{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008657379,0.0002393804,0.0005577657,0.001022936,0.0007847417,0.0003516658,0.0007512984,0.0001650499,0.000002377784],"category_scores_gemma":[0.005836058,0.0001931621,0.0002273727,0.001750127,0.0008398498,0.000839771,0.0002861637,0.0008161801,3.095803e-7],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009515167,"about_ca_system_score_gemma":0.01853912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001917937,"about_ca_topic_score_gemma":0.007179083,"domain_scores_codex":[0.9961301,0.0002661023,0.0009193219,0.0007097903,0.001167776,0.0008068845],"domain_scores_gemma":[0.9965042,0.0007152143,0.0003844926,0.0004274494,0.001657062,0.0003115478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00528818,0.00408843,0.08053403,0.0005222114,0.0001388797,0.0001138813,0.003263811,0.001089788,0.8970765,0.001242982,0.0001020787,0.006539186],"study_design_scores_gemma":[0.02679393,0.01003272,0.02040358,0.0009727137,0.000297509,0.001490248,0.01168352,0.8233884,0.05237206,0.05060602,0.001340369,0.0006189265],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7546318,0.0009018074,0.2358744,0.005983041,0.0001208726,0.002427673,0.00003979533,0.00001922241,0.000001445545],"genre_scores_gemma":[0.9687009,0.000022994,0.02734056,0.003568342,0.0002354113,0.00006545994,0.000007659823,0.00003321625,0.00002538205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8447044,"threshold_uncertainty_score":0.9870248,"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."}}