{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002019008,0.0002745485,0.0002464394,0.0002480586,0.0001549386,0.0001352464,0.0001840024,0.0003726953,0.00000774363],"category_scores_gemma":[0.00004539346,0.0003693081,0.00007456841,0.0002139855,0.00005233687,0.0006822484,0.0002987637,0.0006527897,0.00008749699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006646794,"about_ca_system_score_gemma":0.00003200814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003811721,"about_ca_topic_score_gemma":0.00000660582,"domain_scores_codex":[0.9989099,0.00006010311,0.0002559217,0.0004309544,0.0000931469,0.0002499881],"domain_scores_gemma":[0.9992001,0.00004964428,0.0001673194,0.0003553841,0.0001278924,0.00009963389],"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":[0.00001725051,0.000007673983,0.002499323,0.0001780066,0.00003092859,0.00000132222,0.0001781429,0.9862593,0.008669633,0.0002605465,0.0001579788,0.001739914],"study_design_scores_gemma":[0.0002522273,0.00001106624,0.001229578,0.0001002987,0.0000558738,0.000002794586,0.0001222682,0.9964021,0.0006565157,0.0003385791,0.0004731738,0.0003555612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5396466,0.00003184609,0.4586851,0.00001076719,0.0001551693,0.0001642622,0.000006129909,0.0002773559,0.001022773],"genre_scores_gemma":[0.9981658,0.0002571588,0.001027282,0.00001016203,0.00003509317,5.500254e-7,0.0001573228,0.00003645171,0.0003101983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4585192,"threshold_uncertainty_score":0.9998759,"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."}}