{"id":"W1974294304","doi":"10.1002/cyto.a.22106","title":"FlowRepository: A resource of annotated flow cytometry datasets associated with peer‐reviewed publications","year":2012,"lang":"en","type":"letter","venue":"Cytometry Part A","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":203,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University; BC Cancer Agency","funders":"Terry Fox Research Institute; Terry Fox Foundation; International Society for Advancement of Cytometry; Wallace H. Coulter Foundation","keywords":"Computer science; Flow cytometry; Resource (disambiguation); Peer review; Information retrieval; Data science; Computational biology; World Wide Web; Medicine; Biology; Immunology; Computer network","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0006139275,0.0005871224,0.0008232842,0.000481683,0.0001606423,0.00008261433,0.0007978506,0.001306245,0.0001116931],"category_scores_gemma":[0.001010475,0.0005268422,0.0002939627,0.001388455,0.0002577514,0.00001719161,0.0001585589,0.001011338,0.00003137375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007107271,"about_ca_system_score_gemma":0.0002201543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001618008,"about_ca_topic_score_gemma":0.000012144,"domain_scores_codex":[0.9966742,0.0003132926,0.0007101754,0.0008134496,0.0007598975,0.0007289191],"domain_scores_gemma":[0.9969929,0.0001542707,0.0005990759,0.00149457,0.0005664446,0.000192747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007209363,0.0002542506,0.004340959,0.0002246356,0.0006908145,0.00002274076,0.00003024992,0.000006460524,0.02842525,8.004329e-7,0.9654867,0.0004450401],"study_design_scores_gemma":[0.0007646945,0.0002724245,0.001178253,0.0002255349,0.0004012116,0.00003153862,0.00001049096,0.00003411439,0.01448125,9.670823e-7,0.981966,0.0006335357],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.4345552,0.06786664,0.01726684,0.2996818,0.01349314,0.01215397,0.1238293,0.00185375,0.02929935],"genre_scores_gemma":[0.2745146,0.0007176269,0.002991057,0.1522055,0.013905,0.0005202527,0.5292168,0.0008562919,0.02507291],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4053874,"threshold_uncertainty_score":0.9999903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02821854413470257,"score_gpt":0.2520464578469725,"score_spread":0.2238279137122699,"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."}}