{"id":"W2404951684","doi":"","title":"ARPANI@BIT_DURG: KBP English Slot-filling Task Challenge.","year":2013,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Vocabulary; Knowledge base; Task (project management); Entity linking; Natural language processing; Context (archaeology); Information retrieval; Robustness (evolution); Artificial intelligence; World Wide Web; Linguistics","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":[0.004613884,0.001118394,0.001178638,0.001218357,0.002413616,0.003380413,0.002032986,0.002749137,0.06794083],"category_scores_gemma":[0.01108268,0.0005396231,0.0005165007,0.001513531,0.0007074478,0.004936844,0.003179659,0.00232099,0.05625841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001246183,"about_ca_system_score_gemma":0.001713899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01541411,"about_ca_topic_score_gemma":0.0194388,"domain_scores_codex":[0.9977881,0.0007632425,0.0001382419,0.0005062195,0.0005707669,0.000233364],"domain_scores_gemma":[0.994456,0.002294616,0.0001155152,0.0007333398,0.001627202,0.0007734275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004513261,0.0001958342,0.0004853742,0.0004659481,0.00001948196,0.0003504276,0.0007883617,0.000505548,0.003608237,0.003553521,0.9371028,0.05247315],"study_design_scores_gemma":[0.0002669746,0.0002397428,0.003706239,0.00012118,0.00001889015,0.0005816981,0.002232261,0.01669475,0.008881514,0.008242853,0.9589188,0.00009513419],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1028521,0.003467762,0.1394508,0.03530819,0.01015742,0.003307373,0.3672786,0.07459811,0.2635798],"genre_scores_gemma":[0.1997871,0.0006555778,0.09699412,0.00340432,0.000956322,0.002969147,0.475256,0.006939867,0.2130375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06794083,"threshold_uncertainty_score":0.2272847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006654326583959316,"score_gpt":0.2356791206221886,"score_spread":0.2290247940382293,"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."}}