{"id":"W2790520416","doi":"10.1007/s10334-018-0680-1","title":"Toward faster inference of micron-scale axon diameters using Monte Carlo simulations","year":2018,"lang":"en","type":"article","venue":"Magnetic Resonance Materials in Physics Biology and Medicine","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo method; Scale (ratio); Cylinder; Axon; Materials science; Diffusion; Physics; Geometry; Mathematics; Statistics; Anatomy","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":[0.00240323,0.001018539,0.00204678,0.001631071,0.001077572,0.001668688,0.003731516,0.002850037,0.005815136],"category_scores_gemma":[0.01698843,0.0019345,0.001356111,0.001103308,0.001381316,0.002858086,0.002104725,0.003094119,0.001332381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001714601,"about_ca_system_score_gemma":0.002568255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01915203,"about_ca_topic_score_gemma":0.02488286,"domain_scores_codex":[0.9993536,0.0001978958,0.00003918648,0.0001449307,0.0002067145,0.00005772671],"domain_scores_gemma":[0.9821038,0.01441557,0.0005847813,0.001218871,0.001210426,0.0004665501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001135903,0.00004967815,0.001159395,0.00008289634,0.00008881978,0.00007833476,0.00007152889,0.9587613,0.001697479,0.0150303,0.001138649,0.02172813],"study_design_scores_gemma":[0.000005612207,0.000002529504,0.0000447313,0.000003751061,0.000002930898,0.0000072114,0.000003238304,0.9947156,0.0002201546,0.004889817,0.0001012057,0.000003068395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0282206,0.0002654835,0.9661708,0.0003459815,0.00009007469,0.00005295769,0.0001660387,0.002631821,0.002056163],"genre_scores_gemma":[0.3670833,0.0002283308,0.6281775,0.0003887461,0.0001105689,0.0002255555,0.0004004698,0.0009175534,0.002467882],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01915203,"threshold_uncertainty_score":0.03808111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08719405476780663,"score_gpt":0.3834253672653256,"score_spread":0.2962313124975189,"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."}}