{"id":"W3185361488","doi":"10.1101/2021.07.19.452964","title":"Resolution Enhancement with a Task-Assisted GAN to Guide Optical Nanoscopy Image Analysis and Acquisition","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; Canada First Research Excellence Fund; National Science Foundation","keywords":"Computer science; Artificial intelligence; Segmentation; Microscopy; Task (project management); Materials science; Pipeline (software); Computer vision; Pattern recognition (psychology); Optics; Physics","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":[0.0004842484,0.0004959784,0.0006091018,0.0003867415,0.0001269168,0.000341381,0.0003205697,0.0004512444,0.00003414862],"category_scores_gemma":[0.0001289844,0.0005161158,0.0002273491,0.0008906773,0.0001314109,0.00001564174,0.0006164011,0.0002734139,0.000005812305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000185226,"about_ca_system_score_gemma":0.0003300055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000149947,"about_ca_topic_score_gemma":0.00008245353,"domain_scores_codex":[0.9970274,0.0001519164,0.0005113693,0.001499046,0.0003690425,0.0004412119],"domain_scores_gemma":[0.9970183,0.00001191037,0.0002788655,0.001585509,0.0008129423,0.0002924139],"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":[0.00008385648,0.0001462971,0.001745002,0.00008777656,0.001122675,0.00005472836,0.000004829983,0.00002882289,0.9961433,0.000006510326,0.0005713329,0.000004867707],"study_design_scores_gemma":[0.0002398511,0.0002000519,0.03750273,0.0001394579,0.001357798,5.804933e-8,0.000005575152,0.000194168,0.9581216,1.345265e-7,0.001647944,0.0005906312],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7889019,0.0005988416,0.2097045,0.0001579742,0.00003508956,0.000442292,0.00003658734,0.00008832174,0.00003454919],"genre_scores_gemma":[0.893603,0.0003986176,0.1051648,0.000339887,0.0001494785,0.0002342853,0.00002055204,0.00006878821,0.00002067127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1047011,"threshold_uncertainty_score":0.999729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005179996993481048,"score_gpt":0.2370658492086496,"score_spread":0.2318858522151685,"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."}}