{"id":"W2901515244","doi":"10.1109/igarss.2018.8518369","title":"Dection and Health Analysis of Individual Tree in Urban Environment with Multi-Sensor Platform","year":2018,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Science Foundation","keywords":"Lidar; Hyperspectral imaging; Computer science; Tree (set theory); Cluster analysis; Remote sensing; Ranging; Computer vision; Artificial intelligence; Euclidean distance; Data mining; Geography; Mathematics; 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":[0.0001664705,0.0002997326,0.0002508819,0.0007588468,0.0001904181,0.0002924474,0.0002951729,0.0003504404,0.0004377246],"category_scores_gemma":[0.0001734794,0.000126447,0.0002098783,0.0004118981,0.0001441507,0.0004731434,0.0005084744,0.0001630093,0.000131096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001285294,"about_ca_system_score_gemma":0.0001505354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000864208,"about_ca_topic_score_gemma":0.001636609,"domain_scores_codex":[0.9998806,0.00001675056,0.000004866607,0.00002809007,0.00004612568,0.00002355179],"domain_scores_gemma":[0.9998879,0.00001791698,0.00002345863,0.00001316279,0.00004085923,0.00001655594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005453087,0.0004533573,0.180476,0.0002601035,0.0001783603,0.001245193,0.0006151692,0.1491359,0.4614619,0.001448529,0.001418009,0.2027622],"study_design_scores_gemma":[0.00001143795,0.000345934,0.121584,0.00001459735,0.00006638152,0.0004311616,0.000589274,0.8268962,0.04800283,0.001182582,0.0008353377,0.00004031957],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9200974,0.0001267032,0.07842453,0.00005399201,0.00001580219,0.00002509459,0.0001643004,0.000231147,0.0008610382],"genre_scores_gemma":[0.9833123,0.00005607308,0.01618782,0.00001451179,0.000005847905,0.00001282668,0.0001038698,0.0000067195,0.0002999286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000864208,"threshold_uncertainty_score":0.001718342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02817856040969738,"score_gpt":0.2621391896116272,"score_spread":0.2339606292019299,"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."}}