{"id":"W1967906448","doi":"10.1002/mrm.22173","title":"Quantitative <i>T</i><sub>2</sub> analysis: The effects of noise, regularization, and multivoxel approaches","year":2009,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canadian Institutes of Health Research; Fondation pour la Recherche Médicale; Multiple Sclerosis Society of Canada","keywords":"Regularization (linguistics); Voxel; Noise (video); Physics; Mathematics; Chemistry; Nuclear magnetic resonance; Artificial intelligence; Computer science","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":[0.0002725656,0.0001467448,0.0004537863,0.000194283,0.00005056606,0.000003500873,0.0001004725,0.00006644725,0.000004158491],"category_scores_gemma":[0.0004626265,0.00009342319,0.00004685517,0.001298233,0.0003955598,0.00003777786,0.00002013685,0.0001732325,6.048958e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001738118,"about_ca_system_score_gemma":0.00002043263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001953251,"about_ca_topic_score_gemma":0.00001415461,"domain_scores_codex":[0.9988655,0.00005257768,0.0003787522,0.0002874365,0.0002446697,0.0001710067],"domain_scores_gemma":[0.9990097,0.0003183261,0.0001279679,0.0003944873,0.00008842401,0.00006113072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003992495,0.0005709664,0.01264689,0.0003134033,0.00005595841,0.00003679899,0.003286344,0.0002767681,0.3333204,0.0391627,0.001006068,0.6089244],"study_design_scores_gemma":[0.003466957,0.004123177,0.8794575,0.001051951,0.001197553,0.00002440731,0.0007765304,0.02163218,0.06981978,0.0160268,0.002160516,0.000262687],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7875422,0.0777721,0.1148123,0.01520286,0.00004094454,0.00263407,0.000005842931,0.00009777592,0.001891987],"genre_scores_gemma":[0.9815617,0.003660151,0.01374992,0.0006693382,0.00004336675,0.0001186584,0.00001657885,0.0000109258,0.0001694077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8668106,"threshold_uncertainty_score":0.3809687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0182251054367385,"score_gpt":0.2864100920892547,"score_spread":0.2681849866525162,"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."}}