{"id":"W3174851372","doi":"10.18653/v1/2021.findings-acl.236","title":"GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction","year":2021,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Higher Education Discipline Innovation Project; Canadian Institutes of Health Research; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Relationship extraction; Joint (building); Computer science; Extraction (chemistry); Information extraction; Relation (database); Information retrieval; Data mining; Artificial intelligence; Engineering; Structural engineering; Chromatography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0001871748,0.00007034157,0.00007243515,0.00008204491,0.000128766,0.0002046891,0.00006133576,0.00006356095,0.00002789584],"category_scores_gemma":[0.00006071506,0.00006903109,0.00002668845,0.0001839377,0.000008348312,0.003798605,0.00005914376,0.0001199437,0.00004128809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004474414,"about_ca_system_score_gemma":0.00002986377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001667398,"about_ca_topic_score_gemma":0.00005498295,"domain_scores_codex":[0.9992183,0.00004035145,0.0002503082,0.0001751312,0.0002068087,0.0001090936],"domain_scores_gemma":[0.999459,0.00002547692,0.0001250935,0.0002377639,0.0001073986,0.00004523935],"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":[0.000007967722,0.00007141277,0.001972474,0.00006068464,0.00001645909,0.00001611282,0.001224691,0.001516867,0.02815704,0.1710376,0.0002144379,0.7957042],"study_design_scores_gemma":[0.000449492,0.00001960499,0.05600041,0.00001445902,0.000009219966,0.0002954807,0.00007489785,0.9152643,0.01129508,0.01063749,0.00577467,0.0001648606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0551636,0.00004175703,0.9406378,0.0007912193,0.000537527,0.00007519036,3.191753e-7,0.0001604587,0.002592117],"genre_scores_gemma":[0.9414186,0.00005359614,0.05818016,0.0001266153,0.00004009707,0.000004504414,0.00001178919,0.00000250297,0.0001621444],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9137475,"threshold_uncertainty_score":0.2815006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02143575687510175,"score_gpt":0.2413861483561028,"score_spread":0.219950391481001,"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."}}