{"id":"W4408363127","doi":"10.1093/gigascience/giae098","title":"An analysis of performance bottlenecks in MRI preprocessing","year":2024,"lang":"en","type":"article","venue":"GigaScience","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Addiction and Mental Health; Concordia University","funders":"Concordia University; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Bottleneck; Computer science; Preprocessor; Pipeline (software); Pipeline transport; Segmentation; Data pre-processing; Artificial intelligence; Deep learning; Software; Data mining; Machine learning; Embedded system; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001786205,0.00004095243,0.0001112017,0.0002439541,0.00002916281,0.00001195175,0.00009422591,0.00002036571,0.00002138239],"category_scores_gemma":[0.000009869084,0.00003364769,0.00003155374,0.001933812,0.00009589013,0.000186059,0.0000146458,0.00006354067,0.000002067326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002726517,"about_ca_system_score_gemma":0.00005931294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000117068,"about_ca_topic_score_gemma":0.000007787575,"domain_scores_codex":[0.9994288,0.000003126826,0.0001281832,0.0002171297,0.0001234311,0.0000993453],"domain_scores_gemma":[0.9996657,0.00001225451,0.00002066841,0.0002335436,0.00002907075,0.0000386981],"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":[0.00003248437,0.0003964146,0.2069365,0.0003952833,0.00003855623,0.00002300716,0.003116402,0.05128809,0.3674696,0.01034435,0.00005285191,0.3599065],"study_design_scores_gemma":[0.00005616675,0.0001072482,0.1232364,0.0001982941,0.0001257093,0.00000707074,0.0001408925,0.8216546,0.05252857,0.0003259539,0.001541516,0.00007751051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7741072,0.0002218615,0.223641,0.000157485,0.00001055529,0.00008479304,0.000002367305,0.00007337609,0.001701317],"genre_scores_gemma":[0.972259,0.00008756517,0.02747358,0.00004505782,0.000008970293,0.00001829127,0.000003697102,0.00000340311,0.0001003637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7703665,"threshold_uncertainty_score":0.1372113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01517649722227458,"score_gpt":0.3707363319502028,"score_spread":0.3555598347279282,"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."}}