{"id":"W3084922366","doi":"10.1364/ol.410171","title":"Label-free, non-contact, in vivo ophthalmic imaging using photoacoustic remote sensing microscopy","year":2020,"lang":"en","type":"article","venue":"Optics Letters","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Illumisonics (Canada); 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":"Sclera; In vivo; Photoacoustic imaging in biomedicine; IRIS (biosensor); Microscopy; Biomedical engineering; Preclinical imaging; Medicine; Retina; Materials science; Pathology; Ophthalmology; Optics; Biology; Computer science; Neuroscience; Computer vision","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":[0.0004030698,0.0004903186,0.0002702451,0.0002809418,0.0002018798,0.0004539739,0.0005438529,0.0007722747,0.001017916],"category_scores_gemma":[0.0003778606,0.0002836466,0.0002315495,0.0001202301,0.0004847765,0.0006247647,0.0004196541,0.000523908,0.0004869679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002221978,"about_ca_system_score_gemma":0.000255403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004266001,"about_ca_topic_score_gemma":0.000936592,"domain_scores_codex":[0.9996687,0.00005861121,0.00001384127,0.00007988338,0.0001382378,0.00004072114],"domain_scores_gemma":[0.999657,0.0001142993,0.0001081313,0.0000480188,0.00004828864,0.00002434728],"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":[0.00001195852,0.000007638256,0.00005170104,0.00004245234,0.000002139763,0.00002964621,0.00001288022,0.00005034792,0.9978487,0.00009806859,0.00004970908,0.001794591],"study_design_scores_gemma":[0.000008123242,0.0001814168,0.001307842,0.000007763487,0.00001233349,0.0003384454,0.00002801077,0.002768354,0.9924982,0.0001211258,0.002713921,0.00001437946],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7451833,0.005478061,0.2402407,0.0006126282,0.0001421312,0.0002318092,0.0003112603,0.001039794,0.00676022],"genre_scores_gemma":[0.8326291,0.003482736,0.1550037,0.0003893157,0.0001185664,0.0002366566,0.0002461553,0.0001321556,0.007761507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001017916,"threshold_uncertainty_score":0.003405333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01146861662801583,"score_gpt":0.2269108934120637,"score_spread":0.2154422767840479,"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."}}