{"id":"W2152897948","doi":"10.1109/tmi.2010.2051559","title":"Understanding the Effect of Bias in Fiducial Localization Error on Point-Based Rigid-Body Registration","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Fiducial marker; Artificial intelligence; Computer vision; Noise (video); Image registration; Computer science; Point (geometry); Observational error; Mathematics; Image (mathematics); Statistics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003595882,0.0006071511,0.0007596485,0.0007291363,0.0002756626,0.0009735581,0.000925903,0.001360539,0.0006434219],"category_scores_gemma":[0.03088682,0.0004568414,0.0004811328,0.0007239218,0.001195673,0.002248514,0.001412048,0.0007337033,0.0002920214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009277853,"about_ca_system_score_gemma":0.0008970735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00272193,"about_ca_topic_score_gemma":0.00144545,"domain_scores_codex":[0.9982421,0.0006102489,0.00009798486,0.0001882107,0.0007512154,0.0001102812],"domain_scores_gemma":[0.9893027,0.007897467,0.0009440171,0.0008418917,0.0009477286,0.00006615331],"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":[0.0001410942,0.00003838235,0.005287309,0.0002426968,0.00006567212,0.0002843842,0.0003466341,0.8460017,0.01579055,0.07063581,0.0007401789,0.06042559],"study_design_scores_gemma":[0.00000816018,0.00003299627,0.001388972,0.00003257729,0.00001502111,0.0001388784,0.0000230705,0.9746935,0.00439118,0.01848708,0.0007650707,0.00002342662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03279245,0.0008224406,0.9643067,0.0003340498,0.00003669356,0.00002390405,0.00003452522,0.0001296572,0.001519637],"genre_scores_gemma":[0.8065352,0.001979653,0.1895482,0.0001900742,0.0001113833,0.00008997411,0.00008040575,0.0001994283,0.001265675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003595882,"threshold_uncertainty_score":0.0190171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02798420800724395,"score_gpt":0.2672526903454931,"score_spread":0.2392684823382492,"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."}}