{"id":"W4221114518","doi":"10.5281/zenodo.6370579","title":"Antibody Characterization Report for Progranulin","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Amyotrophic Lateral Sclerosis Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Antibody; Characterization (materials science); Medicine; Immunology; Materials science; Nanotechnology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0009416537,0.00175625,0.0008659253,0.002241102,0.001037862,0.0009039871,0.001442935,0.0008064274,0.01869421],"category_scores_gemma":[0.002195156,0.0006204173,0.0009851017,0.001404525,0.0002601291,0.0006379195,0.0006583569,0.001804611,0.02362402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005713837,"about_ca_system_score_gemma":0.001295942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002124023,"about_ca_topic_score_gemma":0.002576817,"domain_scores_codex":[0.999103,0.0001485456,0.0001245077,0.0002144891,0.0002185138,0.0001910604],"domain_scores_gemma":[0.9983028,0.0003995336,0.0001199916,0.0003065338,0.0006505785,0.0002204922],"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":[0.0002128137,0.0001304137,0.000373502,0.0004644769,0.000054582,0.0004750679,0.0001041026,0.0001473075,0.9785209,0.0007039824,0.006725149,0.01208776],"study_design_scores_gemma":[0.0001581298,0.0007690212,0.008590345,0.0001961867,0.0002416106,0.005346619,0.0001706606,0.001329442,0.4860511,0.0008738756,0.4961967,0.00007632036],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.3077093,0.0390612,0.4836853,0.005491833,0.00408096,0.007609262,0.08059156,0.003054398,0.06871612],"genre_scores_gemma":[0.1961779,0.03817134,0.2389265,0.003320727,0.00148725,0.005729055,0.4296671,0.001230158,0.08529007],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.01869421,"threshold_uncertainty_score":0.06253839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04346731402761918,"score_gpt":0.3016830340484242,"score_spread":0.258215720020805,"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."}}