{"id":"W2051656066","doi":"10.1089/omi.2008.0080","title":"Modeling and Managing Experimental Data Using FuGE","year":2009,"lang":"en","type":"article","venue":"OMICS A Journal of Integrative Biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital Saint-Luc; Centre Hospitalier de l’Université de Montréal","funders":"Biotechnology and Biological Sciences Research Council","keywords":"Consistency (knowledge bases); Context (archaeology); Flexibility (engineering); Data science; Standardization; Computer science; Artificial intelligence; History; Archaeology; Mathematics","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":[0.0002821264,0.0001265548,0.0001969672,0.00005535237,0.00005567556,0.00002448239,0.0002798909,0.0001083571,0.000002946274],"category_scores_gemma":[0.00002858439,0.00009046416,0.00004788899,0.00003088772,0.00006264454,0.00001221231,0.0001406925,0.0001493095,3.055319e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001754453,"about_ca_system_score_gemma":0.00005513351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004994334,"about_ca_topic_score_gemma":0.000003076286,"domain_scores_codex":[0.9993135,0.00003731628,0.0003243788,0.0001415677,0.00003569909,0.0001475478],"domain_scores_gemma":[0.9994737,0.000007272532,0.0001766732,0.0001967606,0.00008489392,0.00006071583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003203059,0.00006732444,0.0001590351,0.000004141204,0.0001631438,0.00000687171,0.0003879586,0.002090052,0.9718227,0.005399446,0.0002823173,0.0192967],"study_design_scores_gemma":[0.001715382,0.002352651,0.00003628722,0.00009343686,0.00006799801,0.0009502235,0.00293507,0.9333241,0.04463083,0.00869952,0.004710319,0.0004841945],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8192109,0.005923359,0.1741869,0.0001345346,0.0001715852,0.00005232939,0.00001780397,0.000001465416,0.0003011337],"genre_scores_gemma":[0.9823843,0.0006022064,0.01634553,0.0003580111,0.000256416,1.779359e-7,0.00003504844,0.000006246359,0.00001210171],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.931234,"threshold_uncertainty_score":0.3689021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02968245230922645,"score_gpt":0.3123334738865147,"score_spread":0.2826510215772882,"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."}}