{"id":"W6981808969","doi":"","title":"Figurative Language in Michif","year":2022,"lang":"en","type":"dissertation","venue":"University Library (University of Saskatchewan)","topic":"Data Analysis and Archiving","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Literal and figurative language; Semantics (computer science); Noun; Lexicalization; Point (geometry); Lexical semantics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001032153,0.0003032635,0.0002882842,0.001388526,0.004105893,0.00181688,0.0006677941,0.0005923019,0.002659672],"category_scores_gemma":[0.001786946,0.0001721429,0.0001575228,0.001334974,0.007212314,0.001817352,0.002717102,0.0007380786,0.000100936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00484187,"about_ca_system_score_gemma":0.001361316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03470513,"about_ca_topic_score_gemma":0.07735229,"domain_scores_codex":[0.9992078,0.0003657999,0.00002852125,0.0001115409,0.0001383498,0.00014813],"domain_scores_gemma":[0.9992889,0.0003653157,0.0001539469,0.0000341571,0.00008697626,0.00007070452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005302796,0.000009935857,0.0119333,0.0001549214,0.000007340355,0.001727348,0.9630409,0.00004403814,0.00625552,0.009027453,0.0003337403,0.007412475],"study_design_scores_gemma":[0.000003838206,0.00003221325,0.05343771,0.00009422295,0.00001111148,0.00108009,0.9169004,0.0001315633,0.0006594275,0.0007462231,0.02688038,0.00002288142],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990813,0.0002600706,0.0004606866,0.000315427,0.000006383525,0.0000175323,0.00004347347,0.000007811972,0.008075669],"genre_scores_gemma":[0.9982362,0.00009532032,0.0002836429,0.0000587168,0.000002339323,0.00001871363,0.00002690595,0.000007555231,0.001270513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03470513,"threshold_uncertainty_score":0.06900626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006043798570391392,"score_gpt":0.212247932624862,"score_spread":0.2062041340544706,"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."}}