{"id":"W4393889966","doi":"10.5281/zenodo.3543693","title":"NanoString dataset for study: Real-time ex vivo perfusion of human lymph nodes invaded by cancer (REPLICANT): a feasibility study","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Cancer Research","funders":"","keywords":"Ex vivo; Lymph; Perfusion; Cancer; Computer science; Medical physics; Medicine; Oncology; Computational biology; Biology; In vivo; Pathology; Internal medicine; Genetics","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":[0.001063403,0.002608319,0.00153026,0.001461209,0.0007199051,0.001835319,0.003554216,0.003254407,0.02963339],"category_scores_gemma":[0.003634019,0.0006006311,0.001722779,0.002009861,0.0005238635,0.0006603867,0.001646525,0.001508981,0.0371336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001164431,"about_ca_system_score_gemma":0.001960682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009402663,"about_ca_topic_score_gemma":0.01921398,"domain_scores_codex":[0.9993722,0.0000832124,0.00006948016,0.0001993761,0.0001820225,0.0000937896],"domain_scores_gemma":[0.9987452,0.0004352945,0.0001097313,0.0003829837,0.0002072371,0.0001194305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006162294,0.0001734277,0.002001697,0.002806748,0.0002465473,0.0001962407,0.00004397062,0.00241224,0.003291938,0.0007763031,0.9783065,0.00912821],"study_design_scores_gemma":[0.002207111,0.0002561467,0.01438649,0.000565393,0.00034445,0.0006582965,0.0001090075,0.007229763,0.00987817,0.004142137,0.9600611,0.0001619603],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009649178,0.0001754293,0.000285502,0.0000982163,0.00003470727,0.00003966583,0.9964729,0.001460649,0.0004679855],"genre_scores_gemma":[0.001690797,0.0000841286,0.0009165066,0.00007509646,0.000007687714,0.0001773914,0.9964677,0.0001446581,0.0004360522],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02963339,"threshold_uncertainty_score":0.09913361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04013635801779868,"score_gpt":0.359913025782333,"score_spread":0.3197766677645343,"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."}}