{"id":"W1969597008","doi":"10.1007/s11263-013-0652-x","title":"Computational Schlieren Photography with Light Field Probes","year":2013,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Optical Imaging Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Optics; Light field; Computational photography; Photography; Computer science; Schlieren; Computer vision; Refractive index; Artificial intelligence; Computer graphics (images); Image processing; Physics; Image (mathematics)","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":[0.0002339303,0.000621897,0.0004769819,0.0005893077,0.0002632682,0.001036564,0.0007482996,0.0009291247,0.005096282],"category_scores_gemma":[0.00177437,0.000527207,0.000433597,0.0004148532,0.0007088751,0.001397716,0.001754343,0.001025324,0.0005435644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004848613,"about_ca_system_score_gemma":0.0004417525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001818553,"about_ca_topic_score_gemma":0.002225775,"domain_scores_codex":[0.9997734,0.00006995675,0.000008983666,0.00004264869,0.00008492704,0.00001999199],"domain_scores_gemma":[0.9994053,0.0003437915,0.0000557472,0.0001119851,0.00005511023,0.00002808709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004795883,0.0001049981,0.001812551,0.0002991686,0.0000778401,0.0004643222,0.0003764143,0.542056,0.07570519,0.171221,0.005535526,0.2018673],"study_design_scores_gemma":[0.00001112295,0.00001418123,0.0001475605,0.000006568319,0.000002859668,0.00004050209,0.00001190102,0.9872153,0.004827757,0.006454901,0.001258811,0.000008522415],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03530728,0.0001609032,0.9575866,0.0001983512,0.00005125767,0.00003937347,0.0000921262,0.0004586204,0.006105463],"genre_scores_gemma":[0.5354055,0.000329731,0.4551645,0.00009768889,0.00005551831,0.00009174135,0.0002219693,0.0002139156,0.008419454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005096282,"threshold_uncertainty_score":0.01704878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00300719961688344,"score_gpt":0.2272219592287821,"score_spread":0.2242147596118987,"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."}}