{"id":"W2902825269","doi":"10.1038/s41467-018-07668-y","title":"A machine learning approach for online automated optimization of super-resolution optical microscopy","year":2018,"lang":"en","type":"article","venue":"Nature Communications","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Task (project management); Artificial intelligence; Optical imaging; Image quality; Superresolution; Quality (philosophy); Computer vision; Machine learning; Image (mathematics); Systems engineering; Optics","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":[0.001354039,0.001339229,0.00142681,0.0007085574,0.0005271652,0.001030014,0.001619098,0.001675534,0.003160356],"category_scores_gemma":[0.003603678,0.00080137,0.0009151833,0.0007346667,0.0008519551,0.001033122,0.001462534,0.001861767,0.0007340445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001097246,"about_ca_system_score_gemma":0.001690674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004230924,"about_ca_topic_score_gemma":0.004477147,"domain_scores_codex":[0.9993641,0.0002027329,0.00003589176,0.0001538655,0.0001719975,0.00007148973],"domain_scores_gemma":[0.998447,0.0009482544,0.0001462306,0.0001357122,0.0002674325,0.00005530468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003870905,0.00004236311,0.0001701456,0.00004453588,0.00002510031,0.00002855937,0.00002169657,0.9483808,0.002077573,0.003410907,0.000698793,0.04506088],"study_design_scores_gemma":[0.000001949267,0.000004513465,0.00001455291,0.000001286793,8.781174e-7,0.00000180819,9.924737e-7,0.9987273,0.0001788761,0.0009661388,0.0001001944,0.000001597248],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004252273,0.00009274785,0.9938766,0.00009224241,0.00001343166,0.00003160604,0.00002702011,0.0007832436,0.0008308454],"genre_scores_gemma":[0.2650925,0.0001498154,0.7306309,0.0002372422,0.00006630408,0.0004545526,0.0002320704,0.0004227597,0.002713811],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004230924,"threshold_uncertainty_score":0.01057243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01465164551885477,"score_gpt":0.3433743322346687,"score_spread":0.3287226867158139,"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."}}