{"id":"W1912319529","doi":"10.1593/neo.101102","title":"Analysis of Cancer Metabolism by Imaging Hyperpolarized Nuclei: Prospects for Translation to Clinical Research","year":2011,"lang":"en","type":"article","venue":"Neoplasia","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":678,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; University of Toronto","funders":"National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering","keywords":"Hyperpolarization (physics); In vivo; Magnetic resonance imaging; Cellular metabolism; Nuclear magnetic resonance; Computer science; Medicine; Bioinformatics; Biology; Chemistry; Computational biology; Metabolism; Biochemistry; Nuclear magnetic resonance spectroscopy; Physics; Biotechnology","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":[0.0002978551,0.00008895441,0.0002844917,0.000122406,0.00008927161,0.00001029808,0.0002007391,0.00008119685,0.000360332],"category_scores_gemma":[0.00005740128,0.00008624052,0.0001699553,0.0006244308,0.00006530931,0.00006422297,0.00002514597,0.0001690058,0.000003599644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002678541,"about_ca_system_score_gemma":0.0000353371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002938077,"about_ca_topic_score_gemma":0.0001078744,"domain_scores_codex":[0.9989108,0.00001720314,0.000351988,0.000316988,0.0001782979,0.0002247201],"domain_scores_gemma":[0.999137,0.0001482374,0.00008965272,0.0003359668,0.0001961595,0.00009296385],"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":[0.0004303301,0.0005292911,0.1233448,0.00009394952,0.0007017867,9.410807e-7,0.001482117,0.00003192382,0.688746,0.006078509,0.003517014,0.1750433],"study_design_scores_gemma":[0.002220715,0.00007405064,0.04312213,0.00007294357,0.002100106,0.000001166552,0.0003961927,0.01548185,0.6304006,0.005867069,0.2996382,0.000625008],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9161667,0.001191331,0.06755954,0.0007871973,0.0000453782,0.001051423,0.0008304477,0.0002597756,0.01210822],"genre_scores_gemma":[0.9497725,0.00009047956,0.04908152,0.00005457979,0.00007167005,0.0004889242,0.00004977797,0.00002514347,0.0003653544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2961211,"threshold_uncertainty_score":0.3945383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1101617115948177,"score_gpt":0.4398072572434648,"score_spread":0.3296455456486471,"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."}}