{"id":"W2604655395","doi":"10.3832/ifor2151-010","title":"Comparison of wood volume estimates of young trees from terrestrial laser scan data","year":2017,"lang":"en","type":"article","venue":"iForest - Biogeosciences and Forestry","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"European Commission","keywords":"Point cloud; Voxel; Volume (thermodynamics); Tree (set theory); Cylinder; Laser scanning; Lidar; Point (geometry); Mathematics; Computer science; Geometry; Laser; Geology; Remote sensing; Physics; Optics; Artificial intelligence; Mathematical analysis","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":[0.00188872,0.0003927021,0.0004167828,0.003335414,0.000203483,0.0007790587,0.0005940698,0.000435218,0.0005226113],"category_scores_gemma":[0.005219517,0.0002725405,0.0004959875,0.001432553,0.0002602542,0.0009965617,0.0005609377,0.0002181185,0.0004327569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002647213,"about_ca_system_score_gemma":0.0002042364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001695999,"about_ca_topic_score_gemma":0.003506846,"domain_scores_codex":[0.9989236,0.0002359078,0.00008348507,0.0001782542,0.0005094697,0.00006925176],"domain_scores_gemma":[0.9957793,0.002503191,0.0003889289,0.0002625771,0.0009903962,0.00007573855],"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":[0.0008891473,0.0001456616,0.353106,0.0006173804,0.000662307,0.0003198686,0.00169482,0.1335866,0.1038312,0.001886542,0.001120705,0.4021398],"study_design_scores_gemma":[0.00002664273,0.0003079399,0.613627,0.00009687219,0.0001506039,0.0008167261,0.0007413784,0.3304233,0.04706797,0.002439319,0.004130967,0.00017129],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.88511,0.0008196321,0.1110311,0.00002502531,0.00002541925,0.00004934776,0.0008919817,0.000431574,0.001615907],"genre_scores_gemma":[0.943009,0.0002859516,0.05431824,0.00001235698,0.00001168168,0.00005283145,0.001837264,0.0001505095,0.0003221705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003335414,"threshold_uncertainty_score":0.009988666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04084057022451576,"score_gpt":0.3112229555865991,"score_spread":0.2703823853620834,"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."}}