{"id":"W1510421640","doi":"10.1007/978-0-387-34347-1_11","title":"Ontoligent Interactive Query Tool","year":2006,"lang":"en","type":"book-chapter","venue":"Semantic web and beyond","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Query language; Web query classification; RDF query language; Information retrieval; Query optimization; Sargable; Web search query; Query expansion; Variety (cybernetics); Syntax; Ontology; Semantic reasoner; Download; World Wide Web; Search engine; Natural language processing; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001121618,0.00129666,0.0009675015,0.002384887,0.0008121464,0.003362772,0.001384706,0.000935188,0.07456325],"category_scores_gemma":[0.003110303,0.0007574568,0.0008599009,0.002057924,0.0004732467,0.003931084,0.003297719,0.001065534,0.03180845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007608366,"about_ca_system_score_gemma":0.00105895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002920843,"about_ca_topic_score_gemma":0.003629711,"domain_scores_codex":[0.9990402,0.0001403143,0.0001044394,0.0001831602,0.0004590967,0.00007279637],"domain_scores_gemma":[0.9989552,0.000532291,0.00004301191,0.0002294895,0.0001750735,0.00006490995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006931006,0.0001218728,0.001097085,0.001038564,0.00009500899,0.0005144321,0.0005412605,0.001230047,0.01843591,0.03109909,0.6733961,0.2717374],"study_design_scores_gemma":[0.00009918339,0.00003686388,0.0009011115,0.0001261105,0.00006564838,0.0007980231,0.0001912881,0.01566111,0.02680968,0.01994893,0.9352647,0.00009731595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006493891,0.0007320507,0.4832596,0.0008348375,0.000339867,0.0003913,0.0526752,0.3822194,0.0730539],"genre_scores_gemma":[0.08986219,0.001625909,0.4648626,0.00262421,0.0002664461,0.001257331,0.2135972,0.07670008,0.149204],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07456325,"threshold_uncertainty_score":0.2494389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008920276348439282,"score_gpt":0.2362088944550439,"score_spread":0.2272886181066046,"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."}}