{"id":"W2070495689","doi":"10.1075/ml.4.3.03mon","title":"Lexical access of mass and count nouns","year":2009,"lang":"en","type":"article","venue":"The Mental Lexicon","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal; McGill University; Université de Montréal; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Plural; Noun; Computer science; Linguistics; Feature (linguistics); Sentence; Context (archaeology); Natural language processing; Priming (agriculture); Artificial intelligence; Sentence processing; History","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":[0.0006411092,0.0002936479,0.0004539882,0.0003677636,0.0003010377,0.0009712684,0.0002733777,0.000430426,0.004389308],"category_scores_gemma":[0.005452467,0.0002343024,0.0002186582,0.0001994964,0.0008655013,0.001031314,0.0008416525,0.0004141521,0.0004006149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005116925,"about_ca_system_score_gemma":0.0001830104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008263561,"about_ca_topic_score_gemma":0.001040911,"domain_scores_codex":[0.9995713,0.00008511045,0.0000317443,0.0001401078,0.0001130442,0.00005879112],"domain_scores_gemma":[0.9974309,0.001627593,0.0004401338,0.0002112257,0.0001243211,0.0001657326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004179719,0.0001361731,0.0217456,0.0005741199,0.00004015723,0.001194121,0.004367255,0.0003474808,0.9244859,0.004393009,0.0003173401,0.03821917],"study_design_scores_gemma":[0.0003283044,0.002725028,0.816824,0.00009122571,0.000179867,0.005323844,0.002268507,0.007134432,0.1363067,0.02165945,0.007027626,0.0001310486],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913163,0.0001901931,0.001265085,0.00005335411,0.00001085616,0.00001927422,0.00006319519,0.00002307132,0.007058673],"genre_scores_gemma":[0.9979758,0.00006364048,0.000989734,0.00003671045,0.00001582521,0.0000179278,0.00009169186,0.00002292832,0.00078574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004389308,"threshold_uncertainty_score":0.01468372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04057942781945716,"score_gpt":0.3279371635310256,"score_spread":0.2873577357115685,"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."}}