{"id":"W2793563559","doi":"10.3390/rs10020347","title":"Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"West Fraser (Canada); Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Agriculture and Forestry","keywords":"Point cloud; Laser scanning; Photogrammetry; Matching (statistics); Remote sensing; Basal area; Yield (engineering); Forest inventory; Computer science; Digital elevation model; Mean squared error; Lidar; Environmental science; Mathematics; Statistics; Forestry; Artificial intelligence; Geography; Forest management; Laser; Optics","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":[0.0007059191,0.0006888263,0.0003489175,0.001520098,0.0002332059,0.0006767152,0.000646931,0.0004732206,0.0006152472],"category_scores_gemma":[0.001566565,0.0002889682,0.0007097851,0.001933347,0.000172619,0.0009328466,0.0004657317,0.000374408,0.000256128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000920009,"about_ca_system_score_gemma":0.0005867861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02187358,"about_ca_topic_score_gemma":0.02543391,"domain_scores_codex":[0.9996574,0.00005566727,0.00002812605,0.0001120178,0.0001158893,0.00003098477],"domain_scores_gemma":[0.9994971,0.0001534875,0.00008274599,0.0001022575,0.0001369192,0.00002759214],"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":[0.00005669019,0.0001427003,0.04671732,0.00005016919,0.00007808919,0.0001327447,0.00005696094,0.8792953,0.005075934,0.000661959,0.0004694413,0.06726274],"study_design_scores_gemma":[0.000005159789,0.00002169773,0.02715253,0.000006566211,0.00001634161,0.00002734821,0.00003095709,0.9697965,0.001907185,0.0004527954,0.0005676075,0.00001531648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8267493,0.0002696398,0.1647782,0.0001409741,0.00004142173,0.0001207225,0.003415716,0.00133716,0.003146767],"genre_scores_gemma":[0.9529028,0.0001268699,0.04506285,0.0000135267,0.00001272925,0.0000583029,0.001494581,0.00004593948,0.0002823847],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02187358,"threshold_uncertainty_score":0.04349256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04931883916757218,"score_gpt":0.263722146367204,"score_spread":0.2144033071996319,"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."}}