{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":21,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":21,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"0c76d1bcefc8","filters":{"venue":"Computer Speech & Language"}},"results":[{"id":"W2597891111","doi":"10.1016/j.csl.2017.01.014","title":"On integrating a language model into neural machine translation","year":2017,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal","funders":"","keywords":"Machine translation; Computer science; Artificial intelligence; Phrase; Natural language processing; Translation (biology); BLEU; Language model; Baseline (sea); Example-based machine translation; Turkish; Machine learning; Linguistics","retraction":null,"screen_n_in":null,"score":{"opus":0.01622676902070584,"gpt":0.3056925943937503,"spread":0.2894658253730444,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003467765,0.0003438322,0.0003028657,0.0002001906,0.0005193943,0.001087925,0.002866655,0.0001380374,0.00001731672],"category_scores_gemma":[0.00009582692,0.0002844595,0.0001408179,0.0001436488,0.00007586322,0.00111774,0.0006095375,0.0005497884,0.00003158267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007442103,"about_ca_system_score_gemma":0.00004194971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003420242,"about_ca_topic_score_gemma":0.0001782415,"domain_scores_codex":[0.9981158,0.00007171216,0.0003077427,0.000672801,0.0004254754,0.0004064203],"domain_scores_gemma":[0.9976716,0.00007851947,0.0002650729,0.001784705,0.0000710696,0.0001290162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001172026,0.00004993957,0.00002480923,0.00002926339,0.00001351145,0.0003124067,0.008423117,0.0001385726,0.005458472,0.009693504,0.0003425785,0.9755021],"study_design_scores_gemma":[0.0003801496,0.0001087894,0.0000546902,0.00009052255,0.000008830203,0.00005263875,0.00003255042,0.9754,0.01612655,0.007355284,0.00003077625,0.0003592676],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03297767,0.002511402,0.9596383,0.001367731,0.0003723477,0.0002657454,0.000006773487,0.001197676,0.00166236],"genre_scores_gemma":[0.5062477,0.00000200406,0.4928146,0.0006334964,0.0001481502,0.000007986948,0.00001076548,0.00001989694,0.0001154352],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9752614,"threshold_uncertainty_score":0.9999608,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3136363192","doi":"10.1016/j.csl.2022.101429","title":"On the effect of dropping layers of pre-trained transformer models","year":2022,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Topic Modeling","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"","keywords":"Transformer; Computer science; Limiting; Sentence; Artificial intelligence; Paraphrase; Machine learning; Natural language processing; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01020937020698539,"gpt":0.2361752071494904,"spread":0.225965836942505,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006593522,0.0001446029,0.0002542182,0.0001089619,0.0001013203,0.00002545134,0.001276457,0.00002805312,0.00006145281],"category_scores_gemma":[0.0000102901,0.0001032252,0.0001501285,0.0002885147,0.00003259606,0.000136569,0.0002630788,0.0002241165,0.000001923246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003700953,"about_ca_system_score_gemma":0.00002978869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006363192,"about_ca_topic_score_gemma":0.000002771019,"domain_scores_codex":[0.9985176,0.0002245439,0.0002811317,0.0002972712,0.0004559568,0.000223472],"domain_scores_gemma":[0.9987996,0.0002898316,0.0001141221,0.0007357628,0.00002209999,0.00003863245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001081229,0.0001395528,0.00008455669,0.0001957385,0.0001485282,0.000101798,0.03234697,0.2837707,0.01595713,0.0798124,0.0006810533,0.5866534],"study_design_scores_gemma":[0.0005144819,0.0005873109,0.00008251704,0.00003212707,0.00001028042,0.00002077758,0.00006993425,0.9647587,0.03249719,0.001220454,0.00006964427,0.000136568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4287985,0.00007406495,0.5692059,0.0002471228,0.0002038043,0.0002393578,0.000004269283,0.00005376849,0.001173182],"genre_scores_gemma":[0.9818287,0.00000118989,0.01775303,0.0002619892,0.00004996991,0.00002064413,0.00000200001,0.00001129785,0.00007116513],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.680988,"threshold_uncertainty_score":0.4209401,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1995193848","doi":"10.1016/j.csl.2012.11.001","title":"Adjusting dysarthric speech signals to be more intelligible","year":2012,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Phonetics and Phonology Research","field":"Psychology","cited_by":54,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Speech recognition; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.0539166861060433,"gpt":0.3787507243040557,"spread":0.3248340381980124,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006265371,0.0002537344,0.0003292156,0.0002983625,0.0001035724,0.00006640716,0.0006279722,0.0001913257,0.00461522],"category_scores_gemma":[0.00004337209,0.000238013,0.0001041042,0.0005276905,0.00007747146,0.00009516943,0.0004752721,0.000479856,0.00366124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004431184,"about_ca_system_score_gemma":0.00002567752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003257788,"about_ca_topic_score_gemma":0.00003831826,"domain_scores_codex":[0.9977192,0.0001625255,0.0003107241,0.00044108,0.000286246,0.001080206],"domain_scores_gemma":[0.9984766,0.0001973336,0.00006768,0.0007557984,0.00007855374,0.0004240307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009468981,0.0006126629,0.007229139,0.00004519703,0.0002080998,0.0008269085,0.09883554,0.00003335566,0.01915016,0.000614101,0.1586616,0.7136885],"study_design_scores_gemma":[0.005139169,0.002746394,0.3244456,0.000299863,0.0002265305,0.003727924,0.01646413,0.003299889,0.2599905,0.0007596874,0.3785013,0.004399025],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9697102,0.00237452,0.008033556,0.001306188,0.001889235,0.0004154081,0.00002456241,0.000201613,0.01604469],"genre_scores_gemma":[0.967781,0.00001111632,0.01918837,0.004859021,0.002074449,0.00003606514,0.0000344752,0.00006557187,0.005949917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7092895,"threshold_uncertainty_score":0.9971145,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4384936706","doi":"10.1016/j.csl.2023.101538","title":"Trends and developments in automatic speech recognition research","year":2023,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Computer science; Discriminative model; Exploit; Variety (cybernetics); Artificial intelligence; Speech recognition; Natural language; Deep learning; SIGNAL (programming language); Machine learning; Natural language processing","retraction":null,"screen_n_in":null,"score":{"opus":0.07391361334822268,"gpt":0.3410425992855118,"spread":0.2671289859372892,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001597546,0.0001894074,0.0002513594,0.001814938,0.0001452407,0.0003407403,0.000667863,0.0001136506,0.0001934733],"category_scores_gemma":[0.00009735337,0.0001857601,0.00005272865,0.003036888,0.00006037616,0.0004656507,0.0005996901,0.0003092589,0.00140319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008988334,"about_ca_system_score_gemma":0.00005847699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006987071,"about_ca_topic_score_gemma":0.000104404,"domain_scores_codex":[0.9975442,0.000295027,0.0003502129,0.0006142408,0.0005891151,0.0006072287],"domain_scores_gemma":[0.9989012,0.0003253058,0.00005623363,0.0004597447,0.00009916261,0.0001583058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0000019703,0.00004629283,0.0003987703,0.00002336029,0.00001009215,0.0008334327,0.001701903,2.332696e-7,0.0002708758,0.0001028719,0.003023831,0.9935864],"study_design_scores_gemma":[0.00389634,0.0004025449,0.5443045,0.0008895153,0.00001885906,0.001302135,0.00144069,0.3763992,0.04701044,0.01002942,0.01220805,0.002098257],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9755607,0.0001297386,0.006037708,0.00176175,0.0006576153,0.0003256246,0.000009703552,0.001150168,0.01436701],"genre_scores_gemma":[0.292143,0.0001015034,0.7042043,0.00066983,0.0003084049,0.00008513875,0.00008256574,0.00004857274,0.002356677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9914881,"threshold_uncertainty_score":0.9993743,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2083400649","doi":"10.1016/j.csl.2013.04.009","title":"Prior and contextual emotion of words in sentential context","year":2013,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Sentence; Context (archaeology); Natural language processing; Set (abstract data type); Word (group theory); Feature (linguistics); Focus (optics); Task (project management); Similarity (geometry); Artificial intelligence; Contrast (vision); Affect (linguistics); Function (biology); Linguistics; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.009483769740171108,"gpt":0.2387547357675971,"spread":0.229270966027426,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001794547,0.0001160267,0.0002377926,0.0001866022,0.00003079476,0.0001370429,0.0003374516,0.00004537707,0.0001270867],"category_scores_gemma":[0.000009128166,0.0001040392,0.00006530175,0.0002504925,0.00004017049,0.000370265,0.0002753769,0.00008794886,0.00003907894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001460862,"about_ca_system_score_gemma":0.00001339529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003349809,"about_ca_topic_score_gemma":0.00004716779,"domain_scores_codex":[0.9989655,0.00006235347,0.000302051,0.0002903152,0.0001901134,0.0001896945],"domain_scores_gemma":[0.9994333,0.00004577509,0.0001133943,0.0002896129,0.00005595769,0.00006197512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003111695,0.00007702397,0.006691636,0.00001461206,0.00002934711,0.00002723921,0.006885943,0.00001387398,0.002269641,0.001844682,0.0007244055,0.9814185],"study_design_scores_gemma":[0.00287441,0.0002476617,0.1847666,0.0001879015,0.00002067206,0.00005948735,0.003288285,0.7959446,0.01116133,0.0002853404,0.0006375893,0.0005261163],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8059755,0.0003142653,0.1927957,0.0002526834,0.0002509078,0.0001474196,6.501259e-7,0.00003597943,0.0002268724],"genre_scores_gemma":[0.9664608,0.000008008082,0.03303508,0.0002178246,0.0001050369,0.000003475359,0.000004648148,0.00000543619,0.0001596839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9808924,"threshold_uncertainty_score":0.4242595,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2148812765","doi":"10.1006/csla.1999.0136","title":"A path-stack algorithm for optimizing dynamic regimes in a statistical hidden dynamic model of speech","year":2000,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Hidden Markov model; Utterance; Speech recognition; Stack (abstract data type); Path (computing); Phone; Reduction (mathematics); Set (abstract data type); Segmentation; Algorithm; Artificial intelligence; Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.01325843623408658,"gpt":0.2679317228246664,"spread":0.2546732865905798,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004008134,0.0003127949,0.0005353741,0.0003033039,0.00006809785,0.0001490929,0.001022673,0.0001475407,0.0002603159],"category_scores_gemma":[0.0000288365,0.0003103375,0.0001756154,0.0004009352,0.00007899398,0.0003557634,0.000189064,0.0002206545,0.00006613815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001281104,"about_ca_system_score_gemma":0.0001113227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005844087,"about_ca_topic_score_gemma":0.00004827825,"domain_scores_codex":[0.9976202,0.0001072574,0.000587945,0.0007225515,0.0003909087,0.0005710861],"domain_scores_gemma":[0.9986186,0.000246937,0.000132993,0.000748742,0.00009648323,0.0001562676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001578083,0.000137913,0.000005122647,0.00004656119,0.00002441828,0.0002284297,0.001116256,0.0003163192,0.0002722192,0.0004558542,0.0003122853,0.9970688],"study_design_scores_gemma":[0.0009268452,0.0001214357,0.0001176632,0.0001010242,0.00001477328,0.0001063903,0.00008634266,0.9948061,0.0008417174,0.002416707,0.0001032073,0.0003577757],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03637788,0.0001778248,0.9610587,0.0002470179,0.0001864838,0.0005122906,0.0001217097,0.0002007526,0.001117384],"genre_scores_gemma":[0.02450428,0.00004897817,0.9737893,0.0002568028,0.00005947689,0.00003949968,0.00005134418,0.00003708016,0.00121323],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9967111,"threshold_uncertainty_score":0.9999349,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2114705006","doi":"10.1016/j.csl.2009.02.004","title":"Syllabification rules versus data-driven methods in a language with low syllabic complexity: The case of Italian","year":2009,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Phonetics and Phonology Research","field":"Psychology","cited_by":17,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University; National Research Council Canada; National Research Council Institute for Biodiagnostics","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Syllabification; Syllabic verse; Computer science; Syllable; Natural language processing; Artificial intelligence; Lexicon; Parsing; Machine translation; Speech recognition","retraction":null,"screen_n_in":null,"score":{"opus":0.08642458988048203,"gpt":0.441595487778507,"spread":0.355170897898025,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007122052,0.0001797797,0.000306774,0.0001727183,0.00006745951,0.00004591591,0.0009433132,0.0001113524,0.0003511487],"category_scores_gemma":[0.0000322059,0.0001279124,0.00004134652,0.0004074013,0.0002453025,0.00006987,0.0002207048,0.0003663325,0.00007666605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003406567,"about_ca_system_score_gemma":0.00004175731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001569031,"about_ca_topic_score_gemma":0.001997575,"domain_scores_codex":[0.9981539,0.000579706,0.0002923248,0.0004641169,0.0001369702,0.0003729763],"domain_scores_gemma":[0.9977316,0.0003261399,0.0001245,0.001684714,0.00005721438,0.00007583893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007625508,0.0007642974,0.0005756235,0.00006018846,0.0002797251,0.01104375,0.06974898,0.00005360325,0.006581152,0.004493678,0.003859792,0.9017767],"study_design_scores_gemma":[0.0355254,0.009805937,0.6946598,0.000656809,0.0005789078,0.02006988,0.06976508,0.1315247,0.02069816,0.00539521,0.007219787,0.004100342],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873309,0.001038531,0.007143363,0.000847862,0.000371005,0.0004246289,0.0001127241,0.00005702838,0.002674005],"genre_scores_gemma":[0.9257321,0.00000724702,0.07345168,0.0002003632,0.0001847066,0.00001098991,0.0001967714,0.00002041998,0.0001957495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8976763,"threshold_uncertainty_score":0.5216116,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2058080055","doi":"10.1016/j.csl.2014.06.002","title":"Unsupervised language model adaptation using LDA-based mixture models and latent semantic marginals","year":2014,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Latent Dirichlet allocation; Artificial intelligence; Language model; Topic model; Probabilistic latent semantic analysis; Pattern recognition (psychology); Scaling; Mixture model; Cluster analysis; Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03687698171248772,"gpt":0.2563463042773522,"spread":0.2194693225648645,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004272985,0.0003056928,0.0003491209,0.0002604329,0.0001465503,0.0003408065,0.0005837039,0.0001342699,0.00003968252],"category_scores_gemma":[0.00002350365,0.000280422,0.0001222509,0.0002965355,0.00004503669,0.0005199662,0.000204978,0.0001837359,0.00003225568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004672932,"about_ca_system_score_gemma":0.00005900077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000908458,"about_ca_topic_score_gemma":0.00002958907,"domain_scores_codex":[0.9980686,0.0001830902,0.0003124738,0.000624863,0.0004031143,0.0004079059],"domain_scores_gemma":[0.9987664,0.0001251216,0.0001208544,0.0006835813,0.0001063256,0.0001976788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002118374,0.0001672592,0.00006820081,0.0001359585,0.00005131261,0.0002249132,0.007279256,0.06652021,0.008417365,0.003206565,0.0003218655,0.9135859],"study_design_scores_gemma":[0.0006655754,0.00004619325,0.0001361575,0.00008171263,0.0000257883,0.0000601082,0.00009136996,0.9906916,0.006590789,0.001212368,0.00004104834,0.0003573095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2351529,0.0002309916,0.7632119,0.0003533243,0.0001508816,0.0002005862,0.000007039787,0.0002740278,0.0004183405],"genre_scores_gemma":[0.525066,0.000005278073,0.4734133,0.001278925,0.0001158824,0.000005736436,0.00001300831,0.00002165865,0.00008023864],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9241714,"threshold_uncertainty_score":0.9999648,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4391421598","doi":"10.1016/j.csl.2024.101685","title":"Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge","year":2024,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Google (Canada)","funders":"Agence Nationale de la Recherche; University of Sheffield","keywords":"Computer science; Speech recognition; Speech enhancement; Leverage (statistics); Task (project management); Noise (video); Distortion (music); Artificial intelligence; Domain adaptation; Noise reduction","retraction":null,"screen_n_in":null,"score":{"opus":0.02378122438405992,"gpt":0.3505703286582682,"spread":0.3267891042742083,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002925975,0.0001744558,0.0002457529,0.0001607449,0.00006506452,0.0001201786,0.0008336949,0.00005908322,0.00001557025],"category_scores_gemma":[0.00007531764,0.0001047922,0.00009061973,0.000797609,0.0000717138,0.0002899618,0.0003944202,0.0002541829,0.000003483703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000767781,"about_ca_system_score_gemma":0.0001684763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006617595,"about_ca_topic_score_gemma":0.00003866739,"domain_scores_codex":[0.9977129,0.0006705476,0.000317315,0.0004386579,0.0006351894,0.0002254529],"domain_scores_gemma":[0.9987978,0.000294551,0.0001441882,0.0005836808,0.0001488081,0.00003097924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000004736234,0.00007511106,0.0000705994,0.00008462054,0.00004088987,0.00002448526,0.02950769,0.00003337633,0.01615166,0.0006567542,0.00003786667,0.9533122],"study_design_scores_gemma":[0.0006639838,0.0002576156,0.008803505,0.0004248424,0.00007490985,0.0000978547,0.00144918,0.143248,0.8376061,0.006938418,0.0001762389,0.0002593353],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4492462,0.009737152,0.5358797,0.0009846133,0.000667694,0.0007839339,0.000005238911,0.00005598168,0.002639566],"genre_scores_gemma":[0.8776155,0.0000340796,0.1220206,0.0001327193,0.000141777,0.00002060785,0.000001455243,0.000009298561,0.00002395668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9530529,"threshold_uncertainty_score":0.4273303,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3214734602","doi":"10.1016/j.csl.2021.101322","title":"Empirical Mode Decomposition articulation feature extraction on Parkinson’s Diadochokinesia","year":2021,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"H2020 Marie Skłodowska-Curie Actions; Horizon 2020; Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Universidad de Antioquia","keywords":"Segmentation; Computer science; Artificial intelligence; Pattern recognition (psychology); Hilbert–Huang transform; Frame (networking); Feature (linguistics); Filter (signal processing); Speech recognition; Computer vision; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.01510550990543507,"gpt":0.377200725557779,"spread":0.3620952156523439,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009216573,0.0001824226,0.0002422189,0.0001046165,0.00007971004,0.00006483129,0.00005911845,0.0001594334,0.0002144382],"category_scores_gemma":[0.00003750814,0.000165283,0.0001500963,0.0002531639,0.00001781046,0.0001358006,0.00003259975,0.0003119031,0.0001629722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001371779,"about_ca_system_score_gemma":0.00007031066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001435333,"about_ca_topic_score_gemma":0.00005499524,"domain_scores_codex":[0.9986937,0.00007064169,0.0001845006,0.0003891541,0.0004177187,0.0002442726],"domain_scores_gemma":[0.9992197,0.00004725634,0.00006040761,0.0003896415,0.0001468795,0.0001360813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005677975,0.002166708,0.03287493,0.0002226898,0.0002481731,0.007510711,0.003602291,0.001289229,0.1231779,0.0002416278,0.106859,0.7212389],"study_design_scores_gemma":[0.004118263,0.0006313955,0.7471077,0.0003471997,0.0002894778,0.002251394,0.0004553843,0.05790489,0.1211782,0.000309017,0.0646885,0.0007186143],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9646677,0.0003900111,0.02411549,0.007075613,0.0004370114,0.0002370821,0.000005431208,0.0001965793,0.002875088],"genre_scores_gemma":[0.9659618,0.00002878186,0.02699783,0.004792794,0.0009864763,0.00001076816,0.0003450252,0.00003080107,0.0008457438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7205203,"threshold_uncertainty_score":0.6740047,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2036126214","doi":"10.1016/j.csl.2014.10.007","title":"Hybrid Arabic–French machine translation using syntactic re-ordering and morphological pre-processing","year":2014,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Machine translation; Natural language processing; Artificial intelligence; Example-based machine translation; Arabic; BLEU; Translation (biology); Verb; Linguistics","retraction":null,"screen_n_in":null,"score":{"opus":0.016871099164201,"gpt":0.2750535633622381,"spread":0.2581824641980371,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005147009,0.000307944,0.000333738,0.0001988779,0.0002248167,0.0005311564,0.0008239946,0.000112728,0.00001770964],"category_scores_gemma":[0.00005503136,0.0002656188,0.00006712072,0.0002984882,0.00006170665,0.0009016871,0.000412518,0.0004046762,0.00000446677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005536317,"about_ca_system_score_gemma":0.00002959437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001695017,"about_ca_topic_score_gemma":0.00001546142,"domain_scores_codex":[0.9980354,0.0001430393,0.0003391952,0.0007255944,0.0003320986,0.0004247288],"domain_scores_gemma":[0.9989392,0.0001125442,0.0001762988,0.0005696396,0.00007018753,0.0001321147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005272799,0.00003654925,0.0001709367,0.00009473618,0.000008591604,0.00022738,0.001217776,0.00004919031,0.009283161,0.0003678826,0.00003232297,0.9885062],"study_design_scores_gemma":[0.0003256047,0.0001095198,0.0005721144,0.0001491709,0.00001913368,0.001053517,0.000009047619,0.9682175,0.02574544,0.00314574,0.000215954,0.0004372443],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.180615,0.006055113,0.8117474,0.0002437776,0.0001652493,0.0001657577,0.000001739827,0.0008455167,0.0001604335],"genre_scores_gemma":[0.5090957,0.000005068855,0.4904591,0.0002644007,0.0001404294,0.000003060502,0.000004714158,0.00001488186,0.0000125715],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9880689,"threshold_uncertainty_score":0.9999796,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2059790057","doi":"10.1016/j.csl.2014.11.003","title":"Native and non-native class discrimination using speech rhythm- and auditory-based cues","year":2014,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières; University of New Brunswick; Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Speech recognition; Support vector machine; Metric (unit); Mixture model; Rhythm; Context (archaeology); Artificial intelligence; Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.01163572744185679,"gpt":0.261708059154234,"spread":0.2500723317123772,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004141741,0.00030169,0.0003276052,0.0002463241,0.0002746267,0.0005184963,0.0004194165,0.0000897162,0.000007472152],"category_scores_gemma":[0.00006259685,0.0002735064,0.00005336162,0.0003358606,0.0001561902,0.0008038821,0.0003962042,0.0002352196,0.000007823315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000075603,"about_ca_system_score_gemma":0.00006960172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004248305,"about_ca_topic_score_gemma":0.00004270186,"domain_scores_codex":[0.9981607,0.0001252689,0.0002559365,0.0006939826,0.0003729249,0.0003911481],"domain_scores_gemma":[0.9988872,0.0001841338,0.0002015488,0.0004000122,0.0001598956,0.0001671717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000136788,0.00009137506,0.0008889656,0.0001644385,0.00003984396,0.0002099006,0.007602998,0.0001384822,0.01134476,0.0008091671,0.0006942608,0.9780021],"study_design_scores_gemma":[0.001418752,0.0002466187,0.00990494,0.0002936646,0.00003289021,0.0002254013,0.0003151391,0.7612678,0.2227023,0.001714933,0.001126999,0.0007505732],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3905953,0.000185613,0.6074079,0.0004388076,0.0004373148,0.0001476979,0.000003131987,0.0001350481,0.0006491597],"genre_scores_gemma":[0.6436732,0.000004806674,0.3549331,0.000651226,0.000639662,0.000004223505,0.000006091523,0.00001865578,0.00006906807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9772515,"threshold_uncertainty_score":0.9999717,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4401281176","doi":"10.1016/j.csl.2024.101695","title":"Speech self-supervised representations benchmarking: A case for larger probing heads","year":2024,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University; Mila - Quebec Artificial Intelligence Institute","funders":"Agence de l'innovation de Défense","keywords":"Benchmarking; Computer science; Ranking (information retrieval); Inference; Task (project management); Downstream (manufacturing); Generalization; Feature (linguistics); Set (abstract data type); Artificial intelligence; Architecture; Machine learning; Benchmark (surveying); Data set; Natural language processing","retraction":null,"screen_n_in":null,"score":{"opus":0.02313783000429004,"gpt":0.2944255563429296,"spread":0.2712877263386396,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006361717,0.0003151436,0.0003158851,0.000400098,0.0002849005,0.001165055,0.0007437792,0.0001324046,0.0003113483],"category_scores_gemma":[0.00004443881,0.0002925408,0.0003190278,0.0008188226,0.00003278946,0.0007019768,0.0003408784,0.0002516861,0.0002381973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001023507,"about_ca_system_score_gemma":0.0001329153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006665369,"about_ca_topic_score_gemma":0.0000501552,"domain_scores_codex":[0.9975308,0.0001280109,0.0004464518,0.0009713273,0.0003514787,0.0005719031],"domain_scores_gemma":[0.9982301,0.0004611732,0.00006875436,0.0008783738,0.0001493296,0.0002122978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004818143,0.0001549872,0.00002955548,0.0001773877,0.0001278164,0.01098001,0.006668122,0.000008635706,0.001159789,0.00409542,0.01239457,0.9641989],"study_design_scores_gemma":[0.0007283291,0.0001313769,0.00008750348,0.0001798701,0.0000764618,0.01066945,0.0003567779,0.9455146,0.01484004,0.001080798,0.02561928,0.0007154884],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05454756,0.0007905558,0.9337468,0.001898477,0.002306395,0.001095933,0.00004756067,0.002075882,0.003490799],"genre_scores_gemma":[0.08816654,0.00001663682,0.9087351,0.0008780144,0.001345278,0.0001501124,0.00004536579,0.00005386992,0.000609111],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9634834,"threshold_uncertainty_score":0.9999527,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1972278020","doi":"10.1006/csla.2000.0143","title":"Tree-structured vector quantization for speech recognition","year":2000,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Smoothing; Hidden Markov model; Computer science; Vector quantization; Pattern recognition (psychology); Speech recognition; Gaussian; Tree (set theory); Entropy (arrow of time); Artificial intelligence; Feature vector; Mixture model; Curse of dimensionality; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0198735002482187,"gpt":0.2541196303712127,"spread":0.234246130122994,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002761165,0.0002848553,0.0003088031,0.0002081421,0.0001761352,0.0003530511,0.0007680595,0.0001575907,0.002172051],"category_scores_gemma":[0.00004634842,0.0002746161,0.0002101346,0.0004839798,0.00003753745,0.0005950735,0.00007359048,0.0001420461,0.0005725228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004970813,"about_ca_system_score_gemma":0.00004532928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002914425,"about_ca_topic_score_gemma":0.0000598027,"domain_scores_codex":[0.9980565,0.0001090892,0.0003854168,0.0006699926,0.0003347788,0.0004441815],"domain_scores_gemma":[0.9987912,0.00015208,0.0001151784,0.000630782,0.0001508444,0.0001599149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002208138,0.00006701264,0.00001545062,0.00002008743,0.00002534538,0.00006213755,0.0004947758,0.000002900614,0.001476386,0.0002609559,0.004478093,0.9930748],"study_design_scores_gemma":[0.005336575,0.0008509218,0.008223173,0.0002399381,0.0001300297,0.001192459,0.0002238924,0.4318211,0.4728848,0.01148214,0.06495636,0.002658599],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1709163,0.0001725005,0.8159174,0.001029288,0.001393608,0.0009599971,0.00007586765,0.001103636,0.008431315],"genre_scores_gemma":[0.06701642,0.00003167888,0.9283493,0.001711488,0.00114133,0.00005872983,0.000224054,0.00004833553,0.001418671],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9904162,"threshold_uncertainty_score":0.9999706,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2766171655","doi":"10.1016/j.csl.2017.10.006","title":"Application of the pairwise variability index of speech rhythm with particle swarm optimization to the classification of native and non-native accents","year":2017,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick; Université de Moncton; Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Particle swarm optimization; Support vector machine; Pairwise comparison; Metric (unit); Artificial intelligence; Speech recognition; Pattern recognition (psychology); Rhythm; Generalization; Point (geometry); Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01579100498665801,"gpt":0.2634387689169221,"spread":0.2476477639302641,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006164135,0.0001195088,0.0002055208,0.00005010821,0.0001528236,0.00007122984,0.0009076523,0.00004430489,0.000008299858],"category_scores_gemma":[0.0001524002,0.00007276427,0.00005002689,0.0002858494,0.0001869494,0.0002815838,0.0003812863,0.00008946082,0.000001828474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003049536,"about_ca_system_score_gemma":0.00004772456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001338014,"about_ca_topic_score_gemma":0.00006481544,"domain_scores_codex":[0.9987693,0.0001538933,0.0002947449,0.0003099776,0.0003450634,0.0001269559],"domain_scores_gemma":[0.9977242,0.0001659463,0.0005354361,0.001155756,0.0003676511,0.00005101788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006101823,0.000264091,0.02949327,0.0000668029,0.00007270918,0.000002814489,0.007908875,0.001541358,0.003016521,0.002024015,0.00006005183,0.9554885],"study_design_scores_gemma":[0.0003636104,0.0000649084,0.3627777,0.00004879289,0.00001613184,0.000008202548,0.0001757147,0.5086588,0.1275449,0.000220517,0.00002585517,0.00009481898],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3679193,0.000006124338,0.6303911,0.000757814,0.00006357316,0.0004701395,0.000009875157,0.00001423435,0.0003679077],"genre_scores_gemma":[0.9153553,0.000002957076,0.08447406,0.00007409378,0.00004346644,0.00002374622,0.000001997661,0.000006523304,0.00001789056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9553937,"threshold_uncertainty_score":0.2967241,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4402910001","doi":"10.1016/j.csl.2024.101723","title":"Speech Generation for Indigenous Language Education","year":2024,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Multilingual Education and Policy","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"University nuhelot'ine thaiyots'i nistameyimâkanak Blue Quills; National Research Council Canada","funders":"UK Research and Innovation","keywords":"Computer science; Indigenous; Natural language processing; Linguistics; Artificial intelligence; Speech recognition; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.04128638080843477,"gpt":0.4452927727766668,"spread":0.404006391968232,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004621027,0.0001233936,0.0001194114,0.0001817082,0.0002879032,0.0003933281,0.0002369855,0.000107683,0.0004985866],"category_scores_gemma":[0.00007958284,0.0001229733,0.00009223609,0.0002943199,0.00004835996,0.0001810469,0.00002631343,0.0001197183,0.0002535256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001621724,"about_ca_system_score_gemma":0.0009609936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003758907,"about_ca_topic_score_gemma":0.001900494,"domain_scores_codex":[0.9989142,0.00008932453,0.0001856049,0.0002968736,0.0002119641,0.0003019633],"domain_scores_gemma":[0.9994191,0.00008381881,0.00004230892,0.0002259305,0.0000920362,0.0001368258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001630258,0.00005974856,0.00002022049,0.00003576177,0.00001147365,0.000008636373,0.2017266,0.000002541755,0.001298742,0.004048528,0.01434376,0.7784424],"study_design_scores_gemma":[0.0002386768,0.00007299041,0.0003068804,0.00007254747,0.00004092493,0.00002595277,0.02086171,0.005542043,0.007729248,0.0003359854,0.9643095,0.0004635828],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9454288,0.004112849,0.009598664,0.002449435,0.008070417,0.00111301,0.00003927576,0.0005813855,0.02860618],"genre_scores_gemma":[0.8776673,0.00007921863,0.04429961,0.003421417,0.02600224,0.0001087709,0.0003522832,0.00005821301,0.04801095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9499657,"threshold_uncertainty_score":0.5682368,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410431605","doi":"10.1016/j.csl.2025.101815","title":"BERSting at the screams: A benchmark for distanced, emotional and shouted speech recognition","year":2025,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Benchmark (surveying); Speech recognition; Emotion recognition; Natural language processing","retraction":null,"screen_n_in":null,"score":{"opus":0.01565752871258395,"gpt":0.2604487816432463,"spread":0.2447912529306624,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000510506,0.00022818,0.0002382663,0.0001658172,0.0004537936,0.0003452665,0.0005801067,0.00009631646,0.0001782477],"category_scores_gemma":[0.000129941,0.0001806578,0.0001386981,0.0004478738,0.00009888331,0.000306313,0.0004410895,0.000151844,0.00003746249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008015053,"about_ca_system_score_gemma":0.00005270909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000355907,"about_ca_topic_score_gemma":0.0001359585,"domain_scores_codex":[0.9983684,0.0001030567,0.0003176718,0.0005967999,0.0002573907,0.000356707],"domain_scores_gemma":[0.9985308,0.0006088314,0.0001144772,0.0004994519,0.0001552068,0.00009120351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002017726,0.00004764696,0.000134582,0.00004149218,0.00004818597,0.00003242083,0.0004761997,0.000001514132,0.0004442186,0.001517634,0.009197186,0.9880387],"study_design_scores_gemma":[0.01134495,0.0007578636,0.03909645,0.002437375,0.0004647987,0.001965131,0.00255356,0.5765508,0.164855,0.05076406,0.1451205,0.004089538],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1277205,0.0006203275,0.8544229,0.003812687,0.001003271,0.0008481196,0.000112748,0.0003473195,0.0111121],"genre_scores_gemma":[0.07464375,0.00004823851,0.914466,0.005409071,0.000693904,0.0001318319,0.0002395434,0.00003380184,0.004333893],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9839492,"threshold_uncertainty_score":0.736701,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2189903590","doi":"10.1016/j.csl.2015.11.002","title":"Speech Production in Speech Technologies: Introduction to the CSL Special Issue","year":2015,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Rehabilitation Institute","funders":"","keywords":"Computer science; Speech production; Speech technology; Production (economics); Context (archaeology); Speech processing; Speech recognition; History","retraction":null,"screen_n_in":null,"score":{"opus":0.01931376996621591,"gpt":0.2584920550608656,"spread":0.2391782850946497,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00140786,0.0003584661,0.0003840965,0.0006464616,0.0001385759,0.0004118594,0.001959138,0.0001918829,0.0002088955],"category_scores_gemma":[0.000569066,0.0002828108,0.0001111643,0.00205352,0.00008957995,0.000641559,0.0008027959,0.0005011263,0.002366249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002968914,"about_ca_system_score_gemma":0.000107605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001278484,"about_ca_topic_score_gemma":0.0004388917,"domain_scores_codex":[0.9967853,0.0002163352,0.0005121881,0.001108757,0.0007498473,0.0006275215],"domain_scores_gemma":[0.9976559,0.00006623336,0.0001455307,0.001710946,0.0002506951,0.0001706793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001646711,0.0001127568,0.00005729879,0.000007650131,0.00001108457,0.000223943,0.002416467,0.00004405549,0.0003471024,0.0002752683,0.1815814,0.8149065],"study_design_scores_gemma":[0.0008584791,0.000353244,0.001244937,0.00007470849,0.000019202,0.001233385,0.003514247,0.007198915,0.1261572,0.002260988,0.8561231,0.0009615614],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.319271,0.0008910685,0.2596703,0.3335293,0.04446959,0.005391302,0.00002504655,0.006128333,0.03062403],"genre_scores_gemma":[0.05519336,0.00005916265,0.8695256,0.003323948,0.06545823,0.0001622346,0.0000324378,0.0001000087,0.006144986],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8139449,"threshold_uncertainty_score":0.9999624,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4402127086","doi":"10.1016/j.csl.2024.101715","title":"Enhancing analysis of diadochokinetic speech using deep neural networks","year":2024,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Government of Ontario; National Institutes of Health; National Science Foundation; Ministry of Science and Technology, Israel; Ontario Brain Institute; United States-Israel Binational Science Foundation","keywords":"Computer science; Speech recognition; Deep learning; Artificial intelligence; Convolutional neural network; Artificial neural network; Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.01106882913646058,"gpt":0.2895941880896504,"spread":0.2785253589531898,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000215783,0.000244761,0.0006275459,0.0006728971,0.00004306762,0.00007871462,0.000183139,0.000125366,0.0004825357],"category_scores_gemma":[0.00002297936,0.0002144835,0.0004506196,0.001821071,0.00005872408,0.0001144984,0.0001162183,0.0003310383,0.00001711637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006371822,"about_ca_system_score_gemma":0.00003648526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002481673,"about_ca_topic_score_gemma":0.000301303,"domain_scores_codex":[0.9983186,0.00005021186,0.0004425236,0.0004431951,0.0003417524,0.0004037881],"domain_scores_gemma":[0.9990917,0.00009728958,0.00007405053,0.0005162169,0.00007483017,0.0001458913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008541244,0.0003014374,0.01143847,0.0007429291,0.004484912,0.006008239,0.006035703,0.03486923,0.03298453,0.0001307043,0.0004763604,0.9024421],"study_design_scores_gemma":[0.0003413587,0.0001195756,0.007452653,0.0001410699,0.002299493,0.0001651798,0.0002907859,0.9840211,0.004758166,0.000009033281,0.0001813287,0.0002201993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.791741,0.005743152,0.2009499,0.0001336884,0.0005189818,0.0002111418,0.000005110661,0.0001754786,0.0005215759],"genre_scores_gemma":[0.9752581,0.00003717436,0.0231576,0.0005460875,0.0006929,0.000002539088,0.00007794213,0.00004468068,0.0001830012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9491519,"threshold_uncertainty_score":0.8746385,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4417072973","doi":"10.1016/j.csl.2025.101923","title":"Enhanced audio-visual speech enhancement with posterior sampling methods in recurrent variational autoencoders","year":2025,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Speech enhancement; Inference; Intelligibility (philosophy); Sampling (signal processing); Autoencoder; Pattern recognition (psychology); Noise reduction; Posterior probability","retraction":null,"screen_n_in":null,"score":{"opus":0.01555674382515423,"gpt":0.3504463130887455,"spread":0.3348895692635913,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000799565,0.0003719021,0.0004617402,0.0004810054,0.0001661228,0.00045981,0.001077984,0.0001112971,0.0000758449],"category_scores_gemma":[0.00004882092,0.0003336559,0.00009472396,0.001263348,0.00005604035,0.0005540515,0.0006009833,0.0003795483,0.00003041851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002683921,"about_ca_system_score_gemma":0.0003707639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003643167,"about_ca_topic_score_gemma":0.0000645201,"domain_scores_codex":[0.9971666,0.0001975864,0.000563702,0.0009488765,0.0004635885,0.0006596668],"domain_scores_gemma":[0.9987049,0.0002011702,0.0002130707,0.0006101768,0.0001471049,0.0001235591],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004009322,0.000194691,0.0001363896,0.00007184671,0.00005141617,0.0001125849,0.002022257,0.000339766,0.03157797,0.0004378413,0.0001255813,0.9648896],"study_design_scores_gemma":[0.00249995,0.0005374305,0.007237718,0.0009358193,0.00003435303,0.00011778,0.000239569,0.1259224,0.8586945,0.001464715,0.001301152,0.00101456],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06439032,0.0005260485,0.9309851,0.0007837521,0.001024128,0.0003621141,0.000001949406,0.0002445806,0.001682039],"genre_scores_gemma":[0.04759343,0.000009897601,0.9504756,0.001279722,0.0001965227,0.00003729799,0.00001546919,0.00001835935,0.0003737063],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.963875,"threshold_uncertainty_score":0.9999115,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4416581044","doi":"10.1016/j.csl.2025.101907","title":"A robust framework for noisy speech recognition using Frequency-Guided-Swin Transformer","year":2025,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Moncton","funders":"","keywords":"Transformer; Robustness (evolution); Convolutional neural network; Word error rate; Pattern recognition (psychology); Deep neural networks; Artificial neural network; Deep learning","retraction":null,"screen_n_in":null,"score":{"opus":0.06383290841893033,"gpt":0.3086372540781378,"spread":0.2448043456592075,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000583971,0.0004044914,0.0004832028,0.000513571,0.0002897349,0.0004755422,0.001158368,0.0003171749,0.0002999068],"category_scores_gemma":[0.0001494904,0.0004026027,0.0003713951,0.0009890613,0.00006696999,0.0006842744,0.0001314543,0.0003567538,0.0001478205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001701499,"about_ca_system_score_gemma":0.0001872152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001076085,"about_ca_topic_score_gemma":0.00004601509,"domain_scores_codex":[0.9973137,0.000127634,0.0006156042,0.0008952772,0.0003699261,0.0006778833],"domain_scores_gemma":[0.998121,0.0004124134,0.0001551178,0.0008600366,0.0002814334,0.0001699597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001292588,0.0001410952,0.00005386869,0.0000982268,0.00008833092,0.0001293336,0.0007119045,0.00001711316,0.002303372,0.004839781,0.002575658,0.9890284],"study_design_scores_gemma":[0.004174306,0.0004301316,0.001092223,0.002000111,0.0003489004,0.0009894788,0.0006421001,0.4060981,0.3895822,0.1824249,0.009201528,0.003016073],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03595994,0.0002775863,0.9545137,0.00118967,0.001765261,0.0008198338,0.00004748227,0.0005455591,0.004881018],"genre_scores_gemma":[0.01046269,0.00001984179,0.9854031,0.003113179,0.0005929616,0.00006098418,0.00004166626,0.00003724818,0.000268313],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9860123,"threshold_uncertainty_score":0.9998426,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}