{"id":"W2407694321","doi":"","title":"SemQuest: University of Houston's Semantics-based Question Answering System.","year":2011,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automatic summarization; Question answering; Computer science; Preprocessor; Redundancy (engineering); Natural language processing; Semantics (computer science); Relevance (law); Extractor; Sentence; Artificial intelligence; Information retrieval; Programming language; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001579762,0.001141911,0.0006219302,0.002530685,0.0006433041,0.001701547,0.001208028,0.0009751446,0.02796982],"category_scores_gemma":[0.005381971,0.0004840633,0.0005204605,0.001289883,0.0003136667,0.003778472,0.001665983,0.0008609516,0.01842411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007410785,"about_ca_system_score_gemma":0.001328773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002655663,"about_ca_topic_score_gemma":0.004558238,"domain_scores_codex":[0.9993101,0.0002164902,0.00006844859,0.0001555115,0.0002002591,0.00004913682],"domain_scores_gemma":[0.9984695,0.000547258,0.0001519464,0.0002041245,0.0004786126,0.0001484954],"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":[0.0006626206,0.0001814772,0.00280517,0.001028364,0.00007277862,0.0004018848,0.0008390805,0.001913911,0.01234167,0.007590712,0.6886654,0.2834969],"study_design_scores_gemma":[0.0004055326,0.0002898643,0.006606184,0.0002011201,0.00007509848,0.0006781439,0.000839828,0.04863558,0.02604844,0.03108261,0.885015,0.0001225826],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"other","genre_scores_codex":[0.02752986,0.002522188,0.2774278,0.002747229,0.0005500607,0.001436005,0.1451357,0.487161,0.0554901],"genre_scores_gemma":[0.1521943,0.001352751,0.4931421,0.001516666,0.0003452051,0.001547475,0.2970924,0.009884541,0.04292462],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02796982,"threshold_uncertainty_score":0.09356833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007996082422250674,"score_gpt":0.2213967534979937,"score_spread":0.213400671075743,"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."}}