{"id":"W6948036045","doi":"10.48448/3d5d-se66","title":"Leveraging English Word Embeddings for Semi-Automatic Semantic Classification in Nêhiyawêwin (Plains Cree)","year":2021,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Word (group theory); Cluster analysis; Ontology; Point (geometry); Semantics (computer science); Natural language","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003062861,0.0007376491,0.0009174782,0.003178535,0.000359819,0.0006228405,0.001674445,0.0004555926,0.001253217],"category_scores_gemma":[0.002626078,0.0007841973,0.0001663316,0.00453607,0.001050136,0.0004710916,0.0003319897,0.0006877306,0.0003536293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001453054,"about_ca_system_score_gemma":0.001755059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003473645,"about_ca_topic_score_gemma":0.002313688,"domain_scores_codex":[0.9939184,0.0001620533,0.0009949021,0.00195397,0.001538071,0.001432607],"domain_scores_gemma":[0.9965174,0.00040255,0.0008862292,0.001337683,0.0005522462,0.000303924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008446754,0.002795311,0.009112287,0.005852904,0.0004328383,0.0002487585,0.02518034,0.002905431,0.09577926,0.01826704,0.7518355,0.0875059],"study_design_scores_gemma":[0.002149736,0.00008191868,0.002280842,0.004838705,0.0001488916,0.0000311647,0.00604827,0.8051901,0.0007513051,0.001059914,0.1754438,0.001975372],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07828584,0.004108395,0.0742446,0.00213614,0.01113231,0.01526792,0.000998591,0.008610171,0.8052161],"genre_scores_gemma":[0.7299013,0.0001047465,0.06297076,0.0006393113,0.002134728,0.0006314195,0.001424759,0.002882441,0.1993105],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8022847,"threshold_uncertainty_score":0.9996598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04710761762701766,"score_gpt":0.3238304432794405,"score_spread":0.2767228256524229,"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."}}