{"id":"W2946378069","doi":"10.3390/rs11101239","title":"Winter Wheat Canopy Height Extraction from UAV-Based Point Cloud Data with a Moving Cuboid Filter","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canadian Space Agency; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Point cloud; Remote sensing; Canopy; Cuboid; Mean squared error; Environmental science; Filter (signal processing); Outlier; Mathematics; Computer science; Statistics; Geology; Geography; Artificial intelligence; Computer vision; Geometry","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":[0.0001596803,0.0005762529,0.0004715549,0.001322466,0.0002232379,0.0004149189,0.0003483106,0.0002687751,0.0006235196],"category_scores_gemma":[0.0004793441,0.0002500582,0.0006502786,0.001328635,0.0001023807,0.0003574493,0.0003177562,0.0003319289,0.0004507437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00029677,"about_ca_system_score_gemma":0.000579541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01230219,"about_ca_topic_score_gemma":0.01154781,"domain_scores_codex":[0.9997768,0.00001629646,0.00001161997,0.00006174783,0.0001025876,0.00003099722],"domain_scores_gemma":[0.9997473,0.00004011014,0.00003499343,0.00002643862,0.0001394196,0.00001162461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003165384,0.00009809906,0.01423071,0.0003161818,0.0001718282,0.0004304493,0.0002607932,0.06045778,0.2772121,0.001255052,0.004359634,0.6408908],"study_design_scores_gemma":[0.00002031324,0.00008955771,0.02957619,0.00002438966,0.00005922521,0.0002904771,0.0001332227,0.9069018,0.05840047,0.0004764408,0.00397914,0.00004875465],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1843389,0.00053633,0.8097937,0.00008163114,0.00007434841,0.00008367208,0.0008380064,0.002590718,0.001662713],"genre_scores_gemma":[0.5989665,0.0005264828,0.3961418,0.00005793777,0.00004026861,0.0001241448,0.002339037,0.0001416042,0.001662204],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01230219,"threshold_uncertainty_score":0.02446115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01469057471022369,"score_gpt":0.2401424612959074,"score_spread":0.2254518865856837,"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."}}