{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008300376,0.0002544496,0.0002560724,0.000303818,0.0009056968,0.004230173,0.0003802179,0.00006081222,0.000004734387],"category_scores_gemma":[0.0002991384,0.000205061,0.00006238627,0.000323575,0.0001285664,0.003746851,0.000162008,0.000492556,0.000003860127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000459606,"about_ca_system_score_gemma":0.000122009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001352104,"about_ca_topic_score_gemma":6.070406e-7,"domain_scores_codex":[0.9984069,0.00004866679,0.0003610794,0.0005434973,0.0002456183,0.0003942123],"domain_scores_gemma":[0.9990928,0.000229832,0.0001541915,0.0001704112,0.0001923252,0.0001604643],"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":[0.00003032139,0.00002566577,0.0001386391,0.0005968703,0.00001565709,0.0001184061,0.0008307889,0.000002923764,0.04193281,0.00209639,0.001386055,0.9528255],"study_design_scores_gemma":[0.00163281,0.001005402,0.0009479284,0.007152823,0.0001121498,0.003839106,0.0002080946,0.5755936,0.05604013,0.3165468,0.0354273,0.00149391],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00305639,0.01035568,0.9806148,0.004383865,0.0001817599,0.0001401273,0.000001819654,0.0004944307,0.0007711747],"genre_scores_gemma":[0.3459151,0.0007490439,0.6514262,0.0009282402,0.0003614081,0.00002097329,0.00000100992,0.00005479438,0.0005432674],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9513316,"threshold_uncertainty_score":0.9968035,"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."}}