{"id":"W3038098942","doi":"10.1364/ao.394341","title":"Improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data based on a multiscale cylindrical neighborhood","year":2020,"lang":"en","type":"article","venue":"Applied Optics","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; Shandong University of Science and Technology; National Natural Science Foundation of China","keywords":"Ranging; Triangulated irregular network; Lidar; Algorithm; Computer science; Terrain; Point cloud; Robustness (evolution); Photogrammetry; Digital elevation model; Remote sensing; Artificial intelligence; Geology; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000654313,0.0006228194,0.0008383176,0.00138809,0.0006208867,0.0009142151,0.001116251,0.0005599826,0.001451935],"category_scores_gemma":[0.002097859,0.0003616631,0.0008918687,0.00116772,0.0003782988,0.001243791,0.0009249036,0.0006408613,0.0005252989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007811327,"about_ca_system_score_gemma":0.001088719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02046449,"about_ca_topic_score_gemma":0.0245879,"domain_scores_codex":[0.9994749,0.00003673344,0.00003921322,0.0001402118,0.000253793,0.00005519087],"domain_scores_gemma":[0.9993692,0.0001400646,0.00005843489,0.00009989421,0.0003017778,0.00003067692],"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":[0.0002327708,0.0000749605,0.006657251,0.00010017,0.00007039074,0.0001804693,0.0003180192,0.2164381,0.02394805,0.007834126,0.004533124,0.7396126],"study_design_scores_gemma":[0.00001355918,0.0000267826,0.001119897,0.000006877947,0.00001479696,0.00008511346,0.00004844308,0.9888565,0.005752325,0.001390844,0.002669431,0.00001544292],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03482592,0.0002042866,0.9626416,0.0001023868,0.0000529233,0.00006036236,0.000137256,0.0007763609,0.001198918],"genre_scores_gemma":[0.2896557,0.0003407072,0.7045578,0.0001102344,0.00005836444,0.0001544653,0.001444424,0.0001720486,0.003506161],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02046449,"threshold_uncertainty_score":0.04069072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02754068357635938,"score_gpt":0.2528753399625775,"score_spread":0.2253346563862181,"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."}}