{"id":"W4378447349","doi":"10.5281/zenodo.7971926","title":"Antibody Characterization Report for Amyloid-beta precursor protein","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Antibody; BETA (programming language); Amyloid (mycology); Chemistry; Characterization (materials science); Amyloid precursor protein; Amyloid beta; Immunology; Biochemistry; Medicine; Pathology; Nanotechnology; Peptide; Computer science; Materials science; Alzheimer's disease; Disease","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001646681,0.002196607,0.001081406,0.002763495,0.001405569,0.001171335,0.001621651,0.001169977,0.03188179],"category_scores_gemma":[0.003229915,0.00105221,0.001045791,0.001716455,0.0003680945,0.000954256,0.0009917581,0.002380137,0.0516542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000573115,"about_ca_system_score_gemma":0.001536082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001286204,"about_ca_topic_score_gemma":0.001783906,"domain_scores_codex":[0.9984555,0.0002297333,0.0002429371,0.0003714985,0.0004748727,0.0002253718],"domain_scores_gemma":[0.9969801,0.0004051978,0.0001575852,0.0006214792,0.001554479,0.0002811617],"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":[0.0002469852,0.0001712247,0.0004084014,0.000773516,0.00006907171,0.0003781806,0.0001705379,0.0001442588,0.9442156,0.001365298,0.02511196,0.02694491],"study_design_scores_gemma":[0.0001269054,0.0004535871,0.003032645,0.0002037882,0.0001809292,0.003128817,0.00009761512,0.001011339,0.3755931,0.0007832118,0.6153094,0.00007852697],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.1097254,0.02558193,0.7001761,0.00345529,0.004880033,0.00819819,0.07470167,0.006709373,0.06657194],"genre_scores_gemma":[0.1013995,0.02834605,0.3931682,0.002389083,0.00152159,0.009412011,0.354388,0.003451305,0.1059242],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.03188179,"threshold_uncertainty_score":0.1066552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04867835892248555,"score_gpt":0.3141539275053558,"score_spread":0.2654755685828702,"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."}}