{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001322329,0.0003495482,0.0003636297,0.0001498559,0.00008695877,0.0001256042,0.0003030219,0.00006554832,0.00002277755],"category_scores_gemma":[0.00009263886,0.0004247605,0.00007091001,0.0003892099,0.00005597754,0.0002177163,0.00008632476,0.0005146265,0.00001568924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002663144,"about_ca_system_score_gemma":0.0000395614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001587757,"about_ca_topic_score_gemma":0.000003618834,"domain_scores_codex":[0.9982673,0.00002305862,0.0003940557,0.0003752327,0.0002155449,0.0007248027],"domain_scores_gemma":[0.9992666,0.0001030446,0.00005880224,0.0003669141,0.00002772103,0.0001769153],"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":[0.00001141196,0.000005433033,0.0001451491,0.0001364167,0.00002508806,0.0005254554,0.0006007027,0.05797816,0.9393793,0.000001359575,0.0008012417,0.0003902297],"study_design_scores_gemma":[0.0009396849,0.000007799338,0.00004611105,0.0002228998,0.00005287438,0.0001535839,0.0001714308,0.9198275,0.07805635,0.00001707707,0.00007426163,0.0004303816],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6945814,0.0001105034,0.3025503,0.0009045029,0.0005032357,0.0002106393,0.00002688663,0.0002099961,0.0009024862],"genre_scores_gemma":[0.8908536,0.00002212442,0.105405,0.003372035,0.0002108973,6.068956e-7,0.000004679783,0.0001230973,0.000008001935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8618494,"threshold_uncertainty_score":0.9998204,"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."}}