{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002608764,0.001100693,0.0004192306,0.001011341,0.0004081406,0.001071965,0.000823753,0.0006864216,0.00130106],"category_scores_gemma":[0.008957532,0.0003871617,0.0003803525,0.0006231269,0.001068657,0.001225423,0.0008998608,0.0007542137,0.0002562826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007543787,"about_ca_system_score_gemma":0.0007542162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001338695,"about_ca_topic_score_gemma":0.001428858,"domain_scores_codex":[0.9993993,0.0001933838,0.00003299537,0.0001324371,0.0002068012,0.00003502905],"domain_scores_gemma":[0.9971792,0.001745382,0.0003759057,0.0002983094,0.0003250708,0.00007619113],"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":[0.001357501,0.0001793709,0.005559449,0.0006127213,0.0002375786,0.0005734598,0.0005041915,0.1600131,0.6712516,0.01045126,0.001104704,0.148155],"study_design_scores_gemma":[0.00004245637,0.0003743968,0.004688034,0.00004217332,0.0001158069,0.0007780654,0.0001008341,0.6650544,0.3161795,0.009344598,0.003180027,0.000099854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1166376,0.0005054729,0.8802076,0.0001706722,0.00003229948,0.00006495469,0.0001042795,0.001163154,0.001113957],"genre_scores_gemma":[0.4882315,0.0003756367,0.5087389,0.0001496823,0.00002781726,0.0001694298,0.0001867301,0.001005683,0.001114682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002608764,"threshold_uncertainty_score":0.01379663,"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."}}