{"id":"W4280651687","doi":"10.3390/s22103755","title":"Active Aberration Correction with Adaptive Coefficient SPGD Algorithm for Laser Scanning Confocal Microscope","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Toronto","funders":"National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Optics; Adaptive optics; Gradient descent; Microscope; Confocal; Optical path; Refractive index; Laser; Field of view; Computer science; Optical aberration; Interference (communication); Materials science; Wavefront; Physics; Artificial intelligence; Artificial neural network","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00009265696,0.0001428093,0.0001131823,0.00004474603,0.00030325,0.0000146392,0.0000893072,0.00006062523,0.00001368524],"category_scores_gemma":[0.00001976684,0.0001430335,0.00004567831,0.0001077507,0.00009990046,0.000004753127,0.00006972755,0.0001345939,0.000001421573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009822297,"about_ca_system_score_gemma":0.00007733616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002739236,"about_ca_topic_score_gemma":0.0000193815,"domain_scores_codex":[0.9991193,0.00005137801,0.0001203748,0.0003651198,0.0001192326,0.0002245608],"domain_scores_gemma":[0.9995547,0.00001347716,0.0001017685,0.0001629389,0.0001256413,0.00004147392],"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":[0.00088088,0.00009493488,0.00006022611,0.000003877911,0.00004364152,0.000004220713,0.000304263,0.01789248,0.9503264,0.000014091,0.004074157,0.02630077],"study_design_scores_gemma":[0.0004704759,0.001471573,0.00006723339,0.00001038155,0.00001540626,0.00003097144,0.00152243,0.01987541,0.9584449,0.000008311388,0.01788273,0.0002001493],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3725459,0.00005765799,0.6255832,0.00004291426,0.0003994155,0.0008293936,0.0001763621,0.00006102019,0.0003040618],"genre_scores_gemma":[0.8389139,0.00001698633,0.1557802,0.0003806926,0.0002080328,0.0004586707,0.0007043526,0.00008349318,0.003453674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4698031,"threshold_uncertainty_score":0.5832735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007714274595076558,"score_gpt":0.2610873025425454,"score_spread":0.2533730279474688,"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."}}