{"id":"W2946363866","doi":"10.1002/cyto.a.23794","title":"Label‐Free Identification of White Blood Cells Using Machine Learning","year":2019,"lang":"en","type":"article","venue":"Cytometry Part A","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"Autodesk (Canada)","funders":"Division of Biological Infrastructure; National Institute of General Medical Sciences; Foundation for the National Institutes of Health; Biotechnology and Biological Sciences Research Council; Universität Rostock; National Institutes of Health; National Science Foundation","keywords":"White (mutation); Identification (biology); Computer science; Artificial intelligence; Machine learning; Computational biology; Medicine; Biology; Biochemistry; Botany","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":[],"consensus_categories":[],"category_scores_codex":[0.0002492882,0.0001266192,0.0001870468,0.0002982084,0.00007765873,0.0001822323,0.0008971014,0.00003305924,0.00003295053],"category_scores_gemma":[0.0001230146,0.0001321331,0.00007279182,0.00106839,0.00004362332,0.0009298511,0.000620006,0.0000991941,0.0001771202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001747163,"about_ca_system_score_gemma":0.00004432175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001001947,"about_ca_topic_score_gemma":4.805339e-7,"domain_scores_codex":[0.9986964,0.00004369084,0.0003173426,0.0003507011,0.0003549768,0.0002368293],"domain_scores_gemma":[0.9986668,0.00005568522,0.000230091,0.0008491215,0.0001078555,0.00009045238],"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":[0.00001298278,0.0008657259,0.4075374,0.0002587824,0.000134757,0.00002204626,0.000484054,0.001598605,0.5826703,0.002635329,0.0004286556,0.003351348],"study_design_scores_gemma":[0.002327923,0.0002516479,0.01375631,0.000192777,0.0001550635,0.00004650654,0.00005650385,0.2618075,0.7149969,0.001391887,0.004274061,0.0007429119],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984818,0.0003986404,0.01219335,0.00004996595,0.0004431323,0.0001450991,0.00002643091,0.0001760768,0.001749303],"genre_scores_gemma":[0.9896886,0.000004862239,0.008521668,0.00005256232,0.000030013,0.000002532945,0.000009357096,0.00001572419,0.001674719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3937811,"threshold_uncertainty_score":0.5388232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01735772500375631,"score_gpt":0.2503082915318015,"score_spread":0.2329505665280451,"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."}}