{"id":"W4404985636","doi":"10.48550/arxiv.2411.14622","title":"Learning Autonomous Surgical Irrigation and Suction with the da Vinci Research Kit Using Reinforcement Learning","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alberta Innovates; China Scholarship Council","keywords":"Suction; Reinforcement learning; Irrigation; Reinforcement; Computer science; Psychology; Artificial intelligence; Engineering; Mechanical engineering; Agronomy; Biology; Social psychology","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":[0.001058589,0.0001701082,0.0001384835,0.00005277259,0.0008385068,0.0004287518,0.0002335804,0.0001348741,0.0001085562],"category_scores_gemma":[0.00001730548,0.00007099573,0.0000587757,0.0004672804,0.0001577189,0.0001887616,0.001033215,0.001199788,0.00003532497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001933955,"about_ca_system_score_gemma":0.00002803699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001841338,"about_ca_topic_score_gemma":0.0001546924,"domain_scores_codex":[0.9984139,0.000405921,0.0001349047,0.0005873085,0.0001827787,0.0002752368],"domain_scores_gemma":[0.9993361,0.0001977056,0.0001639661,0.00008971174,0.0001449121,0.00006756438],"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":[0.0001080822,0.00001956386,0.001252214,0.00006680612,0.00008211008,0.000148742,0.0004310256,0.9667963,0.000297533,0.0257617,0.00004029746,0.004995578],"study_design_scores_gemma":[0.0002698402,0.0005054288,0.002255871,0.0002111684,0.0002071534,0.00002506014,0.004220001,0.9020749,0.00009390682,0.009564691,0.08011714,0.0004548415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930612,0.00005317312,0.000164711,0.001056459,0.0001000352,0.0003358745,0.000001317496,0.00009427719,0.005132939],"genre_scores_gemma":[0.9926334,0.00007722395,0.00001350605,0.00002026747,0.0001651901,0.00000195645,0.00004628147,0.000002659405,0.007039515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08007684,"threshold_uncertainty_score":0.6449201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1336380217758763,"score_gpt":0.235339382764392,"score_spread":0.1017013609885157,"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."}}