{"id":"W1974064720","doi":"10.1118/1.4771958","title":"3D image‐guided robotic needle positioning system for small animal interventions","year":2013,"lang":"en","type":"article","venue":"Medical Physics","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Institute for Cancer Research; Cancer Research Institute","keywords":"Computer vision; Calibration; Computer science; Artificial intelligence; Workspace; Image registration; Scanner; Robot; Flexibility (engineering); Position (finance); Coordinate system; Workflow; Orientation (vector space); Image (mathematics); Mathematics","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":[0.0004545629,0.0004942524,0.0003451691,0.0004343934,0.0001936671,0.0004063429,0.0009506136,0.0007543269,0.002385552],"category_scores_gemma":[0.0006931941,0.0003536636,0.000469539,0.0001796975,0.0003293279,0.0004183163,0.0005556541,0.0004758321,0.001384729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003599639,"about_ca_system_score_gemma":0.0007451071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006193892,"about_ca_topic_score_gemma":0.0009760155,"domain_scores_codex":[0.9995852,0.00005400447,0.00002883718,0.00007470848,0.0002334833,0.00002376556],"domain_scores_gemma":[0.9996389,0.00006903143,0.00009588673,0.00006466389,0.0001055028,0.00002600214],"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":[0.0002100595,0.00007914734,0.002104745,0.0005720808,0.00004888641,0.0005287334,0.0002433467,0.01293178,0.6856913,0.002434365,0.003948813,0.2912067],"study_design_scores_gemma":[0.000158469,0.002621643,0.02015716,0.0002284323,0.0003007369,0.01030064,0.00009222374,0.1892434,0.5935867,0.002166063,0.1807721,0.0003724938],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02106331,0.0007103395,0.9732479,0.0001666191,0.00009693785,0.0001658129,0.0001098077,0.002582868,0.001856398],"genre_scores_gemma":[0.2168754,0.0009134201,0.7757688,0.0003436983,0.00006875355,0.0004812915,0.0003525784,0.000178567,0.00501738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002385552,"threshold_uncertainty_score":0.007980466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02737514527104708,"score_gpt":0.2632817218664393,"score_spread":0.2359065765953922,"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."}}