{"id":"W2001183149","doi":"10.1021/jp062739m","title":"Extracting Biochemical Parameters for Cellular Modeling:  A Mean-Field Approach","year":2006,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry B","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Ribosome; Polymerase; Gene; RNA polymerase; Translation (biology); Gene expression; RNA; Biology; Function (biology); Messenger RNA; Computational biology; Physics; Genetics","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":[0.0003423939,0.0001684933,0.0002398317,0.00001261736,0.00007842217,0.00002005113,0.0003599174,0.0001178545,0.000002517003],"category_scores_gemma":[0.00007578379,0.0001199587,0.0004263369,0.00008728019,0.00005705165,0.000004805774,0.00005688103,0.0002049549,5.434298e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001876717,"about_ca_system_score_gemma":0.00004770607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006038158,"about_ca_topic_score_gemma":2.465058e-7,"domain_scores_codex":[0.9990084,0.00003576234,0.0003259791,0.0001709,0.0002190213,0.0002399913],"domain_scores_gemma":[0.9991266,0.00007535404,0.00026785,0.0002989869,0.000154922,0.00007633883],"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":[0.0001408222,0.0001370233,0.00001441833,0.00002818405,0.0001067633,7.109823e-7,0.00002419878,0.06059189,0.9376717,0.000006655763,0.001148026,0.000129666],"study_design_scores_gemma":[0.000329651,0.00008518214,0.000001686914,0.000009851273,0.000212139,0.00003249075,0.0001095661,0.08070716,0.917616,0.000424002,0.0003354233,0.0001368968],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.937595,0.0005689027,0.06130621,0.00009031723,0.00001822093,0.00006666254,0.00000219336,0.000004198828,0.0003482589],"genre_scores_gemma":[0.9963565,0.000009749002,0.001835112,0.00003658531,0.001576707,0.000004471408,0.00002292242,0.00002290677,0.000135032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0594711,"threshold_uncertainty_score":0.4891775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01317811262798898,"score_gpt":0.2348283189986562,"score_spread":0.2216502063706672,"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."}}