{"id":"W4389675231","doi":"10.1145/3637490","title":"Principal Component Approximation Network for Image Compression","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Image compression; Computation; Computer science; Principal component analysis; Image (mathematics); Feature (linguistics); ENCODE; Set (abstract data type); Compression (physics); Artificial intelligence; Pattern recognition (psychology); Data compression; Component (thermodynamics); Algorithm; 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","sts"],"consensus_categories":[],"category_scores_codex":[0.0005103648,0.0002459795,0.0002568272,0.0002701645,0.001900424,0.0001801268,0.002769219,0.0001075485,0.00000395535],"category_scores_gemma":[0.00004976548,0.0002510167,0.000101241,0.001055166,0.0002126893,0.0004152002,0.0004586533,0.0003556701,0.00005811542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005303176,"about_ca_system_score_gemma":0.00004171124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001315669,"about_ca_topic_score_gemma":0.000005791257,"domain_scores_codex":[0.9981158,0.0001391011,0.0005186874,0.0005912034,0.0002331403,0.0004020066],"domain_scores_gemma":[0.9934006,0.002186225,0.0002336445,0.003808868,0.0002081172,0.0001625601],"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":[0.00001019967,0.0003483598,0.00002537374,0.00004762611,0.00003469076,3.51434e-7,0.0003810562,0.005023171,0.004373061,0.02184886,0.001418313,0.966489],"study_design_scores_gemma":[0.000524308,0.00005917987,0.0006554138,0.00009416842,0.0000199126,0.000007456128,0.00006276478,0.9233363,0.001907489,0.01794704,0.05507336,0.0003125741],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003446366,0.0001060198,0.9925265,0.003116624,0.0001007216,0.001738595,0.00009504724,0.00182872,0.0001431408],"genre_scores_gemma":[0.1270074,0.0004705132,0.8701988,0.0001631583,0.00007195937,0.001650035,0.0003555946,0.00002968026,0.00005283086],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9661763,"threshold_uncertainty_score":0.9999942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03939443647312917,"score_gpt":0.3352102663065216,"score_spread":0.2958158298333924,"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."}}