{"id":"W2154889602","doi":"10.1162/coli.2010.36.1.36101","title":"A Graph-Theoretic Framework for Semantic Distance","year":2010,"lang":"en","type":"article","venue":"Computational Linguistics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Computer science; Semantic similarity; Natural language processing; Artificial intelligence; Coherence (philosophical gambling strategy); Information retrieval; Set (abstract data type); Semantic computing; Semantic Web; Mathematics","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":[0.000121887,0.00009680245,0.00009490502,0.00002348304,0.0001005786,0.00002160792,0.0001518649,0.0001625907,0.000009463297],"category_scores_gemma":[0.006009991,0.00009080475,0.0000670043,0.00005706305,0.0002439068,2.471651e-7,0.00003684827,0.000134147,0.00000621624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002629011,"about_ca_system_score_gemma":0.00006144204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.396849e-7,"about_ca_topic_score_gemma":0.000003993587,"domain_scores_codex":[0.9993603,0.00001152294,0.0001474408,0.0002172727,0.0001024871,0.0001609578],"domain_scores_gemma":[0.9992435,0.000217436,0.00005711381,0.0001561692,0.000259612,0.0000661581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000077827,0.00009179907,0.001626577,0.0000740722,0.00004722706,0.000003683657,0.00005383797,0.0007252026,0.001575549,0.9848264,0.005076896,0.005820945],"study_design_scores_gemma":[0.0003208071,0.0001770184,0.0013471,0.00002380237,0.00002362273,0.000007412606,0.00001858147,0.005926474,0.0009776072,0.7469691,0.2439949,0.0002135745],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03195066,0.0002029458,0.9654597,0.0001616931,0.001441937,0.0001058956,0.00006028356,0.00003208904,0.0005847709],"genre_scores_gemma":[0.700623,0.000006194758,0.2979518,0.0002146649,0.0009579702,0.00001274653,0.0001506206,0.00001130404,0.00007163871],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6686724,"threshold_uncertainty_score":0.7194955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01206336355466009,"score_gpt":0.3012501952456227,"score_spread":0.2891868316909626,"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."}}