{"id":"W2798898815","doi":"10.1101/307421","title":"Genomic, Proteomic and Phenotypic Heterogeneity in HeLa Cells across Laboratories: Implications for Reproducibility of Research Results","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Genetics","funders":"Universitätsspital Zürich; Ministero della Salute; Eidgenössische Technische Hochschule Zürich; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; European Molecular Biology Laboratory; Regione Lombardia; Universität Basel; Universität Zürich; Johns Hopkins University; SystemsX.ch; Cancer Research UK; National Science Foundation; Yale University; Bayer","keywords":"Biology; Phenotype; Proteome; Transcriptome; Computational biology; Genetics; Cell biology; Gene; Gene expression","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":[0.004341907,0.0003596678,0.0004609369,0.0001247354,0.0002224295,0.00006156077,0.0006646242,0.0008609575,0.000001131203],"category_scores_gemma":[0.0009745713,0.0003951323,0.000115956,0.0003897097,0.0007050466,0.000006148558,0.001176761,0.0004421074,0.000002312195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001024468,"about_ca_system_score_gemma":0.0005485846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008330488,"about_ca_topic_score_gemma":0.00004470847,"domain_scores_codex":[0.9956412,0.0002825893,0.0007871378,0.002574532,0.0001286651,0.0005858638],"domain_scores_gemma":[0.993938,0.00004983402,0.0004203044,0.004158214,0.001285888,0.0001477821],"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":[0.0001883557,0.0001568373,0.01514808,0.0002743566,0.00005624792,4.551446e-7,0.00001107253,0.000007596633,0.9835773,0.0002521565,0.0003239606,0.000003650406],"study_design_scores_gemma":[0.0004561177,0.0001929998,0.1204042,0.000075681,0.00001760171,1.984115e-8,0.000002762185,0.00001514369,0.8742445,0.00005295647,0.004217299,0.0003207501],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892491,0.0008596562,0.002899182,0.0003036305,0.000128353,0.002977339,0.003524672,0.00005386136,0.000004239314],"genre_scores_gemma":[0.9802707,0.0005275945,0.01673667,0.00005637045,0.0003206939,0.001993352,0.00001283817,0.00007730322,0.000004435967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1093327,"threshold_uncertainty_score":0.99985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0344829438772697,"score_gpt":0.3332704379513647,"score_spread":0.2987874940740951,"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."}}