{"id":"W2060977279","doi":"10.1103/physreve.73.031912","title":"Network growth models and genetic regulatory networks","year":2006,"lang":"en","type":"article","venue":"Physical Review E","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Science Foundation","keywords":"Counterintuitive; Degree distribution; Node (physics); Computer science; Scaling; Degree (music); Gene regulatory network; Gene; Class (philosophy); Genome; Computational biology; Biology; Genetics; Mathematics; Complex network; Physics; Artificial intelligence; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001270042,0.0002269793,0.000372632,0.00001318584,0.00008534657,0.00001780971,0.0001623682,0.00006822306,0.000006342143],"category_scores_gemma":[0.00001153644,0.0002044108,0.000199249,0.0002031892,0.00009968894,0.000004582163,0.0001369873,0.00008988661,0.000007571192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001001142,"about_ca_system_score_gemma":0.00002228068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000122666,"about_ca_topic_score_gemma":0.000009864408,"domain_scores_codex":[0.9986529,0.0001109494,0.0002575156,0.0004603022,0.0001625865,0.0003557088],"domain_scores_gemma":[0.9992294,0.00001503934,0.0001062343,0.0004663311,0.00007343017,0.0001095402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008460401,0.0005741502,0.01693065,0.001780096,0.0006680994,0.00003713297,0.00001413573,0.5698759,0.03215086,0.03630178,0.3021318,0.03945075],"study_design_scores_gemma":[0.002414557,0.0009221059,0.1446944,0.00283822,0.003629395,0.0002224563,0.000007895314,0.4568625,0.006055313,0.254811,0.1226068,0.004935271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5513511,0.4303846,0.01542132,0.0003313945,0.00008820723,0.0004326055,0.000003028137,0.00004009168,0.001947688],"genre_scores_gemma":[0.9751724,0.02110725,0.0006176762,0.0009268181,0.001885269,0.00003735914,0.00005736463,0.00003582002,0.0001600548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4238213,"threshold_uncertainty_score":0.8335631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008240815115916456,"score_gpt":0.2353828153358101,"score_spread":0.2271420002198936,"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."}}