{"id":"W6948922418","doi":"10.5281/zenodo.10884606","title":"THE LEXICAL CHARACTERISTICS OF CANADIAN FRENCH INFLUENCED BY LANGUAGE INTERFERENCE","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Libraries and Information Services","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Interference (communication); Feature (linguistics)","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":[0.0006627085,0.002002467,0.00105133,0.007448785,0.002712794,0.003000821,0.001620714,0.001384833,0.01933365],"category_scores_gemma":[0.004768601,0.0004177174,0.001237778,0.01116115,0.0007838334,0.000895509,0.001237241,0.001029697,0.01377943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00723451,"about_ca_system_score_gemma":0.0119131,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9150465,"about_ca_topic_score_gemma":0.9370712,"domain_scores_codex":[0.9987769,0.0001193599,0.00006826151,0.0003144752,0.000335501,0.0003855323],"domain_scores_gemma":[0.9968179,0.0008145401,0.0001730916,0.0002518765,0.001626942,0.0003155825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001068323,0.0001253462,0.02971715,0.001276937,0.000225463,0.000433511,0.000890448,0.001239586,0.002687149,0.002062298,0.9323184,0.0279553],"study_design_scores_gemma":[0.0002686322,0.0000708421,0.317087,0.0005125564,0.0002984939,0.0005968289,0.002375307,0.003138905,0.002857769,0.0008714127,0.6717222,0.0002001227],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0375653,0.001213879,0.0001624259,0.0002593501,0.00007680895,0.00003378719,0.9539688,0.0005077404,0.006211885],"genre_scores_gemma":[0.03152904,0.0003224151,0.0003964819,0.0000670843,0.00001972431,0.00008041629,0.9631587,0.0001362368,0.004289857],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08495355,"threshold_uncertainty_score":0.1709077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02618554468321643,"score_gpt":0.2188374558704596,"score_spread":0.1926519111872431,"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."}}