{"id":"W3014900701","doi":"10.1093/nsr/nwaa055","title":"Genetically encoded X-ray cellular imaging for nanoscale protein localization","year":2020,"lang":"en","type":"article","venue":"National Science Review","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Light Source (Canada); University of Saskatchewan","funders":"National Key Research and Development Program of China; Youth Innovation Promotion Association of the Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Nanoscopic scale; Biomolecule; Microscopy; 5-Hydroxymethylcytosine; Nanotechnology; Resolution (logic); Synchrotron; Microscope; Biophysics; Materials science; Chemistry; Biology; DNA methylation; Optics; Physics; Computer science; Biochemistry; Gene; Gene expression","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":[0.0004716899,0.00009502941,0.0001056065,0.00002092992,0.0001372915,0.00002461632,0.0003233119,0.00003238154,0.00001620231],"category_scores_gemma":[0.00100572,0.00008683317,0.00005343024,0.0003344114,0.0002268455,0.0000147253,0.00008101071,0.00003952364,0.000007684772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003644644,"about_ca_system_score_gemma":0.0003202254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.008079e-7,"about_ca_topic_score_gemma":2.464311e-7,"domain_scores_codex":[0.9988554,0.00002203024,0.0002032273,0.0004013936,0.0003272105,0.0001906995],"domain_scores_gemma":[0.9992077,0.000005982638,0.00007769914,0.0001152773,0.0004958179,0.00009750529],"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":[0.000004033742,0.00001084306,0.0000300347,0.0001669956,0.000001202315,1.700922e-7,0.000003485634,0.00004609454,0.9926187,0.001171008,0.001183504,0.004763957],"study_design_scores_gemma":[0.00008559857,0.00006502098,0.000009700293,0.0002196482,0.000005989875,0.00000110865,0.0000017704,0.00406184,0.8952392,0.0006139824,0.09956848,0.0001276407],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005850552,0.01532474,0.9797851,0.002850984,0.00002417012,0.001077104,0.0000164291,0.00002934613,0.0003070539],"genre_scores_gemma":[0.4431079,0.009520226,0.5256574,0.02012253,0.0004417269,0.0006989017,0.0002544157,0.00004840822,0.0001485573],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4541278,"threshold_uncertainty_score":0.3540954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01582230991383309,"score_gpt":0.3169082694545663,"score_spread":0.3010859595407333,"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."}}