{"id":"W2180136417","doi":"10.3390/rs71115605","title":"Automatic Object Extraction from Electrical Substation Point Clouds","year":2015,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canada Foundation for Innovation","keywords":"Point cloud; Computer science; Laser scanning; Key (lock); Lidar; Circuit breaker; Reliability (semiconductor); Scanner; Real-time computing; Remote sensing; Power (physics); Laser; Electrical engineering; Artificial intelligence; Engineering; Geology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002850663,0.0001346216,0.0001407965,0.00004019844,0.0001389377,0.00006335163,0.00006892224,0.00008959461,0.00007318895],"category_scores_gemma":[0.0001445123,0.0001330976,0.00005403255,0.0003633018,0.00006155721,0.0001529494,0.00003283113,0.0001804555,0.00119479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004371864,"about_ca_system_score_gemma":0.00003155214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003520898,"about_ca_topic_score_gemma":0.0001728725,"domain_scores_codex":[0.9987026,0.0001166241,0.0002394964,0.0003222405,0.0003656448,0.0002533431],"domain_scores_gemma":[0.999275,0.0001074148,0.0001105209,0.0003170899,0.00002138989,0.0001685857],"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":[0.00001142655,0.00001883511,0.0000676448,0.000001216506,0.000008781947,0.00001350299,0.0009504305,0.0012753,0.07855128,0.000009853875,0.002290649,0.9168011],"study_design_scores_gemma":[0.0002520867,0.00002997956,0.004648672,0.00001722981,0.00002741041,0.00008445299,0.000170027,0.9758733,0.009478395,0.005388833,0.003837975,0.0001916476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8905726,0.00002556494,0.09451072,0.0004088321,0.0001814412,0.0001505149,9.087778e-7,0.0002125421,0.01393685],"genre_scores_gemma":[0.9098164,0.000005359204,0.08968199,0.0001538226,0.0001334567,8.872179e-9,0.00002263742,0.00002154979,0.0001647091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.974598,"threshold_uncertainty_score":0.9995829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01988147071106455,"score_gpt":0.2652129867875989,"score_spread":0.2453315160765343,"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."}}