{"id":"W2100612282","doi":"10.1109/isbi.2008.4540967","title":"Co-registration of a needle-positioning device with a volumetric x-ray micro-computed tomography scanner for image-guided preclinical interventions","year":2008,"lang":"en","type":"article","venue":"","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Fiducial marker; Computer vision; Artificial intelligence; Imaging phantom; Scanner; Computer science; Segmentation; Centroid; Image registration; Line (geometry); Nuclear medicine; Mathematics; Medicine; Image (mathematics)","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":[0.0001103604,0.0001130786,0.0001750013,0.0002270294,0.0001150358,0.00002827761,0.0001121005,0.00005734049,0.00002535219],"category_scores_gemma":[0.00002933569,0.0001062995,0.0001783769,0.0007506926,0.00007264459,0.0001064769,0.000006934735,0.00007869193,0.000006490907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002034028,"about_ca_system_score_gemma":0.00001995205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004700151,"about_ca_topic_score_gemma":0.00002858911,"domain_scores_codex":[0.9991467,0.00001047191,0.0004330448,0.0001593478,0.0001018437,0.0001486032],"domain_scores_gemma":[0.9993089,0.0001469002,0.000082501,0.0001935792,0.0002079765,0.00006016067],"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":[0.0001891196,0.003066414,0.08360989,0.003169624,0.002396251,0.00001412102,0.001615,0.5511012,0.1832224,0.0104888,0.1548501,0.006277114],"study_design_scores_gemma":[0.005149295,0.000939404,0.1539552,0.0007445303,0.0005489007,0.0001676182,0.0003045809,0.7691592,0.06403645,0.000431207,0.003324204,0.001239445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2000358,0.0001214161,0.7974918,0.00008453862,0.00004160364,0.0004597033,0.00002863974,0.000202417,0.001534123],"genre_scores_gemma":[0.7912476,0.000007066251,0.2083065,0.00002152515,0.00004001171,0.0001097048,0.00009658029,0.00002096761,0.0001499832],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5912119,"threshold_uncertainty_score":0.4334769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04700908318992085,"score_gpt":0.309125414093622,"score_spread":0.2621163309037012,"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."}}