{"id":"W2100830022","doi":"10.3390/f4030575","title":"Genetic Improvement of White Spruce Mechanical Wood Traits—Early Screening by Means of Acoustic Velocity","year":2013,"lang":"en","type":"article","venue":"Forests","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Heritability; Tree breeding; Trait; Context (archaeology); Biology; Particle velocity; Selection (genetic algorithm); Diameter at breast height; Genetic gain; Environmental science; Acoustics; Botany; Genetic variation; Woody plant; Evolutionary biology; Physics; Computer science; Gene; Genetics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005191586,0.0005161468,0.0003533938,0.0004124416,0.0001588029,0.0003316455,0.0003316939,0.0002391913,0.0006222949],"category_scores_gemma":[0.0003931434,0.0001438014,0.0002867595,0.0002009905,0.0001815941,0.0001755013,0.0002874594,0.0003771601,0.0001249862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002529595,"about_ca_system_score_gemma":0.0003099384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001711165,"about_ca_topic_score_gemma":0.004246639,"domain_scores_codex":[0.9997479,0.00005970785,0.00002415899,0.00007576093,0.00005768069,0.00003469967],"domain_scores_gemma":[0.9993964,0.0001984724,0.0001592936,0.00004572777,0.00006329714,0.0001367886],"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":[0.0001194659,0.0001263439,0.007557543,0.00002004667,0.00001983335,0.00007880831,0.000058667,0.0003121683,0.9858495,0.00005915008,0.00001070886,0.005787603],"study_design_scores_gemma":[0.00005166747,0.002894963,0.4644608,0.00002240693,0.0001253576,0.0004616638,0.0002184683,0.006788725,0.522377,0.0001703776,0.002368821,0.00005974562],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967446,0.0001192272,0.002769232,0.000009666819,0.00000343893,0.00001713655,0.00005980418,0.00004815012,0.0002286759],"genre_scores_gemma":[0.9909706,0.0001651784,0.006954222,0.00002746223,0.000005555265,0.00002488169,0.000318902,0.00004085017,0.001492377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001711165,"threshold_uncertainty_score":0.003402472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00851144186135803,"score_gpt":0.183206475061395,"score_spread":0.174695033200037,"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."}}