{"id":"W3111589389","doi":"10.1115/power2020-16902","title":"Optimization and System Identification of a Variable Pico-Scale Hydro Turbine for Pressure Regulation","year":2020,"lang":"en","type":"article","venue":"","topic":"Cavitation Phenomena in Pumps","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Turbine; Genetic algorithm; Control theory (sociology); Particle swarm optimization; Pressure control; Pressure drop; Range (aeronautics); Engineering; Computer science; Automotive engineering; Mechanical engineering; Aerospace engineering; Algorithm; Physics","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":[0.00009078429,0.00006059821,0.0001004164,0.00003398184,0.00002234226,0.00002152968,0.00003951446,0.00004126305,0.00002680321],"category_scores_gemma":[0.0000243787,0.00006524174,0.00001441153,0.0001396502,0.000008580562,0.0001666304,0.000008488963,0.00002150481,0.000001746176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001716908,"about_ca_system_score_gemma":0.000004495159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000280917,"about_ca_topic_score_gemma":3.386671e-7,"domain_scores_codex":[0.9994906,0.000008343308,0.000252715,0.000110699,0.00007630372,0.00006134935],"domain_scores_gemma":[0.9997088,0.00003247127,0.00005432282,0.00009147499,0.00007778511,0.00003516277],"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":[0.00000782897,0.00000410292,0.0000398319,0.0007864639,0.00001945566,1.184103e-8,0.0003229402,0.9410785,0.05222353,0.004947666,0.0004630254,0.0001066251],"study_design_scores_gemma":[0.0002822049,0.00001746792,0.000307034,0.00001542278,0.00003883329,5.458706e-7,0.00009052829,0.9832323,0.01537825,0.0001140714,0.0004591849,0.00006422547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00861956,0.0001170311,0.9889551,0.0001004289,0.0001002086,0.000448053,0.00003646519,0.0002358266,0.001387376],"genre_scores_gemma":[0.9670779,0.000003516731,0.03248743,0.00001339311,0.00004997833,0.00004939682,0.00007154457,0.00001813358,0.0002287324],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9584583,"threshold_uncertainty_score":0.2660481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007159112987056631,"score_gpt":0.1870766263254504,"score_spread":0.1799175133383938,"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."}}