{"id":"W4321020373","doi":"10.1109/sii55687.2023.10039282","title":"Soft Robotic Hand for Sushi Grasping and Handling","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/SICE International Symposium on System Integration (SII)","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Palm; Soft robotics; Computer science; Artificial intelligence; Robot; Fish <Actinopterygii>; Computer vision; Engineering drawing; Engineering; Fishery","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004419551,0.0009565477,0.0005567653,0.0007168954,0.0004382162,0.0004544439,0.0008590393,0.0007094868,0.003998267],"category_scores_gemma":[0.0005380894,0.000342797,0.0004514283,0.0003506567,0.0004621575,0.0008405937,0.0008708577,0.0003772231,0.001151896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002375007,"about_ca_system_score_gemma":0.0004830155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005533622,"about_ca_topic_score_gemma":0.001044954,"domain_scores_codex":[0.9993747,0.00006142317,0.00005606068,0.0001263574,0.0003268764,0.00005458715],"domain_scores_gemma":[0.9995481,0.00008946587,0.000105049,0.00009902204,0.0001173223,0.00004085465],"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":[0.0002791005,0.00006992928,0.0008023906,0.0009849912,0.00004825978,0.0004933281,0.0001491714,0.004943468,0.7758138,0.002219183,0.001247927,0.2129485],"study_design_scores_gemma":[0.000152955,0.004386688,0.0160033,0.0002449371,0.0002479175,0.009867785,0.0003781304,0.1212328,0.7659289,0.003109697,0.07812548,0.0003214441],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2523068,0.005220413,0.7196449,0.0002603311,0.0004205944,0.0004857896,0.0002227767,0.002920349,0.01851793],"genre_scores_gemma":[0.6593422,0.001053493,0.3230637,0.0002391385,0.0000693656,0.0002531245,0.0001986508,0.00007106306,0.01570927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003998267,"threshold_uncertainty_score":0.01337558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02020472284667983,"score_gpt":0.2585154094670066,"score_spread":0.2383106866203267,"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."}}