{"id":"W4402568544","doi":"10.1109/tro.2024.3462943","title":"Robotic Cutting of Fruits and Vegetables: Modeling the Effects of Deformation, Fracture Toughness, Knife Edge Geometry, and Motion","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Plant Surface Properties and Treatments","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Merck Canada Inc. (Canada)","funders":"National Science Foundation","keywords":"Deformation (meteorology); Geometry; Enhanced Data Rates for GSM Evolution; Motion (physics); Fracture (geology); Fracture toughness; Geology; Materials science; Artificial intelligence; Composite material; Computer science; Mathematics","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":[0.00007940609,0.000101146,0.0001378238,0.00002072745,0.00016548,0.00003848152,0.00005142614,0.00006496692,0.000005525862],"category_scores_gemma":[0.000007218022,0.00003542506,0.00004222365,0.0001845118,0.00003866552,0.0001442753,0.000002157019,0.000105945,0.000001701113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001076327,"about_ca_system_score_gemma":0.00000484247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001396992,"about_ca_topic_score_gemma":0.00003975044,"domain_scores_codex":[0.999446,0.00002976381,0.0001704459,0.0001263333,0.0001160661,0.0001113373],"domain_scores_gemma":[0.9996155,0.000238749,0.00004022459,0.00003755102,0.00003656579,0.0000314042],"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":[0.00001299767,0.00008814868,0.0003368288,0.0003743212,0.00008095546,0.000001696134,0.0003805929,0.9379618,0.01117012,0.00004252619,0.000006283149,0.04954376],"study_design_scores_gemma":[0.0003083384,0.0004669657,0.006136846,0.0009127806,0.0003733195,0.00003750853,0.0005249627,0.9500585,0.04039027,0.0004919507,0.00004548853,0.0002531372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9215408,0.002258591,0.07528526,0.0004190347,0.0002253186,0.000206536,0.00001852768,0.00003035465,0.0000155864],"genre_scores_gemma":[0.9989582,0.0007011451,0.0002446612,0.00002830395,0.00001682881,0.000004862456,0.000005372571,0.000001392828,0.0000392863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07741736,"threshold_uncertainty_score":0.1444592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01439340566800564,"score_gpt":0.2042896419209052,"score_spread":0.1898962362528995,"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."}}