{"id":"W2155822479","doi":"10.1109/icme.2010.5583556","title":"Pixel-based motion vector concatenation for Reference Picture Selection","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Concatenation (mathematics); Computer science; Motion vector; Artificial intelligence; Context (archaeology); Selection (genetic algorithm); Video quality; Block-matching algorithm; Block (permutation group theory); Pixel; Computer vision; Motion estimation; Transcoding; Pattern recognition (psychology); Video tracking; Video processing; Image (mathematics); 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":[0.0006570037,0.0005658387,0.0004949713,0.0006190591,0.0003511633,0.0004717449,0.0005947271,0.0003258907,0.002217849],"category_scores_gemma":[0.002139367,0.0002302243,0.000205223,0.0007469478,0.0002704934,0.0008599774,0.0004323404,0.0004567643,0.0008058774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002638773,"about_ca_system_score_gemma":0.0004159188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008015801,"about_ca_topic_score_gemma":0.001661717,"domain_scores_codex":[0.9993813,0.0001612359,0.00004227878,0.0001100005,0.0002714538,0.00003361644],"domain_scores_gemma":[0.9991823,0.0002598987,0.0001059156,0.0002109762,0.0002131668,0.0000277762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005680958,0.00005784688,0.00115169,0.0001496177,0.00006039369,0.0001911818,0.0001045683,0.02267879,0.2041123,0.01249038,0.00347723,0.754958],"study_design_scores_gemma":[0.00007525993,0.0007112706,0.003124405,0.00003432699,0.00008270323,0.001422712,0.0000480819,0.6006555,0.3590088,0.004153288,0.03059017,0.00009345994],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03013241,0.001000906,0.9655097,0.00007805249,0.00008260898,0.00007947353,0.0001021876,0.001477841,0.001536759],"genre_scores_gemma":[0.2917802,0.0007035733,0.7035302,0.00006138897,0.0001175365,0.00009879027,0.0003969412,0.0001201039,0.003191309],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002217849,"threshold_uncertainty_score":0.007419467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02481015513812072,"score_gpt":0.2638623766205528,"score_spread":0.2390522214824321,"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."}}