{"id":"W4282003195","doi":"10.1515/tlr-2022-2089","title":"Corrigendum to: Split noun phrase topicalization in Eshkevarat Gilaki","year":2022,"lang":"en","type":"erratum","venue":"The Linguistic Review","topic":"Linguistics and Cultural Studies","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Topicalization; Noun phrase; Linguistics; Determiner phrase; Noun; Phrase; Computer science; Natural language processing; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005255446,0.0003912178,0.0008451574,0.00005497175,0.0006532784,0.0001846279,0.0006426516,0.00007058943,0.01175466],"category_scores_gemma":[0.003174654,0.0002396073,0.0002079452,0.0001425373,0.000119984,0.00001295257,0.0003148045,0.0007530823,0.0004363042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001550123,"about_ca_system_score_gemma":0.0001050653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006403622,"about_ca_topic_score_gemma":0.001658972,"domain_scores_codex":[0.9978092,0.000167167,0.0007573942,0.000424343,0.0004752067,0.000366674],"domain_scores_gemma":[0.9986739,0.00006454117,0.0002910074,0.0005673861,0.0003291032,0.00007406185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003382784,0.00002761738,0.000002225676,0.001672717,0.00003216285,0.00003006839,0.004473705,5.134523e-7,2.290026e-8,0.2001588,0.7924791,0.001119678],"study_design_scores_gemma":[0.00006483571,0.00005097208,0.00001161857,0.003415326,0.0002429544,0.000002356292,0.0001995865,0.00001774017,3.850049e-8,0.003118278,0.9925314,0.0003448687],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00000224289,0.2246308,0.000003608822,0.00221667,0.09307741,0.001142749,0.0001850403,0.00005887085,0.6786826],"genre_scores_gemma":[0.0003722444,0.1019543,0.00001800166,0.008927342,0.01666624,0.000345125,0.0007454773,0.00005825424,0.870913],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2000523,"threshold_uncertainty_score":0.9891487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07834942964285377,"score_gpt":0.2944070291009011,"score_spread":0.2160575994580473,"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."}}