{"id":"W2492878132","doi":"10.1109/icip.1996.560849","title":"Extracting buildings from aerial topographic maps","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Exploit; Range (aeronautics); Object (grammar); Interpretation (philosophy); 3D modeling; Pattern recognition (psychology); Engineering","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":[0.00006561627,0.000670661,0.0004183831,0.002595546,0.0002434461,0.0007546562,0.0003345783,0.0003966152,0.002180481],"category_scores_gemma":[0.0005380851,0.0003505258,0.0005089176,0.001976993,0.0002654031,0.0005827103,0.0005232448,0.0002781046,0.001621267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002102154,"about_ca_system_score_gemma":0.0003736902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005418744,"about_ca_topic_score_gemma":0.009517711,"domain_scores_codex":[0.9998826,0.000006656433,0.000005247004,0.00002633901,0.00005575861,0.0000234057],"domain_scores_gemma":[0.9998909,0.00001739609,0.00001274249,0.0000233118,0.00004653633,0.000009072243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001202816,0.00005442876,0.004972232,0.0003626789,0.00006692873,0.0009128191,0.0002212672,0.04185526,0.07739311,0.002611732,0.006035141,0.8653941],"study_design_scores_gemma":[0.00005891913,0.0001948009,0.05013262,0.0001081271,0.0002441189,0.002160036,0.001708331,0.7855477,0.1045425,0.0163069,0.03889201,0.0001039548],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3276207,0.001430283,0.6450722,0.0003214049,0.0001042021,0.0003515285,0.004073258,0.0067666,0.01425987],"genre_scores_gemma":[0.6835315,0.001730253,0.3016904,0.00006387615,0.00006694355,0.0001023057,0.007746279,0.0002304568,0.004837935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005418744,"threshold_uncertainty_score":0.01077443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02621700815243932,"score_gpt":0.2570973193621852,"score_spread":0.2308803112097459,"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."}}