{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003665908,0.0005108046,0.0002324484,0.0002348302,0.0001344354,0.0004160047,0.000400946,0.0004960028,0.001043784],"category_scores_gemma":[0.0002258994,0.0003464559,0.0002289092,0.0001813878,0.0003114619,0.0004545399,0.0003205577,0.0008866717,0.0004006008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002809488,"about_ca_system_score_gemma":0.0001544364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002896407,"about_ca_topic_score_gemma":0.0004123356,"domain_scores_codex":[0.9998581,0.00002359713,0.00000761765,0.0000447835,0.00004056183,0.00002531805],"domain_scores_gemma":[0.9997951,0.00006389114,0.00005451479,0.00002622637,0.00002713698,0.00003312935],"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":[0.00001122176,0.000005109565,0.000009926996,0.000006791945,7.371543e-7,0.000005861576,0.000003328681,0.00003206323,0.9994505,0.00007583849,0.00001170165,0.0003869573],"study_design_scores_gemma":[0.000002878383,0.00002309805,0.0001669831,0.000001213264,0.000002180044,0.00005653394,0.000004041225,0.001177689,0.997963,0.00003389796,0.0005667247,0.000001893578],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7747425,0.001570533,0.2175952,0.0003545921,0.0001031735,0.00008587843,0.00032815,0.0009011169,0.004318756],"genre_scores_gemma":[0.8820037,0.001881795,0.1052068,0.0001718056,0.00003540075,0.0001284679,0.0004471981,0.0001848086,0.009940143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001043784,"threshold_uncertainty_score":0.003491819,"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."}}