{"id":"W2884939853","doi":"10.1002/cyto.a.23495","title":"Tellurium‐based mass cytometry barcode for live and fixed cells","year":2018,"lang":"en","type":"article","venue":"Cytometry Part A","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fluidigm (Canada); University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Mass cytometry; Barcode; Microscale chemistry; Flow cytometry; Cytometry; Biology; Sample preparation; Chemistry; Computational biology; Molecular biology; Chromatography; Computer science; Biochemistry; Phenotype; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002285875,0.0002227756,0.0002422037,0.0001913254,0.0001625648,0.0000417314,0.000157815,0.0002302407,0.00001310227],"category_scores_gemma":[0.0001586779,0.0001981567,0.0001400474,0.0003493013,0.00029478,0.000004944885,0.0000738391,0.00007829666,0.00001579188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001638835,"about_ca_system_score_gemma":0.00004072201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002198409,"about_ca_topic_score_gemma":0.000005041629,"domain_scores_codex":[0.9987094,0.00004284965,0.0002301296,0.0005383404,0.0001308614,0.000348449],"domain_scores_gemma":[0.9990839,0.00005165187,0.0001067265,0.0004258936,0.0002008486,0.0001310163],"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":[0.0001237078,0.00004316393,0.00117722,0.00002678294,0.00005674464,0.000001652887,0.000007994532,8.055999e-7,0.9909606,0.000006345699,0.005526838,0.002068132],"study_design_scores_gemma":[0.0003983824,0.0005973534,0.000227512,0.00002039089,0.0000530881,0.000004654732,0.00003950518,0.0004114909,0.9208308,0.00006256814,0.07708444,0.0002697545],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7491738,0.0006238216,0.2475441,0.0002551338,0.0003021278,0.0005782171,0.0002478493,0.0001612895,0.001113741],"genre_scores_gemma":[0.9513559,0.00008609259,0.04603374,0.000640791,0.0006520377,0.00002404469,0.0001282812,0.00003281443,0.001046331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2021821,"threshold_uncertainty_score":0.8080595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01443999623773564,"score_gpt":0.2870282726106211,"score_spread":0.2725882763728855,"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."}}