{"id":"W2010225204","doi":"10.1364/boe.2.000771","title":"Tyrosinase as a dual reporter gene for both photoacoustic and magnetic resonance imaging","year":2011,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta; Cancer Research Institute","keywords":"Photoacoustic imaging in biomedicine; Magnetic resonance imaging; Reporter gene; Nuclear magnetic resonance; Tyrosinase; Dual (grammatical number); Gene; Optics; Medicine; Biology; Physics; Genetics; Gene expression; Radiology; Enzyme; Art","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":[0.0007960795,0.000845045,0.0006274469,0.0005493305,0.0002676884,0.0006194368,0.0005224581,0.0009223898,0.001294572],"category_scores_gemma":[0.0003600219,0.0005069137,0.0007044261,0.000413229,0.0005697586,0.0004608595,0.0004049281,0.001636677,0.0006986887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006024733,"about_ca_system_score_gemma":0.0004164889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00087882,"about_ca_topic_score_gemma":0.001400995,"domain_scores_codex":[0.9994407,0.000101656,0.0000362716,0.0001693148,0.0001819899,0.00007010155],"domain_scores_gemma":[0.9996791,0.00009196212,0.00007286898,0.00003971055,0.00006159988,0.00005485787],"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":[0.0000179899,0.00001026703,0.00002271067,0.00001752759,0.000001871107,0.00001232363,0.00000769426,0.00001915676,0.999163,0.00009262132,0.00002453055,0.0006102511],"study_design_scores_gemma":[0.000005326355,0.00005477602,0.000184457,0.000002840395,0.000007265914,0.000162723,0.000005930583,0.0007438873,0.9969614,0.00002410698,0.001843068,0.000004198087],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5778823,0.003004198,0.4117797,0.0007560359,0.0002897213,0.0002972774,0.0008991862,0.001124182,0.003967389],"genre_scores_gemma":[0.6047403,0.002935356,0.3680406,0.0002590116,0.00008244193,0.0006151067,0.002039015,0.0003623505,0.02092574],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001294572,"threshold_uncertainty_score":0.004371226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0122119301661093,"score_gpt":0.2157642972815095,"score_spread":0.2035523671154002,"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."}}