{"id":"W2406663039","doi":"","title":"Open Information Extraction to KBP Relations in 3 Hours.","year":2013,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Extractor; Computer science; Precision and recall; Set (abstract data type); Extraction (chemistry); Ontology; Open source; Information extraction; Data mining; Information retrieval; Chromatography; Engineering; Programming language; Process engineering; 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":[0.004901494,0.001464385,0.001090762,0.002219791,0.001597749,0.002632975,0.001644013,0.001091469,0.008962791],"category_scores_gemma":[0.03589176,0.0009807294,0.001162898,0.004773217,0.0006930329,0.003297598,0.003356137,0.00179709,0.006557893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009045502,"about_ca_system_score_gemma":0.002117159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008129802,"about_ca_topic_score_gemma":0.01145888,"domain_scores_codex":[0.9915092,0.002081244,0.000880424,0.002029616,0.003040299,0.0004591915],"domain_scores_gemma":[0.9750196,0.01427779,0.0009069673,0.00477405,0.004542496,0.0004791407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001128835,0.0006043406,0.006040093,0.001164706,0.0002331668,0.0009263472,0.004581966,0.005668531,0.02117704,0.003560385,0.06996295,0.8849517],"study_design_scores_gemma":[0.0007868601,0.001476469,0.03599593,0.0005649684,0.0005643723,0.002016491,0.006342665,0.1310413,0.1596821,0.0419483,0.6192377,0.0003429038],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.289649,0.003770091,0.5466806,0.002587771,0.001191627,0.003087198,0.03189171,0.08640699,0.03473493],"genre_scores_gemma":[0.3697834,0.0007877462,0.5574433,0.0008724928,0.0001627019,0.001412108,0.04807825,0.006012182,0.01544788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008962791,"threshold_uncertainty_score":0.02998346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007067271636542768,"score_gpt":0.2781092129664362,"score_spread":0.2710419413298935,"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."}}