{"id":"W2966942977","doi":"10.1093/jmicro/dfz029","title":"Efficient fluorescence recovery using antifade reagents in correlative light and electron microscopy","year":2019,"lang":"en","type":"article","venue":"Microscopy","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute for Basic Biology; University of Tokyo; RIKEN; Japan Society for the Promotion of Science; Centre for Studies in Religion and Society, University of Victoria; University of Cambridge","keywords":"Correlative; Fluorescence; Reagent; Electron microscope; Microscopy; Fluorescence microscope; Materials science; Chemistry; Photochemistry; Biophysics; Optics; Computer science; Nanotechnology; Physics; Biology; Organic chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002354507,0.0003362066,0.0003115552,0.000149532,0.00009265663,0.00005310394,0.0002640735,0.0002847541,0.0000181758],"category_scores_gemma":[0.00005676451,0.0003533876,0.00007254796,0.000246852,0.0001187227,0.00001241915,0.0001874797,0.000284445,0.00003143183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001338627,"about_ca_system_score_gemma":0.0001174311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005394893,"about_ca_topic_score_gemma":0.00001172605,"domain_scores_codex":[0.9979447,0.00008818777,0.0003698846,0.0008434387,0.0001406672,0.0006131633],"domain_scores_gemma":[0.9991584,0.00001655665,0.000160806,0.0005040038,0.00007158417,0.00008865986],"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":[0.0002693554,0.00008124713,0.02487119,0.00006846304,0.00001332062,0.000005722091,0.0001037798,0.0001312127,0.9740398,0.00001033978,0.0002067665,0.0001988049],"study_design_scores_gemma":[0.0006116728,0.000355934,0.002460977,0.0003984024,0.000008792417,0.00002652673,0.0000564892,0.000525379,0.9936886,0.00002840723,0.001449639,0.0003892129],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904323,0.001986148,0.006329724,0.0000328164,0.0002838527,0.0006333335,0.00002404858,0.00003606391,0.0002416921],"genre_scores_gemma":[0.9132013,0.0006382003,0.08513356,0.0003098627,0.00007597702,0.00002115569,0.00006612731,0.00008685164,0.0004669035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07880384,"threshold_uncertainty_score":0.9998918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005719391806680518,"score_gpt":0.295346026487197,"score_spread":0.2896266346805165,"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."}}