{"id":"W4235829070","doi":"10.1017/s1446181117000219","title":"IMAGE INPAINTING FROM PARTIAL NOISY DATA BY DIRECTIONAL COMPLEX TIGHT FRAMELETS","year":2017,"lang":"en","type":"article","venue":"The ANZIAM Journal","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Alberta","funders":"","keywords":"Inpainting; Image (mathematics); Artificial intelligence; Algorithm; Mathematics; Shearlet; Computer science; Pattern recognition (psychology); Computer vision","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":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002314084,0.0001564469,0.0002061079,0.00003887221,0.00247459,0.002826754,0.005305922,0.00005475966,0.0002114559],"category_scores_gemma":[0.0004913314,0.0001062251,0.00008220701,0.00006698866,0.0001977474,0.001833536,0.00135881,0.0006244651,0.0001118604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002624354,"about_ca_system_score_gemma":0.00007348025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002554325,"about_ca_topic_score_gemma":0.000009122239,"domain_scores_codex":[0.998039,0.0004421909,0.0003105879,0.0003405862,0.0005009527,0.0003666578],"domain_scores_gemma":[0.9971593,0.0003277959,0.0004036458,0.001853792,0.0001207751,0.0001347068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008602765,0.0001907584,0.001566576,0.000006217922,0.0002307369,0.0003459475,0.001358328,0.00001715777,0.2336564,0.001269488,0.4559855,0.3052869],"study_design_scores_gemma":[0.003662521,0.0001847756,0.06874644,0.0001872018,0.0001355017,0.001545988,0.00008560369,0.3907543,0.03733811,0.05717227,0.4389987,0.001188624],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01908394,0.0002980287,0.967595,0.008799491,0.001248529,0.00005807387,0.00003720636,0.00005722892,0.002822447],"genre_scores_gemma":[0.4957111,0.000100878,0.4966668,0.002508423,0.004217071,0.000002767131,0.00003983103,0.00003216089,0.0007209396],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4766272,"threshold_uncertainty_score":0.9988241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08423448207167587,"score_gpt":0.3471199530578696,"score_spread":0.2628854709861937,"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."}}