{"id":"W4400651701","doi":"10.1364/boe.528579","title":"Image metric-based multi-observation single-step deep deterministic policy gradient for sensorless adaptive optics","year":2024,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Adaptive optics and wavefront sensing","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology; NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research; National Institute for Health and Care Research; Engineering and Physical Sciences Research Council; Moorfields Eye Charity; Moorfields Eye Hospital NHS Foundation Trust","keywords":"Adaptive optics; Computer science; Metric (unit); Optics; Image processing; Artificial intelligence; Image quality; Computer vision; Image (mathematics); Algorithm; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003868765,0.0006804441,0.0005492378,0.0001923984,0.0002202453,0.0004390372,0.001019836,0.0007802073,0.001181833],"category_scores_gemma":[0.00148266,0.0003530022,0.000355701,0.0002027185,0.0005732807,0.000543672,0.0008288027,0.001102871,0.0002377733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007358723,"about_ca_system_score_gemma":0.00152973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006020062,"about_ca_topic_score_gemma":0.007644156,"domain_scores_codex":[0.9998515,0.00002844181,0.000006895566,0.00003468044,0.00005396427,0.0000244401],"domain_scores_gemma":[0.999681,0.0001404767,0.00004560629,0.0000345458,0.00006436567,0.00003404066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006703226,0.00003898277,0.000614071,0.00006698861,0.00003722832,0.00006001986,0.00004411719,0.925393,0.006683466,0.005961549,0.001253565,0.05977997],"study_design_scores_gemma":[0.000002778554,0.00001203075,0.00003548049,0.000001520105,0.000001702869,0.000005921219,0.000001409138,0.9983469,0.0007463173,0.0006922792,0.0001514053,0.000002202711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02353955,0.0002384208,0.9732032,0.0002285005,0.00005277536,0.00003487137,0.00004243411,0.0009284965,0.001731711],"genre_scores_gemma":[0.7721591,0.0001590136,0.2238057,0.0002455892,0.00003306524,0.0001278651,0.0001526698,0.0001562608,0.003160756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006020062,"threshold_uncertainty_score":0.01197004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04232175617739681,"score_gpt":0.294924926565268,"score_spread":0.2526031703878712,"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."}}