{"id":"W3046278280","doi":"10.1007/978-3-030-54407-2_5","title":"Semantic Learning for Image Compression (SLIC)","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Image compression; Compression (physics); Artificial intelligence; Image (mathematics); Data compression; Computer vision; Image processing","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005769335,0.0004340542,0.0004912844,0.0004311303,0.0003614996,0.0006485148,0.002827697,0.0002572198,0.00001327064],"category_scores_gemma":[0.0001969407,0.0003857749,0.0001771081,0.0005013142,0.0004548358,0.000586366,0.001089726,0.0008181106,0.0000528675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001591163,"about_ca_system_score_gemma":0.0003137967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004533649,"about_ca_topic_score_gemma":0.000002082516,"domain_scores_codex":[0.9968649,0.00003549788,0.0004752823,0.001411651,0.0007270414,0.0004855998],"domain_scores_gemma":[0.9978722,0.0004563298,0.0003351095,0.0008272522,0.0003388338,0.0001702706],"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":[0.0000131048,0.00002935238,0.00001259162,0.0001758483,0.00001082996,0.00005103808,0.0005120505,0.0006381221,0.01229984,0.03695066,0.0001043496,0.9492022],"study_design_scores_gemma":[0.0002481918,0.0002860482,0.00003066282,0.0004087582,0.00000932384,0.00003446924,1.860594e-7,0.7920914,0.05882869,0.1352495,0.0121476,0.000665146],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00000330104,0.0002379379,0.9936664,0.002370627,0.0005767373,0.0005935752,0.000002848757,0.0005257635,0.00202278],"genre_scores_gemma":[0.06074137,0.00007951763,0.9359797,0.00162168,0.0005166383,0.00003152011,0.00001529284,0.00005597351,0.0009583479],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9485371,"threshold_uncertainty_score":0.9998594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0223752880543376,"score_gpt":0.2680967905953072,"score_spread":0.2457215025409696,"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."}}