{"id":"W2021194850","doi":"10.4028/www.scientific.net/amm.20-23.1476","title":"Research on Video Sequences Quality Based on Motion Intensity","year":2010,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Video quality; Computer science; Subjective video quality; Coding (social sciences); Metric (unit); Artificial intelligence; Computer vision; Quality (philosophy); Perception; Video tracking; Video processing; Distortion (music); Video compression picture types; Image quality; Bandwidth (computing); Engineering; Mathematics; Telecommunications","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":[0.004861779,0.0001361191,0.0002275121,0.0001222457,0.000249594,0.0003929327,0.000396428,0.0001180405,0.00004325547],"category_scores_gemma":[0.00007260548,0.0001131405,0.00002354861,0.0001631715,0.00003062528,0.0001236141,0.0002030886,0.0002500993,0.00007326344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002687282,"about_ca_system_score_gemma":0.00005900774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001363344,"about_ca_topic_score_gemma":0.00001664097,"domain_scores_codex":[0.9982324,0.0001985362,0.0002697245,0.0004957254,0.0005001302,0.0003034394],"domain_scores_gemma":[0.9988025,0.000229497,0.00009284987,0.000641631,0.0001372085,0.00009635924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003601018,0.00005479862,3.190652e-7,0.00001918904,0.000002427782,0.000002111811,0.00005654591,0.000002056178,0.2951379,0.7013962,0.0001240829,0.003168363],"study_design_scores_gemma":[0.0003333692,0.0002003628,0.0002391655,0.00002018516,0.000002863736,0.000001641436,0.000100337,0.001693124,0.6997449,0.2967082,0.0007698731,0.0001859505],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3229361,0.000002490344,0.6694022,0.002874614,0.001472356,0.0005709396,0.0000203336,0.0001611875,0.00255986],"genre_scores_gemma":[0.9819667,0.000005972708,0.01616862,0.001661508,0.0001074468,0.00005135577,0.000008436939,0.00000785316,0.00002214711],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6590306,"threshold_uncertainty_score":0.4613735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09182238446918133,"score_gpt":0.3864175722305616,"score_spread":0.2945951877613802,"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."}}