{"id":"W3004400843","doi":"10.1002/nbm.4264","title":"A multisample 7 T dynamic nuclear polarization polarizer for preclinical hyperpolarized MR","year":2020,"lang":"en","type":"article","venue":"NMR in Biomedicine","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Electric (Canada)","funders":"","keywords":"Polarizer; Spins; Hyperpolarization (physics); Polarization (electrochemistry); Nuclear magnetic resonance; Liquid helium; Cryostat; Physics; Materials science; Atomic physics; Chemistry; Optics; Helium; Condensed matter physics; Nuclear magnetic resonance spectroscopy; Superconductivity; Birefringence","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":[0.0001139372,0.0001623263,0.0003012635,0.00005953007,0.00008059056,0.00001338533,0.0002439875,0.0002142396,0.0003890063],"category_scores_gemma":[0.0003862093,0.0001521655,0.00007645565,0.0003362104,0.0001096412,0.00007833121,0.00006689029,0.0002644873,0.00002100858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006595788,"about_ca_system_score_gemma":0.00002698181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006806055,"about_ca_topic_score_gemma":0.000008876736,"domain_scores_codex":[0.9987135,0.00001045561,0.0004483042,0.0004080869,0.0001561404,0.0002635373],"domain_scores_gemma":[0.9992293,0.000164655,0.0001201945,0.000276197,0.00005431964,0.0001554046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001861871,0.0001576753,0.001659971,0.0001262305,0.00001700189,0.000002433002,0.0002377729,0.000009785436,0.9831985,0.001283508,0.0005155985,0.01260536],"study_design_scores_gemma":[0.009506647,0.0004359969,0.001912102,0.0002377946,0.0001143665,0.00001544331,0.0004448908,0.218814,0.01333758,0.004159511,0.7502459,0.000775696],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4155754,0.001085624,0.509101,0.06297746,0.000144007,0.003022802,0.00154234,0.002396431,0.004154894],"genre_scores_gemma":[0.9203556,0.00005898365,0.07619745,0.002007964,0.0002533713,0.0001212023,0.0005978151,0.0000648659,0.0003427852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9698609,"threshold_uncertainty_score":0.620513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03493633392925403,"score_gpt":0.341248464126628,"score_spread":0.306312130197374,"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."}}