{"id":"W4210304078","doi":"10.20944/preprints202201.0474.v1","title":"Application of Biosensors in Cancers, An Overview","year":2022,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Emergent BioSolutions (Canada)","funders":"","keywords":"Biosensor; Analyte; Cancer detection; Biomarker; Cancer biomarkers; Cancer; Nanotechnology; Function (biology); Computer science; Medicine; Computational biology; Biology; Chemistry; Materials science; Internal medicine","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":[0.0009191097,0.0002303226,0.0003312685,0.0001581476,0.00004272989,0.000008007341,0.0008874525,0.0003790309,0.000347254],"category_scores_gemma":[0.000222274,0.0002421102,0.0001459418,0.0001593266,0.0001841516,0.000003359628,0.002442386,0.0005113783,0.00004700188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001241851,"about_ca_system_score_gemma":0.0004624601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001179711,"about_ca_topic_score_gemma":0.0003150849,"domain_scores_codex":[0.9977456,0.0001666975,0.0006198134,0.0006689113,0.0004675138,0.0003314039],"domain_scores_gemma":[0.998053,0.00001442859,0.0002844648,0.00135821,0.0001285607,0.0001612999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002282264,0.0004780999,0.6325805,0.001510014,0.000157524,0.000004760643,0.000899129,0.002309793,0.3463972,0.0001431926,0.0001981418,0.01509342],"study_design_scores_gemma":[0.0009977306,0.0003444601,0.5528694,0.0001512794,0.00004728491,0.000005255663,0.0008511907,0.003229816,0.3339115,0.001141442,0.1055939,0.0008566931],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955484,0.001196498,0.0001783977,0.0001530296,0.0002251213,0.0006975791,0.0001285117,0.00001436559,0.001858067],"genre_scores_gemma":[0.9910508,0.006867017,0.0003573398,0.0001231552,0.0001349639,0.0002043166,0.000838498,0.00002662545,0.000397307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1053958,"threshold_uncertainty_score":0.9872966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1152014295304953,"score_gpt":0.3958355022325039,"score_spread":0.2806340727020086,"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."}}