{"id":"W2079523732","doi":"10.1115/fedsm2002-31171","title":"Vector Positioning for Cross Correlation PIV","year":2002,"lang":"en","type":"article","venue":"","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Particle image velocimetry; Digital image correlation; Vector field; Computer science; Cross-correlation; Dispersion (optics); Process (computing); Correlation; Physics; Plane (geometry); Optics; Computer vision; Artificial intelligence; Mathematics; Mechanics; Statistics; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009701049,0.0007191731,0.0004858706,0.0006464965,0.0004235168,0.0009726088,0.0009968475,0.00068458,0.00456316],"category_scores_gemma":[0.002221632,0.000467646,0.0002267025,0.0009028582,0.0004752836,0.0008012697,0.00139552,0.0008216113,0.002270275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005489159,"about_ca_system_score_gemma":0.0009209311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001478478,"about_ca_topic_score_gemma":0.001622747,"domain_scores_codex":[0.9989491,0.0002997331,0.000037496,0.0001644784,0.0004858871,0.00006334259],"domain_scores_gemma":[0.9993111,0.0001775501,0.00006538683,0.0001454541,0.0002734924,0.00002704001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000393373,0.00008740646,0.003141176,0.0003464096,0.00005405357,0.000344142,0.0003167709,0.09933829,0.1482199,0.1090003,0.01020915,0.628549],"study_design_scores_gemma":[0.00004025506,0.0002555886,0.002454896,0.00005418384,0.00002470814,0.0005443828,0.00007078492,0.8610087,0.07093672,0.01310949,0.05142012,0.00008020088],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003527321,0.000231271,0.9926603,0.00003885971,0.00009903179,0.00004070428,0.00005330095,0.0008595457,0.002489748],"genre_scores_gemma":[0.1145022,0.0004331988,0.8801515,0.00008161741,0.00006058804,0.0001757001,0.0003281846,0.0002376649,0.004029411],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00456316,"threshold_uncertainty_score":0.01526529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008994990112526866,"score_gpt":0.2027034465022033,"score_spread":0.1937084563896765,"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."}}