{"id":"W1702922849","doi":"10.1007/s10590-015-9172-5","title":"Complexity of alignment and decoding problems: restrictions and approximations","year":2015,"lang":"en","type":"article","venue":"Machine Translation","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Decoding methods; Viterbi algorithm; Computer science; Parameterized complexity; Computational complexity theory; Sentence; Sequential decoding; Algorithm; Word (group theory); Polynomial; Iterative Viterbi decoding; List decoding; Theoretical computer science; Mathematics; Artificial intelligence; Block code","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01006818,0.001891587,0.003931131,0.003218443,0.002405343,0.009407007,0.0049419,0.004281409,0.01322206],"category_scores_gemma":[0.1000432,0.002105214,0.003775869,0.005510364,0.004954841,0.02423318,0.006180656,0.01015623,0.001979402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00520528,"about_ca_system_score_gemma":0.004695951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005218279,"about_ca_topic_score_gemma":0.005499694,"domain_scores_codex":[0.9847564,0.006953578,0.000905888,0.002347145,0.003299385,0.001737565],"domain_scores_gemma":[0.7760564,0.2066484,0.003301672,0.008820168,0.003634064,0.001539215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001334746,0.0006514283,0.004574554,0.0012579,0.0002364887,0.0005119529,0.001170884,0.4043621,0.001588876,0.4336743,0.03366266,0.1169739],"study_design_scores_gemma":[0.00006436505,0.00003850956,0.000514509,0.00005751204,0.00003906355,0.0001592699,0.0001480708,0.4812514,0.000597914,0.5154575,0.001634606,0.00003734607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08368531,0.004751166,0.8683016,0.01350511,0.0005752726,0.0002537823,0.002042134,0.001004671,0.02588087],"genre_scores_gemma":[0.6980473,0.004990638,0.269344,0.00190987,0.003389953,0.0008609836,0.004942419,0.001769095,0.01474582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01322206,"threshold_uncertainty_score":0.05324626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08034618598023999,"score_gpt":0.2992751910534103,"score_spread":0.2189290050731703,"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."}}