{"id":"W1690016138","doi":"10.1007/s00348-015-2062-z","title":"Quantification and adjustment of pixel-locking in particle image velocimetry","year":2015,"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":"","funders":"Engineering and Physical Sciences Research Council; European Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Pixel; Particle image velocimetry; Vector field; Metric (unit); Computer science; Histogram; Basis (linear algebra); Motion vector; Computer vision; Artificial intelligence; Optics; Physics; Mathematics; Image (mathematics); Turbulence; Geometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001122079,0.000329896,0.0003810926,0.0005424819,0.0004348661,0.001023444,0.0008637412,0.0008228673,0.001638011],"category_scores_gemma":[0.003760508,0.0004135116,0.0001558636,0.000540728,0.0007455127,0.0009840487,0.0007813809,0.0007264612,0.0004007327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005615877,"about_ca_system_score_gemma":0.0006655523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00050993,"about_ca_topic_score_gemma":0.00061064,"domain_scores_codex":[0.9992254,0.0001541763,0.00004283244,0.0002434453,0.0002564555,0.00007763079],"domain_scores_gemma":[0.9989166,0.0003974018,0.0001826603,0.0002355708,0.0002030875,0.00006473954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001816395,0.00006589734,0.001601096,0.00003765375,0.000008314605,0.00001867562,0.00005485332,0.001625136,0.9739463,0.001435008,0.0001400825,0.02088543],"study_design_scores_gemma":[0.00001756444,0.0000998727,0.005313269,0.000008450254,0.00001203316,0.00008009598,0.00002008836,0.04844059,0.9445359,0.0005308081,0.0009159124,0.00002548567],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6350524,0.0005540734,0.360444,0.0001951943,0.0001456918,0.0001084455,0.0002465808,0.001111166,0.002142336],"genre_scores_gemma":[0.8374096,0.0002195841,0.1603035,0.0001137942,0.00002849528,0.0001367056,0.0001780693,0.0003289251,0.001281334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001638011,"threshold_uncertainty_score":0.005934179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02497127808539772,"score_gpt":0.2700095030087818,"score_spread":0.2450382249233841,"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."}}