{"id":"W4415818199","doi":"10.3390/ijgi14110429","title":"An Automated Workflow for Generating 3D Solids from Indoor Point Clouds in a Cadastral Context","year":2025,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geomembrane Technologies (Canada); Université Laval; Centre de Géomatique du Québec","funders":"Mitacs","keywords":"Workflow; Point cloud; Modular design; Context (archaeology); Cadastre; Geospatial analysis; Ceiling (cloud); Level of detail; Interoperability; Segmentation","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":[0.001080082,0.001592795,0.0007285834,0.002005506,0.0008469542,0.002241059,0.001779865,0.0009618812,0.007968774],"category_scores_gemma":[0.002529042,0.0009434767,0.001953939,0.00126357,0.0006493454,0.001136811,0.002258163,0.001279729,0.005464382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005356904,"about_ca_system_score_gemma":0.002060946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007001259,"about_ca_topic_score_gemma":0.01394762,"domain_scores_codex":[0.9991742,0.00010009,0.00006038052,0.0002287885,0.0003601319,0.00007629072],"domain_scores_gemma":[0.9988224,0.000280874,0.00008352957,0.0004563098,0.000290705,0.0000661434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002839466,0.0002185034,0.008191564,0.000824039,0.0001533777,0.0008306374,0.001537273,0.1306683,0.07221952,0.01439339,0.02419773,0.7464817],"study_design_scores_gemma":[0.00008707016,0.0002161724,0.006952276,0.0002155484,0.00007342896,0.000995383,0.001456571,0.8295951,0.07162677,0.02106682,0.06753571,0.0001791606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008979794,0.00008140001,0.9686278,0.0001056431,0.00003771126,0.000202049,0.001471063,0.01800568,0.002488946],"genre_scores_gemma":[0.0939826,0.0001636483,0.8965102,0.00007233173,0.00001776149,0.0002110907,0.005919436,0.00166158,0.001461216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007968774,"threshold_uncertainty_score":0.02665824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006316657413475295,"score_gpt":0.2723902280535594,"score_spread":0.2660735706400841,"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."}}