{"id":"W1987366328","doi":"10.1016/j.cad.2014.11.004","title":"Outlier detection for scanned point clouds using majority voting","year":2014,"lang":"en","type":"article","venue":"Computer-Aided Design","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outlier; Point cloud; Anomaly detection; Artificial intelligence; Pattern recognition (psychology); Computer science; Object (grammar); Computer vision","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001499811,0.0002636285,0.0003095499,0.0002520888,0.0004838484,0.0004096499,0.0006976137,0.0001440574,0.000003086041],"category_scores_gemma":[0.0001258822,0.0002697133,0.0001776781,0.0004395861,0.00004138706,0.0006977499,0.0002355631,0.0001911251,0.00001952097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000166457,"about_ca_system_score_gemma":0.00006279221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000501038,"about_ca_topic_score_gemma":0.000004738092,"domain_scores_codex":[0.9978907,0.0003228785,0.0004186891,0.0006398304,0.0002389571,0.0004889294],"domain_scores_gemma":[0.9984332,0.000352336,0.0002158631,0.0006126449,0.0002627212,0.0001232863],"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":[0.00005569601,0.00009351019,0.00002632782,0.00006432761,0.00003893069,0.000007208795,0.0003119005,0.002695461,0.04052088,0.003018549,0.001129131,0.952038],"study_design_scores_gemma":[0.0003496333,0.0003922089,0.00008705462,0.00002804613,0.000009925252,0.00004169186,0.000003262044,0.6377939,0.3535896,0.006781789,0.0006849415,0.0002380141],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003252454,0.00002447396,0.9933843,0.00009646187,0.001350395,0.0006466198,7.999302e-7,0.001157367,0.00008712742],"genre_scores_gemma":[0.4973282,7.966372e-7,0.5018133,0.0003424636,0.0004392156,0.00002955462,6.165359e-7,0.00001954553,0.00002634027],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9518,"threshold_uncertainty_score":0.9999755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03235090185648512,"score_gpt":0.2613875194046565,"score_spread":0.2290366175481714,"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."}}