{"id":"W4313422160","doi":"10.1002/ange.202217028","title":"Super‐Resolution Tension PAINT Imaging with a Molecular Beacon","year":2022,"lang":"en","type":"article","venue":"Angewandte Chemie","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Michael Smith Health Research BC; Canada Foundation for Innovation","keywords":"Molecular beacon; Fluorescence; DNA; Fluorescence-lifetime imaging microscopy; Resolution (logic); Superresolution; Substrate (aquarium); Molecular imaging; Materials science; Chemistry; Biophysics; Nanotechnology; Optics; Physics; Computer science; Image (mathematics); Geology; Biology; Computer vision","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":[0.0001402845,0.0001596624,0.0001124547,0.0000386422,0.0001789829,0.00001447437,0.0001756005,0.00004319473,0.00003714179],"category_scores_gemma":[0.00002871768,0.000153175,0.00005800833,0.0001142085,0.00008791462,0.000005974144,0.0002563667,0.0001564535,0.000001702293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006845601,"about_ca_system_score_gemma":0.00005060721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002221482,"about_ca_topic_score_gemma":0.00000376575,"domain_scores_codex":[0.9990038,0.00003350958,0.0001208894,0.0003944957,0.000170101,0.0002771738],"domain_scores_gemma":[0.9994618,0.000005015482,0.00005531643,0.0003570389,0.00006329455,0.00005752949],"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":[0.00009808119,0.00003983064,0.0008830126,0.000007795935,0.00001270573,0.00003683302,0.00003871954,0.0000738801,0.9953045,0.00002219404,0.002941576,0.0005409108],"study_design_scores_gemma":[0.0003081931,0.0001964654,0.00006251655,0.00001220669,0.0000138313,0.00008384165,0.0001794815,0.00003662578,0.9654728,0.00007618852,0.033343,0.0002149164],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9343515,0.003045952,0.05981638,0.0006192935,0.00008188959,0.000442996,0.00004742646,0.0001569153,0.001437662],"genre_scores_gemma":[0.9844963,0.00005659003,0.01377687,0.0009248327,0.00005808763,0.0001211034,0.0003220616,0.00004287781,0.0002013024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05014478,"threshold_uncertainty_score":0.6246296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005614031460080501,"score_gpt":0.2338339208354837,"score_spread":0.2282198893754032,"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."}}