{"id":"W2101370506","doi":"10.1016/j.cell.2013.11.003","title":"Extracting Insight from Noisy Cellular Networks","year":2013,"lang":"en","type":"article","venue":"Cell","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Planning and Budgeting Committee of the Council for Higher Education of Israel; Israeli Centers for Research Excellence; Canadian Institutes of Health Research","keywords":"Biology; Common descent; Meaning (existential); Categorization; Analogy; Organism; Cognitive science; Adaptation (eye); Evolutionary biology; Gene regulatory network; Epistemology; Artificial intelligence; Computer science; Gene; Genetics; Neuroscience; Philosophy; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00006543747,0.0001360142,0.0001059345,0.00001402395,0.00007434305,0.00006221503,0.0001786886,0.0002022383,0.0002815329],"category_scores_gemma":[0.000005994763,0.0001257595,0.00007065047,0.00003998804,0.00003047707,0.000005812309,0.0001109692,0.0001407017,0.0002504988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006749934,"about_ca_system_score_gemma":0.00001817121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008047892,"about_ca_topic_score_gemma":0.000008377048,"domain_scores_codex":[0.999258,0.0000185013,0.0002055649,0.0002012864,0.00006142595,0.0002551817],"domain_scores_gemma":[0.99942,0.00001424935,0.00009170437,0.0003357324,0.00003935004,0.00009890284],"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":[0.00001307826,0.00006669164,0.0006089704,0.00001248344,0.00004045029,0.000003433228,0.0001486484,0.001738969,0.9426889,0.00002370454,0.04012471,0.01452998],"study_design_scores_gemma":[0.00110448,0.0001649878,0.001790122,0.0000246041,0.00004302533,0.000004961521,0.0002790045,0.0571879,0.5012167,0.000719543,0.4366325,0.0008322593],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8926774,0.003852898,0.05717362,0.00008484125,0.0006100778,0.0002669203,0.000006372626,0.00002280493,0.04530505],"genre_scores_gemma":[0.994281,0.0001436447,0.001971726,0.0005630045,0.0007846712,0.00001424093,0.0002266975,0.00002222196,0.001992814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4414722,"threshold_uncertainty_score":0.5128322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005567392057436057,"score_gpt":0.1818646102021055,"score_spread":0.1762972181446695,"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."}}