{"id":"W2946183952","doi":"","title":"Noisy random boolean networks and cell differentiation","year":2010,"lang":"en","type":"article","venue":"Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Boolean network; Boolean function; And-inverter graph; Boolean circuit; Theoretical computer science; Algorithm","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":[0.0003937679,0.0002344418,0.0002639611,0.0005179592,0.0001600361,0.0006008868,0.0004663708,0.0003991996,0.0008143019],"category_scores_gemma":[0.001442643,0.0001518393,0.0003251019,0.000346615,0.0008226242,0.001006491,0.0003590091,0.0004363001,0.0001446731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006835226,"about_ca_system_score_gemma":0.0002299967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001139151,"about_ca_topic_score_gemma":0.001032252,"domain_scores_codex":[0.9997559,0.00009384073,0.00001065269,0.00003998392,0.00006660406,0.00003307328],"domain_scores_gemma":[0.9995022,0.0002738263,0.0001078523,0.00004412607,0.00004027169,0.00003177085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007429346,0.00002756605,0.001164599,0.00005528313,0.0000200699,0.0001904172,0.00007945404,0.473462,0.01360312,0.4996245,0.0003121139,0.01138654],"study_design_scores_gemma":[0.000009254852,0.00002207336,0.0003173798,0.000006009487,0.000007840316,0.00006581398,0.0000122136,0.8739941,0.00186654,0.1226147,0.001074059,0.00001003825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2907464,0.001437079,0.6955728,0.0009158376,0.00007716518,0.00002700916,0.0001379533,0.0002549705,0.01083078],"genre_scores_gemma":[0.9655967,0.0007340399,0.03059292,0.0001031017,0.00004035845,0.00003699969,0.00007793893,0.00002457962,0.002793195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001139151,"threshold_uncertainty_score":0.004959285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005188307607250975,"score_gpt":0.1899252523480404,"score_spread":0.1847369447407895,"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."}}