{"id":"W1982710388","doi":"10.1007/s10851-013-0473-0","title":"Tree-Oriented Analysis of Brain Artery Structure","year":2014,"lang":"en","type":"article","venue":"Journal of Mathematical Imaging and Vision","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Eidgenössische Technische Hochschule Zürich","keywords":"Phylogenetic tree; Mathematics; Pairwise comparison; Descendant; Tree (set theory); Data set; Artificial intelligence; Pattern recognition (psychology); Combinatorics; Computer science; Statistics; Biology; Physics","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":[0.000273458,0.0002596975,0.0002444653,0.001829747,0.0002349335,0.0008024521,0.0002766557,0.0003251738,0.002155543],"category_scores_gemma":[0.0007020069,0.0001619716,0.0003150874,0.0009531525,0.000232114,0.0007319839,0.0002863655,0.0003817764,0.0004364892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002363338,"about_ca_system_score_gemma":0.0004746814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009695578,"about_ca_topic_score_gemma":0.001376569,"domain_scores_codex":[0.9999504,0.00001105247,0.000002587218,0.00001028768,0.00001499713,0.00001061383],"domain_scores_gemma":[0.9997339,0.00008161642,0.0000436678,0.00002540349,0.00009028267,0.00002515924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004616241,0.0001027128,0.01053127,0.0002768391,0.00009481838,0.0004320676,0.0003466838,0.08634469,0.2811256,0.09005711,0.004832759,0.5253937],"study_design_scores_gemma":[0.00001213489,0.00005883473,0.01116151,0.00001781617,0.0000495682,0.0004604598,0.00008673387,0.9299129,0.01725035,0.03801274,0.002954422,0.00002250548],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2425575,0.0005250686,0.7522571,0.0002608983,0.00004412256,0.0000452427,0.0004613565,0.0004890109,0.00335967],"genre_scores_gemma":[0.8199145,0.0009034799,0.1761154,0.00004533995,0.00007871815,0.00003195654,0.0003936839,0.0001955672,0.00232137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002155543,"threshold_uncertainty_score":0.00721103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01475484303061852,"score_gpt":0.3227452765528965,"score_spread":0.307990433522278,"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."}}