{"id":"W4384636956","doi":"10.1145/3539618.3591887","title":"MMEAD: MS MARCO Entity Annotations and Disambiguations","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Global Water Futures; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Computer science; Python (programming language); Information retrieval; Resource (disambiguation); Named entity; Precision and recall; World Wide Web; Database; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002340981,0.002550427,0.001348964,0.005823371,0.001548993,0.003321953,0.00368862,0.001410715,0.08605714],"category_scores_gemma":[0.01246196,0.001310575,0.001746138,0.006493746,0.00065825,0.006402398,0.005740949,0.002662482,0.08501996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001269591,"about_ca_system_score_gemma":0.002475458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01621891,"about_ca_topic_score_gemma":0.03220299,"domain_scores_codex":[0.997705,0.000338172,0.0002272866,0.0008489157,0.000652178,0.0002285364],"domain_scores_gemma":[0.9962191,0.0007200341,0.0002997692,0.001710817,0.0007115295,0.0003388732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001633205,0.00004597845,0.0009312638,0.0005179589,0.00004928939,0.0001003472,0.0001723864,0.0005762607,0.001545158,0.002477423,0.9654189,0.02800166],"study_design_scores_gemma":[0.0001434815,0.00004133579,0.002136117,0.0001422498,0.00003642024,0.0002035314,0.0001633225,0.006544765,0.006290786,0.005073155,0.9791394,0.00008537785],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.004096905,0.0008948049,0.03523836,0.0005753468,0.0005217417,0.0003088339,0.7289186,0.2094178,0.02002769],"genre_scores_gemma":[0.009831889,0.0002647475,0.06720373,0.000324445,0.0001133342,0.0005443889,0.8974802,0.01592663,0.008310714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08605714,"threshold_uncertainty_score":0.2878898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04196631591292097,"score_gpt":0.2830238869274379,"score_spread":0.2410575710145169,"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."}}