{"id":"W3083584224","doi":"10.1364/osac.398269","title":"In vivo combined virtual histology and vascular imaging with dual-wavelength photoacoustic remote sensing microscopy","year":2020,"lang":"en","type":"article","venue":"OSA Continuum","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Photoacoustic imaging in biomedicine; Histology; Biomedical engineering; Modality (human–computer interaction); In vivo; Materials science; Microscopy; Excitation wavelength; Wavelength; Pathology; Optics; Medicine; Computer science; Biology; Optoelectronics; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005673327,0.0006137081,0.0002932811,0.00045272,0.0001679206,0.0005257705,0.0006561909,0.0005937101,0.001468235],"category_scores_gemma":[0.0004658838,0.0005598795,0.0002598476,0.0001744954,0.000542635,0.0007387355,0.0009118306,0.0006496623,0.000489095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001841991,"about_ca_system_score_gemma":0.000273936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001802367,"about_ca_topic_score_gemma":0.000448487,"domain_scores_codex":[0.9995389,0.00008077946,0.00001793315,0.000134294,0.0001721964,0.00005590832],"domain_scores_gemma":[0.9996,0.0001252524,0.00009304757,0.00009648923,0.00004892444,0.00003619016],"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":[0.00004243504,0.00001900932,0.0001332198,0.00003009652,0.000004861841,0.00003181695,0.00001333052,0.000368674,0.9959944,0.0002267587,0.00003561842,0.003099792],"study_design_scores_gemma":[0.00001129787,0.0004222812,0.002016847,0.000007502712,0.00001715288,0.0008051163,0.00002889765,0.01222429,0.9824418,0.0003023058,0.001698839,0.00002376017],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6033694,0.001546076,0.3882612,0.0002708864,0.0001098868,0.0001048207,0.0001423569,0.00138765,0.004807631],"genre_scores_gemma":[0.7505532,0.0007995164,0.2443612,0.000131898,0.00006972618,0.0001210481,0.00009554236,0.0001419942,0.003725844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001468235,"threshold_uncertainty_score":0.00491178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004263816858165688,"score_gpt":0.186636132504207,"score_spread":0.1823723156460413,"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."}}