{"id":"W2129223507","doi":"10.1109/tmi.2014.2370951","title":"Global Linking of Cell Tracks Using the Viterbi Algorithm","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":213,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"National Institute on Aging; National Institutes of Health; National Heart, Lung, and Blood Institute; Vetenskapsrådet; California Institute for Regenerative Medicine","keywords":"Viterbi algorithm; Computer science; Algorithm; False positive paradox; Segmentation; Artificial intelligence; Image segmentation; Image (mathematics); Sequence (biology); Computer vision; Tracking (education); Pattern recognition (psychology); Hidden Markov model","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":[0.0004293535,0.0001278438,0.0001462442,0.00004304131,0.0001129767,0.00002274545,0.0002737737,0.00009710684,0.00005097559],"category_scores_gemma":[0.00001767339,0.0001004516,0.0001769771,0.000168907,0.0001916568,0.000006265154,0.000004914133,0.0001847052,0.000002313072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001931647,"about_ca_system_score_gemma":0.00005156409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007113097,"about_ca_topic_score_gemma":0.00001732643,"domain_scores_codex":[0.9988654,0.0001098028,0.0002530372,0.0002525172,0.0003253822,0.0001938747],"domain_scores_gemma":[0.9993674,0.00003414758,0.00007685168,0.0003611538,0.00007071181,0.00008973595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001217646,0.0002119755,0.0002044807,0.00002666859,0.00006382377,0.000007273687,0.00005303944,0.0007556366,0.5178022,0.000002810734,0.0003009031,0.480559],"study_design_scores_gemma":[0.0002908976,0.00004406009,0.00002579804,0.00004955087,0.0000979512,0.00003970916,0.00005859419,0.1275793,0.8693935,0.00006240037,0.002217877,0.0001403996],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03866497,0.0001079551,0.9601074,0.0001473365,0.00008320054,0.0000680002,0.00000407081,0.00002543213,0.0007915903],"genre_scores_gemma":[0.9871547,0.00007439091,0.011789,0.0007595049,0.0001392251,0.000005914119,0.000005895293,0.00001548735,0.00005591037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9484897,"threshold_uncertainty_score":0.4096299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00592677037792465,"score_gpt":0.2737512557997551,"score_spread":0.2678244854218305,"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."}}