{"id":"W2010856309","doi":"10.1155/2012/484580","title":"Learning to Translate: A Statistical and Computational Analysis","year":2012,"lang":"en","type":"article","venue":"Advances in Artificial Intelligence","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Phrase; Zipf's law; Machine translation; Algorithmic learning theory; Artificial intelligence; Inference; Natural language processing; Statistical inference; Machine learning; Point (geometry); Active learning (machine learning); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003719554,0.00009833161,0.0001523141,0.0002695239,0.0000758369,0.00008594646,0.0002914908,0.00003506593,0.00001827185],"category_scores_gemma":[0.0001716643,0.0000935374,0.00002319702,0.001236662,0.00006451374,0.000860519,0.00008876197,0.000180176,0.00002011989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002579485,"about_ca_system_score_gemma":0.00001311457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000213107,"about_ca_topic_score_gemma":0.000060792,"domain_scores_codex":[0.9989542,0.0000649453,0.0002541923,0.0002654071,0.0001811262,0.000280176],"domain_scores_gemma":[0.9993991,0.0002869002,0.00004277035,0.0001170884,0.00004495127,0.000109164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000007618077,0.00003101384,0.00237565,0.000006951393,0.000006865203,0.000004421704,0.002172339,0.01446355,0.0001025437,0.3223441,0.000001391025,0.6584835],"study_design_scores_gemma":[0.00001734678,0.0001142713,0.001162092,0.00003156859,0.0000253418,0.00001020114,0.0002542018,0.3813045,0.008492342,0.6074302,0.0008002719,0.0003576997],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004920957,0.003342644,0.9910884,0.0002483205,0.00007500657,0.00008157729,0.000001667827,0.000128039,0.0001133089],"genre_scores_gemma":[0.5592808,0.00002719659,0.4405814,0.00007979583,0.00001570533,0.000006993951,0.000001627174,0.00000248625,0.000004029372],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6581258,"threshold_uncertainty_score":0.3814345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02152639447210036,"score_gpt":0.3633712533238113,"score_spread":0.3418448588517109,"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."}}