{"id":"W3210109743","doi":"10.5281/zenodo.3596024","title":"FAIRplus: D1.2 Selection criteria and guidelines for data sources from IMI projects and EFPIA internal databases","year":2019,"lang":"en","type":"report","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"AstraZeneca (Canada)","funders":"Horizon 2020 Framework Programme","keywords":"Selection (genetic algorithm); Database; Computer science; Information retrieval; Data mining; Data science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":["scholarly_communication","open_science"],"category_scores_codex":[0.004101139,0.0003305863,0.0003709255,0.0005474205,0.001367252,0.01514785,0.005593588,0.000108102,0.0004967086],"category_scores_gemma":[0.01212833,0.0003204125,0.00003877714,0.0004377389,0.0001714859,0.01518345,0.02066854,0.0004680711,0.0002455195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001529009,"about_ca_system_score_gemma":0.00006503634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001211794,"about_ca_topic_score_gemma":0.00001867252,"domain_scores_codex":[0.9954048,0.0005351966,0.0005653955,0.001802784,0.001180551,0.0005113088],"domain_scores_gemma":[0.9945681,0.0002385135,0.000501005,0.002209794,0.002272155,0.0002104457],"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":[0.0001269599,0.00007557358,0.00003348861,0.0006922167,0.0002555586,0.0000171411,0.0003438157,0.000003108874,0.0006167491,0.001537878,0.8698003,0.1264972],"study_design_scores_gemma":[0.0005903137,0.0002392847,0.0003243752,0.0002873002,0.00006087633,0.0001514426,0.0001661572,0.01460486,0.0000790904,0.00005969065,0.9831091,0.0003275141],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.00486095,0.003779773,0.9392347,0.005833419,0.001222375,0.00447673,0.01209111,0.001756206,0.02674476],"genre_scores_gemma":[0.04952653,0.1464511,0.3401771,0.002313822,0.01040875,0.000006542688,0.3441204,0.01315775,0.09383797],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5990576,"threshold_uncertainty_score":0.9999328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3828800719573955,"score_gpt":0.4191939654482199,"score_spread":0.03631389349082442,"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."}}