{"id":"W4385216853","doi":"10.1007/978-981-99-2714-2_35","title":"Generating 3D CAD Models from Laser Scanning Point Cloud Data to Monitor and Preserve Heritage Buildings","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"European Commission; Trent University; Nottingham Trent University; Government of the United Kingdom; European Regional Development Fund; Universidad de Sevilla","keywords":"Point cloud; Architectural engineering; Laser scanning; Key (lock); CAD; Building model; Cultural heritage; Dissemination; Construct (python library); Point (geometry); Retrofitting; Building information modeling; 3d model; Computer science; Engineering; Engineering drawing; Geography; Artificial intelligence; Archaeology; Simulation; Laser; Geometry; Computer security; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0004022856,0.0005021822,0.0004998869,0.0001542667,0.0001208442,0.0002154859,0.0005704803,0.0003805659,0.0002442641],"category_scores_gemma":[0.000256908,0.0004618541,0.00005591153,0.0001253964,0.00002460752,0.0003267576,0.000213421,0.0008136927,0.00003895636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002094983,"about_ca_system_score_gemma":0.00002142299,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003985852,"about_ca_topic_score_gemma":0.03135699,"domain_scores_codex":[0.9978846,0.00002595987,0.0003880232,0.0008595089,0.0003424915,0.0004993563],"domain_scores_gemma":[0.9985434,0.000544865,0.00007914989,0.0005923125,0.0000323317,0.000207984],"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":[0.0000123735,0.000001628362,0.001063418,0.00009508179,0.00005248285,0.0001139334,0.0006735502,0.9818176,0.0002138835,0.00001580561,0.0001781405,0.01576209],"study_design_scores_gemma":[0.0001983204,0.00005330683,0.001316353,0.001203117,0.0000378016,0.00001194977,0.00001901381,0.9926045,0.00008550507,0.001176828,0.00235362,0.0009396693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6804478,0.05321716,0.1940474,0.001299616,0.01385876,0.003889363,0.01983246,0.003653191,0.02975431],"genre_scores_gemma":[0.9535034,0.0003560484,0.03699139,0.000277892,0.002369655,0.000007404359,0.002365764,0.0001403733,0.003988128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2730556,"threshold_uncertainty_score":0.9997833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03914576780634589,"score_gpt":0.2223364517028611,"score_spread":0.1831906838965152,"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."}}