{"id":"W2807695162","doi":"","title":"Adept Automatic Knowledge Discovery System for Cold Start Knowledge Base Population.","year":2017,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Knowledge base; Computer science; Knowledge extraction; Population; Base (topology); Data science; Artificial intelligence; Mathematics; Medicine","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":[0.001633278,0.0007237362,0.001256935,0.004829914,0.001351033,0.001963945,0.003100528,0.001501664,0.007034208],"category_scores_gemma":[0.0051688,0.0004123004,0.00108244,0.002543969,0.0003677119,0.002441293,0.001760086,0.00127988,0.004101501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008940495,"about_ca_system_score_gemma":0.002410607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003754493,"about_ca_topic_score_gemma":0.00823234,"domain_scores_codex":[0.9988499,0.0001985094,0.0001223065,0.0003134975,0.0004220296,0.00009379065],"domain_scores_gemma":[0.9976528,0.0008338968,0.0001126771,0.0002996595,0.0009159765,0.0001850236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006445352,0.0007720181,0.005949558,0.0006339169,0.0003429492,0.0006294265,0.0002645431,0.009416939,0.01740956,0.006754964,0.06084508,0.8963365],"study_design_scores_gemma":[0.0005720733,0.0008501196,0.006389999,0.0002694011,0.0009162456,0.00248506,0.0008486332,0.7834184,0.06649093,0.03221228,0.1053665,0.000180346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07663912,0.002920603,0.8549996,0.0007554218,0.0006214431,0.001455219,0.008126989,0.03932918,0.01515243],"genre_scores_gemma":[0.1573119,0.0005971421,0.8089971,0.0005923703,0.0001628021,0.0008744595,0.016406,0.0005270661,0.01453118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007034208,"threshold_uncertainty_score":0.02353179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323208847957848,"score_gpt":0.2903980280086086,"score_spread":0.2771659395290302,"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."}}