{"id":"W2946865217","doi":"10.3390/rs11101248","title":"Higher-Order Conditional Random Fields-Based 3D Semantic Labeling of Airborne Laser-Scanning Point Clouds","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"State Key Laboratory of Geo-Information Engineering; National Natural Science Foundation of China","keywords":"Conditional random field; Cluster analysis; Point cloud; Computer science; Pairwise comparison; Unary operation; Pattern recognition (psychology); Artificial intelligence; Topology (electrical circuits); Algorithm; Data mining; Mathematics; Discrete mathematics; Combinatorics","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":[0.0003197263,0.0001781957,0.000282411,0.0000544151,0.0001441918,0.00002820172,0.0001073395,0.000112387,0.0005119912],"category_scores_gemma":[0.00004991816,0.0001758586,0.0001018604,0.000344498,0.0001393094,0.00007818541,0.00006581704,0.0002156751,0.0004259109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008998667,"about_ca_system_score_gemma":0.00003593691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000726695,"about_ca_topic_score_gemma":0.00004107216,"domain_scores_codex":[0.9985321,0.0000829397,0.0003499408,0.000368195,0.0003615512,0.0003053054],"domain_scores_gemma":[0.9990477,0.0002252838,0.0001750846,0.0004206031,0.00004327786,0.00008808004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002181,0.0001089807,0.0009879242,0.0001280548,0.00009458693,0.00003726477,0.0007012427,0.5014247,0.371254,0.0001201783,0.002334832,0.1225901],"study_design_scores_gemma":[0.002236823,0.00006494496,0.002572889,0.0002366369,0.00006090095,0.00003390809,0.00008443965,0.9524748,0.03509107,0.001168681,0.005577191,0.000397659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.912842,0.00002984775,0.06624684,0.001354541,0.0002652422,0.0003109832,0.000005134711,0.00009823005,0.01884716],"genre_scores_gemma":[0.939936,0.000003379824,0.05858341,0.0006434669,0.00007585358,1.550193e-8,0.00003172406,0.00002733522,0.0006988277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4510501,"threshold_uncertainty_score":0.7171308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009196428009161787,"score_gpt":0.2333356172428991,"score_spread":0.2241391892337373,"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."}}