{"id":"W2791753693","doi":"10.1117/1.jbo.23.2.029801","title":"Tissue perfusion rate estimation with compression-based photoacoustic-ultrasound imaging (Erratum)","year":2018,"lang":"en","type":"erratum","venue":"Journal of Biomedical Optics","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Photoacoustic imaging in biomedicine; Ultrasound; Ultrasonic imaging; Biomedical engineering; Ultrasound imaging; Compression (physics); Materials science; Optics; Radiology; Medicine; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008026279,0.0006235857,0.0009458467,0.0005798941,0.0001865486,0.0002093174,0.0005994153,0.0005143452,0.0002976838],"category_scores_gemma":[0.0005300599,0.0004205176,0.0001732742,0.0004265846,0.0005336209,0.0002412582,0.00004508708,0.00226659,0.00002605852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003567672,"about_ca_system_score_gemma":0.0008495668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007158266,"about_ca_topic_score_gemma":0.000001371094,"domain_scores_codex":[0.9965074,0.0000849863,0.001147846,0.0003003448,0.001305799,0.0006536298],"domain_scores_gemma":[0.9972686,0.0005645785,0.0006941302,0.0003944411,0.0005334286,0.0005447762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008431949,0.0001485381,0.000008205985,0.0006520823,0.0001420634,0.0005645282,0.0001599697,0.01086715,0.02266837,0.000001474576,0.9545727,0.01013065],"study_design_scores_gemma":[0.001353736,0.0003711936,0.00005667674,0.004093485,0.0005916754,0.0009252128,0.0001375073,0.8732163,0.002449619,0.00008977883,0.1160623,0.0006525936],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001408658,0.002127452,0.9500506,0.0004191228,0.03871679,0.0003737009,0.0001680892,0.0002402141,0.006495371],"genre_scores_gemma":[0.4653554,0.006937117,0.4535723,0.002622288,0.03619402,0.0000583831,0.003040478,0.001934045,0.03028593],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8623491,"threshold_uncertainty_score":0.9998246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005425367830359709,"score_gpt":0.2297276358144054,"score_spread":0.2243022679840457,"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."}}