{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005124764,0.0004909868,0.0003395589,0.0007455386,0.0003191869,0.0003714037,0.0002266426,0.000239545,0.0004733796],"category_scores_gemma":[0.0003555835,0.0001821593,0.0003487663,0.0004063933,0.0001934489,0.0003205203,0.0001960239,0.0003081847,0.0001353959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004732,"about_ca_system_score_gemma":0.0001685466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006811511,"about_ca_topic_score_gemma":0.02735975,"domain_scores_codex":[0.9998012,0.000036762,0.00001879368,0.00006888086,0.00004733349,0.00002702163],"domain_scores_gemma":[0.9995446,0.0001596034,0.0001132682,0.00002264245,0.00007947356,0.00008037855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005005709,0.0002333096,0.2651044,0.0001208627,0.0000895315,0.0001521247,0.0001798008,0.001146261,0.7237626,0.00002497297,0.00003911136,0.008646505],"study_design_scores_gemma":[0.000004575959,0.0006371651,0.9632259,0.000004541496,0.00004524358,0.00008023567,0.0001300059,0.0008743125,0.0347797,0.0000107697,0.0001976997,0.00000988359],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990634,0.0002375917,0.0003307457,0.000003361955,0.000001662834,0.00002135364,0.0001507094,0.000007684796,0.0001834535],"genre_scores_gemma":[0.9979711,0.0001384858,0.0009117492,0.00002305688,0.00000266039,0.00003080997,0.0006353724,0.000007173018,0.0002795464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006811511,"threshold_uncertainty_score":0.01354373,"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."}}