{"id":"W1862182248","doi":"10.1139/cjfr-2012-0295","title":"Predicting forest growth based on airborne light detection and ranging data, climate data, and a simplified process-based model","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Itä-Suomen Yliopisto","keywords":"Lidar; Basal area; Scots pine; Canopy; Environmental science; Ranging; Field (mathematics); Remote sensing; Statistics; Mathematics; Geography; Pinus <genus>; Forestry","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":[0.0005392457,0.000589679,0.0004685933,0.0004002454,0.0002237312,0.000519008,0.0007068288,0.000477814,0.0008358514],"category_scores_gemma":[0.00108728,0.0004616415,0.0005556049,0.0004297495,0.0002296827,0.0007317422,0.0003877442,0.0003441753,0.0002047071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006472098,"about_ca_system_score_gemma":0.0007832473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02663803,"about_ca_topic_score_gemma":0.02468636,"domain_scores_codex":[0.9998511,0.00003794695,0.00001233617,0.00004553868,0.00003633106,0.00001670404],"domain_scores_gemma":[0.9996486,0.0002102843,0.00003230179,0.00003196903,0.00005926879,0.00001768065],"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":[0.00004293794,0.00003013385,0.006068434,0.00003053526,0.00003146432,0.00002364829,0.00001472946,0.9813759,0.001697949,0.0001971573,0.00006926592,0.01041787],"study_design_scores_gemma":[0.000007892738,0.00001783174,0.002637519,0.000002172468,0.00001107218,0.000007809196,0.000003316178,0.9967092,0.0002689013,0.0002409198,0.00008962211,0.000003797378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7446631,0.0002508286,0.2504285,0.0001997893,0.00003176183,0.0000881075,0.0009487049,0.0009265355,0.002462575],"genre_scores_gemma":[0.9553848,0.0001900303,0.04230309,0.0000314864,0.00002535093,0.0001373428,0.0007656088,0.00003011422,0.001132193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02663803,"threshold_uncertainty_score":0.05296594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03913890506045978,"score_gpt":0.2996306389419717,"score_spread":0.2604917338815119,"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."}}