{"id":"W4301603763","doi":"10.16995/glossa.8437","title":"Classifiers can be for numerals <em>or</em> nouns: Two strategies for numeral modification","year":2022,"lang":"en","type":"article","venue":"Glossa a journal of general linguistics","topic":"Syntax, Semantics, Linguistic Variation","field":"Arts and Humanities","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Science Foundation","keywords":"Numeral system; Classifier (UML); Noun; Artificial intelligence; Computer science; Natural language processing; Linguistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005679066,0.0005027257,0.0003985828,0.001326912,0.001446665,0.003496736,0.001159799,0.0007578123,0.007098844],"category_scores_gemma":[0.01823105,0.0004274747,0.0005208984,0.001185293,0.007048724,0.009482667,0.002294655,0.001768447,0.001243986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002238167,"about_ca_system_score_gemma":0.001208272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003123075,"about_ca_topic_score_gemma":0.005084654,"domain_scores_codex":[0.995915,0.00127319,0.000272117,0.001147178,0.001116483,0.0002760398],"domain_scores_gemma":[0.9839845,0.007190133,0.002743409,0.004121758,0.001737265,0.0002228779],"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":[0.0002573617,0.00005357328,0.06001147,0.000206463,0.00005737453,0.0003654567,0.0329659,0.0008693547,0.006941555,0.7467169,0.003640116,0.1479145],"study_design_scores_gemma":[0.00008737549,0.0002206292,0.1226511,0.0005318703,0.0002241733,0.002024058,0.02634134,0.02055077,0.02185271,0.6014797,0.2037264,0.0003097526],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5319405,0.0007091747,0.1809073,0.003756661,0.0002328105,0.0002202421,0.0005176159,0.0006076033,0.2811081],"genre_scores_gemma":[0.9740559,0.0001229604,0.02006851,0.0002354062,0.00003853051,0.00007674591,0.0001373501,0.0002046411,0.005059773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007098844,"threshold_uncertainty_score":0.03003418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06334217467914365,"score_gpt":0.2974163104404324,"score_spread":0.2340741357612887,"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."}}