{"id":"W2277746464","doi":"","title":"Non-standard transcription of Innu: An essential ingredient of its documentation","year":2015,"lang":"en","type":"article","venue":"Americanae (AECID Library)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Documentation; Transcription (linguistics); Technical documentation; Linguistics; History; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0001369539,0.0001247379,0.0002376314,0.0002163038,0.00002636105,0.00004080554,0.0007166342,0.00004702164,0.00002955721],"category_scores_gemma":[0.00002119584,0.0001156513,0.00005178613,0.0007875162,0.0001063931,0.003709904,0.0001168419,0.00009383355,0.000002224123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002803473,"about_ca_system_score_gemma":0.0001710951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00111639,"about_ca_topic_score_gemma":0.000004376541,"domain_scores_codex":[0.998747,0.00006193914,0.0003355658,0.0002688338,0.000420087,0.0001665983],"domain_scores_gemma":[0.9990818,0.0000154782,0.0003033555,0.0003467815,0.0001324853,0.0001201227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005640754,0.0006358338,0.03428895,0.0003772165,0.0001376924,0.00006812524,0.02411414,0.0001832121,0.4236364,0.08186693,0.01776689,0.4163605],"study_design_scores_gemma":[0.0005215373,0.001029879,0.003281643,0.00007838633,0.00001354259,0.000004595846,0.0002411701,0.002832654,0.9847471,0.006739023,0.0002786012,0.0002319151],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6075485,0.000793942,0.3896716,0.0006014886,0.0002930559,0.0002349353,0.00001378494,0.0003302503,0.0005124776],"genre_scores_gemma":[0.7826325,0.0000290196,0.2170888,0.0001321528,0.00003742501,0.000007666427,0.00001676974,0.00001156418,0.00004410906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5611106,"threshold_uncertainty_score":0.4716122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0109619178205593,"score_gpt":0.2660928602502713,"score_spread":0.2551309424297121,"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."}}