{"id":"W2898672015","doi":"10.1016/j.pacs.2018.10.002","title":"Wavelength optimization in the multispectral photoacoustic tomography of the lymphatic drainage in mice","year":2018,"lang":"en","type":"article","venue":"Photoacoustics","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; University of Toronto; Toronto Metropolitan University","funders":"Canadian Institutes of Health Research; Ryerson University","keywords":"Multispectral image; Wavelength; Image quality; Materials science; Optics; Tomography; Photoacoustic imaging in biomedicine; Biomedical engineering; Computer science; Image (mathematics); Optoelectronics; Artificial intelligence; Physics; Medicine","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":[0.0004802377,0.0005802357,0.0002704524,0.000450198,0.0001667208,0.0002971171,0.0003672625,0.0003885625,0.0007268587],"category_scores_gemma":[0.0003963331,0.000301332,0.000381307,0.0003456893,0.0002911052,0.0003748382,0.0003087178,0.000370924,0.0001736976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002440952,"about_ca_system_score_gemma":0.0002241362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003053606,"about_ca_topic_score_gemma":0.0005523981,"domain_scores_codex":[0.9998263,0.00003621593,0.00001108128,0.00004301461,0.00005808222,0.00002543663],"domain_scores_gemma":[0.999763,0.00007637004,0.00008482821,0.00002403892,0.00003430699,0.00001745808],"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":[0.0001039549,0.00004592701,0.0002140724,0.00003532069,0.000008493932,0.00002380032,0.00001403837,0.005690923,0.9898528,0.0002236702,0.00002740461,0.003759623],"study_design_scores_gemma":[0.0000105849,0.0001961259,0.001056671,0.000005537858,0.00002225175,0.00007973386,0.00001483304,0.02032751,0.9776208,0.0001795857,0.0004735937,0.0000127389],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8994392,0.0006989774,0.09781761,0.00009583148,0.0000190639,0.00004091688,0.0001218897,0.0001839958,0.001582552],"genre_scores_gemma":[0.8416655,0.0007221855,0.1555618,0.00005974953,0.000006973723,0.0001327914,0.000114904,0.0001150814,0.001621042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007268587,"threshold_uncertainty_score":0.002539754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006068103239051862,"score_gpt":0.2025838659929111,"score_spread":0.1965157627538593,"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."}}