{"id":"W33523939","doi":"10.1126/sciadv.abc0671","title":"The problems in a Question Answering system in the academic domain","year":2007,"lang":"en","type":"book-chapter","venue":"Science Advances","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 Excellent Science; Environment Canada","keywords":"Domain (mathematical analysis); Work (physics); Government (linguistics); Question answering; Library science; Political science; Engineering management; Operations research; Computer science; Engineering; Information retrieval; Philosophy; Mechanical engineering; Linguistics; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.01399508,0.0008316173,0.001455845,0.002363517,0.003336377,0.008517441,0.003045357,0.004923008,0.01594245],"category_scores_gemma":[0.05566556,0.0008292837,0.001292116,0.004101063,0.004633829,0.02242173,0.006069566,0.0036571,0.006597818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002640622,"about_ca_system_score_gemma":0.002529647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003891764,"about_ca_topic_score_gemma":0.002405077,"domain_scores_codex":[0.9876187,0.006612736,0.001080264,0.002396843,0.001882345,0.0004090347],"domain_scores_gemma":[0.964605,0.02800095,0.000824645,0.002784133,0.003282482,0.0005028057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003064083,0.0002294151,0.004318551,0.0009934524,0.00008967946,0.0006586888,0.003928585,0.02176192,0.002967614,0.4606431,0.07789893,0.4262038],"study_design_scores_gemma":[0.00003926203,0.00004520713,0.0008411169,0.0001595551,0.00004850841,0.00058739,0.001552578,0.1781308,0.004298158,0.7036897,0.110557,0.00005071261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02014718,0.002627197,0.9138266,0.03247593,0.000436188,0.0002769731,0.00120043,0.004430965,0.02457854],"genre_scores_gemma":[0.24541,0.001951297,0.7164992,0.00563672,0.001210706,0.0006019794,0.003305077,0.00101757,0.02436744],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01594245,"threshold_uncertainty_score":0.07401395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01743265193902064,"score_gpt":0.3079986525131947,"score_spread":0.290566000574174,"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."}}