{"id":"W4321644667","doi":"10.5281/zenodo.7671286","title":"Antibody Characterization Report for Angiogenin","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Angiogenesis and VEGF in Cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Angiogenin; Antibody; Computational biology; Characterization (materials science); Chemistry; Biology; Nanotechnology; Immunology; Materials science; Genetics; Angiogenesis","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":[0.0009301375,0.001458311,0.0007168134,0.001852334,0.001136701,0.0008543179,0.001351908,0.0009096399,0.01945971],"category_scores_gemma":[0.001602937,0.0007191573,0.0009425778,0.001351555,0.0002874224,0.0006818183,0.0004961854,0.001736939,0.0204147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007529599,"about_ca_system_score_gemma":0.001518116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002421888,"about_ca_topic_score_gemma":0.004013405,"domain_scores_codex":[0.9990761,0.0001533045,0.0001174503,0.0002312636,0.0002081578,0.0002137002],"domain_scores_gemma":[0.9985976,0.0003482196,0.00009782708,0.0002394391,0.0005050557,0.0002118254],"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":[0.0001935241,0.0001462321,0.0002965714,0.0003578245,0.00004114513,0.0003823201,0.00009108966,0.0001055285,0.9834162,0.0008186506,0.00586242,0.008288416],"study_design_scores_gemma":[0.0002281148,0.0007877033,0.01009818,0.0001582586,0.0002166512,0.004863509,0.000128647,0.001529957,0.5179214,0.0008134036,0.4631857,0.00006839118],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.3060333,0.03420423,0.5072781,0.004608976,0.004436418,0.007201539,0.06267308,0.002861071,0.07070328],"genre_scores_gemma":[0.259544,0.03360985,0.2537199,0.003151366,0.001714336,0.006879987,0.3349815,0.001230015,0.1051691],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.01945971,"threshold_uncertainty_score":0.06509918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03089105743938568,"score_gpt":0.288644180365795,"score_spread":0.2577531229264093,"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."}}