{"id":"W2805259170","doi":"","title":"Stanford at TAC KBP 2016: Sealing Pipeline Leaks and Understanding Chinese.","year":2016,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Pipeline (software); Computer science; Petroleum engineering; Engineering; 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":[0.002569292,0.00135637,0.0007144405,0.002506535,0.0027731,0.002841302,0.001588251,0.00140227,0.03236772],"category_scores_gemma":[0.01098131,0.0007886641,0.0005842439,0.002488754,0.001058701,0.009961801,0.003100985,0.002045151,0.01179974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001709277,"about_ca_system_score_gemma":0.003690575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06762685,"about_ca_topic_score_gemma":0.1003587,"domain_scores_codex":[0.9986313,0.0005068986,0.0001020324,0.0002784211,0.0003924824,0.00008885677],"domain_scores_gemma":[0.9968808,0.001193223,0.0001059503,0.0006915586,0.0009121246,0.0002164369],"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":[0.0002625092,0.000127448,0.003587393,0.0005221515,0.00005089676,0.0004951468,0.003141291,0.003073992,0.003577831,0.02159698,0.8303478,0.1332165],"study_design_scores_gemma":[0.0001868134,0.0000864058,0.01079444,0.0004422022,0.000106015,0.0004731136,0.004010038,0.05548503,0.01654152,0.09003159,0.8216603,0.0001824491],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.122338,0.008579005,0.362396,0.03432687,0.004329579,0.0007483465,0.2378146,0.0964513,0.1330162],"genre_scores_gemma":[0.3291695,0.003347529,0.2923169,0.00148796,0.0007001044,0.0005930483,0.2872707,0.009346471,0.07576776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06762685,"threshold_uncertainty_score":0.1344664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01216118300424717,"score_gpt":0.2670602868645314,"score_spread":0.2548991038602842,"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."}}