{"id":"W2314195774","doi":"10.1021/ac102917f","title":"Protein Detection Using Arrayed Microsensor Chips: Tuning Sensor Footprint to Achieve Ultrasensitive Readout of CA-125 in Serum and Whole Blood","year":2011,"lang":"en","type":"letter","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":120,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ontario Institute for Cancer Research","keywords":"Chemistry; Detection limit; Biomarker; Multiplexing; False positive paradox; Microscale chemistry; Biosensor; Detector; Chromatography; Computer science; Biochemistry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000198977,0.0004724324,0.0006388094,0.0001261536,0.00006129184,0.00002809647,0.0001603273,0.001143485,0.000003548293],"category_scores_gemma":[0.0002273097,0.0004631364,0.0002213587,0.0002709676,0.0002497346,0.0000036805,0.0001596567,0.0008953729,0.000001120363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005204604,"about_ca_system_score_gemma":0.00006939643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001967019,"about_ca_topic_score_gemma":0.00004988701,"domain_scores_codex":[0.9977531,0.00008816564,0.00055307,0.0009211337,0.0002083251,0.0004761367],"domain_scores_gemma":[0.9988144,0.00002670366,0.0002743218,0.0005607891,0.0001986935,0.0001251127],"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":[0.0001388151,0.00005853417,0.00015289,0.0001999591,0.000196433,0.0001126863,0.00002373076,0.000003345438,0.9985166,2.243776e-7,0.0001896154,0.0004071296],"study_design_scores_gemma":[0.0003004464,0.000184893,0.0000876427,0.0003122709,0.0002601691,0.0001206682,0.00008639372,0.0001258583,0.9954519,0.00003111918,0.002509233,0.0005294657],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950812,0.00005636043,0.000721886,0.003263373,0.00001595658,0.0003071638,0.0001484064,0.00002859472,0.0003770113],"genre_scores_gemma":[0.9886699,0.00003310276,0.005210839,0.004450542,0.0005830516,0.000009100405,0.0002286149,0.00006563105,0.0007491578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006411287,"threshold_uncertainty_score":0.999782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01473277642562532,"score_gpt":0.2570470378560172,"score_spread":0.2423142614303919,"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."}}