{"id":"W2950150962","doi":"10.1038/npre.2010.5382","title":"SPARQL Assist Language Neutral Query Composer","year":2010,"lang":"en","type":"preprint","venue":"Nature Precedings","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Paul's Hospital; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Technische Universität Dortmund; Canarie; Microsoft Research","keywords":"SPARQL; Computer science; Named graph; XML; Identifier; Task (project management); World Wide Web; Context (archaeology); Semantic Web; Information retrieval; Linked data; RDF; Programming language; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002982461,0.0004025059,0.0003805896,0.00007589934,0.00008474253,0.000091959,0.0007494394,0.004430891,0.00004045073],"category_scores_gemma":[0.0003245688,0.0003496676,0.0002616868,0.00006836109,0.0002585069,0.000002004406,0.000782411,0.003755401,0.0000175738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000018193,"about_ca_system_score_gemma":0.0001191397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005298287,"about_ca_topic_score_gemma":0.0001641308,"domain_scores_codex":[0.9982074,0.000053484,0.0002710621,0.0008180733,0.0002227301,0.0004272014],"domain_scores_gemma":[0.9988163,0.00003526748,0.0001958296,0.0006675442,0.0001103089,0.0001748273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002414692,0.0001893332,0.00368781,0.0003641308,0.0003478724,0.00004740594,0.0005294444,0.00001279279,0.8244704,0.0004276549,0.136252,0.03342969],"study_design_scores_gemma":[0.0008367188,0.0002492972,0.01080057,0.0002644715,0.000141062,0.00008018016,0.0001693902,0.00007838051,0.519085,0.001188923,0.4658542,0.001251719],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.979385,0.006714691,0.0003774287,0.001605096,0.003943364,0.0002617542,0.0001301319,0.0001596052,0.007422966],"genre_scores_gemma":[0.9864521,0.0002201131,0.007444385,0.001070459,0.002139381,0.0000405558,0.0008415493,0.00005444476,0.001737025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3296022,"threshold_uncertainty_score":0.9998955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007474534731342467,"score_gpt":0.2968057852026288,"score_spread":0.2893312504712863,"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."}}