{"id":"W2295712623","doi":"","title":"ualberta at TAC-KBP 2012: English and Cross-Lingual Entity Linking.","year":2012,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Entity linking; Knowledge base; Ambiguity; Information retrieval; Construct (python library); Task (project management); Information extraction; Pace; Natural language processing; Artificial intelligence; Programming language; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.005588348,0.00169825,0.001215831,0.007359455,0.002860438,0.004024334,0.002501699,0.002255007,0.0192695],"category_scores_gemma":[0.02065091,0.001133449,0.0008436313,0.00787737,0.0008017935,0.007188981,0.004781073,0.002309359,0.01433362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002071406,"about_ca_system_score_gemma":0.003683852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08148324,"about_ca_topic_score_gemma":0.08996775,"domain_scores_codex":[0.9958515,0.001251444,0.0005023385,0.0009058768,0.00127773,0.0002112009],"domain_scores_gemma":[0.9878805,0.003958612,0.0006728806,0.002969486,0.003629716,0.0008887608],"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":[0.0003885733,0.0003935842,0.002322161,0.0008680191,0.0001425499,0.0007849924,0.0007951843,0.001319012,0.002705112,0.003389211,0.8633494,0.1235423],"study_design_scores_gemma":[0.000416821,0.00009892959,0.01580928,0.0005222962,0.0001455511,0.0009138499,0.001288503,0.04665036,0.0155421,0.01244688,0.905961,0.0002043379],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.07509057,0.01084336,0.1301828,0.01396978,0.002300497,0.002237879,0.4611645,0.2161789,0.08803166],"genre_scores_gemma":[0.07857665,0.001843233,0.1729103,0.001296109,0.0001922551,0.001328898,0.7123305,0.005117598,0.02640443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08148324,"threshold_uncertainty_score":0.1620179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0449615131411834,"score_gpt":0.3754842903132494,"score_spread":0.330522777172066,"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."}}