{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007937556,0.0009410451,0.001178584,0.0007396886,0.0006685258,0.001536293,0.002162287,0.002061279,0.002516062],"category_scores_gemma":[0.001640515,0.0007246251,0.001250747,0.001011964,0.0008494597,0.001131792,0.000978338,0.001297762,0.0009612967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009318818,"about_ca_system_score_gemma":0.001318599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006445909,"about_ca_topic_score_gemma":0.00417638,"domain_scores_codex":[0.9996811,0.000134692,0.00001751191,0.00004196039,0.00009416154,0.00003057025],"domain_scores_gemma":[0.9995025,0.0002906473,0.00004118974,0.00005361861,0.00007597455,0.00003589664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003532388,0.00002855071,0.0002759244,0.0001315986,0.00007037249,0.00008159578,0.00005231291,0.9503139,0.003403853,0.0366362,0.001231305,0.007739122],"study_design_scores_gemma":[0.000009113561,0.000007122302,0.00005450715,0.000009628593,0.000008066008,0.00001318877,0.000009451386,0.9901839,0.0006192129,0.006262294,0.002814895,0.000008670755],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03227282,0.003332711,0.9456376,0.001141632,0.0002477269,0.0001362489,0.0007968957,0.001340071,0.01509442],"genre_scores_gemma":[0.3427358,0.008258592,0.6323315,0.0006226538,0.0002851852,0.001715693,0.001876824,0.0007906936,0.01138305],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006445909,"threshold_uncertainty_score":0.01281679,"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."}}