{"id":"W4404349826","doi":"10.1186/s13640-024-00657-w","title":"Learned scalable video coding for humans and machines","year":2024,"lang":"en","type":"article","venue":"EURASIP Journal on Image and Video Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Codec; Analytics; Video tracking; Coding (social sciences); Artificial intelligence; Scalability; Videoconferencing; Video processing; Scalable Video Coding; Data compression; Computer vision; Multimedia; Motion compensation; Computer hardware; Data mining; Database","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005333239,0.0006042116,0.0004032295,0.0004787057,0.0002143052,0.0005545161,0.0009868183,0.0006175602,0.002596866],"category_scores_gemma":[0.002462449,0.0001649846,0.0002872767,0.0004795897,0.0005652673,0.001246365,0.00100823,0.001452709,0.0007317428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006105612,"about_ca_system_score_gemma":0.0008128975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005295486,"about_ca_topic_score_gemma":0.006005459,"domain_scores_codex":[0.9996837,0.00004683233,0.00001588059,0.00007924175,0.0001356623,0.00003875013],"domain_scores_gemma":[0.9994011,0.0001985229,0.00005344721,0.0001420787,0.0001727222,0.00003209722],"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":[0.0002578167,0.0001045526,0.0004857206,0.0001278838,0.000039894,0.000145954,0.00009066035,0.1831058,0.04059379,0.02687008,0.01460662,0.7335713],"study_design_scores_gemma":[0.000010442,0.00004251427,0.0001830992,0.00001581403,0.000008094677,0.0000612306,0.0000151542,0.9720958,0.01294772,0.01133243,0.003278544,0.000009083332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009794666,0.0005069593,0.9857895,0.0002725306,0.00008155025,0.00005665178,0.0001262996,0.001603189,0.001768639],"genre_scores_gemma":[0.4351885,0.00123867,0.5540749,0.0004031309,0.0002327491,0.0002359837,0.001178805,0.0002140959,0.00723322],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005295486,"threshold_uncertainty_score":0.01052934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0287996987753089,"score_gpt":0.3382206442904662,"score_spread":0.3094209455151573,"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."}}