{"id":"W2432182850","doi":"10.5194/isprs-archives-xli-b5-615-2016","title":"AUTOMATIC RAILWAY POWER LINE EXTRACTION USING MOBILE LASER SCANNING DATA","year":2016,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Piecewise; Point cloud; Computer science; Line (geometry); Power (physics); Laser scanning; Point (geometry); Trajectory; Artificial intelligence; Computer vision; Real-time computing; Laser; Mathematics; Optics","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":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.001769101,0.0004383007,0.0004101487,0.0005915792,0.001593631,0.0006220684,0.002207872,0.000107024,0.00005174611],"category_scores_gemma":[0.0007580306,0.0002518959,0.0002874244,0.001015514,0.003769432,0.0009109251,0.001794227,0.0003794338,0.00001527869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009554756,"about_ca_system_score_gemma":0.0001783873,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7132505,"about_ca_topic_score_gemma":0.1232482,"domain_scores_codex":[0.9951103,0.0003236647,0.001472979,0.000568945,0.00195445,0.0005697117],"domain_scores_gemma":[0.9958329,0.001141654,0.001754175,0.000926552,0.0001575629,0.0001871941],"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":[0.0000718446,0.00002466756,0.0003227867,0.00001726983,0.00006210253,2.992533e-7,0.00162367,0.004777444,0.01299911,0.00000197108,0.0001036949,0.9799951],"study_design_scores_gemma":[0.0006493499,0.000117106,0.00347589,0.0003644632,0.00005391207,0.0001924672,0.001141431,0.9716141,0.01107923,0.002350621,0.008633403,0.000327989],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05543925,0.00001912286,0.9309239,0.003422278,0.001407579,0.000731429,0.0001381671,0.00005996535,0.007858348],"genre_scores_gemma":[0.9915111,0.0001000817,0.007255989,0.0007771747,0.0001303381,3.430191e-7,0.00003709982,0.00001636098,0.0001715182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9796671,"threshold_uncertainty_score":0.9999933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02343213261353529,"score_gpt":0.2819578709606366,"score_spread":0.2585257383471014,"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."}}