{"id":"W2023917861","doi":"10.1038/npre.2010.5060.1","title":"Bio2RDF: Convert, Provide And Reuse.","year":2010,"lang":"en","type":"preprint","venue":"Nature Precedings","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Université Laval","funders":"","keywords":"Computer science; Identifier; SPARQL; Linked data; RDF; Information retrieval; World Wide Web; Semantic Web; Database; Data 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0002863169,0.0003311266,0.0003063155,0.00006758723,0.00007395796,0.0000782698,0.0006846224,0.004103309,0.00002122268],"category_scores_gemma":[0.001575368,0.0002795421,0.0001098797,0.00005028176,0.0003426505,0.000001822795,0.002106509,0.002581838,0.000005232843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001098731,"about_ca_system_score_gemma":0.0001282828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002406885,"about_ca_topic_score_gemma":0.00003031259,"domain_scores_codex":[0.9984177,0.00003405459,0.0002238756,0.0008208848,0.000190637,0.000312824],"domain_scores_gemma":[0.9987697,0.00003462359,0.0001633746,0.0007549209,0.0001181273,0.0001592558],"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":[0.000252759,0.00009258934,0.005427487,0.0006361202,0.0003160777,0.00001683047,0.0004418964,9.384721e-7,0.7316673,0.0003157066,0.1678314,0.09300093],"study_design_scores_gemma":[0.000520293,0.000214354,0.002330937,0.0001669962,0.00007299392,0.00005442774,0.00004704701,0.00006359045,0.1577286,0.002594606,0.8355932,0.0006129823],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9753374,0.01289446,0.000270847,0.005039871,0.002760267,0.0004541611,0.00009198084,0.0001440349,0.003006967],"genre_scores_gemma":[0.9784811,0.001318461,0.01575209,0.001245318,0.001201592,0.00006178492,0.0002881898,0.00005001597,0.001601421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6677617,"threshold_uncertainty_score":0.9999657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0077923154753697,"score_gpt":0.2767130480593398,"score_spread":0.2689207325839701,"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."}}