{"id":"W146638925","doi":"10.1515/1544-6115.1779","title":"Hessian Calculation for Phylogenetic Likelihood based on the Pruning Algorithm and its Applications","year":2012,"lang":"en","type":"article","venue":"Statistical Applications in Genetics and Molecular Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Hessian matrix; Computation; Pruning; Algorithm; Tree (set theory); Mathematics; Likelihood function; Restricted maximum likelihood; Inference; Computer science; Maximum likelihood; Model selection; Mathematical optimization; Matrix (chemical analysis); Newton's method; Applied mathematics; Estimation theory; Statistics; Artificial intelligence; Combinatorics; Nonlinear system","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":[0.002053069,0.0007745601,0.0006795005,0.001511549,0.000590758,0.0007957761,0.001058787,0.0009853974,0.003768137],"category_scores_gemma":[0.01051674,0.0004204476,0.000788645,0.001559452,0.0007746735,0.001378867,0.00096649,0.001316366,0.00167002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006167561,"about_ca_system_score_gemma":0.001160686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002697396,"about_ca_topic_score_gemma":0.003019284,"domain_scores_codex":[0.9988254,0.0003921116,0.00006748844,0.0001065113,0.0005501149,0.00005842615],"domain_scores_gemma":[0.9973787,0.001560709,0.0002112816,0.0002479973,0.000525983,0.00007531997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007289199,0.00009750362,0.00195327,0.0005188685,0.0001092967,0.0009054674,0.0003805254,0.2106017,0.02337358,0.3461234,0.008594653,0.4072689],"study_design_scores_gemma":[0.00001713355,0.00003845451,0.001123515,0.00005264456,0.0000227612,0.0003946065,0.00002401662,0.8935829,0.005052449,0.09141657,0.008224064,0.00005088423],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002347726,0.0002096854,0.9959769,0.00009876022,0.00003203316,0.00002071351,0.00002147633,0.0001919193,0.001100853],"genre_scores_gemma":[0.05381779,0.000564821,0.942344,0.0001007645,0.00009360614,0.0001241724,0.0001190811,0.0003778436,0.002457938],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003768137,"threshold_uncertainty_score":0.01260573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01117047521445802,"score_gpt":0.2885623222237179,"score_spread":0.2773918470092598,"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."}}