{"id":"W2137994134","doi":"10.18806/tesl.v29i1.1094","title":"Computer Language Settings and Canadian Spellings","year":2012,"lang":"en","type":"article","venue":"TESL Canada Journal","topic":"Digital Communication and Language","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thinkpath Engineering Services (Canada)","funders":"","keywords":"Spelling; Spell; Set (abstract data type); Computer science; Flagging; Linguistics; Word (group theory); English language; American English; Word processing; First language; Natural language processing; History; Programming language; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002818021,0.00007844133,0.00007884482,0.00007454613,0.0001653245,0.0002817053,0.0005044701,0.00002127772,0.0007406888],"category_scores_gemma":[0.00001921475,0.00007015476,0.00001768419,0.0001148346,0.00001763797,0.0004796655,0.00009201101,0.0002054334,0.00001180457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001513818,"about_ca_system_score_gemma":0.0005180215,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.588597,"about_ca_topic_score_gemma":0.7486968,"domain_scores_codex":[0.9992253,0.00003200194,0.0001268556,0.00007606699,0.000188606,0.0003511908],"domain_scores_gemma":[0.9989662,0.00004393553,0.00005764096,0.0002400191,0.0000358974,0.0006563335],"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":[0.000004025443,0.00006420808,0.004483465,0.0000307253,0.00008658287,0.000590701,0.01151753,0.00002793966,0.0002249627,0.09203088,0.6214592,0.2694797],"study_design_scores_gemma":[0.0001824919,0.00001904254,0.008112815,0.00002118177,0.000004099755,0.001244648,0.0002974476,0.001847421,0.0001145881,0.000105477,0.9878256,0.0002251961],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.378996,0.1718467,0.05413992,0.05603814,0.003676082,0.0005535659,0.00006530279,0.0003810865,0.3343032],"genre_scores_gemma":[0.9046038,0.00001155485,0.01026137,0.08454897,0.0001631415,3.714138e-7,0.000002017533,0.000007293177,0.0004014492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5256078,"threshold_uncertainty_score":0.8110023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006678440575515808,"score_gpt":0.2008757347185108,"score_spread":0.1941972941429949,"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."}}