{"id":"W3111670531","doi":"10.1115/1.4049335","title":"Accurate Registration of Point Clouds of Damaged Aeroengine Blades","year":2020,"lang":"en","type":"article","venue":"Journal of Manufacturing Science and Engineering","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Hausdorff distance; Artificial intelligence; Computer vision; Iterative closest point; Computer science; CAD; Metric (unit); Process (computing); Feature (linguistics); Blade (archaeology); Algorithm; Noise (video); Euclidean distance; Point (geometry); Mathematics; Image (mathematics); Geometry; Engineering; Engineering drawing","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":[0.0008006387,0.00007424492,0.0001720507,0.00007181139,0.00004858746,0.00003457397,0.0001915848,0.00002135454,0.00003000418],"category_scores_gemma":[0.0002247173,0.00005376176,0.00003593218,0.000180784,0.00008616266,0.000502043,0.000009621796,0.0001311211,7.272716e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000359995,"about_ca_system_score_gemma":0.00004311115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003828252,"about_ca_topic_score_gemma":0.000006508563,"domain_scores_codex":[0.9991018,0.00001217743,0.0003150397,0.00008519025,0.0003507318,0.0001351191],"domain_scores_gemma":[0.999475,0.00005480197,0.0002035138,0.00005547687,0.00008696269,0.0001242155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001780181,0.00002623188,0.0274047,0.0007558601,0.00006689393,0.00006226292,0.004809522,0.6974909,0.2361375,0.00007130919,0.0002358463,0.03276093],"study_design_scores_gemma":[0.0003356986,0.0004604239,0.6910847,0.0001699718,0.00001975603,0.00007253454,0.0007053558,0.03000423,0.2766704,0.00003208476,0.0002818508,0.0001629075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986256,0.0002271528,0.0005739334,0.0002513795,0.00011795,0.00002538961,0.000003596605,0.000007565776,0.0001674236],"genre_scores_gemma":[0.9984702,0.00007022232,0.001369486,0.00002234344,0.00005991467,3.078365e-8,6.714142e-7,0.00000154702,0.000005604073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6674867,"threshold_uncertainty_score":0.2192341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01994456130527419,"score_gpt":0.2039453499201795,"score_spread":0.1840007886149053,"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."}}