{"id":"W4393826253","doi":"10.5281/zenodo.3380355","title":"PIAS1 (full length) Purification","year":2019,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Genetic Neurodegenerative Diseases","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computational biology; Biology; Computer science","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":[0.0006206257,0.001706366,0.0009764587,0.001636647,0.0009108959,0.0007924998,0.001520127,0.0005667206,0.01485119],"category_scores_gemma":[0.001011421,0.000505852,0.0008430881,0.002614089,0.0003097251,0.0006408197,0.0007334987,0.002031151,0.02836413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008407087,"about_ca_system_score_gemma":0.0008257432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0033945,"about_ca_topic_score_gemma":0.004363393,"domain_scores_codex":[0.9993956,0.00004828817,0.00006078103,0.0001677534,0.0002065478,0.0001210194],"domain_scores_gemma":[0.9996672,0.00003787121,0.00003186692,0.00006217405,0.000134705,0.00006626383],"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":[0.000768985,0.0002674081,0.001395415,0.0007060811,0.0001028706,0.0006857162,0.0001528049,0.0005775534,0.9063466,0.002352862,0.03901126,0.04763249],"study_design_scores_gemma":[0.0001734792,0.0003522295,0.006843183,0.0001263507,0.0001064119,0.001529576,0.0001066088,0.002231954,0.5679495,0.001752201,0.418743,0.00008555316],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.2545395,0.04152457,0.3277932,0.007592243,0.004130916,0.007504765,0.218764,0.02354456,0.1146062],"genre_scores_gemma":[0.1669235,0.01710692,0.1547257,0.001527532,0.0004569043,0.0024701,0.5306376,0.002269661,0.123882],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.01485119,"threshold_uncertainty_score":0.04968214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07230335622691747,"score_gpt":0.2789566870769131,"score_spread":0.2066533308499957,"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."}}