{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004764797,0.0000989525,0.0001234992,0.00007253304,0.0002116784,0.00004782732,0.0002806047,0.0000450168,0.000006026538],"category_scores_gemma":[0.00004906644,0.00006098054,0.0000161383,0.0001837605,0.0002750091,0.0003481826,0.0002460471,0.00004862678,0.000001674428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003025296,"about_ca_system_score_gemma":0.00002294255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004942028,"about_ca_topic_score_gemma":0.000004340124,"domain_scores_codex":[0.9993931,0.00003567359,0.0001555177,0.0002048039,0.00009074599,0.0001201783],"domain_scores_gemma":[0.9991747,0.0003314422,0.0001012239,0.0002921299,0.00005496873,0.00004557948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000166413,0.000006045648,0.0001497666,0.00002909585,0.000004383877,2.310926e-7,0.0003181928,1.53361e-7,0.005730309,0.949045,0.00007099213,0.0446292],"study_design_scores_gemma":[0.0001182526,0.00002030708,0.00002614404,0.00002811929,0.00000589175,0.000009800375,0.0001230892,0.00005463502,0.0266127,0.9719651,0.0009359989,0.00009992963],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006486923,0.003221267,0.9883825,0.0007125707,0.00001467278,0.0001376893,0.000008125685,0.0001763243,0.0008598817],"genre_scores_gemma":[0.977127,0.0002647888,0.02160815,0.00004712342,0.00003560766,0.00003123425,0.000002207882,0.000006537254,0.0008773142],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9706401,"threshold_uncertainty_score":0.2486714,"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."}}