{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000133531,0.00003163153,0.00003008062,0.00002785048,0.00004767831,0.00002409957,0.00009689551,0.00002196084,0.00004543965],"category_scores_gemma":[0.00005225733,0.00001971395,0.00002031832,0.00003039834,0.000002544713,0.0004051425,0.00002670946,0.00002754351,0.00003906696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001794131,"about_ca_system_score_gemma":0.000006413901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009097808,"about_ca_topic_score_gemma":0.000003743527,"domain_scores_codex":[0.9996427,0.00001265948,0.00007415716,0.0001345646,0.0000579583,0.00007797246],"domain_scores_gemma":[0.9997172,0.00009132113,0.00002728056,0.0001243698,0.00002265678,0.00001717199],"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":[0.00000618137,0.00001101629,0.002611839,0.000004524809,0.000004522304,5.651049e-7,0.0001562368,0.0005557945,0.01491253,0.2104837,0.0003143836,0.7709387],"study_design_scores_gemma":[0.0003770552,0.00003289862,0.001222371,0.000009701802,0.000001279045,0.00000464699,0.000001960998,0.9510589,0.002568373,0.01047612,0.03417754,0.00006910568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003301149,0.00001191061,0.991392,0.002066249,0.0001478037,0.00006149188,1.538554e-7,0.0001319203,0.002887319],"genre_scores_gemma":[0.8035516,0.000003122668,0.185792,0.0000779542,0.00005514894,0.000009327255,4.56163e-7,0.000002844469,0.01050759],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9505032,"threshold_uncertainty_score":0.08039115,"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."}}