{"id":"W3038163067","doi":"10.3390/rs12132109","title":"Forest Inventory and Diversity Attribute Modelling Using Structural and Intensity Metrics from Multi-Spectral Airborne Laser Scanning Data","year":2020,"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 Lethbridge; Queen's University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Forest inventory; Environmental science; Mean squared error; Basal area; Random forest; Wavelength; Leaf area index; Computer science; Mathematics; Statistics; Optics; Geography; Forest management; Forestry; Physics; Ecology; Artificial intelligence; Agroforestry","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":[0.0009321847,0.000569196,0.0003591264,0.001538488,0.0004678991,0.001005385,0.0008834389,0.0003147538,0.0005989749],"category_scores_gemma":[0.00192692,0.0003006542,0.0007687844,0.001402623,0.0003075211,0.0006831142,0.0004699064,0.0004207597,0.0001893061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003987655,"about_ca_system_score_gemma":0.002383808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3933823,"about_ca_topic_score_gemma":0.5258382,"domain_scores_codex":[0.9995731,0.0000636803,0.00002788755,0.0001449609,0.000139743,0.00005064236],"domain_scores_gemma":[0.9993917,0.0001885344,0.00009033788,0.00005143072,0.0002368473,0.00004119951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001103522,0.0001430684,0.2919769,0.0001115817,0.0002742917,0.0001477262,0.0004276252,0.6282023,0.004727463,0.001052531,0.000766518,0.07205961],"study_design_scores_gemma":[0.00001373219,0.00003699134,0.09675691,0.0000170158,0.00004224342,0.00004589658,0.0001589365,0.9003899,0.0009873327,0.0006490681,0.0008748571,0.00002706597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9342478,0.0001562417,0.06006158,0.00007825683,0.0000083316,0.0001836047,0.003059868,0.00035971,0.001844601],"genre_scores_gemma":[0.9480892,0.00007619358,0.04787388,0.00001714164,0.000004724328,0.000122718,0.003076609,0.00003715944,0.0007024469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3933823,"threshold_uncertainty_score":0.7821852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1066463029422307,"score_gpt":0.2667002630537034,"score_spread":0.1600539601114727,"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."}}