{"id":"W2077054502","doi":"10.1145/2396761.2398676","title":"Improving the performance of the reinforcement learning model for answering complex questions","year":2012,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Reinforcement learning; Automatic summarization; Computer science; Question answering; Artificial intelligence; Task (project management); Set (abstract data type); Sentence; Natural language processing; Process (computing); Reinforcement; Feature (linguistics); Machine learning","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":[0.0004107406,0.00005894303,0.00005914169,0.00001649127,0.0002967746,0.00002981395,0.0005549489,0.00001674944,0.000003490475],"category_scores_gemma":[0.00003193165,0.00003244268,0.00004511654,0.00007710463,0.0000224502,0.0003399033,0.0002877806,0.00008841253,0.000001258582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002428364,"about_ca_system_score_gemma":0.00003009908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004288255,"about_ca_topic_score_gemma":0.000003849696,"domain_scores_codex":[0.99938,0.00001620148,0.0001631914,0.00009266789,0.0001365798,0.0002113532],"domain_scores_gemma":[0.9994206,0.00005024558,0.00007950224,0.0003784227,0.00004626926,0.0000249458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[7.495543e-7,0.000004672669,0.001153983,0.00001702047,0.000003136117,2.818699e-9,0.001174112,0.9241246,0.003253066,0.06100187,0.00001723342,0.009249561],"study_design_scores_gemma":[0.00006484271,0.0000119774,0.0006190481,0.00001007863,0.000003262393,0.000001101631,0.00003767355,0.9970853,0.00183629,0.00006262391,0.0002200114,0.0000477291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09194391,0.00001979022,0.9066687,0.0002961555,0.0001202945,0.0001758149,1.125253e-7,0.00004267229,0.0007325715],"genre_scores_gemma":[0.9162849,0.00000211277,0.08263391,0.0001355527,0.00003955924,0.0000208912,1.990638e-7,0.000003623084,0.0008793019],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8243409,"threshold_uncertainty_score":0.228258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04739034023367434,"score_gpt":0.2605318500398405,"score_spread":0.2131415098061661,"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."}}