{"id":"W4399828401","doi":"10.32920/26052526.v1","title":"Implementation of a Cyclegan Model for MRI Image Translation","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Translation (biology); Image (mathematics); Computer science; Artificial intelligence; Computer vision; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002874791,0.0004574964,0.0002497516,0.0003194895,0.0001845157,0.0004632321,0.0008651299,0.0005050491,0.004981293],"category_scores_gemma":[0.0005946709,0.0001938635,0.0003643041,0.0001766676,0.000261414,0.0005795999,0.0005027513,0.0005596229,0.001290921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005767777,"about_ca_system_score_gemma":0.0005775254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004012084,"about_ca_topic_score_gemma":0.005133639,"domain_scores_codex":[0.9998915,0.00001771736,0.000005770853,0.00002765119,0.00003865516,0.00001870695],"domain_scores_gemma":[0.999818,0.0000461723,0.00001204791,0.00003890038,0.00007506729,0.000009812002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000487268,0.0001539719,0.001568926,0.0001701431,0.0001110116,0.000341784,0.0001475854,0.5131259,0.08219459,0.02253101,0.008388449,0.3707794],"study_design_scores_gemma":[0.00000782811,0.00004441199,0.0001247143,0.000006304254,0.000007447236,0.00005931175,0.00000658988,0.9784716,0.01652583,0.00159053,0.003147501,0.000007933289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03995952,0.0002557063,0.9438235,0.000291786,0.0001156976,0.0001396648,0.0002083013,0.004420851,0.01078501],"genre_scores_gemma":[0.6509638,0.0003249318,0.3295504,0.0003501424,0.00004621714,0.0002889828,0.0006901126,0.0003886363,0.01739668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004981293,"threshold_uncertainty_score":0.01666403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04660609839772886,"score_gpt":0.3690697124052822,"score_spread":0.3224636140075534,"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."}}