{"id":"W2056258147","doi":"10.1142/s0218213005002028","title":"APPLICATIVE AND COMBINATORY CATEGORIAL GRAMMAR AND SUBORDINATE CONSTRUCTIONS IN FRENCH","year":2005,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence Tools","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Combinatory categorial grammar; Categorial grammar; Combinatory logic; Computer science; Cognitive grammar; Interrogative; Linguistics; Link grammar; Generalization; Grammar; Mildly context-sensitive grammar formalism; Emergent grammar; Head-driven phrase structure grammar; Generative grammar; Natural language processing; Artificial intelligence; Cognition; Programming language; Mathematics; Psychology","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":[0.001198139,0.0005102385,0.0003551057,0.002136652,0.001007145,0.00305053,0.0003629892,0.0006068829,0.001585565],"category_scores_gemma":[0.001235827,0.0002358003,0.0009443491,0.00147168,0.005195691,0.001839197,0.0008164599,0.0005834208,0.0002018186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002997367,"about_ca_system_score_gemma":0.001212896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02346492,"about_ca_topic_score_gemma":0.01429953,"domain_scores_codex":[0.9988561,0.0005403673,0.00005249995,0.0001924113,0.0001917188,0.0001669346],"domain_scores_gemma":[0.9991671,0.0003774362,0.0001257048,0.0001068157,0.0001713778,0.00005163489],"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":[0.00002349976,0.00000946311,0.004421185,0.00003929824,0.00001479362,0.0004174799,0.003272197,0.00263454,0.001974402,0.9709505,0.0003826696,0.01585977],"study_design_scores_gemma":[0.00001737584,0.00005949861,0.02487675,0.00005914379,0.00007403104,0.001723295,0.003069386,0.02203016,0.00276481,0.8915614,0.05367824,0.0000858536],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6199811,0.004247868,0.2829101,0.001649641,0.00008680257,0.00009650946,0.0003661184,0.0007617059,0.08990016],"genre_scores_gemma":[0.9787208,0.0004024846,0.01713314,0.00009060028,0.00002844211,0.0000282067,0.0001092283,0.00004313878,0.003443891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02346492,"threshold_uncertainty_score":0.04665667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02482966883140848,"score_gpt":0.3139610256161994,"score_spread":0.2891313567847909,"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."}}