{"id":"W2187008919","doi":"","title":"CUNY-BLENDER TAC-KBP2010 Entity Linking and Slot Filling System Description","year":2010,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Task (project management); Surprise; Entity linking; Sentence; Baseline (sea); Focus (optics); Space (punctuation); Natural language processing; Artificial intelligence; Information retrieval; Knowledge base; Engineering","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":[0.0006410911,0.00008148992,0.0001122078,0.0000608458,0.0002338224,0.0001091567,0.00025556,0.00006201109,0.000003408756],"category_scores_gemma":[0.00001642956,0.00007587204,0.00001800436,0.000112818,0.0001293239,0.0003630167,0.0001262206,0.0001406852,0.000002887059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005729622,"about_ca_system_score_gemma":0.00002264649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001922269,"about_ca_topic_score_gemma":0.000007443175,"domain_scores_codex":[0.9993545,0.0000399123,0.0001793607,0.0002274566,0.00008977413,0.0001090328],"domain_scores_gemma":[0.9992782,0.0001138341,0.00009107329,0.000393076,0.00007815119,0.00004563613],"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":[0.00000272747,0.000008782849,0.0002915819,0.00007255435,0.000005207468,1.11286e-7,0.0005772347,0.00001635768,0.009839321,0.9685814,0.000001667024,0.0206031],"study_design_scores_gemma":[0.0002778528,0.00002414009,0.001408219,0.00004806169,0.0000379718,0.0000465184,0.001330062,0.02505714,0.0230098,0.9446647,0.003805494,0.0002900857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2134792,0.0002193075,0.7848611,0.00005874539,0.00009937828,0.0001338884,0.000001875608,0.00008235833,0.001064115],"genre_scores_gemma":[0.9806758,0.00002474125,0.01901385,0.00001748964,0.00009038816,0.00005124501,0.000002735608,0.000004881586,0.0001188917],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7671965,"threshold_uncertainty_score":0.3093972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01228903924965486,"score_gpt":0.2272020124718932,"score_spread":0.2149129732222383,"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."}}