{"id":"W2980345032","doi":"10.48550/arxiv.1910.06391","title":"Building Information Modeling and Classification by Visual Learning At A City Scale","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Metadata; Task (project management); Building information modeling; Scale (ratio); Artificial intelligence; Geospatial analysis; Architecture; Deep learning; Retrofitting; Machine learning; Engineering; Systems engineering; World Wide Web; Cartography; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005565218,0.0005593006,0.0005396802,0.001358284,0.0002377271,0.001530008,0.0008729571,0.0006531152,0.00220581],"category_scores_gemma":[0.001170759,0.000287441,0.0007820134,0.001386787,0.000436422,0.001435726,0.0008760402,0.0007471762,0.001037908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001015426,"about_ca_system_score_gemma":0.0005322194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01926089,"about_ca_topic_score_gemma":0.02498603,"domain_scores_codex":[0.9997259,0.00004849869,0.00001388504,0.0001054761,0.00006258785,0.00004358067],"domain_scores_gemma":[0.9996856,0.00006460564,0.00003087886,0.0001120098,0.00008603334,0.00002079449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001147753,0.0001566119,0.004275217,0.00006950246,0.00007985826,0.00007897449,0.00008617478,0.2146405,0.01123784,0.007445164,0.008480483,0.753335],"study_design_scores_gemma":[0.000002333992,0.00001063947,0.0009408463,0.000005102727,0.000007161876,0.00001340754,0.00002575286,0.9914994,0.001985566,0.004031873,0.001472721,0.00000529674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08004259,0.0007428885,0.910099,0.0006635899,0.00008941127,0.00007334906,0.0008051415,0.003396958,0.004087064],"genre_scores_gemma":[0.6319872,0.0008157656,0.356793,0.0001933856,0.00008234675,0.00008428137,0.003341426,0.0002093142,0.006493247],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01926089,"threshold_uncertainty_score":0.03829753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04542063884964868,"score_gpt":0.1876366473321294,"score_spread":0.1422160084824807,"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."}}