{"id":"W2332618398","doi":"10.1061/9780784412329.078","title":"Providing Query Support to Leverage BIM for Construction","year":2012,"lang":"en","type":"article","venue":"Construction Research Congress 2012","topic":"BIM and Construction Integration","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Building information modeling; Leverage (statistics); Ontology; Flexibility (engineering); Software engineering; Systems engineering; Database; Data mining; Engineering; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00113449,0.000257391,0.0002696425,0.0007487162,0.000457061,0.0001897787,0.0002132004,0.0002523119,0.0008929899],"category_scores_gemma":[0.0002342642,0.0002680089,0.00009888976,0.0006550032,0.0004007531,0.001427673,0.00005948596,0.0004854446,0.0003694883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003105367,"about_ca_system_score_gemma":0.0001348073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001696722,"about_ca_topic_score_gemma":0.00002209878,"domain_scores_codex":[0.997511,0.0001317279,0.000465379,0.0003276735,0.0005505112,0.00101371],"domain_scores_gemma":[0.9981359,0.0002198678,0.00006115092,0.0003696803,0.0007162649,0.0004970895],"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":[0.0001310928,0.00004699654,0.008962563,0.0003381018,0.0001327603,0.000001927419,0.000417356,0.0002519045,0.03015972,0.1933088,0.04805848,0.7181903],"study_design_scores_gemma":[0.001128131,0.00019272,0.001330592,0.00013142,0.00004312754,0.0004474696,0.002368121,0.001598211,0.1283912,0.003462092,0.860126,0.0007809151],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4140005,0.002228183,0.4727027,0.001494909,0.04790727,0.0061958,0.0003449438,0.002585619,0.05254018],"genre_scores_gemma":[0.9754335,0.00007976466,0.02063173,0.00005519449,0.001582587,0.0007858081,0.00004751955,0.00007858002,0.001305358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8120676,"threshold_uncertainty_score":0.9999772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06356479648402116,"score_gpt":0.3390595780029677,"score_spread":0.2754947815189465,"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."}}