{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02190619,0.003530595,0.003082039,0.007092821,0.002594585,0.005675374,0.006621343,0.004973075,0.04680147],"category_scores_gemma":[0.03002679,0.003135342,0.003149832,0.004690205,0.001181075,0.005872516,0.007991475,0.005883882,0.09919228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001461512,"about_ca_system_score_gemma":0.00721269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001617902,"about_ca_topic_score_gemma":0.001592633,"domain_scores_codex":[0.9855872,0.003189423,0.002169251,0.002111244,0.005405147,0.001537721],"domain_scores_gemma":[0.9620304,0.007551476,0.001578036,0.01466099,0.0117909,0.002388208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001312092,0.0004835884,0.001319173,0.00225969,0.0003045823,0.0006348392,0.000434683,0.002639242,0.166232,0.04986842,0.3674807,0.4070311],"study_design_scores_gemma":[0.0003606264,0.000256971,0.001316518,0.0005486633,0.0002420284,0.0010022,0.0002250127,0.02202762,0.1886524,0.07543798,0.7096898,0.0002401341],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002942913,0.001062611,0.9234478,0.001049946,0.0008248869,0.0008827205,0.007329929,0.04563266,0.01682656],"genre_scores_gemma":[0.02261944,0.00181475,0.8672794,0.001019818,0.0005297525,0.003361524,0.06036349,0.01756879,0.02544307],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04680147,"threshold_uncertainty_score":0.1565666,"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."}}