{"id":"W7055367286","doi":"","title":"Chapter 8: Case study: Downtown Toronto open loop geothermal cooling system - part II","year":2015,"lang":"en","type":"other","venue":"Espace ÉTS (ETS)","topic":"Laser Design and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Downtown; Water cooling; Geothermal gradient; Loop (graph theory); Closed loop; Passive cooling","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":[0.0002494667,0.0004834355,0.0002262585,0.0003769516,0.002674142,0.001232037,0.001141926,0.001596481,0.01370201],"category_scores_gemma":[0.0004870287,0.0002179435,0.0002845535,0.0008836047,0.001060482,0.0007069353,0.0007429424,0.0006452986,0.001012141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008738091,"about_ca_system_score_gemma":0.005498013,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4473592,"about_ca_topic_score_gemma":0.731885,"domain_scores_codex":[0.9993901,0.00006297136,0.00001657868,0.00007571316,0.0002833056,0.0001713668],"domain_scores_gemma":[0.9997674,0.00006987838,0.00001884211,0.00001842305,0.00007309481,0.00005242661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"case_report","study_design_scores_codex":[0.001442214,0.001152863,0.07120486,0.004061281,0.0001205382,0.08709555,0.03993564,0.2007674,0.1080892,0.05788349,0.2006825,0.2275644],"study_design_scores_gemma":[0.0002130835,0.0008289516,0.09044202,0.0007827173,0.0001025476,0.006854751,0.07512188,0.04743972,0.07151417,0.003318079,0.7031912,0.0001908772],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7298256,0.001083732,0.01447927,0.001454934,0.0002008156,0.001134199,0.002034943,0.0003196938,0.2494669],"genre_scores_gemma":[0.8355535,0.0006021119,0.006578068,0.0001551254,0.0000178929,0.0001701456,0.0006555942,0.00008277759,0.1561847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5526408,"threshold_uncertainty_score":0.8895106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02381368919535325,"score_gpt":0.2582986553892742,"score_spread":0.2344849661939209,"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."}}