{"id":"W2183617971","doi":"","title":"Terrestrial LiDAR Capabilities for 3D Data Acquisition (Indoor and Outdoor) in the Context of Cadastral Modelling: A Comparative Analysis for Apartment Units","year":2014,"lang":"en","type":"article","venue":"Research Repository (Delft University of Technology)","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Cadastre; Lidar; Point cloud; Computer science; Context (archaeology); Remote sensing; 3D city models; Apartment; Geography; Cartography; Data mining; Computer vision; Engineering; Civil engineering; Visualization","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006781886,0.00008754462,0.0002742546,0.0004807884,0.0001770035,0.0000119568,0.0005977307,0.0001319457,5.589264e-7],"category_scores_gemma":[0.00005746424,0.00008654178,0.00004355645,0.000571403,0.0004296961,0.00009425154,0.0001148145,0.0001843502,2.001482e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000072752,"about_ca_system_score_gemma":0.00005544402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007249783,"about_ca_topic_score_gemma":0.000337567,"domain_scores_codex":[0.9990836,0.00007523327,0.0002070238,0.0002405031,0.0001870561,0.000206534],"domain_scores_gemma":[0.9986324,0.0004217951,0.0000640149,0.0005814268,0.0002703425,0.00003000471],"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":[0.001569521,0.000813277,0.01083555,0.001102703,0.002718112,0.000007102556,0.01940344,0.8685699,0.02460578,0.06143289,0.002064148,0.00687757],"study_design_scores_gemma":[0.0007463907,0.0002251278,0.0002662594,0.00002876727,0.0001355347,0.000001057061,0.007514691,0.9846457,0.003621113,0.0008750655,0.001858696,0.00008161669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.714953,0.00007861904,0.2837043,0.000174263,0.00001664973,0.0007696248,0.0001679265,0.00003837663,0.00009722893],"genre_scores_gemma":[0.9774001,0.00001571097,0.02243109,0.000001112924,0.00002034332,0.00001846452,0.00008400623,0.000005212515,0.00002395876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2624471,"threshold_uncertainty_score":0.3529072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1028857245428194,"score_gpt":0.3057724205390798,"score_spread":0.2028866959962604,"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."}}