{"id":"W2004869320","doi":"10.1364/omp.2015.jw2b.4","title":"In Vivo Multispectral Photoacoustic Imaging of Gene Expression using Engineered Reporters","year":2015,"lang":"en","type":"article","venue":"Optics in the Life Sciences","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Photoacoustic imaging in biomedicine; Multispectral image; In vivo; Preclinical imaging; Optical imaging; Gene expression; Molecular imaging; Gene; Biology; Optics; Computer science; Genetics; Physics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001168828,0.0001201389,0.0001665922,0.0001887825,0.00004127448,0.00003277214,0.0003550393,0.00002844369,0.000006978177],"category_scores_gemma":[0.0003235764,0.0000923957,0.00002751423,0.0005137308,0.0001758353,0.0002525317,0.00002298741,0.0001598666,6.086281e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006363849,"about_ca_system_score_gemma":0.00009532627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009928531,"about_ca_topic_score_gemma":0.00000505513,"domain_scores_codex":[0.9987882,0.00003196682,0.0003347167,0.0001680083,0.0003505341,0.0003265104],"domain_scores_gemma":[0.9995281,0.0001402578,0.00005780739,0.0001867123,0.00002418223,0.00006290211],"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":[0.000002627706,0.00001829736,0.001640487,0.00001825373,0.000002326213,0.00003685362,0.002807196,0.6658625,0.3294629,0.00003421067,0.00009575868,0.00001854046],"study_design_scores_gemma":[0.0002426737,0.000008735225,0.0001825543,0.00008194025,0.000008894158,0.00005688404,0.004442851,0.9031106,0.09148879,0.0002499704,0.00000621,0.0001199316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9666793,0.000532669,0.02918723,0.00004577372,0.0006682147,0.0001478072,0.000005822263,0.00003805649,0.00269515],"genre_scores_gemma":[0.9683366,0.00001989867,0.03151637,0.00005198794,0.00005489244,0.000003449659,3.513659e-7,0.0000105112,0.000005878827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2379741,"threshold_uncertainty_score":0.3767787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03186508257400978,"score_gpt":0.2719533913832846,"score_spread":0.2400883088092748,"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."}}