{"id":"W2069530043","doi":"10.1109/bsc.2010.5473009","title":"Exploiting motion estimation resilience to approximated metrics on SIMD-capable general processors: From Atom to Nehalem","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"SIMD; Computer science; Motion estimation; Metric (unit); Computation; Motion (physics); Encoding (memory); Speedup; Parallel computing; Computer engineering; Code (set theory); Resilience (materials science); Algorithm; Artificial intelligence; Theoretical computer science; Programming language","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":[0.0003025059,0.000183978,0.0001736958,0.0004522439,0.0002397659,0.0003918439,0.001340106,0.0001243009,0.00001418427],"category_scores_gemma":[0.0009234062,0.0001554993,0.00003458215,0.001781136,0.00001983991,0.0005857505,0.0004812865,0.0002640348,0.0002239814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004195889,"about_ca_system_score_gemma":0.00003376183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000166528,"about_ca_topic_score_gemma":0.00002128253,"domain_scores_codex":[0.9981709,0.00003127518,0.0002853666,0.0006798127,0.0004768456,0.0003557647],"domain_scores_gemma":[0.9985883,0.0001721205,0.0000974666,0.0008206968,0.000166698,0.0001547413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001824979,0.0001566561,0.0005961034,0.00001712753,0.00000743321,0.000003691725,0.0007889988,0.02667999,0.1277467,0.05843004,0.007321056,0.7782339],"study_design_scores_gemma":[0.0001292036,0.0001395929,0.0006071973,0.00005288013,0.000002425292,0.000001974892,0.00009540427,0.5825592,0.4080938,0.006831242,0.001241303,0.0002458944],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4173385,0.00000468097,0.5793366,0.0008943822,0.0002000843,0.0001760617,0.00000141894,0.001009976,0.001038292],"genre_scores_gemma":[0.6145605,0.000001058906,0.3846842,0.0003840858,0.00003336468,0.00005925164,0.000002463847,0.000007524648,0.0002675206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.777988,"threshold_uncertainty_score":0.6341078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02412211041634861,"score_gpt":0.2718458611837276,"score_spread":0.247723750767379,"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."}}