{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002691124,0.00007510615,0.00008066838,0.00004110987,0.0001521467,0.00001031534,0.0003616812,0.00008969879,0.000001091449],"category_scores_gemma":[0.0001071465,0.00005591168,0.00005101917,0.0002538813,0.0005187729,0.00001311197,0.0001230997,0.00007011982,2.627471e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001586609,"about_ca_system_score_gemma":0.00003114523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005477864,"about_ca_topic_score_gemma":8.857977e-8,"domain_scores_codex":[0.9991869,0.000003305352,0.0001983139,0.0001987397,0.0003046904,0.0001080308],"domain_scores_gemma":[0.9995205,0.00001308044,0.0002684112,0.0000117836,0.000170625,0.00001556249],"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":[0.000005200478,0.00002359382,0.0010397,0.000009502535,0.000005650092,9.47909e-9,0.00000240526,0.00003675199,0.9935421,0.004857926,0.0004093813,0.00006777225],"study_design_scores_gemma":[0.00007529988,0.00004998623,0.007955207,0.00004527008,0.000009550677,0.00003605672,0.000008932241,0.00004316584,0.9762362,0.0134164,0.002042464,0.00008143081],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986511,0.0001586048,0.00003235595,0.0003164814,0.000004623945,0.0002176696,0.00004116717,0.000007881315,0.0005701576],"genre_scores_gemma":[0.9814702,0.00001491411,0.01815721,0.0001739544,0.00005303013,0.00001011967,5.671369e-7,0.000004902278,0.0001150509],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01812485,"threshold_uncertainty_score":0.2280012,"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."}}