{"id":"W4403209991","doi":"10.1109/tip.2024.3472494","title":"A Virtual-Sensor Construction Network Based on Physical Imaging for Image Super-Resolution","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Dalian Science and Technology Innovation Fund; Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Computer vision; Computer science; Artificial intelligence; Image resolution; Image processing; Image sensor; Medical imaging; Image (mathematics)","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":[0.00009344138,0.0002824598,0.0002287837,0.0001553943,0.0005370945,0.0003381993,0.000107323,0.00005365806,0.0000313104],"category_scores_gemma":[0.000009948732,0.0002685426,0.0002078424,0.0004332576,0.0003183128,0.0005438214,0.000002014804,0.0004645704,0.00004445608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001081414,"about_ca_system_score_gemma":0.0000804006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007081902,"about_ca_topic_score_gemma":5.643841e-7,"domain_scores_codex":[0.9985543,0.0000284845,0.0002318403,0.0005305728,0.0001936212,0.0004611549],"domain_scores_gemma":[0.9992514,0.0002808666,0.00005310402,0.0002301162,0.0001197195,0.00006477241],"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":[0.0001596245,0.0002622824,0.000007442797,0.00009353757,0.00003919265,0.000008903663,0.0001058047,0.08162457,0.04101311,0.002616182,0.0002349802,0.8738344],"study_design_scores_gemma":[0.000444838,0.000114184,0.000004021972,0.0003836279,0.00009419623,0.000005017318,0.0002741909,0.9057786,0.08140232,0.0106932,0.00049049,0.0003152465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005907348,0.00002858136,0.9906114,0.000966891,0.0004824162,0.0003360977,0.00006649463,0.0008582233,0.0007425279],"genre_scores_gemma":[0.8247756,8.229259e-7,0.1745777,0.00005822886,0.00035177,0.00008683226,0.00001057237,0.00005815353,0.0000803168],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8735191,"threshold_uncertainty_score":0.9999767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00926019889716287,"score_gpt":0.2700768460277042,"score_spread":0.2608166471305413,"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."}}