{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006879665,0.0005873467,0.0003749527,0.0002409967,0.0002285051,0.0006100511,0.0007088722,0.002029913,0.0006547602],"category_scores_gemma":[0.001274181,0.0002443348,0.0001804439,0.000251267,0.0006509074,0.000600782,0.0002976297,0.0009142376,0.00096954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000887525,"about_ca_system_score_gemma":0.0002117654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002688048,"about_ca_topic_score_gemma":0.0005266555,"domain_scores_codex":[0.9992791,0.0001698444,0.00003575504,0.0001491995,0.0002955228,0.00007065785],"domain_scores_gemma":[0.9995777,0.0002100663,0.00003832673,0.00004312434,0.0001011316,0.00002952151],"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":[0.0001792477,0.00008302519,0.0008554892,0.0001783795,0.00001349643,0.0007742419,0.0001115919,0.0007701599,0.9597241,0.001701779,0.004804676,0.03080376],"study_design_scores_gemma":[0.00004959727,0.0002375163,0.001196377,0.00001294761,0.00001798407,0.001769473,0.00005327286,0.01683811,0.949142,0.001019953,0.02962789,0.00003485416],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5611647,0.02462012,0.3559281,0.02875655,0.004240887,0.0004563955,0.0007573758,0.003466352,0.02060949],"genre_scores_gemma":[0.8180534,0.005641115,0.1568541,0.009066387,0.0009443419,0.0005471904,0.0004004529,0.0001377705,0.008355291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002029913,"threshold_uncertainty_score":0.006439507,"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."}}