{"id":"W1576253662","doi":"10.1002/cyto.a.22698","title":"Classification of blood cells and tumor cells using label‐free ultrasound and photoacoustics","year":2015,"lang":"en","type":"article","venue":"Cytometry Part A","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Canada Excellence Research Chairs, Government of Canada; Canada Foundation for Innovation; Canadian Cancer Society; Ryerson University","keywords":"Photoacoustic Doppler effect; Ultrasound; Photoacoustic imaging in biomedicine; Photoacoustic effect; SIGNAL (programming language); Materials science; Ultrasonic sensor; Melanoma; Absorption (acoustics); Microscope; Biomedical engineering; Optics; Pathology; Medicine; Acoustics; Physics; Computer science; Cancer research","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":[0.0004787952,0.0003558986,0.0002945174,0.001280944,0.0003277221,0.0006740883,0.0004933115,0.001046398,0.00116505],"category_scores_gemma":[0.0006450766,0.0002732247,0.0002669722,0.0004666948,0.0005593627,0.0007871512,0.0004012526,0.0006395098,0.0007047703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003927406,"about_ca_system_score_gemma":0.0002261116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005895939,"about_ca_topic_score_gemma":0.001018055,"domain_scores_codex":[0.9995685,0.0000726545,0.00002895983,0.0001167903,0.0001563821,0.00005670552],"domain_scores_gemma":[0.9995767,0.0001260845,0.00009350146,0.00004947696,0.0001139491,0.00004024229],"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":[0.00009098826,0.00003306555,0.000742181,0.0000937943,0.000006213692,0.00004702001,0.00005730726,0.0001164439,0.9844739,0.0005012273,0.0001700228,0.01366779],"study_design_scores_gemma":[0.00002275023,0.000282059,0.005351261,0.00002329011,0.00003435117,0.0003927072,0.0001021612,0.008710774,0.9772925,0.0005858017,0.007166968,0.00003541679],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6536645,0.009861067,0.3247825,0.0007433621,0.0004238147,0.0004287014,0.0006714151,0.001012044,0.008412543],"genre_scores_gemma":[0.7073094,0.004504391,0.2752478,0.0006741123,0.0001897865,0.0008050571,0.0009356293,0.0001005935,0.01023316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001280944,"threshold_uncertainty_score":0.003897548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03163642958651679,"score_gpt":0.2413074029206665,"score_spread":0.2096709733341497,"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."}}