{"id":"W2124015892","doi":"10.1002/cjs.5550360110","title":"A multivariate von mises distribution with applications to bioinformatics","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":154,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute for Occupational Safety and Health; Engineering and Physical Sciences Research Council","keywords":"Bivariate analysis; Univariate; von Mises distribution; Multivariate statistics; Joint probability distribution; Mathematics; Extension (predicate logic); Marginal distribution; Distribution (mathematics); Maximum likelihood; Statistics; Multivariate normal distribution; von Mises yield criterion; Conditional probability distribution; Applied mathematics; Econometrics; Computer science; Random variable; Mathematical analysis; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001510637,0.0001007024,0.0001884717,0.0001288437,0.0002387897,0.00003373704,0.000150408,0.00004801281,0.0001076473],"category_scores_gemma":[0.0005065377,0.00007599039,0.00002940906,0.0002938183,0.00007268661,0.00007943388,0.000006998966,0.0001472958,0.00002570271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001272115,"about_ca_system_score_gemma":0.0005832021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004413218,"about_ca_topic_score_gemma":0.001244468,"domain_scores_codex":[0.9991286,0.0000213928,0.0004083846,0.00006444826,0.0001651146,0.0002120099],"domain_scores_gemma":[0.9983857,0.0002225502,0.0002612413,0.0001423412,0.0004249177,0.0005631965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005034641,0.0001799911,0.007375637,0.0001262977,0.0001735571,0.0006192501,0.001627619,0.0004516789,0.00006612282,0.7020602,0.2755368,0.0117325],"study_design_scores_gemma":[0.00506012,0.002968724,0.1394514,0.0005987816,0.0007716762,0.005875922,0.00244203,0.006892118,0.0006424117,0.1350721,0.6981516,0.002073091],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007831593,0.0000166408,0.9897295,0.0002522608,0.00005397978,0.0001862492,0.001430404,0.000007148148,0.0004921861],"genre_scores_gemma":[0.328275,0.000007893314,0.671281,0.0001395617,0.00007535862,0.000007861365,0.00004819128,0.00001098691,0.0001541632],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5669882,"threshold_uncertainty_score":0.3098798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04651975538989457,"score_gpt":0.2705272366819816,"score_spread":0.224007481292087,"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."}}