{"id":"W4414249370","doi":"10.1038/s43588-025-00861-2","title":"On the compatibility of generative AI and generative linguistics","year":2025,"lang":"en","type":"review","venue":"Nature Computational Science","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; HEC Montréal","funders":"","keywords":"Generative grammar; Cognitive linguistics; Applied linguistics; Computational linguistics; Language and Communication Technologies; Theoretical linguistics; Quantitative linguistics; Compatibility (geochemistry)","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":[0.001926464,0.0007417582,0.001334419,0.003089225,0.0004773383,0.001957324,0.001447641,0.001819901,0.005526455],"category_scores_gemma":[0.004500469,0.0003682232,0.0003983712,0.00390043,0.002152854,0.004507929,0.001370648,0.002845115,0.002773262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001493099,"about_ca_system_score_gemma":0.00233997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002308325,"about_ca_topic_score_gemma":0.003288619,"domain_scores_codex":[0.9994784,0.0001801483,0.00004742862,0.00008933438,0.0001728768,0.00003187616],"domain_scores_gemma":[0.9959989,0.003218364,0.0001274013,0.0001171668,0.0004571041,0.00008115487],"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":[0.00005457187,0.00004546424,0.0001886114,0.009121332,0.0001090988,0.0001514102,0.0001415539,0.0006055662,0.0004936069,0.1754965,0.05018374,0.7634086],"study_design_scores_gemma":[0.00001485381,0.00003057754,0.0003986069,0.005076227,0.00008816446,0.0004352864,0.00006166827,0.0002473433,0.0002598318,0.06713356,0.9262328,0.00002106476],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00007767876,0.9927879,0.001082191,0.001600015,0.0002996833,0.000002580806,0.00001380237,0.000008641771,0.004127586],"genre_scores_gemma":[0.002438693,0.9929298,0.001246055,0.001338873,0.0007797958,0.00001188464,0.00004142526,0.0000094595,0.001204195],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005526455,"threshold_uncertainty_score":0.01848781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02464684558482763,"score_gpt":0.3833029036527605,"score_spread":0.3586560580679329,"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."}}