{"id":"W2479420012","doi":"10.1101/cshperspect.a026583","title":"Spatial Heterogeneity in the Tumor Microenvironment","year":2016,"lang":"en","type":"review","venue":"Cold Spring Harbor Perspectives in Medicine","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":340,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Cancer Research","funders":"Associazione Italiana per la Ricerca sul Cancro; National Institute for Health and Care Research; Wellcome Trust","keywords":"Tumor microenvironment; Context (archaeology); Spatial heterogeneity; Biology; Spatial contextual awareness; Ecology; Computer science; Computational biology; Data science; Immune system; Artificial intelligence; Immunology","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":[0.0004691808,0.0003263948,0.0007013841,0.0001237313,0.00003754176,0.00001246009,0.0004928162,0.000147315,0.00002597537],"category_scores_gemma":[0.0002801424,0.0002065197,0.0001583692,0.0001055338,0.0002442152,0.000001581353,0.0001758579,0.000273327,0.00001228524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002805327,"about_ca_system_score_gemma":0.0001328933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001732493,"about_ca_topic_score_gemma":0.0002799648,"domain_scores_codex":[0.9983571,0.0001544313,0.0004134673,0.0005985402,0.0001668914,0.0003096168],"domain_scores_gemma":[0.9989911,0.0001062284,0.0001706933,0.0006575861,0.00001721264,0.00005720912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008891347,0.005173859,0.01468157,0.0270433,0.002665486,0.003372232,0.006447115,0.0000779589,0.07255679,0.03527241,0.01228104,0.8195391],"study_design_scores_gemma":[0.0006099942,0.0001845203,0.001579342,0.003568233,0.00007757081,0.000005765205,0.0001033926,0.000001114647,0.0001457759,0.000006322357,0.9934532,0.0002647811],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002777312,0.9957302,0.0001405127,0.0001981246,0.0002584815,0.0006781441,0.00003135962,0.000003864598,0.0001820565],"genre_scores_gemma":[0.01925492,0.9791336,0.00005017837,0.0001773077,0.001144667,0.0001713933,0.000005642212,0.0000426299,0.00001969001],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9811721,"threshold_uncertainty_score":0.8421627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02177829715794722,"score_gpt":0.302551777150967,"score_spread":0.2807734799930198,"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."}}