{"id":"W2805138799","doi":"","title":"The IBM Systems for Entity Discovery and Linking at TAC 2017.","year":2017,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"IBM; Computer science; Operating system; Database; Nanotechnology; Materials science","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.005034649,0.00006192165,0.0001300477,0.0000274781,0.002001146,0.001019371,0.0006532338,0.00002610953,0.000002752938],"category_scores_gemma":[0.0005987335,0.00003695487,0.00002901237,0.00003810922,0.0007564854,0.0005208771,0.0004528782,0.00003026937,0.000006681649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000505303,"about_ca_system_score_gemma":0.00001161964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003644363,"about_ca_topic_score_gemma":0.00005766416,"domain_scores_codex":[0.9991615,0.00009378365,0.0002521444,0.0002010326,0.0001956139,0.00009592145],"domain_scores_gemma":[0.9967424,0.001908637,0.0003120844,0.0009250892,0.00008276413,0.00002904818],"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":[0.00004235811,0.000009304278,0.0003063013,0.00002826228,0.00001311805,4.2918e-8,0.0001774985,0.000002173557,0.00004370406,0.9796886,0.001283827,0.01840484],"study_design_scores_gemma":[0.00008610619,0.00001061756,0.001602837,0.000005225499,0.00001692801,9.051301e-7,0.001842585,0.00002836416,0.00021329,0.68678,0.3093691,0.00004402534],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1949443,0.009949951,0.765132,0.003821607,0.0006221759,0.002527982,0.0006993928,0.00004731559,0.02225532],"genre_scores_gemma":[0.9875858,0.0007093453,0.00005403092,0.00002583869,0.00007309322,0.000203924,0.00001327944,0.00000310086,0.01133162],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7926415,"threshold_uncertainty_score":0.9992981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07877614834923444,"score_gpt":0.3867391988004679,"score_spread":0.3079630504512334,"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."}}