{"id":"W2805208499","doi":"","title":"SPARQL Assist Language-Neutral Query Composer.","year":2010,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Paul's Hospital; University of British Columbia","funders":"","keywords":"SPARQL; Computer science; Identifier; Task (project management); Query language; Named graph; Information retrieval; Composition (language); World Wide Web; Natural language processing; Linguistics; Semantic Web; Programming language; RDF","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":[],"consensus_categories":[],"category_scores_codex":[0.0001059162,0.00009397298,0.00009129167,0.0000170366,0.00004933219,0.00001888406,0.0001731021,0.000197167,0.0001095167],"category_scores_gemma":[0.00006130872,0.00007247701,0.00006070214,0.00003766744,0.0001660373,8.749609e-7,0.00005318286,0.0001537098,0.00005283244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000157519,"about_ca_system_score_gemma":0.00002723954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006042974,"about_ca_topic_score_gemma":0.0004619975,"domain_scores_codex":[0.9994171,0.00001783575,0.0001024537,0.0002067757,0.00006180182,0.000194022],"domain_scores_gemma":[0.9995865,0.00001237724,0.00002512318,0.0002664569,0.00002113649,0.00008839538],"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":[0.00002128485,0.00005528456,0.004419598,0.000006163116,0.00002199328,0.000008985846,0.00003243613,4.389113e-7,0.9453771,0.000576496,0.02797717,0.02150307],"study_design_scores_gemma":[0.0004453613,0.0001866781,0.01995144,0.000004299405,0.00001042843,0.00005250953,0.0001974824,0.00002544543,0.6548519,0.0001345772,0.3238894,0.0002505415],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9792079,0.0001794314,0.0007468199,0.0006107243,0.0004235523,0.00003999389,0.00000654203,0.00005449442,0.01873057],"genre_scores_gemma":[0.988554,0.00001343621,0.005730693,0.0006294203,0.000365716,0.000005332583,0.0000581963,0.000009782898,0.004633439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2959122,"threshold_uncertainty_score":0.2955527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005958568575716292,"score_gpt":0.270039079173995,"score_spread":0.2640805105982787,"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."}}