{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007542063,0.001487406,0.001151905,0.001412367,0.0007724682,0.003125953,0.002117521,0.001272455,0.0275941],"category_scores_gemma":[0.01168664,0.0008263888,0.001070955,0.001259967,0.001057214,0.00407595,0.004583555,0.001737417,0.01264516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008131043,"about_ca_system_score_gemma":0.001052055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001393486,"about_ca_topic_score_gemma":0.001183855,"domain_scores_codex":[0.9936625,0.001522638,0.0007402607,0.001140299,0.002546082,0.00038829],"domain_scores_gemma":[0.9919932,0.002879821,0.0003085039,0.003013649,0.001548947,0.0002559039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004075558,0.0005683285,0.007359402,0.001462658,0.0002950835,0.0031477,0.002721484,0.01184969,0.08616423,0.1105196,0.3191775,0.4526587],"study_design_scores_gemma":[0.0003912577,0.0001972591,0.001621643,0.0001967631,0.000134702,0.002054106,0.0006729798,0.1940622,0.1797463,0.06990321,0.5508119,0.0002077695],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01337817,0.0001870631,0.7046819,0.0007952265,0.0002356597,0.0006246872,0.00364276,0.2605292,0.01592518],"genre_scores_gemma":[0.318639,0.0005203641,0.5586678,0.002843044,0.0004518845,0.0009768454,0.01766556,0.05613244,0.04410315],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.0275941,"threshold_uncertainty_score":0.09231144,"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."}}