{"id":"W2131493488","doi":"10.1109/tmi.2009.2020751","title":"Distribution of Target Registration Error for Anisotropic and Inhomogeneous Fiducial Localization Error","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Fiducial marker; Isotropy; Distribution (mathematics); Image registration; Point (geometry); Algorithm; Computer science; Measure (data warehouse); Artificial intelligence; Point distribution model; Mathematics; Point set registration; Computer vision; Pattern recognition (psychology); Geometry; Image (mathematics); Mathematical analysis; Data mining; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001209075,0.0001245165,0.0001537128,0.00007250451,0.0001162276,0.00002219678,0.00005536403,0.00009503091,0.00002762747],"category_scores_gemma":[0.00003419536,0.0001304894,0.00005256964,0.0001567337,0.0000618885,0.0001090237,3.062903e-7,0.0001168117,0.000001240636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000581621,"about_ca_system_score_gemma":0.00003320269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001053707,"about_ca_topic_score_gemma":0.00001284094,"domain_scores_codex":[0.9990739,0.00002370202,0.0003109385,0.0001635853,0.0002587903,0.0001690808],"domain_scores_gemma":[0.9996188,0.00004493646,0.00004249822,0.0001094873,0.00007948864,0.0001047891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004569358,0.0001092956,0.00004046356,0.00009739721,0.00001882806,0.00000542135,0.0001334675,0.9311312,0.003382212,0.0004878453,0.000592013,0.06395615],"study_design_scores_gemma":[0.0005379901,0.00008277583,0.0001757982,0.0000625152,0.00003468366,0.00001255559,0.0000369778,0.9812502,0.01670254,0.0004330704,0.000541969,0.0001289571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01537236,0.00007862468,0.983203,0.0005745033,0.0003563379,0.0002081163,0.00004358383,0.0001177494,0.00004571554],"genre_scores_gemma":[0.9977922,0.0000626457,0.001778212,0.0001413921,0.00007802318,0.00001014235,0.0001060259,0.00001657289,0.0000147912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9824198,"threshold_uncertainty_score":0.5321201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01050259771769127,"score_gpt":0.2456687470592621,"score_spread":0.2351661493415708,"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."}}