{"id":"W2950181618","doi":"10.1109/jstars.2019.2918272","title":"Multi-Scale Hierarchical CRF for Railway Electrification Asset Classification From Mobile Laser Scanning Data","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Korea Agency for Infrastructure Technology Advancement; Korea Railroad Research Institute; Wuhan University; Ministry of Land, Infrastructure and Transport","keywords":"Electrification; Computer science; Scale (ratio); Conditional random field; Asset (computer security); Range (aeronautics); Spatial analysis; Constraint (computer-aided design); Representation (politics); Artificial intelligence; Margin (machine learning); Data mining; Machine learning; Remote sensing; Engineering; Electricity","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":[0.0004536979,0.0001437763,0.0002473986,0.0001076303,0.0001784369,0.00008088209,0.0002175855,0.0001467743,0.00001320159],"category_scores_gemma":[0.00008058993,0.0001367198,0.00003612781,0.0005656173,0.00007438484,0.0001866241,0.00003137286,0.0003936897,0.000008832407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009384334,"about_ca_system_score_gemma":0.0000802064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001791917,"about_ca_topic_score_gemma":0.0004037383,"domain_scores_codex":[0.9984924,0.00006057435,0.0005606065,0.0003708059,0.0002827097,0.0002329713],"domain_scores_gemma":[0.9987968,0.0002060585,0.000346596,0.0004447574,0.0001187326,0.00008707581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004858645,0.00006337396,0.002484604,0.00001079155,0.00002248673,8.098089e-7,0.0004024015,0.003854208,0.7279,0.00002042682,0.0004060644,0.2647863],"study_design_scores_gemma":[0.00107182,0.00005939624,0.1928512,0.00006698596,0.00004290139,0.00001878088,0.0001741381,0.7682694,0.01380906,0.0007009534,0.02270545,0.000229956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8556753,0.00003425355,0.142763,0.0005206363,0.0001475279,0.0004852611,0.00002219034,0.00002037592,0.000331447],"genre_scores_gemma":[0.6058931,0.00008303466,0.3934016,0.0001104237,0.0001783651,3.202326e-7,0.0001188881,0.00001848618,0.0001957597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7644151,"threshold_uncertainty_score":0.5575271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04205151462872678,"score_gpt":0.2725806199864836,"score_spread":0.2305291053577568,"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."}}