{"id":"W3037296254","doi":"10.3390/rs12122025","title":"Automatic Extraction of Road Points from Airborne LiDAR Based on Bidirectional Skewness Balancing","year":2020,"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":"","funders":"European Regional Development Fund; Universidade de Santiago de Compostela; Ministerio de Educación, Cultura y Deporte; York University; Xunta de Galicia; National Science Foundation","keywords":"Computer science; Correctness; Ranging; Skewness; Lidar; Constraint (computer-aided design); Key (lock); Focus (optics); Data mining; Extraction (chemistry); Artificial intelligence; Computer vision; Remote sensing; Algorithm; Statistics; Geography; Mathematics; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005290063,0.0008766656,0.0006359397,0.002782166,0.0004259483,0.0007556572,0.0008473574,0.0005109546,0.001547119],"category_scores_gemma":[0.001119767,0.0003797013,0.0006613847,0.001600659,0.0002578854,0.001193001,0.001099415,0.0003694734,0.001639259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001711617,"about_ca_system_score_gemma":0.0005777898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00210181,"about_ca_topic_score_gemma":0.004691612,"domain_scores_codex":[0.9994791,0.00005796159,0.00002964824,0.0001282472,0.0002302756,0.00007474715],"domain_scores_gemma":[0.9993942,0.0001055586,0.00007783972,0.0001237341,0.0002733892,0.00002535965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002161476,0.0001280906,0.01049214,0.000249538,0.00009248322,0.0003186657,0.0001993133,0.02945475,0.1823211,0.001563955,0.002829672,0.7721341],"study_design_scores_gemma":[0.00005308995,0.0001917604,0.02874441,0.00005067656,0.00007902854,0.0009379668,0.0003771445,0.8282311,0.1292498,0.00501793,0.00697656,0.00009042815],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1432609,0.0001875533,0.8500484,0.00005063618,0.00003129384,0.000173967,0.0005976082,0.003970133,0.001679618],"genre_scores_gemma":[0.4708976,0.000221246,0.5244895,0.0000345052,0.00002619361,0.0001250752,0.002602686,0.0002877121,0.00131559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002782166,"threshold_uncertainty_score":0.00517565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0132040363841869,"score_gpt":0.2395211758375803,"score_spread":0.2263171394533934,"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."}}