{"id":"W2042626276","doi":"10.1109/tip.2014.2368359","title":"Coded Acquisition of High Frame Rate Video","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Frame rate; Computer vision; Random access; Frame (networking); SIGNAL (programming language); Shutter; Data acquisition; Pixel; Real-time computing; Telecommunications","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":[0.0001037744,0.0001449623,0.0001843222,0.0001384116,0.00009800077,0.00005269337,0.00009625971,0.00008127168,0.00003915297],"category_scores_gemma":[0.000003780312,0.000150644,0.00005756528,0.0001878551,0.00005878969,0.0002581055,6.277659e-7,0.0001861185,0.00001681049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002810013,"about_ca_system_score_gemma":0.000010979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001917959,"about_ca_topic_score_gemma":0.000003264857,"domain_scores_codex":[0.9993193,0.00003324,0.0002096968,0.0001553901,0.0001159942,0.0001663364],"domain_scores_gemma":[0.9995609,0.00005083527,0.00004841089,0.0001983411,0.0001011343,0.00004037892],"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":[0.0000330427,0.0000581095,0.000001250669,0.0001215744,0.00002606013,0.000002194406,0.0001793991,0.05054796,0.8379318,0.00003481287,0.0002179908,0.1108458],"study_design_scores_gemma":[0.000191801,0.00003904553,0.00004673534,0.0002045571,0.00002862327,0.000004717362,0.00001398471,0.1777298,0.8201911,0.001316278,0.00009238816,0.0001409387],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08102574,0.00004688606,0.9170067,0.00004800456,0.0001806482,0.00007664149,0.000006179758,0.0007236059,0.0008855359],"genre_scores_gemma":[0.9768702,0.00002159492,0.02289264,0.00008537371,0.00004283492,0.00001428447,0.000001874659,0.00003917157,0.00003203172],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8958445,"threshold_uncertainty_score":0.6143084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008847911570110218,"score_gpt":0.2268324899177742,"score_spread":0.2179845783476639,"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."}}