{"id":"W3087941343","doi":"10.1109/lgrs.2020.3022282","title":"Forest Change Detection in Lidar Data Based on Polar Change Vector Analysis","year":2020,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Change detection; Point cloud; Remote sensing; Computer science; Data set; Land cover; Reference data; Data mining; Artificial intelligence; Land use; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0004486506,0.0003506057,0.0003702879,0.003462425,0.0002785096,0.0008358542,0.0002561324,0.0002672127,0.0006384179],"category_scores_gemma":[0.001335286,0.0001632806,0.0002374677,0.002906746,0.0002948492,0.0007264387,0.0003931567,0.0003943618,0.0003244906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002304575,"about_ca_system_score_gemma":0.0002837932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003487482,"about_ca_topic_score_gemma":0.004898765,"domain_scores_codex":[0.9996272,0.0000722393,0.00002463255,0.00007512916,0.0001587448,0.0000419958],"domain_scores_gemma":[0.9993743,0.0002151358,0.00008839959,0.00003990817,0.0002567231,0.00002555461],"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":[0.0004547868,0.0002896074,0.04462041,0.0002299906,0.00008971457,0.0005651979,0.0003761807,0.0346971,0.1541934,0.002711781,0.002714314,0.7590575],"study_design_scores_gemma":[0.00003225996,0.0001569445,0.07353071,0.00004339661,0.00004639633,0.0007686418,0.0007413406,0.8527883,0.06445153,0.002470626,0.00490388,0.00006603834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3524359,0.0005734602,0.6406041,0.0002008145,0.0001069324,0.0002323966,0.0009640268,0.001363682,0.003518694],"genre_scores_gemma":[0.7601125,0.0004525066,0.2368502,0.0000730441,0.00005156246,0.000140705,0.001502437,0.0000558169,0.0007612169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003487482,"threshold_uncertainty_score":0.006934345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04806873010653662,"score_gpt":0.250994565376336,"score_spread":0.2029258352697993,"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."}}