{"id":"W2156341121","doi":"10.1109/icip.1995.529754","title":"Image interpolation using a simple Gibbs random field model","year":2002,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Interpolation (computer graphics); Image scaling; Computer science; Nearest-neighbor interpolation; Image (mathematics); Algorithm; Artificial intelligence; Iterative method; Random field; Linear interpolation; Computer vision; Image texture; Binary image; Mathematics; Image processing; Pattern recognition (psychology); Statistics","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003554913,0.0004449059,0.0003501052,0.0005072523,0.0004067885,0.002373198,0.001861162,0.0001422941,0.0002121332],"category_scores_gemma":[0.0006522327,0.0004483165,0.0001178574,0.0005170116,0.0001477496,0.007169249,0.0005192352,0.0005561365,0.00005017045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002056261,"about_ca_system_score_gemma":0.0001148396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008130933,"about_ca_topic_score_gemma":6.4649e-7,"domain_scores_codex":[0.9970319,0.00001689612,0.0006319916,0.0009621271,0.000806773,0.0005503053],"domain_scores_gemma":[0.9972998,0.00006182045,0.000564003,0.0002665282,0.001652445,0.0001553716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002163756,0.0004511235,0.0002679243,0.000314431,0.00006161224,0.00003074546,0.004646887,0.00005906497,0.8125615,0.04850239,0.003470522,0.1294174],"study_design_scores_gemma":[0.0006652616,0.00007006128,0.000004519671,0.0003445327,0.00001242759,0.00005356921,0.0001249698,0.9084492,0.02207315,0.0676419,0.000106963,0.0004535025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002411395,0.00008021556,0.9456464,0.002349346,0.0001406028,0.0002732164,0.0000054196,0.0009189868,0.04817439],"genre_scores_gemma":[0.4832417,0.00002930995,0.5156716,0.000679313,0.00009776694,0.00004457491,0.000003359511,0.00003087045,0.0002015063],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9083901,"threshold_uncertainty_score":0.9997969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06307020455252375,"score_gpt":0.3369980741335701,"score_spread":0.2739278695810464,"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."}}