{"id":"W4224325577","doi":"10.1002/ansa.202100055","title":"Artificial amniotic fluid for nuclear magnetic resonance spectroscopy studies","year":2022,"lang":"en","type":"article","venue":"Analytical Science Advances","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University; St. Michael's Hospital","funders":"","keywords":"Amniocentesis; Imaging phantom; Amniotic fluid; Miscarriage; Magnetic resonance imaging; Nuclear magnetic resonance spectroscopy; Nuclear magnetic resonance; Medicine; Fetus; Prenatal diagnosis; Pregnancy; Nuclear medicine; Radiology; Physics; Biology; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004631644,0.0001102609,0.0002726227,0.0001119095,0.000711004,0.00001895047,0.0002583689,0.00001594062,0.0002991369],"category_scores_gemma":[0.0005759486,0.00008782891,0.0000853474,0.0007966509,0.00148938,0.0001822231,0.0002066736,0.0001481009,0.00004050205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001221758,"about_ca_system_score_gemma":0.00008120878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002271621,"about_ca_topic_score_gemma":0.000002690073,"domain_scores_codex":[0.9983294,0.00001999128,0.0002236498,0.0004844232,0.0004799376,0.0004625801],"domain_scores_gemma":[0.9993771,0.0001472938,0.00003837499,0.0002109441,0.00008864565,0.0001376311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002209103,0.0008126553,0.002542094,0.0002000886,0.00003993236,0.0004935433,0.001162239,0.0001781827,0.2983164,0.2463995,0.002867278,0.444779],"study_design_scores_gemma":[0.001552562,0.012312,0.01196521,0.00007193688,0.000280338,0.0006680482,0.007055134,0.01988108,0.02593148,0.1312901,0.788182,0.0008101168],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9406738,0.03420732,0.0003465562,0.01454466,0.001159726,0.001013257,0.00005731486,0.0001894878,0.007807858],"genre_scores_gemma":[0.9925688,0.0002363565,0.00377672,0.002244586,0.0001679533,0.00003492495,0.000003346369,0.00001251788,0.0009548151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7853147,"threshold_uncertainty_score":0.5487681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02900064592172038,"score_gpt":0.3425075688889853,"score_spread":0.3135069229672649,"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."}}