{"id":"W1991251707","doi":"10.1109/transducers.2013.6627383","title":"Digitizing immunoassay on an antibody nanoarray to improve assay sensitivity","year":2013,"lang":"en","type":"article","venue":"","topic":"Nanofabrication and Lithography Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Immunoassay; Assay sensitivity; Sensitivity (control systems); Antibody; Digitization; Computer science; Nanotechnology; Chromatography; Chemistry; Materials science; Immunology; Medicine; Engineering; Electronic engineering; Pathology; Telecommunications","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":[0.0003851641,0.0001691431,0.0001492017,0.0001869097,0.00005518051,0.0001325522,0.0001016174,0.00008561018,0.000148258],"category_scores_gemma":[0.0000353237,0.000149937,0.00006470192,0.0002718541,0.00001578216,0.0003461553,0.00002065488,0.0001332264,0.0003189896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003816138,"about_ca_system_score_gemma":0.000005988716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001669402,"about_ca_topic_score_gemma":0.00000891174,"domain_scores_codex":[0.9991284,0.00007326313,0.0001742316,0.0002164692,0.0001403983,0.0002672553],"domain_scores_gemma":[0.9992574,0.00008160948,0.00001716049,0.0004197611,0.00007252599,0.0001515115],"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":[0.00000168005,0.0000376978,0.0004180702,0.000008790729,0.00001444501,0.00000193785,0.00008281254,0.00004709364,0.9166692,0.001889052,0.002204479,0.07862475],"study_design_scores_gemma":[0.0002069846,0.0002305711,0.03235681,0.00004605321,0.000008586127,0.000009649892,0.0001726178,0.01236829,0.914553,0.0004411302,0.03883289,0.0007734037],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.47228,0.00002579097,0.3317488,0.000394235,0.0004340957,0.0009705186,0.00001881568,0.004893684,0.1892341],"genre_scores_gemma":[0.9898153,0.000007711168,0.009065164,0.0004944286,0.00005666757,0.00005703876,0.00001585108,0.00003685219,0.0004510137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5175353,"threshold_uncertainty_score":0.6114253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00568809280013934,"score_gpt":0.2396924517494517,"score_spread":0.2340043589493124,"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."}}