{"id":"W4220715687","doi":"10.1038/s41598-022-08508-2","title":"In-vivo functional and structural retinal imaging using multiwavelength photoacoustic remote sensing microscopy","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Centre for Bioengineering and Biotechnology, University of Waterloo; Mitacs; University of Waterloo; illumiSonics; Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Optical coherence tomography; Retina; Retinal; Preclinical imaging; Photoacoustic imaging in biomedicine; In vivo; Biomedical engineering; Optical imaging; Computer science; Optics; Medicine; Ophthalmology; Biology; 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":[],"consensus_categories":[],"category_scores_codex":[0.000844566,0.000201689,0.0001981026,0.0002998564,0.0007068749,0.0002249358,0.0000703183,0.00002475406,0.0002430082],"category_scores_gemma":[0.0000798308,0.0002309848,0.00004986919,0.0004602277,0.0001784882,0.0002671996,0.0001167975,0.0003706579,8.207669e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003579958,"about_ca_system_score_gemma":0.0001079415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001781303,"about_ca_topic_score_gemma":0.000008685097,"domain_scores_codex":[0.9979795,0.00003966775,0.0004395341,0.0005948644,0.0004468803,0.0004995748],"domain_scores_gemma":[0.999348,0.0000519478,0.0001014673,0.0003448816,0.00005859248,0.00009508257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000923601,0.00000445849,0.000909632,0.00004833433,0.00001029404,0.001129439,0.0006392488,0.08630347,0.9073902,0.000001691734,0.00175091,0.001803032],"study_design_scores_gemma":[0.0002031725,0.000003457996,0.0003525604,0.00003816273,0.00002285755,0.006923941,0.000599595,0.9651219,0.02362324,0.001065107,0.001772903,0.0002730626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9667445,0.0003229902,0.02190518,0.00002166762,0.01029074,0.0001961195,0.00001207097,0.0001361608,0.0003705888],"genre_scores_gemma":[0.9898949,0.000001819854,0.009554654,0.00004590417,0.00009279548,0.000001191708,0.0000131619,0.00003862504,0.0003569003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.883767,"threshold_uncertainty_score":0.9419288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008548289492853281,"score_gpt":0.2264526484127285,"score_spread":0.2179043589198752,"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."}}