{"id":"W2986452971","doi":"10.1016/j.jmr.2019.106637","title":"Investigation of TEMPO partitioning in different skin models as measured by EPR spectroscopy – Insight into the stratum corneum","year":2019,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance","topic":"Electron Spin Resonance Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Deutsche Forschungsgemeinschaft; Grantová Agentura České Republiky; European Commission; Univerzita Karlova v Praze","keywords":"Stratum corneum; Electron paramagnetic resonance; Ex vivo; Chemistry; In vivo; Biophysics; Spectroscopy; Epidermis (zoology); Human skin; Dermis; Spin probe; Analytical Chemistry (journal); In vitro; Membrane; Nuclear magnetic resonance; Biochemistry; Chromatography; Pathology; Anatomy; Biology","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":[0.0003329886,0.0001549821,0.0002802167,0.0000492054,0.00004030439,0.00001910418,0.0002595342,0.00008588994,0.00001306931],"category_scores_gemma":[0.00008264854,0.0001068267,0.00008949614,0.000140135,0.0001371775,0.00001415361,0.00005385713,0.0002308337,0.000002111331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003835068,"about_ca_system_score_gemma":0.0001298972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000415873,"about_ca_topic_score_gemma":0.00009120832,"domain_scores_codex":[0.998605,0.0001384638,0.0005046418,0.0001934588,0.0003464668,0.000211944],"domain_scores_gemma":[0.9991491,0.00003064033,0.0003623565,0.0002408576,0.0001707291,0.00004629217],"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":[0.0002065574,0.00004173079,0.01948499,0.00002608177,0.00001709512,0.000002486044,0.0007479569,0.0001449548,0.974904,0.0002234707,0.002093769,0.002106927],"study_design_scores_gemma":[0.001309286,0.00241122,0.1180557,0.0002465597,0.00002123141,0.00002311601,0.0002801133,0.000318397,0.8634416,0.00367812,0.01000947,0.0002052134],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9543517,0.04389467,0.0001298502,0.0006050578,0.0001069316,0.0001835102,0.000002403133,0.000001719592,0.0007242179],"genre_scores_gemma":[0.9944769,0.004548652,0.0003429158,0.0001418269,0.00008877416,0.000005790441,0.000003916535,0.00001418872,0.0003770046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1114624,"threshold_uncertainty_score":0.4356266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009728388163733896,"score_gpt":0.2327508514429325,"score_spread":0.2230224632791986,"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."}}