{"id":"W2788536023","doi":"10.3390/ijgi7010028","title":"A Knowledge Base for Automatic Feature Recognition from Point Clouds in an Urban Scene","year":2018,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Point cloud; Computer science; Geospatial analysis; Knowledge base; Ontology; Lidar; Feature (linguistics); Spatial relation; Representation (politics); Artificial intelligence; Knowledge representation and reasoning; Process (computing); Point (geometry); Semantic feature; 3D city models; Information retrieval; Remote sensing; Geography; Visualization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001116959,0.001184818,0.001027015,0.005487591,0.00116227,0.003744944,0.003073791,0.001728646,0.005927491],"category_scores_gemma":[0.004873815,0.0007551404,0.002124028,0.003406682,0.0009678952,0.003424653,0.001925042,0.00165646,0.002970271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001284991,"about_ca_system_score_gemma":0.002744132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01524976,"about_ca_topic_score_gemma":0.01369125,"domain_scores_codex":[0.998903,0.0001291877,0.0001878768,0.0002971476,0.0004028539,0.00007983323],"domain_scores_gemma":[0.9982978,0.0007075055,0.0001521414,0.0003695608,0.0004025645,0.00007056484],"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":[0.0003042558,0.0009362056,0.005407873,0.002788084,0.0005067805,0.006084191,0.001316233,0.1344469,0.02793296,0.09390964,0.0389384,0.6874285],"study_design_scores_gemma":[0.0001881286,0.0002258557,0.007802428,0.002050006,0.001172359,0.003249924,0.001522689,0.5902941,0.04529802,0.1436831,0.204207,0.0003065942],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01245775,0.0005048364,0.9654686,0.0005263126,0.0001011536,0.0005193984,0.006695055,0.004968808,0.00875816],"genre_scores_gemma":[0.1414187,0.001684756,0.8301419,0.0003932015,0.00007125345,0.0009213512,0.02192125,0.0002668105,0.003180791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01524976,"threshold_uncertainty_score":0.03032202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01496868140487073,"score_gpt":0.2692147016158331,"score_spread":0.2542460202109623,"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."}}