{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007802709,0.0003043008,0.000319064,0.00006324298,0.0000680612,0.00003339444,0.0001341895,0.0005596868,0.00008520656],"category_scores_gemma":[0.00003138407,0.0002558849,0.0001357281,0.00001131514,0.0002541436,0.000001741912,0.0001661532,0.000228916,0.00004055495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001402413,"about_ca_system_score_gemma":0.00009421459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002189896,"about_ca_topic_score_gemma":0.0001092448,"domain_scores_codex":[0.9988736,0.00001401209,0.0002557298,0.0004750752,0.0001500944,0.0002315181],"domain_scores_gemma":[0.9994377,0.00002468152,0.0001247357,0.0002912034,0.00005050406,0.00007112577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004076309,0.0002355516,0.0004843802,0.0005656255,0.001277844,0.0003562747,0.0002761424,0.000003112679,0.08141982,0.01596495,0.577739,0.3212697],"study_design_scores_gemma":[0.0003573464,0.0003097809,0.000177604,0.0001239261,0.00009768645,0.00006718512,0.00004165812,0.00001874536,0.001850247,0.002919177,0.9935825,0.0004541181],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0291904,0.010961,0.0004568936,0.0006562734,0.0008815893,0.0003275943,0.0001962581,0.00006578193,0.9572642],"genre_scores_gemma":[0.1710182,0.001706413,0.000661769,0.0007750574,0.001022354,0.00001232243,0.0005836792,0.00006901924,0.8241512],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4158435,"threshold_uncertainty_score":0.9999893,"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."}}