{"id":"W4248002101","doi":"10.1186/gb-2010-11-s1-i5","title":"Personalized oncogenomics","year":2010,"lang":"en","type":"article","venue":"Genome Biology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"","keywords":"Computer science","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":[0.0001343208,0.0001413646,0.0001417574,0.00003775859,0.0000766553,0.00001610978,0.0002513852,0.0002421862,0.0002176363],"category_scores_gemma":[0.00009974642,0.000139963,0.00009510696,0.00004591289,0.0001926176,9.213513e-7,0.0001451527,0.0001366869,0.0001016253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000116996,"about_ca_system_score_gemma":0.0001295713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002723021,"about_ca_topic_score_gemma":0.0001465333,"domain_scores_codex":[0.9991455,0.00002073636,0.0001589574,0.0003499156,0.00003280283,0.0002920805],"domain_scores_gemma":[0.9993652,0.00001782942,0.00005975796,0.0003843877,0.00006204949,0.0001107804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004693546,0.00002567184,0.0027744,0.000002440169,0.00003068854,0.000001543225,0.00002601034,0.000004741915,0.9892747,0.004988613,0.0006098922,0.002214324],"study_design_scores_gemma":[0.0005355335,0.0002163329,0.003063531,4.561618e-7,0.00001179446,0.0000339377,0.00001889037,0.000007230578,0.02624219,0.0007543268,0.9688941,0.0002216334],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912673,0.0009481205,0.001025379,0.0002341748,0.0009030197,0.0001330458,0.0001291309,0.00001505828,0.005344751],"genre_scores_gemma":[0.9932225,0.0004870365,0.002532989,0.001088059,0.001308847,0.00002383896,0.0004428507,0.00002794858,0.0008659604],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9682842,"threshold_uncertainty_score":0.5707526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007895697282593442,"score_gpt":0.2481023919261001,"score_spread":0.2402066946435067,"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."}}