{"id":"W2056715273","doi":"10.1016/j.bpj.2014.08.028","title":"Super-resolution Microscopy Approaches for Live Cell Imaging","year":2014,"lang":"en","type":"review","venue":"Biophysical Journal","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":251,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; European Research Council; Centre National de la Recherche Scientifique; Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche; Fondation pour la Recherche Médicale; Agence Nationale de la Recherche","keywords":"Superresolution; Microscopy; Live cell imaging; Resolution (logic); Super-resolution microscopy; Fluorescence microscope; Nanotechnology; Nanoscopic scale; Image resolution; Diffraction; Computer science; Fluorescence; Optics; Materials science; Physics; Biology; Computer vision; Cell; Artificial intelligence; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001013091,0.001623196,0.001660403,0.002377735,0.0003806995,0.001077172,0.002157083,0.001798277,0.004437949],"category_scores_gemma":[0.0008817154,0.0006811084,0.0005910015,0.002743519,0.00113937,0.002637693,0.001326513,0.002838297,0.004088388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009706041,"about_ca_system_score_gemma":0.0008789256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008893552,"about_ca_topic_score_gemma":0.00160763,"domain_scores_codex":[0.9997019,0.0000314364,0.00002512108,0.00005076786,0.0001594739,0.0000312718],"domain_scores_gemma":[0.9995415,0.0002123464,0.00004901432,0.00002988629,0.0001355421,0.00003170034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004960121,0.00007122359,0.00005926945,0.007652625,0.00004477132,0.0002036438,0.00004045639,0.0006202366,0.01667865,0.008528494,0.03053416,0.9355168],"study_design_scores_gemma":[0.00001503281,0.00006289213,0.0003301059,0.0009217202,0.00006357398,0.001162582,0.00002781391,0.0006758246,0.01012392,0.005611045,0.9809622,0.0000432977],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002528323,0.9866561,0.008601679,0.0004691668,0.0007943089,0.0000219368,0.00005295557,0.00008842057,0.003062578],"genre_scores_gemma":[0.001542902,0.9882476,0.005856934,0.0003608349,0.0005009974,0.00003443339,0.00009987167,0.00001923151,0.00333729],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004437949,"threshold_uncertainty_score":0.01484644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02932020179662132,"score_gpt":0.3299558517968174,"score_spread":0.3006356500001961,"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."}}