{"id":"W7133073814","doi":"","title":"Characterizing contrast origins and noise contribution in spin-echo BOLD at 3 T","year":2018,"lang":"","type":"dissertation","venue":"TSpace","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Noise (video); Communication noise; Contrast (vision); Limiting; Sensitivity (control systems); SIGNAL (programming language); Background noise; Signal-to-noise ratio (imaging)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000807634,0.0004257494,0.0003282133,0.000270804,0.0002012397,0.0004621987,0.000387447,0.0006862999,0.0006605347],"category_scores_gemma":[0.002165833,0.0002068914,0.0004142429,0.0002284435,0.000375048,0.0005883295,0.0003327102,0.0005847051,0.000297263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002916791,"about_ca_system_score_gemma":0.0004072903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001226245,"about_ca_topic_score_gemma":0.001986542,"domain_scores_codex":[0.9998485,0.00003631707,0.000005244464,0.00004215993,0.00004594718,0.0000218862],"domain_scores_gemma":[0.9994616,0.0002964216,0.00006415007,0.00005125716,0.0001007765,0.00002582608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002819784,0.00009084056,0.004363001,0.000176866,0.00006661711,0.0002528311,0.000200985,0.03801377,0.9287391,0.002722348,0.0004119916,0.02467976],"study_design_scores_gemma":[0.00001591749,0.000437252,0.01802805,0.00003919612,0.00008582834,0.0004783442,0.00008177171,0.3510567,0.6222977,0.005423999,0.001978232,0.0000769742],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6299335,0.0006985749,0.3659264,0.0002437848,0.00002631799,0.00004435268,0.0002188427,0.0004869059,0.002421355],"genre_scores_gemma":[0.923372,0.0008552267,0.07306702,0.0001405405,0.00001592787,0.00006415338,0.0004799778,0.000170244,0.001834894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001226245,"threshold_uncertainty_score":0.004271209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01558835930364151,"score_gpt":0.3750090350994444,"score_spread":0.3594206757958029,"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."}}