{"id":"W4404577095","doi":"10.23919/eusipco63174.2024.10715006","title":"Automated Inventory of Electrical Distribution Assets Based on Image Recognition and Ground LiDAR","year":2024,"lang":"en","type":"article","venue":"","topic":"Smart Grid and Power Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Lidar; Computer science; Remote sensing; Computer vision; Image (mathematics); Artificial intelligence; Distribution (mathematics); Geology; Mathematics","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":[0.0001124179,0.00007284112,0.00008549697,0.00006448654,0.00001281242,0.0000375951,0.00002162144,0.00005609037,0.00004353874],"category_scores_gemma":[0.00001487875,0.00006480495,0.00002763112,0.0001667766,0.00001086894,0.00007898043,0.000003303414,0.00007456895,0.00006160569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005040248,"about_ca_system_score_gemma":0.00001080067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001956775,"about_ca_topic_score_gemma":0.0000025975,"domain_scores_codex":[0.99957,0.00002284281,0.0001293847,0.00009234575,0.00008945726,0.00009594678],"domain_scores_gemma":[0.9998199,0.00005592428,0.000007726419,0.00006210274,0.00001739027,0.00003699589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001365017,0.0003355629,0.005904514,0.004437787,0.0003969019,0.0001832377,0.0005679984,0.001028109,0.1736277,0.00240726,0.7696295,0.04134491],"study_design_scores_gemma":[0.0001923668,0.000106251,0.007531531,0.0001666568,0.00001834629,0.000008699855,0.00000655496,0.9702728,0.01301809,0.0000442081,0.008498755,0.000135676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9658837,0.0005176468,0.02241435,0.00005561769,0.002521552,0.0001984853,0.0001514803,0.002374136,0.005883027],"genre_scores_gemma":[0.9994763,0.00001040846,0.00005432043,0.000009396298,0.0001693225,0.000007608351,0.0002308236,0.00001248257,0.00002931072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9692448,"threshold_uncertainty_score":0.2642669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01268076678976392,"score_gpt":0.2249004533979211,"score_spread":0.2122196866081571,"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."}}