{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001789383,0.0008720297,0.000884268,0.002142371,0.0004909734,0.001714513,0.001276269,0.001570225,0.002478508],"category_scores_gemma":[0.003356183,0.0003518079,0.0007215346,0.0009509265,0.0007164549,0.001326766,0.00113967,0.001497992,0.002877267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006434805,"about_ca_system_score_gemma":0.0005528474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003997358,"about_ca_topic_score_gemma":0.0006932463,"domain_scores_codex":[0.9983236,0.0003890828,0.00009374321,0.000543414,0.0005249687,0.0001251072],"domain_scores_gemma":[0.9984084,0.0006034031,0.0002697145,0.0003094855,0.0003443774,0.00006479218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003361725,0.0002901993,0.005587742,0.0005014684,0.0001169402,0.0001820175,0.000136682,0.009202311,0.5466888,0.004029473,0.005744365,0.4271838],"study_design_scores_gemma":[0.00004637334,0.0002572684,0.005868195,0.0001009829,0.00007124157,0.0005826366,0.00005502944,0.3840618,0.5789084,0.01129785,0.01864366,0.0001066433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05554683,0.001681071,0.9345867,0.0005001768,0.0002015604,0.000171223,0.0007063829,0.003354735,0.003251393],"genre_scores_gemma":[0.2750682,0.001393267,0.7137752,0.0008189802,0.0002319485,0.000530832,0.001927037,0.0004116688,0.00584293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002478508,"threshold_uncertainty_score":0.009463251,"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."}}