{"id":"W4409071198","doi":"10.1075/ml.24035.loo","title":"Long-lag morphological priming and inflectional paradigm size effectsin Estonian and Finnish text reading","year":2024,"lang":"en","type":"article","venue":"The Mental Lexicon","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Concordia University; University of Windsor","funders":"","keywords":"Estonian; Reading (process); Lag; Computer science; Priming (agriculture); Linguistics; Natural language processing; Inflection; Psychology; Artificial intelligence; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002207082,0.0001314806,0.0001154322,0.00003510412,0.0002737106,0.0001316127,0.0001042204,0.0000629576,0.00005305065],"category_scores_gemma":[0.0001617832,0.00008341484,0.00003164221,0.0001025761,0.0003965206,0.000104671,0.0001204425,0.0002459931,0.00002870915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002337365,"about_ca_system_score_gemma":0.000015241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000221667,"about_ca_topic_score_gemma":0.000005915827,"domain_scores_codex":[0.9990478,0.0001474003,0.0001185791,0.0003797348,0.0000983205,0.0002081461],"domain_scores_gemma":[0.9989337,0.0008799605,0.00002626468,0.0001067274,0.000002314567,0.0000510672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004678314,0.00002449991,0.0008343344,0.00002533245,0.000006532363,0.001008858,0.001252354,0.000001041767,0.9897519,0.001455211,0.0002299401,0.005363168],"study_design_scores_gemma":[0.0008054472,0.0007622428,0.01662018,0.0001698434,0.00006363932,0.02152847,0.0001811031,0.0006731292,0.9471855,0.005904139,0.005630716,0.0004756085],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955242,0.000679569,0.000007156185,0.002245517,0.0003497121,0.0002134555,0.000008918458,0.000108596,0.0008629255],"genre_scores_gemma":[0.9972073,0.00007224419,0.00004011326,0.001949201,0.0001220019,0.000009757259,0.000002006518,0.00001148304,0.0005859035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04256646,"threshold_uncertainty_score":0.3401558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02335824390555351,"score_gpt":0.2934311353078894,"score_spread":0.2700728914023359,"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."}}