{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01644763,0.001367592,0.0008510987,0.00229209,0.001161612,0.005543817,0.002977696,0.003672774,0.01098561],"category_scores_gemma":[0.01562885,0.001544512,0.001351853,0.001870148,0.002051557,0.006511709,0.002805506,0.004108751,0.01035611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001761763,"about_ca_system_score_gemma":0.003226141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002290222,"about_ca_topic_score_gemma":0.001827793,"domain_scores_codex":[0.9937936,0.002108243,0.001790034,0.0004238691,0.001514713,0.0003694648],"domain_scores_gemma":[0.9863462,0.007595319,0.0009835134,0.00310531,0.001585624,0.0003840275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001268791,0.0003381706,0.002629701,0.001895138,0.0001170539,0.001222497,0.00334539,0.01355888,0.06862275,0.4829912,0.1266994,0.2973112],"study_design_scores_gemma":[0.0002281592,0.0001872239,0.001000746,0.000826921,0.00006766026,0.0009047336,0.0002623488,0.041101,0.07559711,0.1067987,0.7727672,0.0002582078],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002020307,0.0002673871,0.951939,0.0006556593,0.0002190448,0.0004153408,0.004131228,0.03566884,0.004683202],"genre_scores_gemma":[0.04320681,0.001252027,0.903569,0.00284322,0.0002469641,0.001981853,0.02349396,0.01104509,0.01236104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01644763,"threshold_uncertainty_score":0.08698446,"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."}}