{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005010619,0.0001059464,0.0001911164,0.00009073126,0.0003721265,0.0001844594,0.0003813496,0.00006017356,0.0003004405],"category_scores_gemma":[0.0008080063,0.00008403563,0.00003528261,0.0002422136,0.0007199414,0.0007979064,0.000516722,0.00007298891,0.0000700098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008113756,"about_ca_system_score_gemma":0.00001395945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002574696,"about_ca_topic_score_gemma":0.00003703505,"domain_scores_codex":[0.9986556,0.0001767581,0.00038547,0.0002617444,0.0003284242,0.0001920133],"domain_scores_gemma":[0.997428,0.001493712,0.000194089,0.0005841753,0.0001909538,0.0001090453],"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":[0.00004223051,0.00006564095,0.00813046,0.00002437375,0.00001522836,5.557068e-8,0.003497409,0.000001385171,0.00006726971,0.9540112,0.001031162,0.03311357],"study_design_scores_gemma":[0.0001674471,0.00001956761,0.008621433,0.000003754362,0.00002831551,0.000001785276,0.002660838,0.000002337236,0.002374243,0.49829,0.4877085,0.0001218148],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9497654,0.002977674,0.01972047,0.0002127636,0.0002555465,0.0004340525,0.0001473465,0.00005522332,0.02643154],"genre_scores_gemma":[0.9900098,0.0001474828,0.0002081202,0.0001145502,0.000204565,0.00005270129,0.00004076865,0.000005807842,0.009216212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4866773,"threshold_uncertainty_score":0.3426874,"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."}}