{"id":"W4400913536","doi":"10.12688/f1000research.130587.3","title":"A guide to selecting high-performing antibodies for human Midkine for use in Western blot and immunoprecipitation","year":2024,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Proteoglycans and glycosaminoglycans research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Genentech; 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":"Midkine; Immunoprecipitation; Open peer review; Western blot; Antibody; Plant biology; Biology; Medicine; Neuroscience; Molecular biology; Cell biology; Computational biology; Immunology; Biochemistry; Gene; Growth factor; Botany","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"],"consensus_categories":[],"category_scores_codex":[0.001527396,0.0003258933,0.0003772106,0.0005780777,0.0002344777,0.0004422124,0.0004712211,0.0003884289,0.000005428497],"category_scores_gemma":[0.0007937886,0.0003254689,0.0001213613,0.0002055912,0.00009849107,0.00001003837,0.002039781,0.00058621,0.000004328611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001203704,"about_ca_system_score_gemma":0.0003594772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00296488,"about_ca_topic_score_gemma":0.005065086,"domain_scores_codex":[0.9971913,0.0001053384,0.000482931,0.001053529,0.0003491525,0.0008177462],"domain_scores_gemma":[0.9985887,0.0001514814,0.00006316694,0.0005262595,0.0005141539,0.0001562088],"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":[0.0007496563,0.0000917462,0.005069563,0.002337572,0.0002073083,0.00001502302,0.0004385885,0.0003268965,0.9613707,0.0002274185,0.004617758,0.02454774],"study_design_scores_gemma":[0.004665709,0.004624121,0.0203887,0.002347786,0.0001132588,0.00004090064,0.0005548273,0.00745794,0.8511442,0.00624893,0.1004788,0.001934853],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896409,0.001385102,0.003623407,0.000641583,0.0001741464,0.003919564,0.0005031264,0.0000271945,0.00008498881],"genre_scores_gemma":[0.9656005,0.0003489657,0.01598869,0.00007086108,0.0006452858,0.003243727,0.001259209,0.0001477859,0.01269496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1102265,"threshold_uncertainty_score":0.9999197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04765540620358782,"score_gpt":0.4027820850963985,"score_spread":0.3551266788928107,"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."}}