{"id":"W2530152826","doi":"10.1145/2872518.2889397","title":"A Machine learning Filter for Relation Extraction","year":2016,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Polytechnique Montréal","funders":"","keywords":"Computer science; Relation (database); Relationship extraction; Extraction (chemistry); Filter (signal processing); Artificial intelligence; Machine learning; Data mining; Computer vision; Chromatography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005729255,0.001806917,0.001961378,0.006858823,0.002239906,0.002857724,0.002093416,0.002848552,0.004397559],"category_scores_gemma":[0.01177485,0.0006887094,0.002023052,0.00427291,0.0006932433,0.003458564,0.001394041,0.002108897,0.005178005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001567718,"about_ca_system_score_gemma":0.002830342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0110816,"about_ca_topic_score_gemma":0.01230586,"domain_scores_codex":[0.9958271,0.0006115429,0.0005111162,0.001400179,0.001299194,0.000350777],"domain_scores_gemma":[0.9893045,0.005985909,0.0006197849,0.001179757,0.00272489,0.0001852199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000581982,0.0003873927,0.009168535,0.0005231689,0.0002617381,0.0004412078,0.0004733466,0.005708155,0.04742853,0.004559679,0.02869168,0.9017745],"study_design_scores_gemma":[0.0001602479,0.0006646754,0.0177485,0.0002368868,0.0005553979,0.001824128,0.000457255,0.6528383,0.2072719,0.01436914,0.1036779,0.0001957737],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04337211,0.00145278,0.9216021,0.0006713828,0.0003261298,0.000485149,0.003722352,0.0259233,0.002444616],"genre_scores_gemma":[0.1642357,0.0004260016,0.8164495,0.0004856552,0.0002699789,0.0005660616,0.0091518,0.0007988673,0.007616447],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0110816,"threshold_uncertainty_score":0.0302996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03431274194028436,"score_gpt":0.2715054275684631,"score_spread":0.2371926856281787,"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."}}