{"id":"W2741988747","doi":"10.18653/v1/w17-2322","title":"Painless Relation Extraction with Kindred","year":2017,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"Compute Canada","keywords":"Computer science; Relation (database); Relationship extraction; Python (programming language); Task (project management); Biomedical text mining; Data science; Data mining; Information retrieval; Text mining; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003549734,0.002275951,0.001363587,0.005017547,0.002358514,0.004592876,0.002675079,0.001479622,0.02717942],"category_scores_gemma":[0.01851454,0.001829925,0.003462711,0.005198837,0.001360103,0.00634872,0.005979188,0.002821258,0.02591759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00102048,"about_ca_system_score_gemma":0.002802568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002648287,"about_ca_topic_score_gemma":0.006838808,"domain_scores_codex":[0.9963422,0.0006111087,0.0004627325,0.000904956,0.00145014,0.0002288511],"domain_scores_gemma":[0.9913315,0.003635582,0.0006228547,0.003196883,0.0009983934,0.0002148674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000487992,0.0001179571,0.004066833,0.003222611,0.0002994646,0.001205907,0.001316097,0.003803163,0.01689623,0.05184015,0.4948526,0.421891],"study_design_scores_gemma":[0.00007846491,0.00006617857,0.002710787,0.0004573498,0.0001344147,0.002132736,0.0003235066,0.04582841,0.03859331,0.1457609,0.7637323,0.0001816638],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006293595,0.001259564,0.6694618,0.001423738,0.0007541292,0.0003192377,0.03780313,0.269115,0.01356976],"genre_scores_gemma":[0.04974384,0.001321053,0.8181841,0.001524828,0.000277997,0.0006808437,0.07800524,0.03453053,0.01573169],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02717942,"threshold_uncertainty_score":0.0909242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02022296523828725,"score_gpt":0.3020006754100079,"score_spread":0.2817777101717207,"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."}}