{"id":"W1880858962","doi":"10.1007/3-540-45526-4_5","title":"Metric Lexical Analysis","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"semigroups and automata theory","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; Queen's University","funders":"","keywords":"Linguistics; Computer science; Mathematics; Philosophy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001177445,0.0005498265,0.0008016829,0.003477,0.0002370808,0.0007102902,0.00488481,0.0003607713,0.0001317074],"category_scores_gemma":[0.00009602646,0.000476067,0.0003921408,0.005093535,0.0005960827,0.0005836416,0.00161706,0.0007802922,0.0001246513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002790031,"about_ca_system_score_gemma":0.0003009293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000347764,"about_ca_topic_score_gemma":0.00004839496,"domain_scores_codex":[0.9955044,0.00004517693,0.0005637165,0.00185028,0.001253685,0.0007827787],"domain_scores_gemma":[0.9966089,0.000658389,0.0002692341,0.002046114,0.0001704291,0.0002469839],"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":[0.00000275988,0.00003709151,0.0001558321,0.00001144229,0.00009640253,0.0002399817,0.0002067131,0.02709989,0.00002216335,0.1058492,0.00003549755,0.866243],"study_design_scores_gemma":[0.000155808,0.00009631777,0.000424735,0.00007124893,0.00007589892,0.00006976071,7.412014e-8,0.7063438,0.0002572023,0.2882593,0.003530207,0.0007156188],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00007045348,0.0008143497,0.9831056,0.0005425959,0.0009953824,0.0001744384,0.000003937126,0.000277048,0.01401622],"genre_scores_gemma":[0.4439034,0.0002068151,0.548317,0.004327646,0.001036939,0.00001206619,0.0000171793,0.00006572682,0.002113215],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8655274,"threshold_uncertainty_score":0.9997691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01520070267838556,"score_gpt":0.2470185585541154,"score_spread":0.2318178558757299,"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."}}