{"id":"W1930004075","doi":"10.1139/cjfr-2014-0297","title":"Using semi-global matching point clouds to estimate growing stock at the plot and stand levels: application for a broadleaf-dominated forest in central Europe","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Forest inventory; Point cloud; Canopy; Mathematics; Laser scanning; Statistics; Environmental science; Random forest; Remote sensing; Forestry; Geography; Computer science; Forest management; Laser; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001410385,0.000932711,0.0008106805,0.001246397,0.0003202372,0.0007046034,0.001163193,0.0008336941,0.0005639881],"category_scores_gemma":[0.001400443,0.0004709717,0.0008637638,0.001259159,0.0002863685,0.0006540546,0.0005259574,0.0003151844,0.0001713445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007245537,"about_ca_system_score_gemma":0.0007794987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05186968,"about_ca_topic_score_gemma":0.03690013,"domain_scores_codex":[0.999709,0.00008398529,0.00001974396,0.00009885876,0.00004388539,0.0000445249],"domain_scores_gemma":[0.9995545,0.000197764,0.0000561202,0.00006535861,0.00009038784,0.00003588036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003108718,0.0002524506,0.05238945,0.00006300992,0.0002139357,0.0001484767,0.0001464661,0.8501277,0.006760259,0.0001776393,0.0002271709,0.08918261],"study_design_scores_gemma":[0.00001393552,0.00002508142,0.01295217,0.000003883767,0.00001495511,0.00001344103,0.00003484034,0.986042,0.0007489249,0.00008547358,0.00005459391,0.00001065998],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9589273,0.0001264488,0.03949801,0.00003300505,0.000008939902,0.00004637913,0.0004060504,0.0007038121,0.0002500689],"genre_scores_gemma":[0.9735134,0.00004651683,0.02559023,0.000009150557,0.000004395534,0.0000232264,0.0005975614,0.00003084601,0.0001846716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05186968,"threshold_uncertainty_score":0.1031355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.046872249274581,"score_gpt":0.3406405222607704,"score_spread":0.2937682729861894,"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."}}