{"id":"W4232972677","doi":"10.3763/inbi.2009.si05","title":"The CABA Building Intelligence Quotient programme","year":2009,"lang":"en","type":"article","venue":"Intelligent Buildings International","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium","funders":"","keywords":"Building automation; Automation; Knowledge management; Computer science; Engineering; Data science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.006012267,0.0007502711,0.0003854418,0.003931779,0.001336218,0.005642923,0.001269693,0.001058127,0.0231906],"category_scores_gemma":[0.008221241,0.0002745319,0.0003089113,0.003134161,0.0009657775,0.00194659,0.002910793,0.001551264,0.01143209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003954688,"about_ca_system_score_gemma":0.007785503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02809645,"about_ca_topic_score_gemma":0.01390807,"domain_scores_codex":[0.993813,0.001141191,0.0001591327,0.0003289843,0.00408656,0.0004711256],"domain_scores_gemma":[0.993336,0.0006455649,0.0003175087,0.0006417285,0.00391779,0.001141567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000344423,0.0004457573,0.007178599,0.0002874141,0.00001630986,0.0001078705,0.0006011325,0.003508462,0.003271689,0.1261286,0.4034854,0.4546243],"study_design_scores_gemma":[0.0000188133,0.00006072674,0.006350363,0.00003505622,0.000002495068,0.00004115757,0.0001096831,0.001020553,0.0007321092,0.002926431,0.9886881,0.00001453771],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01755353,0.003365604,0.02885908,0.00519472,0.001321372,0.001506811,0.01610749,0.003955002,0.9221365],"genre_scores_gemma":[0.3000109,0.004852486,0.104778,0.002422012,0.0009987652,0.00340855,0.06008549,0.002786613,0.5206571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02809645,"threshold_uncertainty_score":0.07758027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01222186025200202,"score_gpt":0.2388168075047312,"score_spread":0.2265949472527292,"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."}}