{"id":"W2021173709","doi":"10.1016/j.neuroimage.2014.12.008","title":"The impact of gradient strength on in vivo diffusion MRI estimates of axon diameter","year":2014,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":115,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"NIH Blueprint for Neuroscience Research; National Institute of Biomedical Imaging and Bioengineering; National Center for Research Resources; National Institute of Mental Health; Canadian Institutes of Health Research; National Institutes of Health; Radiological Society of North America","keywords":"Diffusion MRI; In vivo; Axon; Diffusion; Biomedical engineering; Chemistry; Materials science; Neuroscience; Physics; Biology; Medicine; Magnetic resonance imaging; Radiology; Biotechnology; Thermodynamics","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":[0.00009528764,0.0001025009,0.0001905269,0.00007744635,0.000034381,0.000004986402,0.0001089694,0.00002192192,0.00001109831],"category_scores_gemma":[0.0002048954,0.0000643184,0.00009851067,0.0001479238,0.0001063054,0.0000259457,0.00004361771,0.0001474928,0.000001666924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001923133,"about_ca_system_score_gemma":0.00001064745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005092621,"about_ca_topic_score_gemma":0.00000258214,"domain_scores_codex":[0.9992847,0.00003061202,0.0002280849,0.0001808014,0.0001290904,0.000146644],"domain_scores_gemma":[0.9989721,0.0003443192,0.0001172264,0.0004880923,0.00003376894,0.00004448005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002390125,0.0008764646,0.06131263,0.00006195126,0.00001070103,0.000009040044,0.00009711775,0.0001370185,0.9012448,0.002824685,0.002112281,0.03107434],"study_design_scores_gemma":[0.001047509,0.001952258,0.7284862,0.0001817144,0.00002900796,0.00001656802,0.00001030297,0.0159769,0.2464996,0.003638834,0.002030408,0.0001307898],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949987,0.00001551795,0.00201754,0.0005607192,0.00002092892,0.0003197121,0.00001431563,0.00004776311,0.002004803],"genre_scores_gemma":[0.9969786,0.0000994182,0.002757027,0.00006813814,0.00001565288,0.00001831767,0.000002720921,0.00001840682,0.00004167983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6671735,"threshold_uncertainty_score":0.2622828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0328762899737219,"score_gpt":0.3480370503872482,"score_spread":0.3151607604135263,"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."}}