{"id":"W2403380339","doi":"","title":"Cross-Language Entity Linking in Maryland during a Hurricane.","year":2011,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Task (project management); Natural language processing; Machine translation; Artificial intelligence; Transliteration; Software; Measure (data warehouse); Information retrieval; Data mining; Programming language","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":[0.002605533,0.0004434955,0.0003268873,0.001127862,0.003023127,0.001412581,0.000556816,0.001350532,0.006698395],"category_scores_gemma":[0.006086201,0.0002425633,0.0002791534,0.002370893,0.0003940366,0.001913302,0.002385581,0.0009142486,0.004558777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001437879,"about_ca_system_score_gemma":0.00179838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02299319,"about_ca_topic_score_gemma":0.0687058,"domain_scores_codex":[0.9987634,0.0004128589,0.00007548154,0.0003128295,0.0002532991,0.0001821187],"domain_scores_gemma":[0.9974104,0.0008853604,0.000204561,0.0003761409,0.0007998019,0.0003238387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002893758,0.000863973,0.0656947,0.001100572,0.0002509346,0.007928758,0.01993915,0.01058927,0.03203974,0.005440697,0.4329273,0.4203312],"study_design_scores_gemma":[0.0001403763,0.000434005,0.1671319,0.0001522744,0.0001167813,0.001764323,0.0198133,0.01589251,0.05660715,0.004362261,0.7333888,0.0001963178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8353914,0.001573279,0.02714265,0.006984077,0.002831872,0.0005486695,0.03440676,0.01178847,0.07933281],"genre_scores_gemma":[0.8345705,0.0004629117,0.03469713,0.001312401,0.0003211327,0.0003520521,0.06482637,0.001078955,0.06237861],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02299319,"threshold_uncertainty_score":0.04571867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00806790300983532,"score_gpt":0.2635963014563376,"score_spread":0.2555283984465023,"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."}}