{"id":"W4283815139","doi":"10.3390/rs14133157","title":"A Deep Learning Time Series Approach for Leaf and Wood Classification from Terrestrial LiDAR Point Clouds","year":2022,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; University of Alberta","keywords":"Point cloud; Computer science; Deep learning; Artificial intelligence; Residual; Convolutional neural network; Pattern recognition (psychology); Lidar; Feature (linguistics); Series (stratigraphy); Remote sensing; Algorithm; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003261291,0.0001466211,0.0001726842,0.00003517091,0.0008801832,0.00008282975,0.00009441484,0.00005894578,0.00005374536],"category_scores_gemma":[0.00007337602,0.0001590806,0.00006443947,0.0001901157,0.0001224562,0.0001007251,0.0001698214,0.0002578751,0.00003198215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001755006,"about_ca_system_score_gemma":0.00001362268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005940063,"about_ca_topic_score_gemma":0.00001634482,"domain_scores_codex":[0.9986652,0.0001602763,0.0002180057,0.0004722959,0.0002352499,0.0002490354],"domain_scores_gemma":[0.9994072,0.0001043872,0.0001324022,0.0002687099,0.000006658409,0.00008066011],"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":[0.0001766693,0.00005294677,0.00009449846,0.000008229184,0.00003717136,0.000004284353,0.003292768,0.02447679,0.2567358,0.00002551749,0.0005870796,0.7145083],"study_design_scores_gemma":[0.0004218621,0.00008077032,0.0007150057,0.000005665493,0.00003595458,0.0000605898,0.001586911,0.9715092,0.000977422,0.0008618025,0.02352822,0.000216657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7560933,0.00005850879,0.2348428,0.0009084527,0.0001310176,0.0005819197,0.000008202704,0.0001823647,0.007193509],"genre_scores_gemma":[0.8553988,0.0000083004,0.1434415,0.00008521764,0.0001832372,2.070128e-7,0.0001417329,0.00003452437,0.0007065687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9470323,"threshold_uncertainty_score":0.6769747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01577397759820315,"score_gpt":0.2246897100821598,"score_spread":0.2089157324839566,"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."}}