{"id":"W4244275492","doi":"10.22215/etd/2006-06365","title":"Rician noise corrected multi-component analysis of the MR diffusion signal decay for human brain in vivo","year":2006,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Canadian Heritage","funders":"","keywords":"Rician fading; Noise (video); Physics; Computer science; Telecommunications; Artificial intelligence","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":[0.0004366465,0.0004883245,0.0002199513,0.0007510193,0.0001971098,0.0004169108,0.0003241406,0.0004140287,0.002110406],"category_scores_gemma":[0.001688533,0.0001916019,0.0004250621,0.0005109644,0.0002344479,0.000450121,0.0002403684,0.0004607196,0.001281155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000305533,"about_ca_system_score_gemma":0.000730044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002687294,"about_ca_topic_score_gemma":0.005234917,"domain_scores_codex":[0.9999105,0.00002497317,0.000006794696,0.00001825648,0.00003024267,0.00000925547],"domain_scores_gemma":[0.9997352,0.00008287952,0.00002423645,0.0000588039,0.00008798658,0.00001096192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003940205,0.00009668659,0.0009590919,0.0004564563,0.0001358334,0.0001812649,0.000229395,0.08224913,0.3080036,0.0174099,0.01227823,0.5776064],"study_design_scores_gemma":[0.00001925854,0.00009147673,0.007513005,0.0000367553,0.0001150224,0.000400796,0.00005507309,0.8473879,0.1194413,0.006420379,0.01846096,0.00005808717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02592132,0.001128364,0.9698395,0.0002641355,0.00009558474,0.00004505726,0.0002190499,0.001038051,0.001448948],"genre_scores_gemma":[0.2664304,0.003620507,0.7106765,0.0001153746,0.0001279902,0.0001173154,0.001603268,0.001107993,0.01620071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002687294,"threshold_uncertainty_score":0.007059991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03715492550859682,"score_gpt":0.3631355201565886,"score_spread":0.3259805946479917,"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."}}