{"id":"W2805588680","doi":"","title":"Multi-lingual Extraction and Integration of Entities, Relations, Events and Sentiments into ColdStart++ KBs with the SAFT System.","year":2017,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Extraction (chemistry); Natural language processing; Information retrieval; Artificial intelligence; Chromatography; Chemistry","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":[0.001784786,0.00223727,0.001456273,0.005604145,0.001554809,0.003368511,0.001663463,0.001267937,0.0192021],"category_scores_gemma":[0.008293246,0.001384592,0.001857963,0.004343774,0.0005883942,0.005806079,0.004029442,0.002227919,0.02602782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009462162,"about_ca_system_score_gemma":0.00229521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01015407,"about_ca_topic_score_gemma":0.02126738,"domain_scores_codex":[0.9981207,0.0004229596,0.000330078,0.0006162301,0.000407071,0.0001029628],"domain_scores_gemma":[0.9952592,0.001728459,0.0003080349,0.0008152733,0.001696522,0.0001926141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008452745,0.0003311551,0.007801243,0.004816362,0.000603839,0.002126701,0.004025173,0.004142345,0.04326824,0.0146527,0.4654509,0.4519361],"study_design_scores_gemma":[0.000261332,0.0002853522,0.01812024,0.001195093,0.0007651709,0.002222323,0.004149284,0.1168974,0.0454298,0.04007875,0.7701864,0.0004089449],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02674937,0.001507832,0.5306786,0.001335285,0.000823733,0.001240778,0.2729259,0.14281,0.0219285],"genre_scores_gemma":[0.06084169,0.0006358948,0.4895794,0.0003292843,0.0001990595,0.0007464706,0.4315456,0.006563396,0.00955914],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0192021,"threshold_uncertainty_score":0.06423736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01279260957349717,"score_gpt":0.2729504194354924,"score_spread":0.2601578098619953,"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."}}