{"id":"W4417311052","doi":"10.1515/mim-2025-0026","title":"Enhancing STED microscopy via fluorescence lifetime unmixing and filtering in two-species SPLIT-STED","year":2025,"lang":"en","type":"article","venue":"Methods in microscopy","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Occupational Cancer Research Centre","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Canada Foundation for Innovation; Sentinelle Nord, Université Laval; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"STED microscopy; Microscopy; Fluorophore; Fluorescence; Fluorescence microscope; Fluorescence-lifetime imaging microscopy; Stimulated emission; Light sheet fluorescence microscopy; Resolution (logic)","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001339091,0.0004620614,0.0005674831,0.0004256732,0.0001524879,0.0001004825,0.0005050668,0.0002841188,0.00001812205],"category_scores_gemma":[0.0004813392,0.0005184737,0.00008299621,0.0007691694,0.0003744982,0.00002832368,0.0006317123,0.0004956147,0.000002842041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001696298,"about_ca_system_score_gemma":0.0001220765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001675296,"about_ca_topic_score_gemma":0.0002579645,"domain_scores_codex":[0.9967833,0.0005015742,0.0007842863,0.001056562,0.000113042,0.0007612074],"domain_scores_gemma":[0.9988064,0.0001615242,0.0001640417,0.0006975491,0.00008330338,0.00008718731],"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":[0.0001760491,0.00006065543,0.01341484,0.0001236053,0.00001603428,0.00001129099,0.000186418,0.00001847286,0.9777378,0.00005222437,0.00004832335,0.008154238],"study_design_scores_gemma":[0.0008681189,0.00008648878,0.005305228,0.0006506174,0.00001133361,0.00001095572,0.0001462519,0.0003468666,0.9894641,0.0003693027,0.002291765,0.0004489468],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3203118,0.002700164,0.6757842,0.0000640677,0.0002676191,0.000515496,0.00001436606,0.00006553149,0.0002767094],"genre_scores_gemma":[0.02835787,0.000676892,0.9697987,0.0003997776,0.00006420171,0.00008990183,0.0000337599,0.00005825167,0.0005205924],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2940145,"threshold_uncertainty_score":0.9997267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.011090003678814,"score_gpt":0.3962460230510063,"score_spread":0.3851560193721923,"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."}}