{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002618988,0.0001926183,0.0002186015,0.000217948,0.000106056,0.0002399171,0.0003772016,0.0004339538,0.001102303],"category_scores_gemma":[0.0002699037,0.0002076725,0.0001608713,0.0001967561,0.0002430643,0.0003791862,0.0004578217,0.0005098031,0.0002743021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003236567,"about_ca_system_score_gemma":0.0001343237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003411324,"about_ca_topic_score_gemma":0.0002927411,"domain_scores_codex":[0.9998493,0.00002046087,0.000006410746,0.00004013214,0.00005759738,0.00002606412],"domain_scores_gemma":[0.9998777,0.00003576162,0.00003565707,0.000009397787,0.00002125231,0.00002028592],"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":[0.00001335914,0.000003179087,0.00001994839,0.00001192162,8.355431e-7,0.00001390301,0.000006298995,0.0001679398,0.9988869,0.0001169385,0.00002600756,0.0007328156],"study_design_scores_gemma":[0.000008162328,0.00007056692,0.0002959404,0.000002912372,0.000002586637,0.00006654358,0.000006603748,0.006388227,0.9921929,0.00006165737,0.0008979882,0.000005948304],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8988298,0.001431671,0.09661911,0.0002942571,0.00004512261,0.00003508168,0.0001231892,0.0003841802,0.002237568],"genre_scores_gemma":[0.9331681,0.0006355946,0.06259408,0.0000891679,0.00001714241,0.0000806565,0.0001216491,0.00004541972,0.003248208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001102303,"threshold_uncertainty_score":0.003687561,"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."}}