{"id":"W4385894310","doi":"10.2139/ssrn.4524100","title":"Amplifying Precision: Tracking Muc1 Tumor Marker with Direct Electrochemical Aptamer Sensor","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Aptamer; Tracking (education); MUC1; Tumor marker; Electrochemistry; Nanotechnology; Computer science; Computational biology; Chemistry; Materials science; Biology; Molecular biology; Cancer; Genetics; Electrode; Psychology","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":[0.0006188429,0.0005491514,0.0004283781,0.0003588418,0.0001629872,0.0007141842,0.0008096136,0.001175419,0.001966712],"category_scores_gemma":[0.001005929,0.0005062604,0.0002086369,0.0002914623,0.0002539994,0.0007073754,0.0006325826,0.0007956469,0.001783671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004524599,"about_ca_system_score_gemma":0.000220924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000256109,"about_ca_topic_score_gemma":0.0004221277,"domain_scores_codex":[0.9990932,0.00006909917,0.0000493652,0.0002964,0.0004296072,0.00006238073],"domain_scores_gemma":[0.9995466,0.000142095,0.00008054362,0.00009440133,0.0001088499,0.00002742419],"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":[0.0000255681,0.000005836869,0.00006727778,0.00002568208,0.000002267985,0.0000174727,0.0000154927,0.00008218924,0.992825,0.0001362719,0.0001151939,0.006681683],"study_design_scores_gemma":[0.000003790619,0.00002940949,0.000208473,0.000001782841,0.000005048934,0.0001294956,0.000004516129,0.002160434,0.9956137,0.00007763295,0.00176053,0.000005112645],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4510554,0.00434938,0.5265133,0.001050356,0.000629139,0.0002039645,0.0006109638,0.005275431,0.01031213],"genre_scores_gemma":[0.7841061,0.001138383,0.1949313,0.0004270961,0.0001296315,0.0001008398,0.0004101621,0.0002892676,0.01846717],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001966712,"threshold_uncertainty_score":0.00657934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01349674852394659,"score_gpt":0.2778969941431695,"score_spread":0.2644002456192229,"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."}}