{"id":"W2404631988","doi":"","title":"WebTLab: A cooccurrence-based approach to KBP 2010 Entity-Linking task.","year":2010,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Intuition; Computer science; Task (project management); Information retrieval; Knowledge base; Artificial intelligence; Machine learning; Data mining; Cognitive science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003579149,0.002230236,0.001475477,0.01167508,0.002607587,0.003844966,0.003610913,0.003632192,0.01195611],"category_scores_gemma":[0.02136804,0.0008956766,0.001223549,0.009008932,0.0006799158,0.007253157,0.005693182,0.002803903,0.012418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001144184,"about_ca_system_score_gemma":0.002917637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01109379,"about_ca_topic_score_gemma":0.01542274,"domain_scores_codex":[0.9942821,0.002097994,0.0004774169,0.001291809,0.001597378,0.0002532734],"domain_scores_gemma":[0.9916638,0.004537858,0.0006141495,0.001261897,0.001468483,0.000453792],"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":[0.0009657913,0.001280497,0.004979157,0.002531435,0.0005693302,0.001041064,0.001387016,0.01490172,0.0188986,0.01572374,0.1700695,0.7676521],"study_design_scores_gemma":[0.0003453776,0.00041549,0.01090716,0.0004240777,0.0004548696,0.002548184,0.002269638,0.6134267,0.03692909,0.08737871,0.2445343,0.0003664131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02204172,0.001646987,0.8953854,0.00110695,0.0005606908,0.001876343,0.02707683,0.03644153,0.01386359],"genre_scores_gemma":[0.08075626,0.0005172377,0.8517305,0.0003936769,0.0001595344,0.001656651,0.05702734,0.001347302,0.006411478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01195611,"threshold_uncertainty_score":0.03999722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01283418924114096,"score_gpt":0.2486989356056279,"score_spread":0.235864746364487,"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."}}