{"id":"W2946689488","doi":"10.5194/isprs-archives-xlii-2-w11-419-2019","title":"THE EVOLUTION OF MODELLING PRACTICES ON CANADA’S PARLIAMENT HILL: AN ANALYSIS OF THREE SIGNIFICANT HERITAGE BUILDING INFORMATION MODELS (HBIM)","year":2019,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Parliament; Timeline; Digitization; Workflow; Scope (computer science); Data science; Visualization; Building information modeling; Block (permutation group theory); Computer science; Scheme (mathematics); Engineering; Architectural engineering; Operations research; Political science; Database; Data mining; Geography; Operations management; Archaeology; Telecommunications; Law","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.004154827,0.0003779501,0.0004342932,0.002919154,0.004728526,0.006058145,0.00214927,0.0005263341,0.002141817],"category_scores_gemma":[0.01271449,0.0005818673,0.000748393,0.009533727,0.00306842,0.001699833,0.002797545,0.001021256,0.0003129071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07150859,"about_ca_system_score_gemma":0.05005487,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9793079,"about_ca_topic_score_gemma":0.9892665,"domain_scores_codex":[0.9964688,0.0005062027,0.0001287489,0.0002907885,0.00215648,0.0004489275],"domain_scores_gemma":[0.9908122,0.001918749,0.0006074242,0.001164023,0.004883396,0.0006140345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004414995,0.0002794111,0.4095975,0.0006422065,0.0002448356,0.001107521,0.06570102,0.1480521,0.004860401,0.04963714,0.01404056,0.305396],"study_design_scores_gemma":[0.00003886505,0.0001698061,0.5082537,0.0006146014,0.000180838,0.00033115,0.1128467,0.1657683,0.00801051,0.004129721,0.1993543,0.0003015063],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9339045,0.0003840205,0.01376522,0.0009862364,0.00002131242,0.0003011786,0.003137569,0.0004385936,0.04706132],"genre_scores_gemma":[0.9649737,0.0003633107,0.02569047,0.00007887324,0.000002363751,0.0001013708,0.002847978,0.0002678731,0.00567408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07150859,"threshold_uncertainty_score":0.5188335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02581839928730222,"score_gpt":0.2422837645133608,"score_spread":0.2164653652260586,"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."}}