{"id":"W2980514386","doi":"10.1101/807875","title":"Quantitative Visualization of Hypoxia and Proliferation Gradients Within Histological Tissue Sections","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cancer, Hypoxia, and Metabolism","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"","keywords":"Hypoxia (environmental); Tumor hypoxia; Biology; Pathology; Stain; Computational biology; Cancer research; Bioinformatics; Radiation therapy; Staining; Medicine; Internal medicine; Chemistry; Oxygen","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":[0.0005675498,0.0003614071,0.0001341786,0.001384197,0.0002097913,0.0005607423,0.0002611689,0.0003449584,0.003657077],"category_scores_gemma":[0.0005443289,0.0003061202,0.000135146,0.0005776225,0.0002539701,0.0003403125,0.0002692123,0.0003970976,0.0007422189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003678886,"about_ca_system_score_gemma":0.0002718888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001297349,"about_ca_topic_score_gemma":0.00190993,"domain_scores_codex":[0.9997899,0.00002815884,0.00001465637,0.00005712505,0.0000842043,0.00002592568],"domain_scores_gemma":[0.9995567,0.0001677178,0.00006692822,0.00005465941,0.0001288295,0.00002516711],"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":[0.00006819211,0.00002017726,0.001160412,0.0001041252,0.00001230676,0.00006673942,0.00006358769,0.002083525,0.9824265,0.0009158915,0.0003300653,0.01274846],"study_design_scores_gemma":[0.00002254833,0.00009348609,0.03637204,0.00003957948,0.00003065184,0.000539546,0.0001516541,0.06621395,0.8870218,0.001486507,0.007989636,0.00003861435],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4635228,0.001349933,0.5203313,0.0003085109,0.0001179347,0.0001638048,0.003624851,0.004688275,0.005892655],"genre_scores_gemma":[0.4366489,0.001065828,0.5500606,0.00007416605,0.0000456319,0.0002550898,0.002124709,0.001021914,0.008703067],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003657077,"threshold_uncertainty_score":0.01223415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01669824869871461,"score_gpt":0.260163775017274,"score_spread":0.2434655263185594,"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."}}