{"id":"W4413856557","doi":"10.1093/noajnl/vdaf166.027","title":"23 INVESTIGATING METABOLIC DEPENDENCIES DURING THE EVOLUTION OF GLIOBLASTOMA","year":2025,"lang":"en","type":"article","venue":"Neuro-Oncology Advances","topic":"Cancer, Lipids, and Metabolism","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Glioblastoma; Computational biology; Computer science; Biology; Cancer research","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001398174,0.0001227049,0.0001961931,0.000534884,0.0002227224,0.0005016403,0.0001449774,0.0001632873,0.000959368],"category_scores_gemma":[0.0001834252,0.000100401,0.0002182335,0.0003988572,0.0001517616,0.0001690387,0.0002617922,0.0002320025,0.0002740131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001371151,"about_ca_system_score_gemma":0.000592571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0311716,"about_ca_topic_score_gemma":0.03718965,"domain_scores_codex":[0.9999276,0.000004651999,0.000004216051,0.00001680946,0.00002388884,0.00002293842],"domain_scores_gemma":[0.999905,0.000007402472,0.00002090614,0.000004779267,0.00004191151,0.00001994728],"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":[0.0005504199,0.00005380974,0.06298821,0.0001210144,0.00004397809,0.0006293583,0.0001922135,0.0005927571,0.9166644,0.00040032,0.0006104847,0.01715318],"study_design_scores_gemma":[0.000009426808,0.000512149,0.7421686,0.00003914688,0.00008308326,0.001616385,0.0008433213,0.004206586,0.2351537,0.0004964132,0.01482809,0.00004304157],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911165,0.001788339,0.001267558,0.00008880395,0.00001500858,0.00003343437,0.002573902,0.00004009585,0.003076396],"genre_scores_gemma":[0.9925694,0.001144527,0.001188654,0.0000658533,0.000003029185,0.00003405754,0.002690803,0.00001954201,0.002284109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0311716,"threshold_uncertainty_score":0.06198037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005828910997577627,"score_gpt":0.2621872628829101,"score_spread":0.2563583518853324,"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."}}