{"id":"W1608072211","doi":"10.1007/978-3-642-38868-2_7","title":"Tree-Space Statistics and Approximations for Large-Scale Analysis of Anatomical Trees","year":2013,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"H. Lundbeck A/S; Lundbeckfonden; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Danmarks Frie Forskningsfond; Villum Fonden; Strategiske Forskningsråd; AstraZeneca","keywords":"Tree (set theory); Geodesic; Metric (unit); Computer science; Principal component analysis; Metric space; Mathematics; Algorithm; Artificial intelligence; Combinatorics; Geometry; Discrete mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003556293,0.00008314711,0.0002409947,0.000314311,0.00009139114,0.00006475465,0.000197305,0.0000509257,0.00005067686],"category_scores_gemma":[0.0004475514,0.00006205934,0.00004299247,0.001425238,0.0001483289,0.000101614,0.00009084315,0.00007038069,7.131771e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001655723,"about_ca_system_score_gemma":0.00002565987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002328957,"about_ca_topic_score_gemma":0.0001907606,"domain_scores_codex":[0.9991358,0.00002646181,0.0002225881,0.0002566593,0.0001621813,0.0001963003],"domain_scores_gemma":[0.9982404,0.001275248,0.00008957162,0.0002148962,0.0001256091,0.00005424828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002279142,0.001650904,0.08011051,0.0002689951,0.000348215,0.000003778787,0.005724977,0.02669178,0.008696267,0.4200484,0.001445831,0.4549876],"study_design_scores_gemma":[0.000153214,0.000038712,0.02424812,0.000005734341,0.0000461754,6.407396e-7,0.000005047619,0.8681918,0.0007104301,0.1065172,0.00001260656,0.0000703093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.12031,0.00001274182,0.8790346,0.000330211,0.00003493128,0.0001979245,0.00005168157,0.00001342335,0.00001448917],"genre_scores_gemma":[0.4863246,9.554848e-7,0.5135944,0.00005262782,0.00001032564,0.00001002274,0.000003509781,0.000001935022,0.000001620943],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8415,"threshold_uncertainty_score":0.2530707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02122944916714245,"score_gpt":0.2914429356819674,"score_spread":0.2702134865148249,"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."}}