{"id":"W2114824209","doi":"10.1093/llc/fqs048","title":"Defining dialect regions with interpretations: Advancing the multidimensional scaling approach","year":2013,"lang":"en","type":"article","venue":"Literary and Linguistic Computing","topic":"Linguistic Variation and Morphology","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Multidimensional scaling; Scaling; Computer science; Natural language processing; Linguistics; Artificial intelligence; Mathematics; Philosophy; Machine learning","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006248991,0.0001126089,0.0001423061,0.00006125436,0.001360629,0.00016725,0.0001201445,0.00004962086,0.00004459332],"category_scores_gemma":[0.002966519,0.00007986271,0.00003163263,0.0002002907,0.0002222077,0.00007025366,0.00007145423,0.0002138271,0.00001086678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000240136,"about_ca_system_score_gemma":0.0001135907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000900753,"about_ca_topic_score_gemma":0.00003479018,"domain_scores_codex":[0.9987806,0.0002476609,0.0002439807,0.0002560753,0.0001665131,0.0003051808],"domain_scores_gemma":[0.9977943,0.001655127,0.0001200191,0.0001276736,0.0001948242,0.0001080676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000139262,0.0000477332,0.008067651,0.00002768918,0.00003922681,0.00001235381,0.167586,0.000562786,0.0000126989,0.8067888,0.0002178935,0.01662324],"study_design_scores_gemma":[0.002403378,0.0002436306,0.02967227,0.001261334,0.0002501596,0.0002398978,0.03331789,0.6927938,0.00001935135,0.09125167,0.1470423,0.001504283],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2957864,0.002765493,0.508917,0.003446999,0.002552461,0.001542851,0.000008439592,0.0005590019,0.1844214],"genre_scores_gemma":[0.9354938,0.000007554669,0.0627506,0.0008596468,0.0006298653,0.0000141469,0.00001341059,0.00001019635,0.0002208351],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7155371,"threshold_uncertainty_score":0.9999394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009216159006169917,"score_gpt":0.2661869069054667,"score_spread":0.2569707478992968,"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."}}