{"id":"W2186748791","doi":"","title":"FRDC's Cross-lingual Entity Linking System at TAC 2013","year":2013,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Ranking (information retrieval); Entity linking; Acronym; Information retrieval; Lexicon; Population; Artificial intelligence; Cluster analysis; Natural language processing; Heuristic; Knowledge base; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004339494,0.00008758777,0.0001266058,0.00004544785,0.0002715446,0.0001383492,0.0004623281,0.00005219853,0.00002255988],"category_scores_gemma":[0.00001253021,0.00007888023,0.00002973482,0.0001258268,0.0001607311,0.0003684977,0.0002889543,0.0000670765,0.00006975092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002013978,"about_ca_system_score_gemma":0.00002371433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009739735,"about_ca_topic_score_gemma":0.00000458844,"domain_scores_codex":[0.9992353,0.0000517708,0.0002237027,0.0002369218,0.0001133081,0.0001389887],"domain_scores_gemma":[0.999015,0.0001532848,0.0001108328,0.0005354874,0.0001338808,0.00005153456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000001970207,0.00001004181,0.0007321281,0.00006553206,0.000007181693,1.214194e-7,0.0005977906,0.00009080959,0.0006794881,0.9857642,0.00002313849,0.01202757],"study_design_scores_gemma":[0.0003189247,0.00003180513,0.002578322,0.00004887278,0.00002273922,0.00002780077,0.0008884791,0.01188764,0.03030709,0.9456954,0.007824943,0.000367961],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3292755,0.000497533,0.6676149,0.00008910793,0.0000656378,0.0002346646,0.00000227641,0.0001126814,0.002107593],"genre_scores_gemma":[0.9938369,0.00002089537,0.005061609,0.00002666767,0.00009064107,0.0001478486,0.000003724703,0.00000521216,0.0008065156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6645613,"threshold_uncertainty_score":0.3216642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009312109205930782,"score_gpt":0.2486992344290475,"score_spread":0.2393871252231167,"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."}}