{"id":"W4385700889","doi":"10.12688/f1000research.139867.1","title":"Identification of high-performing antibodies for amyloid-beta precursor protein for use in Western Blot, immunoprecipitation and immunofluorescence","year":2023,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Genentech; Canadian Institutes of Health Research; Innovative Medicines Initiative; Government of Canada; Ontario Genomics Institute; Emory University; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Mitacs; Ontario Genomics; Genome Canada; Bristol-Myers Squibb; Bayer; Pfizer","keywords":"Immunoprecipitation; Western blot; Antibody; Amyloid precursor protein; Immunofluorescence; Amyloid beta; Molecular biology; Amyloid (mycology); Transmembrane protein; BETA (programming language); Blot; Biology; Chemistry; Alzheimer's disease; Immunology; Biochemistry; Pathology; Medicine; Disease; Gene; Computer science; Receptor","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":[0.0031732,0.002698207,0.001197728,0.002334185,0.001330439,0.001243171,0.001511418,0.001185319,0.02112421],"category_scores_gemma":[0.00381944,0.001148401,0.0009189064,0.001621994,0.0005771558,0.0009113446,0.0009581329,0.00198143,0.02500325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009597305,"about_ca_system_score_gemma":0.000884091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001085977,"about_ca_topic_score_gemma":0.002730192,"domain_scores_codex":[0.9980201,0.0004396496,0.0003627806,0.0003109361,0.0006258587,0.0002408466],"domain_scores_gemma":[0.9980155,0.0004810062,0.0001649273,0.0003619475,0.0008302225,0.0001464272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00014953,0.00005693425,0.0003773879,0.0005060902,0.00002837222,0.0001472752,0.000100846,0.0001140614,0.9746795,0.0008427291,0.008862643,0.0141346],"study_design_scores_gemma":[0.00008519524,0.0001737484,0.006581053,0.0001523528,0.00007986837,0.001293419,0.0001063074,0.001235012,0.8258227,0.0005666342,0.1638453,0.00005851041],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.221863,0.01673253,0.6385013,0.005140719,0.002837369,0.00534955,0.03544115,0.01238527,0.06174917],"genre_scores_gemma":[0.1076728,0.008772403,0.7660127,0.001061578,0.0003501505,0.005819211,0.06169472,0.004696423,0.04391996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02112421,"threshold_uncertainty_score":0.07066756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08564029400022578,"score_gpt":0.3868237603011783,"score_spread":0.3011834663009525,"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."}}