{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.1450239,0.002366623,0.003341447,0.02060718,0.003760491,0.01971584,0.007163874,0.005277935,0.1505733],"category_scores_gemma":[0.3374124,0.003734432,0.004585152,0.01877481,0.00235798,0.008147855,0.01582539,0.003972906,0.1077125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005217552,"about_ca_system_score_gemma":0.03060054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006750857,"about_ca_topic_score_gemma":0.009101579,"domain_scores_codex":[0.9110785,0.03669763,0.02698379,0.005347823,0.01671205,0.003180108],"domain_scores_gemma":[0.7065875,0.1392258,0.01820941,0.06147216,0.06589106,0.008614101],"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":[0.001844023,0.0002154394,0.004054184,0.01043029,0.0003284505,0.0003901891,0.002207233,0.001671834,0.002849367,0.03407059,0.8601277,0.08181068],"study_design_scores_gemma":[0.0006882582,0.00008554177,0.002938332,0.004269273,0.00009307449,0.0001784402,0.000487495,0.001047836,0.002458453,0.01189884,0.975727,0.0001275286],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.002926296,0.001125431,0.1461501,0.006544683,0.0009132043,0.05621748,0.7050287,0.02471858,0.05637543],"genre_scores_gemma":[0.008793317,0.0008158049,0.3406979,0.003669554,0.0003733052,0.1353836,0.4807043,0.01029486,0.0192673],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9928361,"threshold_uncertainty_score":0.766969,"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."}}