{"id":"W3013187059","doi":"10.3791/60740","title":"Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment","year":2020,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Cancer Cells and Metastasis","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier de l’Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Université de Montréal; Canadian Institutes of Health Research; Canadian Liver Foundation; Public Health Agency; Public Health Agency of Canada; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Computer science; Tumor microenvironment; Immune system; Computational biology; Visualization; Tumour heterogeneity; Biology; Artificial intelligence; Cancer; Immunology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002997302,0.00008524108,0.0002924638,0.0001004255,0.00003596182,0.00001358622,0.00008005879,0.00002036397,0.00008211179],"category_scores_gemma":[0.00004465571,0.00006118184,0.0000849184,0.0002200855,0.00003980071,0.00007048688,0.00002115232,0.00008282416,0.000001866092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000360491,"about_ca_system_score_gemma":0.00004111869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002500602,"about_ca_topic_score_gemma":4.618687e-7,"domain_scores_codex":[0.9987195,0.00008637815,0.0007295702,0.00009508433,0.0002785983,0.00009092508],"domain_scores_gemma":[0.9992481,0.00002151307,0.0004903534,0.0001054845,0.00006251564,0.0000720066],"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":[0.0002395028,0.0004825193,0.01190159,0.0001099316,0.00004099283,0.0000144684,0.009562534,0.00001965609,0.9764599,0.0002107201,0.000663028,0.0002952202],"study_design_scores_gemma":[0.007045039,0.0006399108,0.08529606,0.0002725487,0.0001767938,0.00009642094,0.0122131,0.0003178073,0.8752258,0.00004595008,0.01851639,0.000154179],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876753,0.006201664,0.004561557,0.001112227,0.00008305875,0.0002585354,0.000003594036,0.000002784538,0.0001012916],"genre_scores_gemma":[0.9958598,0.0004557329,0.002941924,0.0006416926,0.00005740398,0.000004626655,0.000007108601,0.00001117181,0.00002051473],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.101234,"threshold_uncertainty_score":0.2494923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.081234312088494,"score_gpt":0.4121631756668766,"score_spread":0.3309288635783826,"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."}}