{"id":"W4214486869","doi":"10.1007/978-1-0716-2051-9_14","title":"Super-Resolution Radial Fluctuations (SRRF) Microscopy","year":2022,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Workflow; Resolution (logic); Computer science; Software; Microscopy; Plug-in; Focus (optics); Superresolution; Microscope; Sample (material); Super-resolution microscopy; Fluorescence microscope; Artificial intelligence; Computer vision; Computer graphics (images); Nanotechnology; Optics; Materials science; Fluorescence; Physics; Image (mathematics); Scanning confocal electron microscopy; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005149468,0.0004872465,0.0004388332,0.0004846358,0.0003167992,0.0004818495,0.000899013,0.0007249526,0.002967543],"category_scores_gemma":[0.0007156382,0.0003481474,0.0002576063,0.0005246391,0.0004723174,0.0009360379,0.0008207924,0.001243754,0.001312196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003459609,"about_ca_system_score_gemma":0.0002945811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004970649,"about_ca_topic_score_gemma":0.0007715218,"domain_scores_codex":[0.9994203,0.00009804962,0.00002441635,0.000127077,0.0002557378,0.00007436457],"domain_scores_gemma":[0.9994332,0.0001893489,0.00007471201,0.0001650512,0.00008688244,0.00005085145],"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":[0.0001290788,0.00004044661,0.000358285,0.0002193204,0.00002688481,0.0001367535,0.0000990492,0.004107316,0.9185189,0.02345256,0.005304094,0.04760718],"study_design_scores_gemma":[0.00003730566,0.0001020955,0.00159239,0.00002697172,0.00002365997,0.0004734098,0.00004861721,0.1196982,0.8467405,0.00840662,0.02276614,0.00008404659],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1518771,0.002717059,0.8171958,0.0007159116,0.0002217939,0.0001248391,0.0008323448,0.005959456,0.0203557],"genre_scores_gemma":[0.5697125,0.002094396,0.4145112,0.0004381644,0.0001473386,0.0001984822,0.000696821,0.0006789938,0.01152219],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002967543,"threshold_uncertainty_score":0.009927392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01384339835168038,"score_gpt":0.4007824383661148,"score_spread":0.3869390400144344,"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."}}