{"id":"W4394721167","doi":"10.21203/rs.3.pex-2607/v1","title":"A consensus platform for antibody characterization","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; University of Toronto","funders":"National Institute on Aging; Genentech; Mitacs; Motor Neurone Disease Association; Ontario Genomics Institute; Government of Canada; Emory University; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Bayer; ALS Society of Canada; Ontario Genomics; Genome Canada; Bristol-Myers Squibb; Silicon Valley Community Foundation; Bill and Melinda Gates Foundation","keywords":"Characterization (materials science); Antibody; Computer science; Computational biology; Biology; Nanotechnology; Immunology; Materials science","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001665543,0.0003493219,0.0006488372,0.0009231937,0.0002788135,0.0002481792,0.0003010308,0.0005536184,0.0003794322],"category_scores_gemma":[0.0006418967,0.0002764397,0.000463063,0.000486431,0.0003407418,0.00002448222,0.001662491,0.002834408,0.0006541227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000290024,"about_ca_system_score_gemma":0.001608743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002078057,"about_ca_topic_score_gemma":0.00001571905,"domain_scores_codex":[0.9957359,0.0001249713,0.000488225,0.0008951638,0.001696931,0.001058789],"domain_scores_gemma":[0.9966887,0.000665318,0.00006828547,0.0006597884,0.001458986,0.0004589596],"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":[0.01062348,0.001371922,0.01073719,0.1184959,0.002058132,0.002654928,0.002060635,0.00001583459,0.7038435,0.03477892,0.04313781,0.07022174],"study_design_scores_gemma":[0.003017068,0.003653506,0.06577129,0.01625816,0.000311061,0.0004287271,0.0006286167,0.02874649,0.01594818,0.03846823,0.8254547,0.001314039],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9607279,0.002728132,0.0001903035,0.01728213,0.0007725937,0.00577729,0.003105907,0.0002310579,0.009184645],"genre_scores_gemma":[0.9084047,0.00230808,0.001133039,0.0002089638,0.003553226,0.001235013,0.02323963,0.0002124982,0.05970484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7823168,"threshold_uncertainty_score":0.9999688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1298003722880252,"score_gpt":0.4789742660807735,"score_spread":0.3491738937927483,"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."}}