{"id":"W3177345707","doi":"10.6000/1927-520x.2021.10.07","title":"Buffalo as a New Model for Long COVID-19 Study and for Recovering the SARS CoV-2 Polyclonal Neutralizing Antibody, Using an Online Affinity Column Adsorption Technology, for Therapeutic Interventional Modality in Humans: A New Multi-Purpose COVID Research Proposal","year":2021,"lang":"en","type":"article","venue":"Journal of Buffalo Science","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Polyclonal antibodies; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Antibody; Modality (human–computer interaction); Virology; Column (typography); Neutralizing antibody; Medicine; Immunology; Computer science; Infectious disease (medical specialty); Internal medicine; Artificial intelligence; Disease","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001569536,0.0006523718,0.0005199698,0.0007966753,0.0006858507,0.0009060118,0.0007288776,0.0006326742,0.00485792],"category_scores_gemma":[0.0003308197,0.0002284936,0.0005611545,0.0004307217,0.0007039618,0.001196631,0.0008097902,0.00117905,0.0009377706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000549606,"about_ca_system_score_gemma":0.001643614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001315628,"about_ca_topic_score_gemma":0.002274673,"domain_scores_codex":[0.999558,0.0001193239,0.00002369182,0.000101733,0.0001149687,0.00008215014],"domain_scores_gemma":[0.9997409,0.00002992599,0.00003929307,0.0000283786,0.00004929451,0.0001121226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009913187,0.001154979,0.003746387,0.0002298692,0.00003794103,0.0001922915,0.0001385284,0.0001062497,0.9621221,0.005175082,0.002624919,0.02348033],"study_design_scores_gemma":[0.0008090747,0.02488511,0.0335752,0.0002348524,0.0005232926,0.004903058,0.000816191,0.005482653,0.6786525,0.007040141,0.2428962,0.0001817674],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"protocol","genre_scores_codex":[0.7454767,0.03689214,0.1683117,0.01075369,0.00322677,0.003823877,0.003024832,0.0007145786,0.02777574],"genre_scores_gemma":[0.707229,0.04307179,0.1945358,0.00366579,0.00150111,0.003750982,0.005983114,0.0002113547,0.04005094],"genre_candidate":"protocol","genre_consensus":null,"teacher_disagreement_score":0.00485792,"threshold_uncertainty_score":0.01625139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4151345991996232,"score_gpt":0.5418642238060905,"score_spread":0.1267296246064673,"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."}}