{"id":"W1966077509","doi":"10.1073/pnas.0510921103","title":"Severe acute respiratory syndrome diagnostics using a coronavirus protein microarray","year":2006,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital","funders":"National Institutes of Health; Damon Runyon Cancer Research Foundation","keywords":"Coronavirus; Microarray; Protein microarray; Virology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Antibody; DNA microarray; Outbreak; Coronaviridae; Severe acute respiratory syndrome; Respiratory system; Immunofluorescence; Coronavirus disease 2019 (COVID-19); Medicine; Immunology; Biology; Gene; Pathology; Internal medicine; Gene expression; Disease; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003164628,0.0003010356,0.0003202641,0.0004552738,0.0002426508,0.00041933,0.0001894333,0.0004321109,0.00089735],"category_scores_gemma":[0.0004666361,0.0001677009,0.0001809944,0.0002696364,0.0001147744,0.0003154766,0.0001883196,0.000318543,0.0004874588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004545622,"about_ca_system_score_gemma":0.000258664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001223192,"about_ca_topic_score_gemma":0.00223491,"domain_scores_codex":[0.9997235,0.00004800505,0.00001252927,0.00007864884,0.0001076185,0.00002968659],"domain_scores_gemma":[0.9998395,0.00005625411,0.00001936566,0.0000133617,0.00004797918,0.00002348417],"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":[0.0001865702,0.00007483435,0.00667688,0.00004437909,0.00002553523,0.00004284444,0.00002045181,0.001687476,0.9650673,0.0001717169,0.0007777751,0.0252242],"study_design_scores_gemma":[0.00003212181,0.0007454935,0.04860893,0.00001684626,0.00005879237,0.0005641638,0.0000617295,0.1269451,0.8121872,0.0006082858,0.01012963,0.00004171127],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.874812,0.002106562,0.112638,0.0008033607,0.0001349781,0.000206834,0.00261702,0.00193131,0.004749987],"genre_scores_gemma":[0.821818,0.0008599687,0.1700646,0.0003486733,0.00004095617,0.0002243623,0.00228128,0.00002303532,0.004339087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001223192,"threshold_uncertainty_score":0.003298104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03772357784558855,"score_gpt":0.3256561499499823,"score_spread":0.2879325721043937,"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."}}