemma":[0.9974357,0.0000742693,0.0005375379,0.000008545316,0.000001061318,0.00002288012,0.0001318972,0.000004759012,0.001783298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06964153,"threshold_uncertainty_score":0.2707512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0129784084218957,"score_gpt":0.1940582589574494,"score_spread":0.1810798505355537,"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."}}