{"id":"W2162239134","doi":"10.1108/14714171111124149","title":"House construction CO<sub>2</sub> footprint quantification: a BIM approach","year":2011,"lang":"en","type":"article","venue":"Construction Innovation","topic":"BIM and Construction Integration","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Natural Resources","funders":"","keywords":"Carbon footprint; Greenhouse gas; Framing (construction); Building information modeling; Construction industry; Engineering; Architectural engineering; Process (computing); Originality; Civil engineering; Environmental science; Computer science; Construction engineering; Compatibility (geochemistry)","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":[0.0009318579,0.0008193026,0.0006616836,0.003235568,0.0005464504,0.002770939,0.001045809,0.0008861742,0.002465317],"category_scores_gemma":[0.002046683,0.0005062314,0.0005852188,0.003486836,0.000727817,0.002056705,0.00161228,0.0005633552,0.0008116343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001422645,"about_ca_system_score_gemma":0.001117447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004865025,"about_ca_topic_score_gemma":0.0053547,"domain_scores_codex":[0.9986206,0.0003749664,0.00004808425,0.0001233312,0.0007437572,0.00008919177],"domain_scores_gemma":[0.99932,0.0001633209,0.00009135402,0.0001449597,0.0002479239,0.00003240621],"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":[0.0001838229,0.0003020466,0.02134894,0.0006174892,0.0002018118,0.0002863327,0.000576407,0.5029883,0.01692658,0.05511612,0.005091919,0.3963602],"study_design_scores_gemma":[0.00001474416,0.00006691115,0.01325275,0.0001357786,0.00005926276,0.0003044748,0.0007413878,0.9249562,0.02066418,0.0207319,0.01899319,0.00007923716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09723882,0.0005130594,0.8561935,0.000346717,0.00003752601,0.0002746037,0.001746989,0.002021564,0.04162718],"genre_scores_gemma":[0.5830882,0.0005899622,0.4114462,0.0000759112,0.00001535432,0.0002885123,0.001524894,0.0002449618,0.002726089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004865025,"threshold_uncertainty_score":0.01032203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03518559669408424,"score_gpt":0.2161228232726665,"score_spread":0.1809372265785822,"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."}}