{"id":"W1969343214","doi":"10.1145/1836845.1836925","title":"Quality-preserving image downsizing","year":2010,"lang":"en","type":"article","venue":"","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Image quality; Image (mathematics); Quality (philosophy); Noise (video); Computer graphics (images)","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":[],"consensus_categories":[],"category_scores_codex":[0.0001167082,0.00006058952,0.00005621425,0.0000273056,0.00005851677,0.00006902595,0.0001682778,0.00003952299,0.0003126003],"category_scores_gemma":[0.00002461106,0.00005706552,0.00002221333,0.00008870469,0.00001879481,0.0001493409,0.00004043805,0.0001828915,0.00007313088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004900869,"about_ca_system_score_gemma":0.000004047466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002287058,"about_ca_topic_score_gemma":0.00002088105,"domain_scores_codex":[0.9996352,0.000002548895,0.000106825,0.00007823468,0.00005371426,0.0001234418],"domain_scores_gemma":[0.9996582,0.00001983863,0.000009203787,0.0002502801,0.0000286285,0.00003384472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[1.903016e-7,0.000005499089,0.00006069204,0.0000286677,0.000002365456,3.670826e-7,0.00003442327,0.00001007557,0.9683706,0.009728567,0.007252723,0.01450585],"study_design_scores_gemma":[0.0001573697,0.000004738556,0.001774029,0.00001605595,0.000006695565,0.0000125025,0.0000738231,0.06143743,0.7474949,0.0270706,0.1614609,0.0004909597],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06619928,0.00003382369,0.5011781,0.0004465148,0.0001183582,0.0000950854,0.00000257917,0.003045353,0.4288809],"genre_scores_gemma":[0.7054027,0.000004914053,0.2939905,0.0000599942,0.00006218783,0.00002396571,0.000002344415,0.00001662619,0.0004368285],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6392034,"threshold_uncertainty_score":0.3422754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01408654679704519,"score_gpt":0.2927184865693124,"score_spread":0.2786319397722672,"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."}}