{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003237217,0.0001413277,0.0001956151,0.0004303121,0.00005550588,0.0002001964,0.0004096021,0.0001139167,0.00002743801],"category_scores_gemma":[0.0001100873,0.0001476485,0.00008429446,0.0001359068,0.00001760687,0.001809028,0.00002541524,0.0002409727,0.00001409558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003394855,"about_ca_system_score_gemma":0.0001286895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002001444,"about_ca_topic_score_gemma":0.0001384168,"domain_scores_codex":[0.9984474,0.00001899656,0.000996215,0.00006944763,0.0002938004,0.000174186],"domain_scores_gemma":[0.9988278,0.00009429205,0.0002593825,0.00012661,0.0006333046,0.00005856858],"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":[0.0001032128,0.00004201162,0.0008479212,0.00001999536,0.0001504605,0.000001678319,0.001510384,0.8718076,0.002090845,0.001037356,0.002519282,0.1198692],"study_design_scores_gemma":[0.001321108,0.0000390333,0.002283615,0.0001543831,0.00001885374,0.000008150228,0.0002715572,0.99022,0.00161509,0.0009917105,0.002951268,0.0001253085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3187716,0.00004305774,0.6787645,0.0004069252,0.00138096,0.0002078884,0.0001224984,0.0001241703,0.0001784466],"genre_scores_gemma":[0.9187599,0.00001738858,0.08016335,0.0005147877,0.0002578349,0.0000400975,0.0002324303,0.000009953645,0.000004217803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5999883,"threshold_uncertainty_score":0.6020933,"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."}}