{"id":"W2960127460","doi":"10.3390/rs11161915","title":"An Analysis of Ground-Point Classifiers for Terrestrial LiDAR","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Lidar; Artificial intelligence; Terrain; Computer science; Remote sensing; Outlier; Pattern recognition (psychology); Geology; Cartography; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005302204,0.0006140595,0.0005972226,0.002865676,0.0005593539,0.00116081,0.0007475548,0.0006918121,0.00119892],"category_scores_gemma":[0.01220645,0.0001593231,0.0006135681,0.002668798,0.0003086166,0.001194234,0.0005548944,0.0004926029,0.0006119334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001002608,"about_ca_system_score_gemma":0.0009893483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009360377,"about_ca_topic_score_gemma":0.01015196,"domain_scores_codex":[0.9967684,0.0004621743,0.0002471103,0.0005678193,0.001710237,0.0002442933],"domain_scores_gemma":[0.9897003,0.004735956,0.000703101,0.0008366941,0.003856726,0.0001672434],"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":[0.0005258909,0.0002651164,0.1543705,0.0004675205,0.0003765572,0.0001955838,0.0003478442,0.1008336,0.01281164,0.002072562,0.004176374,0.7235568],"study_design_scores_gemma":[0.00002382503,0.0005765394,0.1421552,0.0001462832,0.0001345416,0.0002899456,0.0007153279,0.8292029,0.01909379,0.001775759,0.005834279,0.00005162245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8644887,0.002511921,0.1215607,0.0001897088,0.0001035329,0.0002738163,0.001748245,0.001272388,0.007850806],"genre_scores_gemma":[0.9523197,0.0003484649,0.04345584,0.0000416398,0.00002412313,0.00009041867,0.00287745,0.00006185708,0.0007804839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009360377,"threshold_uncertainty_score":0.02804106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160256408162935,"score_gpt":0.2658009688865862,"score_spread":0.2497753280702927,"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."}}