{"id":"W2383617580","doi":"","title":"The Application of Butterworth Filter in POCS Super-resolution Reconstruction","year":2006,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Filter (signal processing); Enhanced Data Rates for GSM Evolution; Oscillation (cell signaling); Computer vision; Butterworth filter; Artificial intelligence; Sequence (biology); Algorithm; Low-pass filter; Control theory (sociology); High-pass filter","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001067033,0.0001129387,0.000103785,0.00009104933,0.0001432514,0.00004233941,0.000239554,0.00006672817,0.000002587152],"category_scores_gemma":[2.380915e-7,0.0001029858,0.00004435698,0.0004099868,0.00008153555,0.00009459048,0.00003054082,0.0001227085,0.00001820156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006461371,"about_ca_system_score_gemma":0.00001231237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001057648,"about_ca_topic_score_gemma":0.0001128072,"domain_scores_codex":[0.9991851,0.00001111957,0.0003741636,0.0001785935,0.00007207051,0.0001789819],"domain_scores_gemma":[0.9995114,0.00003903934,0.00006205614,0.000300979,0.00006827762,0.00001823599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003372914,0.00009987896,0.001973107,0.00007138497,0.00001005438,1.257269e-7,0.00005331857,0.007951322,0.1004975,0.01683892,0.004496686,0.8680044],"study_design_scores_gemma":[0.0003557105,0.00001276064,0.01423641,0.00004259733,0.0000200245,0.00003996223,0.00002710257,0.1916395,0.06737226,0.02745364,0.6984079,0.0003921714],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01412049,0.0003421748,0.9831057,0.0002512716,0.00001245587,0.0006396492,0.00001692531,0.0002802089,0.001231136],"genre_scores_gemma":[0.7945897,0.00006524882,0.2032381,0.00003007777,0.0001558371,0.001737056,0.00008952386,0.00002903918,0.00006543185],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8676122,"threshold_uncertainty_score":0.419964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004062317080807246,"score_gpt":0.2023121798964946,"score_spread":0.1982498628156873,"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."}}