{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001830525,0.0001472914,0.0002454529,0.00008093379,0.0001925152,0.000008629458,0.0002846388,0.0001277616,0.000006437869],"category_scores_gemma":[0.0005037716,0.0001169573,0.00009926,0.0002496221,0.0003730501,0.00001319407,0.000165512,0.0001587806,0.000002856629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000295368,"about_ca_system_score_gemma":0.0002926191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003110755,"about_ca_topic_score_gemma":0.000609304,"domain_scores_codex":[0.9988726,0.0001643917,0.0002840407,0.0003179435,0.0001134949,0.0002475714],"domain_scores_gemma":[0.9992778,0.00008852568,0.0001785187,0.0003128837,0.0001016546,0.00004065212],"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":[0.0001047712,0.00002719732,0.008031613,0.00004135024,0.00003731095,1.660931e-7,0.00007314036,0.000429086,0.9836581,0.001922204,0.001167362,0.004507738],"study_design_scores_gemma":[0.0005461079,0.0001386642,0.01741134,0.00001372011,0.00004930317,0.00002066749,0.0004680256,0.00002032511,0.3761192,0.0006138314,0.6044968,0.0001019728],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.958272,0.03223871,0.0004726,0.0003725065,0.006084006,0.0002144747,0.00001231841,0.00002356632,0.002309865],"genre_scores_gemma":[0.9933217,0.002544424,0.0003791316,0.0005143899,0.002587471,0.00006828595,0.000004375116,0.00001319403,0.0005670123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6075389,"threshold_uncertainty_score":0.476938,"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."}}