{"id":"W4410221936","doi":"10.1088/1361-6501/add6ca","title":"Modified <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> D Laplacian for smooth pressure reconstruction based on time-resolved velocimetry (2): experiments and perspectives","year":2025,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Velocimetry; Particle image velocimetry; Computer science; Applied mathematics; Mechanics; Materials science; Mathematics; Physics; Turbulence","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":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00277955,0.0005921782,0.0003330819,0.0009521881,0.002006658,0.0007517715,0.001413237,0.001184414,0.002253772],"category_scores_gemma":[0.001364815,0.0008380915,0.0004261438,0.001363105,0.00258646,0.001052273,0.0004695898,0.0009433611,0.0002408668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003043351,"about_ca_system_score_gemma":0.0009585646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001520827,"about_ca_topic_score_gemma":0.0002358281,"domain_scores_codex":[0.9937406,0.0001930271,0.0008936877,0.001710111,0.001977212,0.001485432],"domain_scores_gemma":[0.9970598,0.0004049493,0.0007718529,0.0009803883,0.0003377625,0.0004452037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001201977,0.00034139,0.0002809519,0.0005746553,0.0006574361,0.0001217845,0.001344854,0.0005843306,0.01736107,0.8770872,0.05647769,0.0439667],"study_design_scores_gemma":[0.001437404,0.00163256,0.0002017393,0.0006471618,0.0004134731,0.0002411185,0.003303285,0.2595991,0.7222202,0.002368409,0.007028013,0.0009074389],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9424453,0.002279626,0.002380493,0.002218633,0.001521629,0.000190504,0.0002279362,0.0006635436,0.04807233],"genre_scores_gemma":[0.9932989,0.0004195478,0.003959012,0.001322057,0.000288979,0.0003382724,0.0001415983,0.00009607126,0.0001355531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8747187,"threshold_uncertainty_score":0.999407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02211606486300022,"score_gpt":0.2317574229807336,"score_spread":0.2096413581177334,"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."}}