{"id":"W4313343470","doi":"10.1007/978-3-662-66544-2_3","title":"Named Entity Recognition on CORD-19 Bio-Medical Dataset with Tolerance Rough Sets","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Winnipeg; University of Alberta","funders":"","keywords":"Computer science; Named-entity recognition; Task (project management); Artificial intelligence; Natural language processing; Process (computing)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001222428,0.0005352612,0.0005152759,0.0005745167,0.0004362284,0.0004334048,0.004210044,0.0002838219,0.0003155285],"category_scores_gemma":[0.0001749458,0.0004737659,0.00008112508,0.0006535628,0.0005359484,0.0007634042,0.001815806,0.001556277,0.00008002445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004723384,"about_ca_system_score_gemma":0.001039515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001232644,"about_ca_topic_score_gemma":0.0002047563,"domain_scores_codex":[0.9939182,0.00008769446,0.0005262793,0.002213311,0.002566778,0.0006877121],"domain_scores_gemma":[0.9967662,0.0004960568,0.0003144004,0.001968016,0.0001299621,0.000325337],"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":[0.00004263813,0.00007349116,0.00005878869,0.00005288747,0.00001352613,0.0005605945,0.0003256131,0.0257127,0.000004776721,0.004324822,0.0003784824,0.9684517],"study_design_scores_gemma":[0.0008039597,0.0007441984,0.00007185611,0.000683109,0.00001272275,0.0003763537,2.526268e-7,0.9260402,0.0001481968,0.04169927,0.02827953,0.001140379],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005005444,0.0001362454,0.9930143,0.002126511,0.002150726,0.0004611673,0.0002071855,0.0001931104,0.001210168],"genre_scores_gemma":[0.1244279,0.0002121896,0.8424107,0.02991729,0.001378243,0.0001007121,0.001195189,0.0001186453,0.0002392033],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9673113,"threshold_uncertainty_score":0.9997714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03383488766186366,"score_gpt":0.2720463831629888,"score_spread":0.2382114955011252,"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."}}