{"id":"W212569231","doi":"10.22260/isarc2013/0120","title":"Autonomous Modeling of Pipes within Point Clouds","year":2013,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Point cloud; Laptop; Computer science; Reverse engineering; Computer graphics (images); Point (geometry); Download; Photogrammetry; Laser scanning; Cloud computing; Scanner; Computer vision; Artificial intelligence; Laser; Operating system; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003020249,0.0007256102,0.000738075,0.001003572,0.0004896634,0.001273302,0.001183691,0.0009724082,0.00142477],"category_scores_gemma":[0.001006864,0.0007499976,0.001007066,0.001187622,0.0006599686,0.001106492,0.001391258,0.0007545904,0.0006853395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004918742,"about_ca_system_score_gemma":0.0008223542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01000035,"about_ca_topic_score_gemma":0.01152295,"domain_scores_codex":[0.999632,0.00005317801,0.00001960273,0.00008654162,0.0001684653,0.00004006621],"domain_scores_gemma":[0.9996023,0.0001369943,0.00005177456,0.00008987436,0.00009168394,0.00002756379],"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":[0.00005891146,0.00002509754,0.001609676,0.00005412463,0.00003223341,0.0001424377,0.0001320101,0.9456525,0.01471965,0.002359161,0.000547366,0.03466682],"study_design_scores_gemma":[0.000002305603,0.000005403114,0.0002905038,0.000003390029,0.00000216303,0.00001828448,0.00001725789,0.9955679,0.00267934,0.0008577065,0.0005507735,0.000004921609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03843439,0.00007167143,0.9578269,0.00005349575,0.00001554189,0.00006467717,0.0002955509,0.002259013,0.0009787921],"genre_scores_gemma":[0.6097353,0.0004038427,0.3853115,0.00002954992,0.00002060561,0.0001711199,0.001299315,0.0004055109,0.00262337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01000035,"threshold_uncertainty_score":0.01988429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01823914118518898,"score_gpt":0.1925929483126052,"score_spread":0.1743538071274162,"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."}}