{"id":"W2166868970","doi":"10.1080/14399776.2002.10781143","title":"An Empirical Discharge Coefficient Model for Orifice Flow","year":2002,"lang":"en","type":"article","venue":"International Journal of Fluid Power","topic":"Hydraulic and Pneumatic Systems","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; University of Illinois at Urbana-Champaign; Nanjing University of Science and Technology; University of Science and Technology of China; Zhejiang University of Technology; University of Saskatchewan","keywords":"Flow coefficient; Body orifice; Discharge coefficient; Bernoulli's principle; Pressure drop; Orifice plate; Mechanics; Reynolds number; Bandwidth throttling; Pressure coefficient; Flow (mathematics); Flow conditioning; Mathematics; Control theory (sociology); Thermodynamics; Physics; Engineering; Turbulence; Mechanical engineering; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007021343,0.000966003,0.00094536,0.001026019,0.0005472088,0.001281076,0.002745882,0.002086968,0.004116197],"category_scores_gemma":[0.004410752,0.0005985127,0.000788689,0.001114781,0.0008199352,0.003059383,0.0007423579,0.002304206,0.002619259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001145968,"about_ca_system_score_gemma":0.00128605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004833909,"about_ca_topic_score_gemma":0.002450157,"domain_scores_codex":[0.9993452,0.00006670571,0.00002766984,0.0002035066,0.0002850409,0.00007181707],"domain_scores_gemma":[0.9988086,0.0005360994,0.0001245447,0.0001591229,0.0003353882,0.00003622522],"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":[0.00005474117,0.00008956152,0.001101303,0.0001255581,0.00001459402,0.000172912,0.0001599042,0.9080949,0.008671544,0.04130393,0.003292738,0.03691839],"study_design_scores_gemma":[0.000007843045,0.0000193012,0.0002235954,0.00001085954,0.000004740467,0.0001200388,0.000006938692,0.9885512,0.001270134,0.004881317,0.00488713,0.00001697248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01529417,0.0009429871,0.9704648,0.0003464298,0.0001162996,0.0001332915,0.0004167252,0.001014649,0.01127078],"genre_scores_gemma":[0.7593411,0.004195299,0.1726007,0.0004356529,0.0002086988,0.0008262724,0.001535725,0.0007672949,0.06008921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004833909,"threshold_uncertainty_score":0.0137701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02801048819359333,"score_gpt":0.2893657071314977,"score_spread":0.2613552189379044,"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."}}