{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002476285,0.0001420241,0.0001514139,0.0003027708,0.001747849,0.002898534,0.00131453,0.00007411771,0.03536261],"category_scores_gemma":[0.0001624564,0.0001061024,0.00004512387,0.0001811355,0.000338818,0.0003176709,0.0006440179,0.0003743954,0.01553727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005270276,"about_ca_system_score_gemma":0.00001531915,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01341518,"about_ca_topic_score_gemma":0.001521449,"domain_scores_codex":[0.9988909,0.00008654274,0.0003544175,0.0001730815,0.0002389429,0.0002561606],"domain_scores_gemma":[0.9989523,0.00002954368,0.000157602,0.0003930885,0.0003309538,0.000136552],"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":[0.000006392274,0.00001090297,4.703951e-8,0.0001484612,0.00003455759,0.000002583176,0.004979271,8.909949e-8,0.000006061619,0.00651788,0.9817044,0.006589328],"study_design_scores_gemma":[0.00005480189,0.0000846266,0.000008110781,0.00007027436,0.00001663794,0.000004910283,0.00188625,0.00001602489,0.00001175014,0.00005122357,0.9976721,0.0001232974],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001990799,0.000163514,0.000002156343,0.0006554851,0.0003145542,0.0001627828,0.9332338,0.0001073945,0.06516127],"genre_scores_gemma":[0.00465573,0.0003798922,0.000002305472,0.0003691376,0.0002764407,5.672507e-8,0.9919406,0.0002701853,0.002105674],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06305559,"threshold_uncertainty_score":0.9995517,"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."}}