{"id":"W3113276760","doi":"10.1063/1.5143319","title":"Hyperspectral super-resolution imaging with far-red emitting fluorophores using a thin-film tunable filter","year":2020,"lang":"en","type":"article","venue":"Review of Scientific Instruments","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Optics; Fluorophore; Materials science; Microscopy; Spectral resolution; Wavelength; Spectral imaging; Detector; Image resolution; Fluorescence; Optoelectronics; Physics; Computer science; Spectral line; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0003101052,0.000199139,0.0002691234,0.00004697356,0.0001899682,0.00005717123,0.0003309962,0.0000490532,0.00004299373],"category_scores_gemma":[0.0001083783,0.0001689928,0.0001026729,0.0003353722,0.0002893594,0.00003279985,0.0001887924,0.0001119362,0.000005004901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004908446,"about_ca_system_score_gemma":0.0001255633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001392658,"about_ca_topic_score_gemma":0.000001209765,"domain_scores_codex":[0.9983606,0.00005625793,0.0003659476,0.0005721453,0.000294258,0.0003508184],"domain_scores_gemma":[0.9991375,0.000002910294,0.0002012559,0.0003767326,0.0001831447,0.00009845831],"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":[0.00002536135,0.0000257154,0.002157092,0.0007346218,0.00001718972,0.000002438168,0.00005519541,0.00001415255,0.9906629,0.0000113447,0.001233765,0.005060169],"study_design_scores_gemma":[0.0002642365,0.0001127749,0.00007501847,0.002331235,0.00004260877,0.00002443395,0.00009314117,0.00384836,0.9780526,0.00001169095,0.01490927,0.0002346803],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798004,0.0130285,0.005184077,0.0004591946,0.0002067003,0.0008092721,0.00006550138,0.00005582835,0.0003905251],"genre_scores_gemma":[0.8078684,0.004275462,0.1859902,0.00123432,0.0001176391,0.00002704747,0.0002613594,0.00005620445,0.0001693823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1808061,"threshold_uncertainty_score":0.6891326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01609163768538637,"score_gpt":0.2742147712153987,"score_spread":0.2581231335300123,"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."}}