{"id":"W2185312150","doi":"","title":"SemLinker system for KBP2013: A disambiguation algorithm based on mutual relations of semantic annotations inside a document.","year":2013,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Entity linking; Annotation; Information retrieval; Process (computing); Ranking (information retrieval); Natural language processing; Artificial intelligence; Programming language; Knowledge base","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005437464,0.001988926,0.001497528,0.01222677,0.003082131,0.003987569,0.00263368,0.00239741,0.0186814],"category_scores_gemma":[0.01981914,0.001365302,0.001411915,0.007283324,0.0009575607,0.008147365,0.006238181,0.002184491,0.01680133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001321002,"about_ca_system_score_gemma":0.002886007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003891592,"about_ca_topic_score_gemma":0.00714209,"domain_scores_codex":[0.9955089,0.001238359,0.0005505578,0.001198303,0.001335645,0.000168258],"domain_scores_gemma":[0.9933158,0.002955009,0.0006161999,0.001408227,0.00151826,0.0001865617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006599011,0.0002443721,0.002828824,0.00237625,0.0003999431,0.0007818418,0.001944007,0.006354941,0.01733692,0.04122384,0.2092137,0.7166355],"study_design_scores_gemma":[0.0003460379,0.0002068961,0.003387395,0.0006377249,0.0003382335,0.001349363,0.001186668,0.1436817,0.06101599,0.07690242,0.7105858,0.0003617918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007241247,0.00105995,0.8533456,0.0003989338,0.0003986326,0.0005576756,0.01301183,0.1143423,0.009643856],"genre_scores_gemma":[0.03291826,0.0004183518,0.919867,0.0002311046,0.00009586726,0.0004993087,0.0325087,0.006939243,0.006522195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0186814,"threshold_uncertainty_score":0.06249553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00913259396379512,"score_gpt":0.2455256997393294,"score_spread":0.2363931057755343,"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."}}