{"id":"W2087374705","doi":"10.1002/cyto.a.20637","title":"Gating‐ML: XML‐based gating descriptions in flow cytometry","year":2008,"lang":"en","type":"article","venue":"Cytometry Part A","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Gating; Computer science; Interoperability; XML; Data exchange; Bottleneck; Cytometry; Flow cytometry; Data mining; Database; Embedded system; World Wide Web; Medicine","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":[0.0002735144,0.0002570325,0.0002716609,0.00031581,0.000224949,0.00003379046,0.0002608751,0.0002540938,0.00009520941],"category_scores_gemma":[0.0002925329,0.0002746125,0.0001698889,0.000901476,0.0001540542,0.00001057165,0.00005980328,0.0002580537,0.00004655154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005007462,"about_ca_system_score_gemma":0.0001397438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003785871,"about_ca_topic_score_gemma":0.00005684878,"domain_scores_codex":[0.998257,0.00008915754,0.0004189455,0.0005073978,0.0002367887,0.0004907049],"domain_scores_gemma":[0.9991618,0.00004319144,0.00009454309,0.0004519408,0.00009080334,0.0001577246],"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":[0.00008426855,0.0003013367,0.2717021,0.00004631121,0.00003400174,0.00004267269,0.0001291467,0.0005855994,0.7238089,0.000009330114,0.001844985,0.001411374],"study_design_scores_gemma":[0.005381141,0.0008591406,0.09134466,0.0002452025,0.00006282089,0.0001701938,0.0003414639,0.01911216,0.8334275,0.00005696365,0.047377,0.001621688],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862326,0.000554872,0.01017577,0.00007265075,0.0004210811,0.0001889249,0.00003867613,0.0000564865,0.002258927],"genre_scores_gemma":[0.9906023,0.00008073812,0.007564707,0.0005628793,0.0004148097,0.00003835899,0.0002052823,0.0000510641,0.0004799087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1803574,"threshold_uncertainty_score":0.9999706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04037916908186798,"score_gpt":0.2498454277657902,"score_spread":0.2094662586839223,"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."}}