{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003041847,0.0002387656,0.0001303649,0.0001432529,0.0002360498,0.0002944796,0.0008030019,0.00009797717,0.00008490554],"category_scores_gemma":[0.00009134832,0.0001945252,0.0001273515,0.0002712963,0.00007670029,0.000236726,0.00005857883,0.0002874286,0.00002304096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000251791,"about_ca_system_score_gemma":0.00001878955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000143435,"about_ca_topic_score_gemma":0.00000824541,"domain_scores_codex":[0.9983835,0.00001504083,0.0004522633,0.0002653919,0.0004274294,0.000456405],"domain_scores_gemma":[0.9993057,0.0001005476,0.00007644566,0.0002631477,0.0001452319,0.0001089636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001648409,0.00004038566,0.000106215,0.000003036147,0.00006443984,0.000004231893,0.0001146804,0.4858693,0.0007908324,0.1822583,0.001672427,0.3290596],"study_design_scores_gemma":[0.0001317755,0.0001088795,0.0001540259,0.0000959325,0.00002033124,0.00006146768,0.0001494158,0.4870437,0.06908734,0.02334396,0.4192729,0.0005302834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05999436,0.001142786,0.9175471,0.002248831,0.005171244,0.0003281584,0.000006283326,0.001110017,0.01245122],"genre_scores_gemma":[0.9871956,0.0008424293,0.01073649,0.000264569,0.0003521573,0.00003580744,0.00001676234,0.0000318827,0.0005243055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9272012,"threshold_uncertainty_score":0.7932507,"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."}}