{"id":"W1568656855","doi":"10.1109/robot.1992.220069","title":"Soft materials for robotic fingers","year":2003,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"GRASP; Computer science; Artificial intelligence; Soft materials; Object (grammar); Computer vision; Robotic hand; Robot; Robot hand; Grippers; Natural rubber; Mechanical engineering; Engineering; Materials science; Nanotechnology; Composite material","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008742967,0.00004663268,0.00006086833,0.00002296547,0.00002571922,0.00002426969,0.00002214707,0.00002462619,0.001078904],"category_scores_gemma":[0.00005775804,0.00004482618,0.00001690156,0.00002989789,0.000002748307,0.00003978633,0.00000134681,0.00001913494,0.00009540603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001473245,"about_ca_system_score_gemma":0.000003217043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001215814,"about_ca_topic_score_gemma":0.000001159895,"domain_scores_codex":[0.999728,0.000006881884,0.00007788024,0.00004814495,0.00003751892,0.0001016286],"domain_scores_gemma":[0.9998823,0.00002583128,0.000006224049,0.00005272814,0.00001206956,0.00002086579],"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":[5.965123e-7,0.000001511016,0.0000379568,0.00002158647,0.000006106345,1.610542e-7,0.00003686649,0.9784592,0.01063896,0.007344243,0.003167677,0.0002851565],"study_design_scores_gemma":[0.001036213,0.00004523381,0.001695699,0.00003140692,0.00002942048,0.000009524211,0.0001914599,0.7076165,0.1273302,0.001392799,0.159964,0.0006575964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01042429,0.00003503156,0.9609151,0.00003558641,0.0005690574,0.0001261012,8.404021e-8,0.0003506536,0.02754406],"genre_scores_gemma":[0.9912801,0.000001501804,0.006696825,0.00005707845,0.00002848925,0.000009759664,0.000002573896,0.00001664025,0.001907022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9808558,"threshold_uncertainty_score":0.9998342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02538604410812245,"score_gpt":0.2339208272775461,"score_spread":0.2085347831694236,"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."}}