{"id":"W4306317500","doi":"10.1145/3511808.3557588","title":"Early Stage Sparse Retrieval with Entity Linking","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","topic":"Topic Modeling","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Information retrieval; Question answering; Boosting (machine learning); Artificial intelligence; Task (project management); Ranking (information retrieval); Natural language processing","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":[0.001715157,0.001233747,0.001600301,0.002927542,0.0007057323,0.00179229,0.002346088,0.001368967,0.006752098],"category_scores_gemma":[0.005650797,0.0005281981,0.001126383,0.003513107,0.0007875248,0.006290796,0.003237185,0.001195758,0.006641953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005510416,"about_ca_system_score_gemma":0.001233929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004306952,"about_ca_topic_score_gemma":0.008328473,"domain_scores_codex":[0.998723,0.000241357,0.0001233918,0.0003031627,0.0004606679,0.0001485064],"domain_scores_gemma":[0.9973288,0.0006717388,0.0001622838,0.00130761,0.0004538195,0.00007582719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000700877,0.0007517256,0.003567568,0.0006168268,0.0002013222,0.0004279318,0.000474772,0.04811241,0.05650311,0.00998266,0.02850808,0.8501527],"study_design_scores_gemma":[0.0002277561,0.000895071,0.002937778,0.00005658355,0.0002338926,0.001318187,0.0003458598,0.837341,0.09633876,0.02852564,0.03163812,0.0001413078],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06778096,0.00123245,0.8983613,0.000309624,0.0001269715,0.0006124886,0.001413614,0.02467937,0.005483081],"genre_scores_gemma":[0.3393449,0.0007476377,0.6327599,0.0005175835,0.0002075076,0.0002970387,0.008613119,0.0008165161,0.01669584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006752098,"threshold_uncertainty_score":0.02258801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05496141773196674,"score_gpt":0.2754014181884152,"score_spread":0.2204400004564485,"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."}}