{"id":"W2075694192","doi":"10.1186/1471-2105-13-s1-s2","title":"SPARQL Assist language-neutral query composer","year":2012,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"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; Canarie; Microsoft Research","keywords":"SPARQL; Computer science; Named graph; Semantics (computer science); World Wide Web; Identifier; Information retrieval; Semantic Web; Query language; Context (archaeology); Task (project management); RDF query language; SPARK (programming language); RDF; Web search query; Web query classification; Programming language; Search engine","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":[0.00797061,0.001554844,0.001036246,0.001084883,0.0008773252,0.002866116,0.002615093,0.00124982,0.0251652],"category_scores_gemma":[0.01108167,0.0007917891,0.001292955,0.000994721,0.001178591,0.004105926,0.003864757,0.001857828,0.01041182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001000369,"about_ca_system_score_gemma":0.001442632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001698491,"about_ca_topic_score_gemma":0.001298712,"domain_scores_codex":[0.9949695,0.001167425,0.0006684251,0.001162925,0.001707846,0.0003238031],"domain_scores_gemma":[0.991935,0.003208643,0.0003642478,0.002426716,0.001750306,0.0003151635],"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":[0.004936181,0.0007104271,0.01055856,0.002712228,0.0004522796,0.002781829,0.004665582,0.01211208,0.103064,0.105029,0.3405975,0.4123803],"study_design_scores_gemma":[0.0004842621,0.0003087338,0.002458111,0.0002723296,0.0001993458,0.002141246,0.0008689084,0.1984183,0.1681265,0.06158101,0.5648383,0.0003030993],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01211819,0.0002077351,0.677407,0.000780641,0.0001632945,0.0007055208,0.003930005,0.2904435,0.01424413],"genre_scores_gemma":[0.2810033,0.0005890022,0.6183768,0.003500245,0.0003633308,0.001238376,0.02266144,0.04674813,0.02551946],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0251652,"threshold_uncertainty_score":0.08418596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02285940336895057,"score_gpt":0.2831718068025856,"score_spread":0.260312403433635,"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."}}