{"id":"W2036798998","doi":"10.15376/biores.7.2.2444-2460","title":"CHARACTERIZING CHANGBAI LARCH THROUGH VENEERING. PART 1: EFFECT OF STAND DENSITY","year":2012,"lang":"en","type":"article","venue":"BioResources","topic":"Forest ecology and management","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"FPInnovations","funders":"Chinese Academy of Forestry; FPInnovations; National Natural Science Foundation of China","keywords":"Larch; Veneer; Thinning; Softwood; Materials science; Young's modulus; Composite material; Green wood; Laminated veneer lumber; Pulp and paper industry; Forestry; Environmental science; Botany; Engineering; Geography; Wood drying; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003760225,0.00008792937,0.0001271786,0.00001491148,0.00007773792,0.000005759377,0.0001160008,0.00004865528,0.0005929882],"category_scores_gemma":[0.00002104194,0.00007003202,0.00003742539,0.00007725561,0.0001477531,0.0001301952,0.0002075775,0.00005207822,0.0002221972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003348605,"about_ca_system_score_gemma":0.000001004916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007285938,"about_ca_topic_score_gemma":0.00002580485,"domain_scores_codex":[0.9993286,0.00005469452,0.00009869612,0.0001197505,0.0001300603,0.0002681922],"domain_scores_gemma":[0.9996994,0.0000420451,0.00005687436,0.0001510662,0.000001552239,0.00004908657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005363629,0.00006065478,0.9863229,0.00005774121,0.00002476722,0.000003436286,0.001684634,0.00001579299,0.008575016,0.0003369268,0.001228259,0.00163624],"study_design_scores_gemma":[0.0001710732,0.0001367519,0.7817788,0.00001373442,0.00002053693,0.000001952052,0.00003501413,0.00001219268,0.04158854,0.00003257519,0.1761151,0.00009371345],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952915,0.0002099118,0.00007004554,0.0000668535,0.0001887196,0.0001446058,0.000003032274,0.0000265372,0.003998741],"genre_scores_gemma":[0.999471,0.00002110048,0.00008920721,0.0000837172,0.00008072536,0.00001344453,0.000003030257,0.000006964209,0.0002308252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2045441,"threshold_uncertainty_score":0.6492805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01125869951776285,"score_gpt":0.2245482469650043,"score_spread":0.2132895474472415,"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."}}