{"id":"W4404576619","doi":"10.1109/tmrb.2024.3503894","title":"Encoding Desired Deformation Profiles in Endoscope-Like Soft Robots","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Robotics and Bionics","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Emerging Frontiers in Research and Innovation; National Science Foundation; National Institutes of Health; Division of Civil, Mechanical and Manufacturing Innovation; National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada","keywords":"Endoscope; Deformation (meteorology); Robot; Encoding (memory); Artificial intelligence; Computer science; Computer vision; Materials science; Medicine; Composite material; Surgery","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000190918,0.0001526199,0.0001453784,0.0001810824,0.0001014815,0.00009218235,0.0001005816,0.0001811526,0.00005037731],"category_scores_gemma":[0.000009624816,0.0001359099,0.00005140185,0.000379501,0.00005573484,0.0001148869,0.000001796721,0.0004150991,0.00004371064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007799158,"about_ca_system_score_gemma":0.00006458128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009569154,"about_ca_topic_score_gemma":0.00005191315,"domain_scores_codex":[0.9989879,0.00001470272,0.0003003044,0.0001874712,0.0002733154,0.000236374],"domain_scores_gemma":[0.9995149,0.0001617494,0.00001199131,0.0001278103,0.00001883714,0.0001646623],"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":[0.000003846395,0.00007458917,0.00001361567,0.0001890828,0.00003324575,0.00001585321,0.0001812525,0.9217178,0.0008638112,0.002506196,0.0002150157,0.07418571],"study_design_scores_gemma":[0.0002045062,0.00003982867,0.00004408516,0.0002665273,0.00002723075,0.00002382483,0.00005716442,0.995855,0.002057884,0.0003936261,0.0008566267,0.0001737244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007797154,0.0006330599,0.98921,0.0007557819,0.0008811926,0.0002003817,0.00001261577,0.0003004878,0.0002093438],"genre_scores_gemma":[0.9917218,0.002078089,0.005928178,0.00007761528,0.00005381637,0.00004517004,0.000009946702,0.00002913694,0.00005621183],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9839247,"threshold_uncertainty_score":0.5542245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01651771522175055,"score_gpt":0.2440598877334991,"score_spread":0.2275421725117485,"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."}}