{"id":"W6931402990","doi":"10.5281/zenodo.3908145","title":"CINECA: Common Infrastructure for National Cohorts in Europe, Canada, and Africa - Kick Off Report","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission","keywords":"Interoperability; Data sharing; Excellence; Leverage (statistics); Deliverable; Population; Work (physics); Global health; European union","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.04599317,0.001820553,0.0009830976,0.005873315,0.005919515,0.01346809,0.00806285,0.004492671,0.02741304],"category_scores_gemma":[0.04583713,0.001719695,0.002283628,0.005939813,0.002273335,0.007836808,0.01900669,0.004548352,0.01671106],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02732711,"about_ca_system_score_gemma":0.2241955,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7800627,"about_ca_topic_score_gemma":0.6313267,"domain_scores_codex":[0.9748916,0.003816962,0.001244639,0.002289169,0.009858848,0.007898759],"domain_scores_gemma":[0.9073268,0.005247276,0.00188346,0.01047573,0.03866415,0.03640263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002956516,0.00013618,0.005907953,0.0003419611,0.00008086372,0.0002259288,0.0008649294,0.0013136,0.0009870863,0.01850754,0.9189228,0.05241553],"study_design_scores_gemma":[0.0001210745,0.00007941825,0.01331001,0.000426988,0.00003059718,0.00008308083,0.0008789726,0.001322464,0.0008492934,0.002046183,0.9807436,0.00010819],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.02742127,0.006749693,0.09965324,0.1494426,0.01244417,0.01883804,0.4166976,0.04695377,0.2217997],"genre_scores_gemma":[0.07316903,0.00614785,0.1468486,0.01668309,0.001739176,0.01205835,0.5953013,0.0080715,0.1399811],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9919372,"threshold_uncertainty_score":0.442465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008779943257861545,"score_gpt":0.2113023682818504,"score_spread":0.2025224250239889,"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."}}