{"id":"W2090475106","doi":"10.1088/0031-9155/57/15/4905","title":"Tracking the motion trajectories of junction structures in 4D CT images of the lung","year":2012,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; National Research Council Canada","keywords":"Artificial intelligence; Computer vision; Computer science; Trajectory; Tracking (education); Maxima and minima; Voxel; Metric (unit); Image registration; Motion estimation; Pattern recognition (psychology); Mathematics; Image (mathematics); Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0004063651,0.00004508168,0.0001141107,0.00003355885,0.00002144077,0.000002505648,0.0001640751,0.00001580778,0.00000341925],"category_scores_gemma":[0.0001177599,0.00002117056,0.00001241372,0.0002175308,0.0003419559,0.000118051,0.00004838144,0.0001015952,2.355887e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000083955,"about_ca_system_score_gemma":0.000009332956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001900143,"about_ca_topic_score_gemma":0.000008444806,"domain_scores_codex":[0.9994814,0.000130814,0.0001652866,0.0000717249,0.00006957709,0.0000811764],"domain_scores_gemma":[0.9995922,0.000158306,0.00009477143,0.0001202611,0.00002419835,0.00001023119],"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":[0.000007748251,0.00007453174,0.2786197,0.0000938235,0.00001297247,3.312837e-7,0.007556801,0.00001368295,0.1578325,0.07316501,0.0003985233,0.4822243],"study_design_scores_gemma":[0.0005090436,0.0001135388,0.4785519,0.0001114446,0.00001426903,0.000006717804,0.0007731818,0.001454016,0.4367894,0.08155989,0.00003745679,0.00007919611],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4169685,0.0006974764,0.5807083,0.0009990733,0.0003670243,0.0001428673,7.163752e-7,0.00001112932,0.0001049428],"genre_scores_gemma":[0.9977356,0.00006782011,0.001901123,0.0001889859,0.00009861875,0.000004081456,0.000001156376,0.000001271375,0.000001335884],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5807671,"threshold_uncertainty_score":0.1259951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1233026524263476,"score_gpt":0.391696619214017,"score_spread":0.2683939667876694,"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."}}