{"id":"W4236158887","doi":"10.31234/osf.io/hdftz","title":"Estonian case inflection made simple. A case study in Word and Paradigm morphology with Linear Discriminative Learning.","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Inflection; Computer science; Morpheme; Comprehension; Linguistics; Artificial intelligence; Natural language processing; Discriminative model; Estonian; Analogy; Noun","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004635052,0.0004069204,0.0004867919,0.0005209286,0.0001390999,0.000271305,0.0004889445,0.0002899251,0.000008467046],"category_scores_gemma":[0.0000882487,0.0003131863,0.00003610379,0.0004448568,0.0001042419,0.0003754798,0.001663883,0.001782402,0.00000350615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001629325,"about_ca_system_score_gemma":0.0001726019,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01184577,"about_ca_topic_score_gemma":0.006174854,"domain_scores_codex":[0.9976026,0.0003625837,0.0003448789,0.001118358,0.0002121929,0.0003593195],"domain_scores_gemma":[0.9986492,0.0001689819,0.000276091,0.000714328,0.00009608111,0.00009532559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"case_report","study_design_gemma":"case_report","study_design_scores_codex":[0.0002275831,0.001148727,0.07088223,0.0006335771,0.0002174669,0.6772171,0.0902257,0.004210457,0.0002380649,0.00336688,0.0001117811,0.1515204],"study_design_scores_gemma":[0.004673544,0.006509598,0.005504913,0.0007592089,0.0002884164,0.5652163,0.02186842,0.3586244,0.003922056,0.02821007,0.0001644269,0.004258636],"study_design_candidate":"case_report","study_design_consensus":"case_report","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7895684,0.0003457355,0.2080541,0.0002718036,0.00008564907,0.001069146,0.000001889681,0.000507412,0.0000958233],"genre_scores_gemma":[0.8619421,0.000009648304,0.1375805,0.00007995722,0.00002847226,0.0001170583,0.000005023816,0.00002547169,0.0002117832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.354414,"threshold_uncertainty_score":0.9999321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02496418147281704,"score_gpt":0.3216952408774313,"score_spread":0.2967310594046143,"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."}}