{"id":"W2996217617","doi":"","title":"Molecular modeling of biomolecules by paramagnetic NMR and computational hybrid methods Proteins and proteomics","year":2017,"lang":"en","type":"article","venue":"Biochimica et Biophysica Acta","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biomolecule; Paramagnetism; Variety (cybernetics); Computer science; Chemistry; Mechanism (biology); Proteomics; Nanotechnology; Biological system; Physics; Materials science; Artificial intelligence; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006945926,0.0001659399,0.0001998396,0.0000250119,0.0001833291,0.00007512516,0.0002405174,0.0000617582,0.00000418382],"category_scores_gemma":[0.00004607293,0.000163328,0.00004515138,0.00002897996,0.0002681838,0.00008675635,0.0001958205,0.0001178922,3.72267e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009202862,"about_ca_system_score_gemma":0.00002416965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003421417,"about_ca_topic_score_gemma":2.518195e-7,"domain_scores_codex":[0.9991971,0.00001889186,0.0002065674,0.0003318549,0.0001011441,0.0001444673],"domain_scores_gemma":[0.9991726,0.00003040993,0.0002256103,0.0004435608,0.00005563883,0.00007214712],"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":[0.00001371633,0.00007749241,0.000002868763,0.00006926319,0.00001695984,5.638263e-7,0.00002231859,0.0001108449,0.9952533,0.002033915,0.00001935765,0.0023794],"study_design_scores_gemma":[0.0002065893,0.00003118088,0.00001972751,0.00004304981,0.00002131474,0.00000534786,0.000008056745,0.1342653,0.85203,0.01308211,0.0001201071,0.000167225],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8578712,0.00009732744,0.1403516,0.0008185879,0.000001966331,0.0002414732,0.0001524628,0.00004895682,0.0004164715],"genre_scores_gemma":[0.7396487,0.00007460993,0.2600762,0.00003519371,0.00001072253,0.00007822863,0.00004185538,0.00001948261,0.00001502588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1432233,"threshold_uncertainty_score":0.6660323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01625179711620138,"score_gpt":0.3206085453206401,"score_spread":0.3043567482044388,"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."}}