{"id":"W3134270751","doi":"10.2139/ssrn.3711439","title":"Stroma Amount in Human Cancer","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Stroma; Cancer; Biology; Computational biology; Genetics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002660308,0.0001270169,0.0001399592,0.001306779,0.0002865977,0.0005128121,0.00012811,0.00034065,0.004589073],"category_scores_gemma":[0.0004168494,0.00023061,0.0001477314,0.0006685727,0.0002389594,0.0003799813,0.0002428878,0.0003099798,0.0006386538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002397225,"about_ca_system_score_gemma":0.0001645258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001033837,"about_ca_topic_score_gemma":0.0008606904,"domain_scores_codex":[0.999862,0.00003140006,0.000008987588,0.00003468855,0.00003695465,0.00002609798],"domain_scores_gemma":[0.9996843,0.0001002171,0.00006899054,0.00002698536,0.00005241486,0.00006709628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.006124398,0.0001590528,0.111468,0.0006579357,0.0001819805,0.001601785,0.0009210602,0.001517125,0.8130264,0.005440123,0.001645769,0.05725629],"study_design_scores_gemma":[0.0001184266,0.0008871703,0.6859916,0.00005045581,0.0002842862,0.0104869,0.001025602,0.009393398,0.2621185,0.002381359,0.02720951,0.00005266766],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861,0.004556904,0.001658819,0.00009181894,0.00002478316,0.00001173132,0.0005207831,0.00009957085,0.00693545],"genre_scores_gemma":[0.9947464,0.00087931,0.000633446,0.00002563706,0.00001188726,0.00001261159,0.0003099803,0.00001438138,0.00336636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004589073,"threshold_uncertainty_score":0.01535201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01066218610717845,"score_gpt":0.3059807119440701,"score_spread":0.2953185258368917,"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."}}