{"id":"W3097759449","doi":"10.1158/1538-7445.tumhet2020-po-049","title":"Abstract PO-049: Exploiting tumor acidic microenvironment for improved therapeutics","year":2020,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer, Hypoxia, and Metabolism","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Tumor microenvironment; Cancer research; Antibody; Tumor progression; Tumor hypoxia; Immune system; Cancer; Chemistry; Medicine; Immunology; Tumor cells; Internal medicine; Radiation therapy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001329334,0.0003033191,0.0001684321,0.000177581,0.00007736343,0.0002718929,0.0002560928,0.0003554932,0.001953838],"category_scores_gemma":[0.00007807499,0.00009188843,0.0001656422,0.0001137842,0.0001261199,0.0002322606,0.0001934371,0.0004826693,0.000692611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002085512,"about_ca_system_score_gemma":0.0001942884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002498957,"about_ca_topic_score_gemma":0.0002434593,"domain_scores_codex":[0.9999355,0.00001011742,0.000003937584,0.00001149562,0.000020029,0.00001889157],"domain_scores_gemma":[0.9999586,0.000004644138,0.0000123765,0.000003021635,0.000009964651,0.00001134621],"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":[0.0001020638,0.0001035642,0.0001387251,0.00008097391,0.000006033493,0.00007982784,0.000007842921,0.00035284,0.9891722,0.0006636992,0.0003762009,0.008916007],"study_design_scores_gemma":[0.00003965849,0.0008891447,0.0009525212,0.000007457994,0.00001649376,0.0003254076,0.000007211198,0.001747858,0.9815891,0.0001426058,0.01427631,0.000006228345],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9064212,0.00778356,0.05569677,0.00126844,0.0002497259,0.0003267243,0.0006905753,0.0005544263,0.02700858],"genre_scores_gemma":[0.9637553,0.002352825,0.0222455,0.000458559,0.00004858421,0.0001008167,0.00047854,0.00004051117,0.01051929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001953838,"threshold_uncertainty_score":0.006536245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08455431797527364,"score_gpt":0.3656363877435302,"score_spread":0.2810820697682566,"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."}}