{"id":"W2727822501","doi":"10.1007/s00348-017-2369-z","title":"Instantaneous PIV/PTV-based pressure gradient estimation: a framework for error analysis and correction","year":2017,"lang":"en","type":"article","venue":"Experiments in Fluids","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pressure gradient; Curl (programming language); Truncation error; Divergence (linguistics); Vortex; Vector field; Mathematical analysis; Adverse pressure gradient; Physics; Boundary (topology); Wake; Mathematics; Mechanics; Turbulence; Flow separation; 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":[0.001859185,0.001157448,0.001150551,0.001236523,0.0007578629,0.002261006,0.002723362,0.001352945,0.002224984],"category_scores_gemma":[0.005597296,0.0007725164,0.0006658791,0.001133317,0.001216556,0.002482632,0.002114067,0.00228329,0.0009763871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005523254,"about_ca_system_score_gemma":0.00187603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004061677,"about_ca_topic_score_gemma":0.003104327,"domain_scores_codex":[0.9986061,0.0002646932,0.00007037329,0.0003090967,0.0006389874,0.000110728],"domain_scores_gemma":[0.9985766,0.0003086516,0.0001557398,0.0003657796,0.0005234093,0.00006978397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003059857,0.0001457239,0.002051716,0.0003675066,0.0001243951,0.0001202572,0.0001593692,0.3877034,0.05338049,0.08311053,0.003825616,0.4687051],"study_design_scores_gemma":[0.000006292801,0.00003226537,0.0002854185,0.00001085815,0.00001036886,0.00004266948,0.00001054539,0.9800361,0.01148166,0.006677145,0.001378527,0.00002819853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00114794,0.00004854792,0.9980931,0.00001845439,0.00002958604,0.000009034934,0.0000309811,0.0003228145,0.0002995785],"genre_scores_gemma":[0.1860196,0.0002975237,0.8097818,0.00005682819,0.0001048773,0.00008912238,0.0002998576,0.000461725,0.002888734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004061677,"threshold_uncertainty_score":0.009832442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01427196487132284,"score_gpt":0.2845293249871365,"score_spread":0.2702573601158137,"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."}}