{"id":"W2082161040","doi":"10.1139/x09-111","title":"Genetic analysis of longitudinal height data using random regression","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Heritability; Scots pine; Statistics; Selection (genetic algorithm); Trait; Mathematics; Regression; Regression analysis; Legendre polynomials; Pinus <genus>; Restricted maximum likelihood; Random effects model; Biology; Maximum likelihood; Computer science; Evolutionary biology; Botany","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":[0.008731176,0.0004921521,0.0006075505,0.001910793,0.0003111383,0.0005008326,0.0005730248,0.0002713403,0.0009713322],"category_scores_gemma":[0.01697869,0.0002960181,0.0008930929,0.001988721,0.0004272965,0.000552437,0.0004260281,0.0005721693,0.0003080328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004642402,"about_ca_system_score_gemma":0.0006329864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008387747,"about_ca_topic_score_gemma":0.01066798,"domain_scores_codex":[0.994608,0.004000073,0.0001502385,0.0007082354,0.0003962375,0.0001371814],"domain_scores_gemma":[0.9922014,0.005401854,0.0008178856,0.0008615779,0.0005945707,0.0001226045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001143243,0.0004696062,0.4358677,0.000208117,0.001958686,0.0006715606,0.0006583968,0.2570959,0.03238015,0.01044082,0.00114445,0.2579615],"study_design_scores_gemma":[0.0001017655,0.0006707103,0.2307274,0.00005257398,0.0003539298,0.0004410721,0.0001330267,0.749581,0.006132543,0.009683956,0.001960309,0.0001615734],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5613548,0.0001621742,0.436318,0.00006949968,0.00001612958,0.0000843934,0.0005367562,0.0006695192,0.0007888094],"genre_scores_gemma":[0.8567173,0.0001412421,0.1406571,0.00002573195,0.0000163379,0.0001314162,0.001429963,0.0001478934,0.0007329732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008731176,"threshold_uncertainty_score":0.04617542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08255788807580558,"score_gpt":0.3506562920111279,"score_spread":0.2680984039353222,"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."}}