{"id":"W3185599435","doi":"10.1109/tmrb.2021.3097123","title":"Extending Reach Inside the MRI Bore: A 7-DOF, Low-Friction, Hydrostatic Teleoperator","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Robotics and Bionics","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"CRC Robotics","funders":"","keywords":"Teleoperation; Imaging phantom; Overshoot (microwave communication); Hydrostatic equilibrium; Mechanism (biology); Computer science; Simulation; Robot; Biomedical engineering; Engineering; Physics; Artificial intelligence; Optics","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":[0.0001805326,0.0001770359,0.0001750785,0.00005693871,0.0004008653,0.0000995712,0.0001372483,0.0001604522,0.00009434072],"category_scores_gemma":[0.00004093178,0.00013531,0.00007671675,0.0004720462,0.0001072277,0.00005452004,0.000003445348,0.0005416803,0.00003163514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006290261,"about_ca_system_score_gemma":0.0001214324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001117012,"about_ca_topic_score_gemma":0.00008401884,"domain_scores_codex":[0.9987267,0.00003431001,0.0003122163,0.000240997,0.0003816695,0.0003040851],"domain_scores_gemma":[0.9990401,0.000255083,0.00002375142,0.0003211993,0.0000792844,0.0002806326],"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":[0.000007330573,0.0003702571,0.00003648199,0.0001223803,0.0001893167,0.00006139722,0.000433372,0.9441207,0.002397471,0.005708278,0.002494285,0.04405873],"study_design_scores_gemma":[0.0006001082,0.00006247214,0.0002462422,0.0001681526,0.0001466449,0.0001766047,0.0003511005,0.9772891,0.009242021,0.0009220464,0.01039359,0.000401894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01267388,0.0006794081,0.9801894,0.005000947,0.0008049732,0.0001699226,0.00002281584,0.0001875656,0.0002711161],"genre_scores_gemma":[0.9869097,0.007304699,0.004704887,0.0007077571,0.0001148585,0.00004267224,0.00001292126,0.00004132865,0.0001611925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9754845,"threshold_uncertainty_score":0.5517783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008712759264998163,"score_gpt":0.2271834473958525,"score_spread":0.2184706881308544,"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."}}