{"id":"W2028559162","doi":"10.1889/1.3069850","title":"65.3: Next Generation of Frame‐Rate Conversion Algorithm","year":2008,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Frame (networking); Frame rate; Quality (philosophy); Algorithm; Computer engineering; Real-time computing; Telecommunications; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002005419,0.0001594331,0.0003229198,0.0001167805,0.00007237437,0.000009123596,0.0001210707,0.000320237,0.00004420969],"category_scores_gemma":[0.00003962548,0.0001488678,0.0001516321,0.0002628116,0.00009653148,0.0001413841,0.00002804822,0.0002128199,0.00001949346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008052085,"about_ca_system_score_gemma":0.00002756878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006462525,"about_ca_topic_score_gemma":0.000002851496,"domain_scores_codex":[0.998836,0.00004375607,0.0004907327,0.000183032,0.000264908,0.000181555],"domain_scores_gemma":[0.9993921,0.00006840314,0.0001093765,0.0002728744,0.0000804383,0.00007684219],"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":[0.00001248636,0.00002594147,0.000110097,0.00003267767,0.00001676093,0.000004437643,0.00003713158,0.002881736,0.9959157,0.00004114265,0.00063478,0.0002871044],"study_design_scores_gemma":[0.0006176204,0.0003324782,0.001473067,0.00007301203,0.00002470937,0.00004381599,0.00004074229,0.001848512,0.9890227,0.000004266026,0.006275415,0.0002436742],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8836436,0.0002750508,0.0004778894,0.00009103583,0.001719595,0.0005723489,0.00003203779,0.0005303381,0.1126581],"genre_scores_gemma":[0.9992039,0.0002354887,0.0001767062,0.00001817735,0.0002019601,0.00001248665,0.00001309242,0.00002722648,0.0001109128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1155603,"threshold_uncertainty_score":0.6070653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03383514993130998,"score_gpt":0.2230711535105613,"score_spread":0.1892360035792513,"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."}}