{"id":"W4387970980","doi":"10.3390/fluids8110285","title":"An Enhanced Python-Based Open-Source Particle Image Velocimetry Software for Use with Central Processing Units","year":2023,"lang":"en","type":"article","venue":"Fluids","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Python (programming language); Computer science; Particle image velocimetry; Software; Computational science; Workstation; Computer graphics (images); Image processing; Operating system; Artificial intelligence; Physics; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001265801,0.0001946957,0.000183192,0.00006833826,0.0001517708,0.00034994,0.0002882174,0.00006971017,0.00002133585],"category_scores_gemma":[0.00006633877,0.0001810429,0.00002717352,0.0008878848,0.00003455965,0.0005120816,0.00003697809,0.0001036094,0.00002701646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000564469,"about_ca_system_score_gemma":0.00008991976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002390019,"about_ca_topic_score_gemma":0.00002530948,"domain_scores_codex":[0.9988338,0.00001707251,0.0001799541,0.000268172,0.0001533111,0.0005477378],"domain_scores_gemma":[0.9992981,0.00007523575,0.00001801243,0.0002990269,0.00014566,0.000163966],"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":[0.0002595914,0.0001439435,0.00497515,0.0005519501,0.0000818651,0.00003532138,0.0012416,0.7584829,0.1865172,0.0001505823,0.005330857,0.04222902],"study_design_scores_gemma":[0.001020382,0.0001230716,0.003089778,0.00007628111,0.00002086291,0.000001014987,0.00004424843,0.9611636,0.03238166,0.00001506218,0.001776663,0.0002873545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6675923,0.00003453265,0.3309118,0.00002801199,0.0001113954,0.000339014,0.0000615695,0.0008851872,0.00003621593],"genre_scores_gemma":[0.9723594,0.000005063823,0.02668416,0.00008061576,0.00007805318,0.0001088014,0.0002546555,0.0001327893,0.0002964316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3047672,"threshold_uncertainty_score":0.7382716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01624799373975662,"score_gpt":0.2398423084354496,"score_spread":0.223594314695693,"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."}}