{"id":"W2537452330","doi":"10.1109/nlpke.2005.1598743","title":"Semantic Context Classification by Means of Fuzzy Set Theory","year":2006,"lang":"en","type":"article","venue":"","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Fuzzy set; Probabilistic logic; Fuzzy logic; Focus (optics); Set (abstract data type); Context (archaeology); Natural language; Comprehension; Representation (politics)","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":[0.001462182,0.0003616509,0.0008990428,0.003314917,0.0009892894,0.002045289,0.001076622,0.0009280767,0.001450475],"category_scores_gemma":[0.003960479,0.0002592794,0.001416401,0.001497444,0.001372431,0.002539675,0.000845411,0.0008900316,0.0003019837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001242924,"about_ca_system_score_gemma":0.0009349648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003542648,"about_ca_topic_score_gemma":0.00300373,"domain_scores_codex":[0.9988891,0.0002626622,0.0001084579,0.0002230184,0.0004421235,0.00007459533],"domain_scores_gemma":[0.9991905,0.000437189,0.00006402165,0.00007153842,0.0002070408,0.00002981391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002316412,0.0001407086,0.00406008,0.0004304204,0.000228177,0.0002875477,0.00111718,0.1268568,0.01512881,0.3266967,0.004213503,0.5206084],"study_design_scores_gemma":[0.0000261341,0.00005654141,0.001259084,0.00006718084,0.0000610502,0.0001188132,0.000171828,0.7645561,0.003035895,0.2264293,0.004171536,0.0000464783],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0156615,0.0005337916,0.9812723,0.000221692,0.00005356833,0.00007021656,0.00007869718,0.0001320743,0.001976136],"genre_scores_gemma":[0.3687154,0.0005435144,0.6286875,0.0001271294,0.0001529865,0.0001990376,0.0002564857,0.00002587273,0.001292171],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003542648,"threshold_uncertainty_score":0.009018064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01437797947590901,"score_gpt":0.214338417010998,"score_spread":0.199960437535089,"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."}}