{"id":"W2167056143","doi":"10.5194/isprsannals-ii-5-w2-19-2013","title":"Automatic extraction of insulators from 3D LiDAR data of an electrical substation","year":2013,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Insulator (electricity); Electrical conductor; Computer science; Electrical equipment; Segmentation; Point (geometry); Principal component analysis; Artificial intelligence; Engineering; Electrical engineering; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006977791,0.0001095404,0.0001950649,0.0001227353,0.0001966215,0.00007823183,0.0003635824,0.00006891086,0.00004310287],"category_scores_gemma":[0.0002527259,0.0000794491,0.00004636633,0.0007999048,0.0005528887,0.0007921309,0.0001277779,0.00009494397,0.000008703772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001184897,"about_ca_system_score_gemma":0.00003169705,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4265102,"about_ca_topic_score_gemma":0.004612478,"domain_scores_codex":[0.9983838,0.0001089743,0.0005925807,0.0001848603,0.000559562,0.0001702619],"domain_scores_gemma":[0.9984978,0.0001475418,0.0006825844,0.0005024618,0.00009628685,0.00007335149],"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":[0.000005517522,0.00002130111,0.0007016595,0.000007536692,0.000007011551,2.389052e-8,0.0006345157,0.0004567248,0.0140821,0.000001606259,0.00007071257,0.9840113],"study_design_scores_gemma":[0.00008529351,0.00007500223,0.08291367,0.00003015558,0.0000129338,0.000004113274,0.0003293644,0.8721736,0.04315941,0.0009426556,0.0001941085,0.0000796573],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9031634,0.00001346728,0.09562429,0.0002633542,0.00008474036,0.0002436397,0.00002117701,0.00001769936,0.0005682614],"genre_scores_gemma":[0.9877859,0.00002488911,0.01203653,0.0001054171,0.00001368746,7.631512e-8,0.00002587952,0.000003891895,0.00000374088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9839317,"threshold_uncertainty_score":0.5773088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04468428679319689,"score_gpt":0.3129261357931706,"score_spread":0.2682418489999737,"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."}}