{"meta":{"query_hash":"156f0c5b8dc6","filters":{"venue":"International Conference on Computational Linguistics"},"cohort_total":34,"direct_labels_cover":0,"predictions_cover":34,"exported":34,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/156f0c5b8dc6","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Conference+on+Computational+Linguistics"},"results":[{"id":"W169800830","doi":"","title":"Scaling up Analogical Learning","year":2008,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Scalability; Artificial intelligence; Simple (philosophy); Scaling; Identification (biology); Space (punctuation); Limit (mathematics); Machine learning; Theoretical computer science; Mathematics; Epistemology","score_opus":0.06614344725555805,"score_gpt":0.337967393007861,"score_spread":0.27182394575230295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W169800830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022224395,0.00006214048,0.9673051,0.00075317296,0.0016063881,0.00007240859,0.00000725238,0.0006292759,0.027341783],"genre_scores_gemma":[0.6920134,0.000013548477,0.3067534,0.00041984048,0.00033238917,0.0000048390934,0.000041784388,0.000007343567,0.00041346895],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982907,0.00005765601,0.00030546737,0.00039822227,0.0007503287,0.00019757953],"domain_scores_gemma":[0.99781406,0.0003331936,0.00015743134,0.00015671863,0.001448158,0.00009044632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017486926,0.00016973092,0.00014870397,0.0002012664,0.00026562304,0.00019190546,0.0010126207,0.000081148384,0.000076573844],"category_scores_gemma":[0.0023593635,0.00016114558,0.0000665748,0.00020182277,0.0000966845,0.00010450367,0.00021259004,0.0004284042,0.000100891164],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011363213,0.000043005028,0.00034159355,0.0000029261134,0.00001582775,0.00009344731,0.00018442264,0.006375367,0.00003428585,0.9870905,0.0004734883,0.005333735],"study_design_scores_gemma":[0.00022582112,0.0000713791,0.00064261974,0.00005558119,0.000002640692,0.00007903213,0.000013047864,0.6709031,0.0003109695,0.32203487,0.005411971,0.00024898048],"about_ca_topic_score_codex":0.000010526663,"about_ca_topic_score_gemma":5.040163e-7,"teacher_disagreement_score":0.68979096,"about_ca_system_score_codex":0.0000874697,"about_ca_system_score_gemma":0.00018400131,"threshold_uncertainty_score":0.65713257},"labels":[],"label_agreement":null},{"id":"W1815301076","doi":"","title":"Measuring the Non-compositionality of Multiword Expressions","year":2010,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Principle of compositionality; Computer science; Artificial intelligence; Natural language processing; Semantics (computer science); Metric (unit); Natural language; Question answering; Combinatory categorial grammar; Distributional semantics; Expression (computer science); Information extraction; Programming language; Semantic similarity; Link grammar; Rule-based machine translation","score_opus":0.0730969691908334,"score_gpt":0.32514661388054306,"score_spread":0.25204964468970964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1815301076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0268742,0.0000036714723,0.92831004,0.0024296052,0.0039252583,0.00014638428,0.000042697193,0.00006808791,0.038200043],"genre_scores_gemma":[0.85870135,0.0000012473104,0.14047962,0.00024886543,0.0004621551,0.000010842348,0.000021622502,0.0000057787774,0.000068523565],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983724,0.00004056933,0.00038280492,0.00029408737,0.00077271,0.00013743412],"domain_scores_gemma":[0.99720633,0.00052512233,0.00020593053,0.00036640957,0.0016302937,0.00006588496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032068774,0.00012906907,0.00012416337,0.00010182549,0.00016116418,0.00012525376,0.0013425349,0.000056549954,0.00007263871],"category_scores_gemma":[0.0009448598,0.00010370395,0.00007467823,0.00010590366,0.00010322797,0.00006612753,0.00023374503,0.00037362584,0.000029534394],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007971683,0.00008515404,0.00040351652,0.0000041521116,0.000022262795,0.0000031376337,0.0001825618,0.030654222,0.0020202445,0.9647896,0.00008612073,0.001741044],"study_design_scores_gemma":[0.0002198029,0.000018722325,0.005855537,0.000048242222,0.000003731573,0.0000061603887,0.000015136462,0.85349864,0.0010567015,0.13734834,0.0018108261,0.000118181095],"about_ca_topic_score_codex":0.00003293107,"about_ca_topic_score_gemma":0.000011363132,"teacher_disagreement_score":0.83182716,"about_ca_system_score_codex":0.00002584872,"about_ca_system_score_gemma":0.00020471872,"threshold_uncertainty_score":0.42289242},"labels":[],"label_agreement":null},{"id":"W1956103381","doi":"","title":"Automatic Acquisition of Lexical Formality","year":2010,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Formality; Computer science; Word (group theory); Natural language processing; Artificial intelligence; Word Association; Metric (unit); Similarity (geometry); Association (psychology); Task (project management); Synonym (taxonomy); Linguistics; Speech recognition; Psychology","score_opus":0.0284122819659165,"score_gpt":0.3393629900576921,"score_spread":0.3109507080917756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1956103381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016594892,0.000012034675,0.95946515,0.0013062259,0.002318448,0.00012418794,0.000042709027,0.00038165614,0.019754691],"genre_scores_gemma":[0.60364646,6.2728276e-7,0.39592645,0.00019706879,0.00016772709,0.0000039898555,0.0000329827,0.0000037876841,0.000020948592],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986449,0.000026638916,0.00035313974,0.00023764915,0.000611286,0.0001264143],"domain_scores_gemma":[0.9977387,0.00021368319,0.00022032166,0.00022265878,0.0015440814,0.000060555027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002495009,0.00012199782,0.00013340412,0.0001590228,0.00006324582,0.00013311212,0.0009412477,0.00008486478,0.00015987628],"category_scores_gemma":[0.0009964185,0.000115544695,0.000056896566,0.00013255267,0.00009273527,0.00012005184,0.00015635861,0.00030174293,0.000025152644],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066279326,0.000079355494,0.0001428719,0.000011605924,0.000011484414,0.000005897304,0.000062755316,0.00013771142,0.0006442547,0.9895677,0.00015573071,0.009174014],"study_design_scores_gemma":[0.00011650862,0.000045927667,0.0008053833,0.00003452084,0.0000024131436,0.000009297484,0.0000029447208,0.47959164,0.0022433626,0.5167366,0.00031598166,0.00009540895],"about_ca_topic_score_codex":0.000012073575,"about_ca_topic_score_gemma":0.0000034941854,"teacher_disagreement_score":0.5870515,"about_ca_system_score_codex":0.00003112158,"about_ca_system_score_gemma":0.00016840381,"threshold_uncertainty_score":0.47117758},"labels":[],"label_agreement":null},{"id":"W2108711363","doi":"","title":"A Strategy of Mapping Polish WordNet onto Princeton WordNet","year":2012,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"WordNet; Premise; Computer science; Set (abstract data type); Natural language processing; Focus (optics); Artificial intelligence; Lexical database; Range (aeronautics); Information retrieval; Linguistics; Programming language; Philosophy","score_opus":0.061792355341779494,"score_gpt":0.3410952297029126,"score_spread":0.2793028743611331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108711363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043475945,0.00031690026,0.93602747,0.00068787375,0.0025738743,0.00021741244,0.000068842106,0.00039822678,0.05536181],"genre_scores_gemma":[0.6895747,0.0000060580614,0.3094447,0.00024874508,0.00048940297,0.000007066586,0.000050848303,0.000008672107,0.0001698116],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981744,0.000053100583,0.0004505859,0.0002890493,0.0007446898,0.00028812085],"domain_scores_gemma":[0.9976868,0.00022287077,0.0003284934,0.00024509046,0.0013932481,0.00012350766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031632607,0.00019330005,0.00020074725,0.00027279955,0.000069225556,0.00017987561,0.0011347376,0.000088154164,0.0000743099],"category_scores_gemma":[0.0008660381,0.00019173471,0.00006465364,0.00024595245,0.00007667436,0.0002184713,0.00024506004,0.00026231844,0.000027473081],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012372942,0.00012752625,0.0008569326,0.000016243239,0.000029725941,0.0000043912623,0.00032773835,0.00082595495,0.000118545584,0.98780996,0.0005176783,0.009352963],"study_design_scores_gemma":[0.0005687383,0.00016149956,0.006746856,0.00045356064,0.000012807277,0.00003069092,0.00009955362,0.2752703,0.0021637268,0.7009943,0.012841175,0.0006567837],"about_ca_topic_score_codex":0.00005004643,"about_ca_topic_score_gemma":0.000004317156,"teacher_disagreement_score":0.6852271,"about_ca_system_score_codex":0.000122020756,"about_ca_system_score_gemma":0.00023717414,"threshold_uncertainty_score":0.78187144},"labels":[],"label_agreement":null},{"id":"W2250555305","doi":"","title":"On Panini and the Generative Capacity of Contextualized Replacement Systems","year":2012,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"semigroups and automata theory","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Generative grammar; Formalism (music); Sanskrit; Computer science; Grammar; Rewriting; Linguistics; Mildly context-sensitive grammar formalism; Adaptive grammar; Emergent grammar; Natural language processing; Programming language; Mathematics; Artificial intelligence; Philosophy; Literature","score_opus":0.0576393092193052,"score_gpt":0.3040569194395782,"score_spread":0.24641761022027298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250555305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06274259,0.00014777816,0.8784258,0.0015613276,0.0063969675,0.00046664802,0.00014055731,0.000098067096,0.05002029],"genre_scores_gemma":[0.9905042,0.000008538659,0.008398157,0.0006430855,0.00028776928,0.000017182432,0.000019759016,0.0000051617603,0.00011615969],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866474,0.0001705745,0.00032497823,0.00019162399,0.0005163509,0.00013175432],"domain_scores_gemma":[0.9975234,0.0012927584,0.00024476528,0.0001856432,0.00069375307,0.000059644863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006345115,0.00012645902,0.00017815144,0.00007223896,0.00009338295,0.00012232843,0.0004201435,0.000035466273,0.00002504318],"category_scores_gemma":[0.0011373517,0.00008835353,0.000041343534,0.000060276005,0.00018152526,0.000054789445,0.00010835211,0.00012095926,0.00001767926],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008515586,0.00007595046,0.00012445523,0.0000051165125,0.0000528793,7.240073e-7,0.0007271527,0.0032759318,0.000015173023,0.9950448,0.00030970146,0.00028290658],"study_design_scores_gemma":[0.0013291875,0.00009729306,0.0007275254,0.00007456549,0.000007871564,0.0000075478815,0.00010852598,0.77147734,0.00012382168,0.22461854,0.001300295,0.00012747225],"about_ca_topic_score_codex":0.000029814073,"about_ca_topic_score_gemma":5.947985e-7,"teacher_disagreement_score":0.9277616,"about_ca_system_score_codex":0.000044617682,"about_ca_system_score_gemma":0.0000537947,"threshold_uncertainty_score":0.36029524},"labels":[],"label_agreement":null},{"id":"W2250571015","doi":"","title":"Towards Automatic Topical Question Generation","year":2012,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Computer science","score_opus":0.09053207617422618,"score_gpt":0.3518676068766896,"score_spread":0.2613355307024634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250571015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007267001,0.000016773522,0.9484622,0.0017068832,0.0056102173,0.00009595219,0.000008237667,0.00018981098,0.03664296],"genre_scores_gemma":[0.77135795,0.0000032351925,0.22577465,0.00058120384,0.0020643047,0.000011070143,0.000061603016,0.0000064532146,0.00013952945],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984193,0.000072827745,0.0003413005,0.0002672665,0.0006787103,0.00022059614],"domain_scores_gemma":[0.99865323,0.0001023356,0.00012056972,0.00021519371,0.0007840598,0.00012461621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030585565,0.00014639525,0.00011899358,0.00013798513,0.000100851335,0.00021747958,0.00055224943,0.000072658535,0.00012403337],"category_scores_gemma":[0.00089436065,0.00014802381,0.0000509272,0.0000955902,0.000028795692,0.00018367321,0.00012272406,0.00016734257,0.00018073316],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017307542,0.00008132509,0.00043346873,0.0000036292909,0.00001474234,0.0000025557952,0.00014949049,0.0087219095,0.00003067025,0.9670547,0.0003380186,0.02316773],"study_design_scores_gemma":[0.00015975558,0.00003538997,0.002811624,0.000024551427,0.0000041126636,0.000009906261,0.000006919269,0.927909,0.00010041576,0.064972535,0.0038118116,0.00015397252],"about_ca_topic_score_codex":0.000015414309,"about_ca_topic_score_gemma":0.0000018568387,"teacher_disagreement_score":0.91918707,"about_ca_system_score_codex":0.00014895962,"about_ca_system_score_gemma":0.0001515167,"threshold_uncertainty_score":0.6036236},"labels":[],"label_agreement":null},{"id":"W2251004534","doi":"","title":"Fourteen Light Tasks for comparing Analogical and Phrase-based Machine Translation","year":2014,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Transliteration; Machine translation; Phrase; Computer science; Scripting language; Natural language processing; Artificial intelligence; Rule-based machine translation; Translation (biology); Example-based machine translation; Machine translation software usability; Programming language","score_opus":0.058601602908245884,"score_gpt":0.33216916151273396,"score_spread":0.27356755860448806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2251004534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028720303,0.00007797384,0.990849,0.0028504767,0.00045692487,0.00016331603,0.000030862615,0.00023919364,0.0050450307],"genre_scores_gemma":[0.5826117,0.0000011247948,0.41656354,0.00048529185,0.00019605862,0.000012118374,0.000110348075,0.0000063251273,0.000013495751],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871075,0.00004878831,0.00029039872,0.00038137782,0.00041179787,0.00015689655],"domain_scores_gemma":[0.9983335,0.00047284833,0.00015094911,0.00014411796,0.0008189243,0.00007965272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002969774,0.00016901025,0.000178517,0.0001809949,0.00013638464,0.00030037708,0.0005619133,0.00007385787,0.000011182954],"category_scores_gemma":[0.00084999704,0.00015753924,0.000052550335,0.00009782946,0.000048257793,0.00007565219,0.000062093895,0.00017123623,0.0000051240627],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003786175,0.000054466927,0.00027111924,0.000016859345,0.000015384456,0.0000025837853,0.000049046215,0.0036359532,0.00008307872,0.97248393,0.00014749855,0.023202186],"study_design_scores_gemma":[0.00042078664,0.00009994984,0.00020166887,0.000048901846,0.0000058509468,0.000002830129,0.0000013983954,0.73310906,0.00021671025,0.26225066,0.0035001135,0.00014210296],"about_ca_topic_score_codex":0.000008855496,"about_ca_topic_score_gemma":0.000007713835,"teacher_disagreement_score":0.72947305,"about_ca_system_score_codex":0.000044931185,"about_ca_system_score_gemma":0.00007222492,"threshold_uncertainty_score":0.6424264},"labels":[],"label_agreement":null},{"id":"W2251031731","doi":"","title":"A System for Multilingual Sentiment Learning On Large Data Sets","year":2012,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Sentiment analysis; Artificial intelligence; Generalization; Natural language processing; Set (abstract data type); Empirical research; Machine learning; Mathematics","score_opus":0.12093133476971414,"score_gpt":0.3898499392076454,"score_spread":0.26891860443793125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2251031731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043184967,0.000038561833,0.96704525,0.00054738217,0.006859912,0.00028261097,0.0002675401,0.00023174961,0.020408526],"genre_scores_gemma":[0.9288105,0.0000026131252,0.06829973,0.00025873168,0.0011511521,0.000013439361,0.0011663319,0.000013277382,0.0002842578],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797434,0.000068834466,0.0004003835,0.00047200266,0.00076081156,0.00032361815],"domain_scores_gemma":[0.9978701,0.0005516747,0.00025461233,0.00036308126,0.00083520066,0.00012533048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070937723,0.00018896481,0.00018459567,0.00020622418,0.00024061368,0.0002825494,0.0011121567,0.00005584858,0.00004771337],"category_scores_gemma":[0.0009112898,0.00018652984,0.00008254754,0.00011655855,0.000019469488,0.00013583293,0.0003532126,0.00018030741,0.00017230956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029610448,0.00025856582,0.0011899193,0.000013971458,0.00013094752,0.0000040566106,0.00031533177,0.019700747,0.0000104056235,0.97354877,0.0012232164,0.0035744864],"study_design_scores_gemma":[0.0006025393,0.000075915326,0.00051716453,0.0000966155,0.000016904976,0.0000033052459,0.00015844143,0.96592945,0.00010690965,0.0011870121,0.031107498,0.0001982393],"about_ca_topic_score_codex":0.000004136902,"about_ca_topic_score_gemma":9.0783857e-7,"teacher_disagreement_score":0.97236174,"about_ca_system_score_codex":0.000113658025,"about_ca_system_score_gemma":0.00010043914,"threshold_uncertainty_score":0.76064664},"labels":[],"label_agreement":null},{"id":"W2252045241","doi":"","title":"Flexible Structural Analysis of Near-Meet-Semilattices for Typed Unification-Based Grammar Design","year":2012,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Unification; Parsing; Computer science; Programming language; Head-driven phrase structure grammar; Grammar; Rule-based machine translation; Type (biology); Theoretical computer science; Algorithm; Natural language processing; Artificial intelligence; Generative grammar; Linguistics","score_opus":0.07928709696875265,"score_gpt":0.3722236565658698,"score_spread":0.2929365595971172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2252045241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00093314203,0.00008316852,0.9965557,0.00034968645,0.00072020484,0.00018942285,0.00009401834,0.00016712677,0.00090751704],"genre_scores_gemma":[0.5286627,6.721307e-7,0.47089964,0.00013894733,0.00009913861,0.000012794942,0.0001490124,0.0000056319695,0.000031452255],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985493,0.000056555356,0.00038924936,0.000270823,0.00053014996,0.00020396516],"domain_scores_gemma":[0.9961326,0.0009399392,0.00035725735,0.00023731437,0.0022514367,0.00008141622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003786348,0.00016090307,0.0002191138,0.00037844328,0.00012047458,0.00018841201,0.0008799777,0.00007602112,0.000042558237],"category_scores_gemma":[0.0013804316,0.00015270876,0.00011424355,0.00053727423,0.00008651095,0.00012599744,0.00006190196,0.00010308345,0.000004954157],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042912478,0.000059576585,0.0009423052,0.000014207792,0.00018659954,5.499352e-7,0.0001473361,0.055246215,0.00012208385,0.9413903,0.0001529701,0.0016949759],"study_design_scores_gemma":[0.00017159412,0.000059459693,0.0015536761,0.000024525067,0.00008406212,5.230623e-7,0.000006631241,0.8708527,0.0017524374,0.12492312,0.0004152882,0.00015597747],"about_ca_topic_score_codex":0.000018282994,"about_ca_topic_score_gemma":0.0000021727683,"teacher_disagreement_score":0.81646717,"about_ca_system_score_codex":0.00008207402,"about_ca_system_score_gemma":0.00027608773,"threshold_uncertainty_score":0.6227282},"labels":[],"label_agreement":null},{"id":"W2572452825","doi":"","title":"Selective Co-occurrences for Word-Emotion Association","year":2016,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Word (group theory); Computer science; Association (psychology); Natural language processing; Word Association; Artificial intelligence; Task (project management); Emotion classification; Semantic similarity; Psychology; Linguistics","score_opus":0.05802061708972685,"score_gpt":0.35514580214277025,"score_spread":0.2971251850530434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2572452825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010120701,0.000006001338,0.9739011,0.004010374,0.0027232936,0.00015145044,0.00010625158,0.00010153036,0.017987924],"genre_scores_gemma":[0.9608734,0.000012483683,0.03677686,0.00029573642,0.0007965018,0.000025056053,0.00011862093,0.0000071443915,0.0010941917],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830574,0.00005142739,0.0003781285,0.00038738514,0.0006837547,0.00019353839],"domain_scores_gemma":[0.9962581,0.0010307,0.000377358,0.00012085479,0.0021504206,0.00006261266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040859033,0.0001414785,0.00014780587,0.00021454872,0.00014587623,0.0002438938,0.00053956703,0.00006570327,0.00010249375],"category_scores_gemma":[0.002201059,0.00011364209,0.00010421245,0.00015099758,0.000025381167,0.000131635,0.000047583773,0.0000754021,0.00012499679],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002032222,0.000076356584,0.002899518,0.0000024188084,0.00010350122,7.841238e-7,0.00010239977,0.0013835565,0.00007570817,0.9732523,0.00616001,0.015923144],"study_design_scores_gemma":[0.0011447313,0.00018413778,0.008441832,0.0001417642,0.000018022047,0.0000012089371,0.00003473619,0.7146528,0.00085092714,0.23990935,0.034253526,0.00036700763],"about_ca_topic_score_codex":0.0000032826756,"about_ca_topic_score_gemma":0.0000019800984,"teacher_disagreement_score":0.95986134,"about_ca_system_score_codex":0.00027022007,"about_ca_system_score_gemma":0.00016381581,"threshold_uncertainty_score":0.463419},"labels":[],"label_agreement":null},{"id":"W2573346038","doi":"","title":"Predicting sentential semantic compatibility for aggregation in text-to-text generation","year":2016,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Sentence; Natural language processing; Artificial intelligence; Task (project management); Cluster analysis; Process (computing); Context (archaeology); Information retrieval","score_opus":0.07688502906661919,"score_gpt":0.3287613563504564,"score_spread":0.2518763272838372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573346038","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0830668,0.00000328529,0.9096187,0.002924894,0.0024675967,0.00035816326,0.000053615116,0.00008403627,0.0014229426],"genre_scores_gemma":[0.9130513,0.0000023950508,0.08542663,0.00036236097,0.00092402194,0.000042815147,0.000060068665,0.000010443759,0.00011997],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979912,0.00006451212,0.0005554918,0.0005788722,0.0005854228,0.00022448954],"domain_scores_gemma":[0.99753165,0.00048379428,0.00018111129,0.00024020742,0.0014728089,0.000090441215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004128842,0.0001672073,0.00016391861,0.00027396582,0.000102097816,0.00018703575,0.000594028,0.000064849184,0.000030201116],"category_scores_gemma":[0.0023235618,0.00014932337,0.00005975924,0.00014735306,0.000031645464,0.00013662847,0.00013031777,0.000097385135,0.000049973933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037785267,0.000117362244,0.007682639,0.000010887648,0.000020357973,0.000004482955,0.0002330678,0.066537745,0.00059209735,0.8960314,0.00015853482,0.028573679],"study_design_scores_gemma":[0.000677692,0.0000795795,0.005149633,0.00014007142,0.0000032723478,0.0000026810253,0.000010073255,0.9368224,0.00034419607,0.056050204,0.0005494456,0.00017075529],"about_ca_topic_score_codex":0.00004430134,"about_ca_topic_score_gemma":0.000108620385,"teacher_disagreement_score":0.8702847,"about_ca_system_score_codex":0.00023668872,"about_ca_system_score_gemma":0.00018901392,"threshold_uncertainty_score":0.608923},"labels":[],"label_agreement":null},{"id":"W2573843450","doi":"","title":"Determining the Multiword Expression Inventory of a Surprise Language","year":2016,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Treebank; Surprise; Computer science; Natural language processing; Identification (biology); Artificial intelligence; Language model; Natural language; Language identification; Parsing; Psychology","score_opus":0.03742044249948087,"score_gpt":0.33147871213364416,"score_spread":0.29405826963416326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573843450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018833863,0.000074404306,0.97176236,0.0013142984,0.0013428864,0.00016579832,0.000044027893,0.0002513844,0.006210985],"genre_scores_gemma":[0.8409463,0.0000045710244,0.15849352,0.00021045413,0.0001789644,0.000007973231,0.000009281038,0.000007226874,0.00014172144],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99869984,0.00006121897,0.0002933827,0.00024824057,0.00057030737,0.0001269837],"domain_scores_gemma":[0.997772,0.00053353485,0.00025084082,0.0002502586,0.0011494728,0.000043914344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021911784,0.00012528496,0.00011284751,0.00012695105,0.00007054997,0.000081858474,0.0011915605,0.000049988852,0.000032512857],"category_scores_gemma":[0.0021393106,0.0000734663,0.000053699714,0.0000984055,0.0001014001,0.00009272188,0.00025210725,0.000121266465,0.000014339725],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026647533,0.00010198807,0.0018899216,0.000010837147,0.000020275294,0.000015455693,0.00067220925,0.00014457614,0.0031394847,0.94384444,0.00069675845,0.04943741],"study_design_scores_gemma":[0.0020371205,0.00032132128,0.004696784,0.0015925979,0.000017745233,0.000015576408,0.0001678546,0.28964153,0.05728692,0.6397289,0.003693107,0.00080051593],"about_ca_topic_score_codex":0.000006458141,"about_ca_topic_score_gemma":0.0000021997487,"teacher_disagreement_score":0.82211244,"about_ca_system_score_codex":0.0000574611,"about_ca_system_score_gemma":0.00011833764,"threshold_uncertainty_score":0.29958686},"labels":[],"label_agreement":null},{"id":"W2574914175","doi":"","title":"An interactive system for exploring community question answering forums","year":2016,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Question answering; Computer science; World Wide Web; Interface (matter); Information retrieval; User interface; Graphical user interface; Human–computer interaction","score_opus":0.13824046408852536,"score_gpt":0.3595167125248636,"score_spread":0.22127624843633825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2574914175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016022194,0.0000019807067,0.97483075,0.0005541528,0.0036095742,0.00017518376,0.00006283727,0.00028848447,0.004454857],"genre_scores_gemma":[0.852754,0.0000019804336,0.1464039,0.00010627235,0.00057363557,0.00007106653,0.000038655864,0.000013019059,0.000037456968],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985219,0.00013511088,0.00036882013,0.0003474826,0.00042075838,0.00020591082],"domain_scores_gemma":[0.99680316,0.0008224573,0.0001863806,0.00034934882,0.0017382666,0.00010037425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004914549,0.00017076405,0.00015126659,0.00018073285,0.00024686454,0.00024271742,0.0010463805,0.00005122716,0.000007678556],"category_scores_gemma":[0.0009951064,0.00014724073,0.00006113352,0.00006768989,0.000037409154,0.00039007084,0.00013845124,0.0001796165,0.000024904384],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003885642,0.00007224022,0.00015232986,0.000014141148,0.000026126996,0.0000032842227,0.00035857796,0.024433872,0.00026198957,0.9572736,0.000017636266,0.017347328],"study_design_scores_gemma":[0.0005244271,0.00019338002,0.00052950106,0.0003687999,0.0000046530527,0.000007632851,0.00022104499,0.900135,0.0007102156,0.096229196,0.0008663151,0.00020985877],"about_ca_topic_score_codex":0.00004560974,"about_ca_topic_score_gemma":0.000012146428,"teacher_disagreement_score":0.8757011,"about_ca_system_score_codex":0.00033071564,"about_ca_system_score_gemma":0.00012152233,"threshold_uncertainty_score":0.60043025},"labels":[],"label_agreement":null},{"id":"W2574986170","doi":"","title":"plWordNet 3.0 - a Comprehensive Lexical-Semantic Resource.","year":2016,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"WordNet; Computer science; Set (abstract data type); Natural language processing; Resource (disambiguation); Word (group theory); Artificial intelligence; Lexical database; Information retrieval; Word list; Linguistics; Class (philosophy); Programming language","score_opus":0.04651737362330788,"score_gpt":0.32945800977683776,"score_spread":0.2829406361535299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2574986170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00073555275,0.00008256294,0.9741423,0.006242764,0.0011336056,0.0001335051,0.0000627561,0.0005663318,0.016900638],"genre_scores_gemma":[0.76659536,0.000008256341,0.23127213,0.0012969624,0.00040221692,0.00001057315,0.000027484459,0.000012343058,0.00037470163],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99807626,0.00006397274,0.00035647134,0.00049061264,0.0007721082,0.00024059034],"domain_scores_gemma":[0.997327,0.0005798507,0.00019091893,0.000308566,0.0014826945,0.00011091797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012566787,0.00021334487,0.0001785324,0.00023452587,0.000112989204,0.0002501734,0.0013617086,0.000086885324,0.00010247188],"category_scores_gemma":[0.0011080189,0.0001581747,0.0000730142,0.00017286268,0.00012761042,0.00011065252,0.00030128102,0.00019246772,0.00017389779],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019897792,0.00005496016,0.00007150807,0.000005653246,0.000030645446,0.000033843527,0.0000495565,0.00016274992,0.00019887826,0.9855726,0.002766723,0.011033029],"study_design_scores_gemma":[0.00046992325,0.00020222981,0.00033579924,0.00026995645,0.0000051337515,0.000028388893,0.000009070711,0.06910053,0.0007950668,0.89029634,0.03815393,0.00033362853],"about_ca_topic_score_codex":0.00000587257,"about_ca_topic_score_gemma":0.0000020775308,"teacher_disagreement_score":0.7658598,"about_ca_system_score_codex":0.00011824268,"about_ca_system_score_gemma":0.00016000753,"threshold_uncertainty_score":0.6450177},"labels":[],"label_agreement":null},{"id":"W2575224319","doi":"","title":"Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs.","year":2016,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Interpretability; Computer science; Coreference; Artificial intelligence; Task (project management); Machine learning; Mechanism (biology); Recurrent neural network; Natural language processing; Long short term memory; Artificial neural network; Resolution (logic)","score_opus":0.04258849303752668,"score_gpt":0.33589017408603444,"score_spread":0.29330168104850773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2575224319","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015857743,0.000066438166,0.97373766,0.000775917,0.0012792906,0.0001463005,0.0000351462,0.00030097394,0.0078005055],"genre_scores_gemma":[0.7666558,0.0000037832956,0.23288146,0.000102152975,0.00023660553,0.000008590987,0.0000082968645,0.0000090245585,0.0000943041],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850345,0.00007144704,0.00039472233,0.00037050067,0.00044981108,0.00021008532],"domain_scores_gemma":[0.9984285,0.00034703696,0.00015599708,0.00019233821,0.0008083162,0.00006781024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031557152,0.00015766479,0.00013523287,0.00031233768,0.000072086295,0.00018152173,0.00070054934,0.00007243262,0.00004058356],"category_scores_gemma":[0.0011821083,0.00012724173,0.000038879487,0.00022084209,0.000059333117,0.00022216082,0.00018566914,0.00016904194,0.00005011727],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012370565,0.00011482926,0.0010246373,0.0000117941945,0.000013193055,0.000028064322,0.00024924122,0.0018992694,0.0015337153,0.9866354,0.00007602554,0.008401462],"study_design_scores_gemma":[0.00038221732,0.000036658097,0.0008949774,0.0003741676,0.0000030404815,0.00001396921,0.000011388256,0.62941664,0.0012789395,0.36710155,0.00032514412,0.00016129849],"about_ca_topic_score_codex":0.000021200034,"about_ca_topic_score_gemma":0.000012589083,"teacher_disagreement_score":0.75079805,"about_ca_system_score_codex":0.00030917308,"about_ca_system_score_gemma":0.00021185448,"threshold_uncertainty_score":0.5188767},"labels":[],"label_agreement":null},{"id":"W2577720462","doi":"","title":"Named Entity Disambiguation for little known referents: a topic-based approach","year":2016,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Referent; Exploit; Task (project management); Property (philosophy); Named-entity recognition; Natural language processing; Entity linking; Artificial intelligence; Information retrieval; Training set; Named entity; Linguistics; Knowledge base","score_opus":0.09194997485170264,"score_gpt":0.3341074863322806,"score_spread":0.24215751148057796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577720462","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010354781,0.000004304798,0.9760732,0.0033903804,0.00213664,0.00022848757,0.0000959793,0.00012692466,0.01690861],"genre_scores_gemma":[0.7667977,0.0000016291608,0.23119992,0.0003538147,0.0005883559,0.000055330558,0.00013574219,0.00000900552,0.0008585043],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981164,0.000047657388,0.00037411487,0.00049994857,0.00075897016,0.00020291959],"domain_scores_gemma":[0.9966441,0.00047379863,0.00017562145,0.00029245176,0.0023285588,0.000085518695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002872382,0.00016760426,0.0001492453,0.00015501808,0.000112122216,0.00021323134,0.00083297695,0.000076803895,0.000055720175],"category_scores_gemma":[0.0023635796,0.00013661364,0.00008780042,0.000089775654,0.00004844483,0.00010781084,0.0001010787,0.00009457371,0.000044480737],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002813419,0.00014136374,0.0002259744,0.00000952115,0.000023758768,0.0000010704802,0.000053823864,0.0111279255,0.000038597573,0.9708794,0.0004173593,0.017053122],"study_design_scores_gemma":[0.0008285116,0.00006916049,0.0005414755,0.00006439068,0.0000052019996,9.824636e-7,0.0000063679913,0.7943559,0.00012466856,0.1919757,0.011853969,0.00017367413],"about_ca_topic_score_codex":0.0000115798075,"about_ca_topic_score_gemma":0.000004585816,"teacher_disagreement_score":0.783228,"about_ca_system_score_codex":0.00018584577,"about_ca_system_score_gemma":0.00026582842,"threshold_uncertainty_score":0.5570943},"labels":[],"label_agreement":null},{"id":"W2579534470","doi":"","title":"Training Data Enrichment for Infrequent Discourse Relations","year":2016,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Parsing; Computer science; Training set; Relation (database); Natural language processing; Artificial intelligence; Training (meteorology); Confidence interval; Quality (philosophy); Machine learning; Data mining; Statistics","score_opus":0.1427233374709916,"score_gpt":0.40637640218257554,"score_spread":0.2636530647115839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2579534470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000061224324,0.000035791654,0.9840076,0.0069629983,0.0016662081,0.00017511938,0.0003456669,0.00026042562,0.0064849644],"genre_scores_gemma":[0.52868617,0.0000041961803,0.47017154,0.0002536436,0.00036470982,0.000022330843,0.00017976903,0.000007794642,0.0003098719],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99838924,0.000028854156,0.00034476738,0.0005062629,0.0005349302,0.00019597044],"domain_scores_gemma":[0.9971665,0.00089777267,0.00020306939,0.00046741258,0.0011858646,0.00007933894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003140328,0.00015696697,0.00012689346,0.00016735743,0.00013231518,0.00021587849,0.0019163494,0.00005846129,0.000050074468],"category_scores_gemma":[0.0036186075,0.00011826264,0.000043073425,0.00010257388,0.00007586392,0.00024308458,0.0003506139,0.000111409994,0.000039237726],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001067716,0.00004730846,0.00004087094,0.0000030226122,0.000027072307,0.0000049701116,0.00012151894,0.00027660673,0.0000860484,0.9503368,0.001997226,0.047047846],"study_design_scores_gemma":[0.0003684966,0.00007320254,0.00007592183,0.00014397326,0.000007717819,0.0000060060383,0.000019504369,0.2679378,0.0001301859,0.71319914,0.017829627,0.00020846007],"about_ca_topic_score_codex":0.0000048092807,"about_ca_topic_score_gemma":0.0000037132827,"teacher_disagreement_score":0.5286249,"about_ca_system_score_codex":0.00014331267,"about_ca_system_score_gemma":0.000363407,"threshold_uncertainty_score":0.48226103},"labels":[],"label_agreement":null},{"id":"W2579773546","doi":"","title":"Reddit Temporal N-gram Corpus and its Applications on Paraphrase and Semantic Similarity in Social Media using a Topic-based Latent Semantic Analysis.","year":2016,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Paraphrase; Computer science; Latent semantic analysis; SemEval; Natural language processing; Artificial intelligence; Social media; Semantic similarity; Similarity (geometry); n-gram; Probabilistic latent semantic analysis; Text corpus; Information retrieval; Language model; World Wide Web","score_opus":0.08700156074749933,"score_gpt":0.3337642859727162,"score_spread":0.24676272522521686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2579773546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16318132,0.000023890703,0.83335555,0.0022574428,0.00040527704,0.00022096036,0.00008504613,0.000063724554,0.0004067803],"genre_scores_gemma":[0.980411,0.000009162615,0.018923284,0.0002984686,0.00027960824,0.000017113021,0.000041243402,0.0000069446733,0.000013209814],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983712,0.00007016067,0.00038878268,0.0004945421,0.0004992218,0.00017610106],"domain_scores_gemma":[0.9983282,0.00071571127,0.00017684235,0.00015074862,0.0005401173,0.0000883569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002258312,0.00017088845,0.0002264366,0.00041355332,0.00012777762,0.00014703398,0.0003316296,0.00007845032,0.000016403006],"category_scores_gemma":[0.000547536,0.00015080869,0.00005407234,0.00028720923,0.000068733825,0.000060042486,0.00010615305,0.00014765878,0.000005684808],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026855989,0.0001353125,0.010789025,0.000016346788,0.00007696198,0.000030359011,0.00015148078,0.024942795,0.000060441555,0.9586119,0.000008975372,0.0051495577],"study_design_scores_gemma":[0.00053627905,0.00002777036,0.020556921,0.00006952812,0.00002927894,0.000002694648,0.000005783508,0.9114118,0.000024590066,0.0670229,0.0001422006,0.00017020863],"about_ca_topic_score_codex":0.000032087537,"about_ca_topic_score_gemma":0.00006765493,"teacher_disagreement_score":0.891589,"about_ca_system_score_codex":0.000121321274,"about_ca_system_score_gemma":0.00015248917,"threshold_uncertainty_score":0.61498},"labels":[],"label_agreement":null},{"id":"W2759162401","doi":"","title":"Named Entity Recognition and Hashtag Decomposition to Improve the Classification of Tweets","year":2016,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec; Université du Québec à Montréal","funders":"","keywords":"WordNet; Computer science; Artificial intelligence; Natural language processing; Preprocessor; Named-entity recognition; Segmentation; Field (mathematics); Task (project management); Information retrieval; Semantics (computer science)","score_opus":0.0829395137147931,"score_gpt":0.3343411440966311,"score_spread":0.25140163038183805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2759162401","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078173,0.0000036372276,0.91128826,0.0053536925,0.0012340853,0.00017699426,0.00008106416,0.000044313347,0.003644927],"genre_scores_gemma":[0.95691425,0.000006566814,0.042415455,0.00031761668,0.00024095344,0.000017603987,0.000027454562,0.000005002535,0.00005511688],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878395,0.000054115644,0.00031576667,0.00030789524,0.00043413666,0.00010415314],"domain_scores_gemma":[0.99781305,0.00040632798,0.00018107543,0.00019395114,0.0013441582,0.00006143817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024928074,0.00010065699,0.000095235795,0.000121531644,0.00007847357,0.00010492556,0.0004519029,0.000041569387,0.000024529221],"category_scores_gemma":[0.00090022705,0.00007144276,0.000031530224,0.00008501862,0.00005905599,0.00008829712,0.00012230226,0.00007388331,0.000050779392],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030715637,0.0000633549,0.000465759,0.0000067186056,0.000026192165,0.0000013771917,0.00019912339,0.0005215629,0.0044217724,0.8693155,0.00011731975,0.124830626],"study_design_scores_gemma":[0.0005224178,0.00014994442,0.020084893,0.0001658303,0.000009443099,0.000005733115,0.00002482508,0.5916139,0.0014672203,0.3845559,0.0012072216,0.00019264301],"about_ca_topic_score_codex":0.00001679173,"about_ca_topic_score_gemma":0.00000734106,"teacher_disagreement_score":0.8787412,"about_ca_system_score_codex":0.00007058802,"about_ca_system_score_gemma":0.00009294194,"threshold_uncertainty_score":0.29133514},"labels":[],"label_agreement":null},{"id":"W2759598007","doi":"","title":"UQAM-NTL: Named entity recognition in Twitter messages.","year":2016,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec; Université du Québec à Montréal","funders":"","keywords":"Conditional random field; Named-entity recognition; Computer science; Task (project management); Conjunction (astronomy); Artificial intelligence; Natural language processing; Named entity; Entity linking; Information retrieval; Machine learning; Knowledge base; Engineering","score_opus":0.0937972724176686,"score_gpt":0.3230069221583068,"score_spread":0.22920964974063818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2759598007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019049108,0.0000065755935,0.9360234,0.00469883,0.0028657434,0.00014042664,0.000038435133,0.00013020988,0.03704726],"genre_scores_gemma":[0.9438475,0.000012431442,0.05451508,0.00072035217,0.00043278123,0.000016570166,0.000035164114,0.000009395787,0.0004107694],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981959,0.00006975246,0.00041235055,0.00046754308,0.000644113,0.00021033108],"domain_scores_gemma":[0.9981953,0.0003671062,0.00015028649,0.00022781584,0.0009835942,0.00007592225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028458732,0.00015902019,0.00014415268,0.00027258304,0.000051956922,0.00016455301,0.0007511997,0.00007374392,0.000229068],"category_scores_gemma":[0.0012211556,0.0001355789,0.000053184936,0.00013235732,0.000051254574,0.00014819886,0.00016406881,0.000161785,0.0003177382],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027130733,0.00014642059,0.0021480438,0.0000068461195,0.000027036649,0.00004993502,0.00019787002,0.0024045845,0.00012925793,0.94958085,0.00048315266,0.0447989],"study_design_scores_gemma":[0.00094015594,0.000050183982,0.0057529556,0.00023029548,0.0000033274341,0.000008008007,0.000013211597,0.45514938,0.00018925064,0.5329611,0.0044102124,0.000291957],"about_ca_topic_score_codex":0.00002714723,"about_ca_topic_score_gemma":0.000017552773,"teacher_disagreement_score":0.92479837,"about_ca_system_score_codex":0.00018794168,"about_ca_system_score_gemma":0.0001724314,"threshold_uncertainty_score":0.5528747},"labels":[],"label_agreement":null},{"id":"W2785621828","doi":"","title":"Lexfom: a lexical functions ontology model","year":2016,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec","funders":"","keywords":"Syntagmatic analysis; Computer science; Lexical item; Natural language processing; Lexical density; Lexical grammar; Lexical choice; Lexical functional grammar; Artificial intelligence; Relation (database); Function (biology); Ontology; Linguistics; Representation (politics); Perspective (graphical); Generative grammar; Phrase structure rules","score_opus":0.0599011662098063,"score_gpt":0.34416787995760867,"score_spread":0.2842667137478024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785621828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011262773,0.000028500885,0.9624685,0.0075840624,0.0011519413,0.00007091679,0.000040888215,0.0004240035,0.028118534],"genre_scores_gemma":[0.63897604,0.000003865226,0.35848737,0.0009484882,0.00025491707,0.000012886141,0.000017728094,0.000007569424,0.001291159],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99847734,0.00003687394,0.00030043643,0.00043257728,0.0005430039,0.00020977724],"domain_scores_gemma":[0.99777865,0.00031149003,0.00013409193,0.00024582606,0.0014351088,0.00009481381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014164603,0.00016946466,0.00014048188,0.00020442774,0.00011293628,0.00015703203,0.0010334543,0.00009741529,0.000095232885],"category_scores_gemma":[0.001339459,0.0001266185,0.00006520253,0.00011874815,0.00010788773,0.00013233404,0.00020695014,0.00017990713,0.00018679233],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017685234,0.000065475426,0.000054147167,0.0000020441169,0.000018336314,0.00001509279,0.000032253403,0.0016329249,0.00015163259,0.9840209,0.0026935104,0.011295987],"study_design_scores_gemma":[0.0001811806,0.000048155573,0.00003551412,0.00004141956,0.000002526423,0.000008636712,0.0000018353628,0.4151944,0.00017995994,0.58237237,0.0018067346,0.00012728051],"about_ca_topic_score_codex":0.000008400101,"about_ca_topic_score_gemma":0.000006220353,"teacher_disagreement_score":0.6388634,"about_ca_system_score_codex":0.00014072082,"about_ca_system_score_gemma":0.00030144982,"threshold_uncertainty_score":0.51633525},"labels":[],"label_agreement":null},{"id":"W2787587581","doi":"","title":"A Proposal for combining “general” and specialized frames","year":2016,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"FrameNet; Merge (version control); Computer science; Domain (mathematical analysis); Resource (disambiguation); Semantics (computer science); Natural language processing; Representation (politics); Artificial intelligence; Information retrieval; Programming language; Parsing; Mathematics","score_opus":0.034748774668634734,"score_gpt":0.33961479867365135,"score_spread":0.3048660240050166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787587581","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004203661,0.000040349114,0.9876851,0.0061886515,0.0011066656,0.00019069186,0.000051388055,0.00024585586,0.004070927],"genre_scores_gemma":[0.43529162,0.000006419914,0.5636195,0.00035576624,0.00039249804,0.000019702507,0.000014619405,0.0000073045026,0.00029256698],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988519,0.000027424137,0.00024387444,0.00035252984,0.00036829617,0.00015596277],"domain_scores_gemma":[0.9979729,0.00046368295,0.00014185088,0.00013085344,0.0012255935,0.000065086635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017720496,0.00014229288,0.0001356056,0.00013894332,0.00010273657,0.00026614254,0.0005823572,0.00006204737,0.000024844005],"category_scores_gemma":[0.0017081014,0.00010522239,0.000039170096,0.000067534296,0.000085144646,0.00010058439,0.0001505289,0.00008648442,0.0000101475225],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028082753,0.000029671834,0.000069381655,0.000006009928,0.000017239592,0.0000051720012,0.00005868185,0.00002270626,0.0003454275,0.97903365,0.0007310698,0.019652924],"study_design_scores_gemma":[0.0005304716,0.000107266686,0.00009254018,0.000112701346,0.0000033441538,0.0000073684105,0.0000034132047,0.10203789,0.00092685345,0.89079374,0.005220731,0.0001637058],"about_ca_topic_score_codex":0.0000043639457,"about_ca_topic_score_gemma":0.0000020360715,"teacher_disagreement_score":0.43487126,"about_ca_system_score_codex":0.00005903938,"about_ca_system_score_gemma":0.00023572872,"threshold_uncertainty_score":0.42908445},"labels":[],"label_agreement":null},{"id":"W2814750830","doi":"","title":"Learning Emotion-enriched Word Representations","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Similarity (geometry); Word (group theory); Natural language processing; Computer science; Meaning (existential); Affect (linguistics); Representation (politics); Artificial intelligence; Emotion classification; Contrast (vision); Psychology; Cognitive psychology; Linguistics; Communication","score_opus":0.057740884668665775,"score_gpt":0.35367466296883426,"score_spread":0.2959337783001685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2814750830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050258185,0.0000064666106,0.82220846,0.0020532424,0.0034357784,0.000088459725,0.0000066407883,0.00021666156,0.16695847],"genre_scores_gemma":[0.9243928,0.00000730432,0.07194726,0.0004458211,0.0013309659,0.0000065236713,0.00012550916,0.000009444323,0.001734342],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817306,0.00007567129,0.00038482758,0.00045139602,0.0007245983,0.00019043387],"domain_scores_gemma":[0.99695504,0.0002867272,0.0002207473,0.00022541726,0.002220281,0.00009181636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023908321,0.00015484134,0.00013996637,0.00030172477,0.0003125866,0.00044039192,0.000758279,0.000050845043,0.0005223796],"category_scores_gemma":[0.0012240275,0.00016182422,0.00008851523,0.0003587859,0.00009401874,0.00011991018,0.00017095626,0.00020181648,0.0006260067],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009502215,0.00007344177,0.0017801689,0.0000013655009,0.0000688461,0.0000065166105,0.00046026034,0.01387535,0.00004252797,0.97630155,0.0014809481,0.0058995155],"study_design_scores_gemma":[0.00028950037,0.00010998708,0.0070225727,0.000028482893,0.000008248462,0.000003818248,0.00013056723,0.9045191,0.00006357,0.076565005,0.011067213,0.00019194627],"about_ca_topic_score_codex":0.00001355371,"about_ca_topic_score_gemma":0.0000033260872,"teacher_disagreement_score":0.919367,"about_ca_system_score_codex":0.00006480315,"about_ca_system_score_gemma":0.0001287022,"threshold_uncertainty_score":0.8046258},"labels":[],"label_agreement":null},{"id":"W2816262648","doi":"","title":"Farewell Freebase: Migrating the SimpleQuestions Dataset to DBpedia.","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Leverage (statistics); Question answering; Knowledge graph; Information retrieval; Benchmark (surveying); Entity linking; Task (project management); Simple (philosophy); Graph; World Wide Web; Knowledge base; Theoretical computer science; Artificial intelligence","score_opus":0.07890198444019045,"score_gpt":0.36377097578953593,"score_spread":0.2848689913493455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2816262648","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010568486,0.0000049300443,0.96742874,0.008967657,0.0029817324,0.000169657,0.0006373204,0.00011951272,0.018633584],"genre_scores_gemma":[0.8395382,0.0000014390265,0.1533947,0.0044544567,0.0019799513,0.000012418915,0.00044731874,0.00000940284,0.00016210259],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817353,0.00006412683,0.00036853054,0.00045512087,0.0007084048,0.00023026366],"domain_scores_gemma":[0.9970835,0.00049847626,0.00012947815,0.0004791639,0.0016841163,0.00012526216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031632342,0.00016882556,0.00011080917,0.00014568123,0.0003287831,0.0004282828,0.0016218196,0.00004478699,0.00014087283],"category_scores_gemma":[0.002267076,0.00014205101,0.000039448038,0.00021646412,0.00009414969,0.00008025678,0.00038687352,0.0002078154,0.0005387528],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000063198518,0.00003680315,0.000111656336,0.0000017611441,0.000017890221,0.000007912092,0.00026888866,0.0197946,0.000018899953,0.95930827,0.016994407,0.0034325765],"study_design_scores_gemma":[0.00013187164,0.0000891457,0.0004035502,0.00003636598,0.000004549107,0.00000787545,0.000029919056,0.80946857,0.00007132688,0.08628757,0.10329831,0.00017095587],"about_ca_topic_score_codex":0.00008973041,"about_ca_topic_score_gemma":0.00008549015,"teacher_disagreement_score":0.8730207,"about_ca_system_score_codex":0.00007817386,"about_ca_system_score_gemma":0.00023442293,"threshold_uncertainty_score":0.6924757},"labels":[],"label_agreement":null},{"id":"W2847160827","doi":"","title":"Reproducing and Regularizing the SCRN Model","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Dropout (neural networks); Tying; Computer science; Language model; Task (project management); Artificial intelligence; Data modeling; Machine learning; Algorithm; Engineering","score_opus":0.07632542245516838,"score_gpt":0.3238737291177107,"score_spread":0.2475483066625423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2847160827","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002407446,0.000015749678,0.9516423,0.0050505577,0.0013040444,0.00008159338,0.0000059890517,0.000089085384,0.039403263],"genre_scores_gemma":[0.800552,0.0000035316846,0.19722733,0.0010563149,0.0008459774,0.0000041760654,0.000005962748,0.0000061290048,0.00029857355],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998622,0.000032317366,0.00024826828,0.00046893363,0.0004810317,0.00014745856],"domain_scores_gemma":[0.9980487,0.00017626431,0.000111255635,0.00036319403,0.0012482776,0.000052311854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003833792,0.00011746,0.00008758241,0.00008667384,0.0002550443,0.0003335202,0.00074962084,0.00003782657,0.000013063377],"category_scores_gemma":[0.0011506042,0.00009654317,0.000026687807,0.00009022042,0.0001323675,0.00007456341,0.00028570183,0.00016578891,0.00003145669],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050923995,0.000012784115,0.00006801968,0.0000017081384,0.000013176434,0.0000024625583,0.0003538792,0.03933914,0.000025552972,0.95483917,0.00022379802,0.005115246],"study_design_scores_gemma":[0.000086392276,0.000022193253,0.0001820346,0.00002547495,0.0000021374344,0.000007080674,0.000012865165,0.68797153,0.00005992164,0.31053784,0.001015207,0.00007733034],"about_ca_topic_score_codex":0.00001340285,"about_ca_topic_score_gemma":0.000004431365,"teacher_disagreement_score":0.7981446,"about_ca_system_score_codex":0.000042640964,"about_ca_system_score_gemma":0.00015091097,"threshold_uncertainty_score":0.3936916},"labels":[],"label_agreement":null},{"id":"W2848618769","doi":"","title":"Authorship Identification for Literary Book Recommendations","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Authorship Attribution and Profiling","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Reading (process); Recommender system; Pleasure; Computer science; Identification (biology); Style (visual arts); Factor (programming language); Writing style; Qualitative analysis; Information retrieval; World Wide Web; Natural language processing; Artificial intelligence; Qualitative research; Psychology; Linguistics; Literature; Art; Sociology","score_opus":0.11276954657335071,"score_gpt":0.39432093701642357,"score_spread":0.28155139044307287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2848618769","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011200179,0.000022777573,0.96345025,0.011269848,0.005665537,0.00024208608,0.000171358,0.00020920257,0.018856961],"genre_scores_gemma":[0.8414903,0.000007946948,0.15083973,0.0025086135,0.0016488105,0.00005499136,0.00069071795,0.000013473756,0.0027453941],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983007,0.00008636202,0.000495565,0.0004604366,0.0004383945,0.00021858841],"domain_scores_gemma":[0.99561113,0.0005019096,0.00025787813,0.0002627001,0.0032489772,0.000117383934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006030019,0.00016412637,0.00012497573,0.00024267306,0.00035746174,0.0004701507,0.0008780558,0.00009485103,0.0002619854],"category_scores_gemma":[0.001964726,0.00017896622,0.00008219432,0.00020907451,0.00008986887,0.0002026511,0.00010751396,0.00017792052,0.0003390498],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026639587,0.000060238133,0.000050786628,0.0000050838967,0.000024727391,0.0000010772727,0.000363049,0.0002176466,0.000037864367,0.9802175,0.011078936,0.007916442],"study_design_scores_gemma":[0.00020793077,0.00008575285,0.000320438,0.00003551267,0.0000042985116,0.0000028738013,0.000008227743,0.43387675,0.00040105052,0.43370846,0.13120253,0.00014616834],"about_ca_topic_score_codex":0.000002047842,"about_ca_topic_score_gemma":0.0000017506117,"teacher_disagreement_score":0.84137833,"about_ca_system_score_codex":0.00010585708,"about_ca_system_score_gemma":0.00022732276,"threshold_uncertainty_score":0.7298031},"labels":[],"label_agreement":null},{"id":"W2849292054","doi":"","title":"Automatically Extracting Qualia Relations for the Rich Event Ontology","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Qualia; Computer science; Ontology; Commonsense knowledge; Event (particle physics); Artificial intelligence; Focus (optics); Semantics (computer science); Structuring; Ontology learning; Natural language processing; Upper ontology; Suggested Upper Merged Ontology; Knowledge extraction; Semantic Web; Epistemology; Consciousness","score_opus":0.08858363997313826,"score_gpt":0.3894872429872838,"score_spread":0.30090360301414554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2849292054","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009133612,0.000014137122,0.95209366,0.009172087,0.0039428007,0.00019950906,0.000018789287,0.0001301546,0.03351551],"genre_scores_gemma":[0.82015365,0.000002113815,0.17738391,0.0008385814,0.00091524405,0.000031318734,0.00002076722,0.0000061224127,0.00064828957],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998548,0.000056021552,0.00041259572,0.00032007782,0.00045380692,0.00020953278],"domain_scores_gemma":[0.99431443,0.0030265753,0.00021790709,0.00025285257,0.002138378,0.000049875343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042242778,0.00013988154,0.00013329812,0.00010275541,0.00037484683,0.00024217987,0.0010360503,0.00006966593,0.00013556205],"category_scores_gemma":[0.005358311,0.00010901999,0.000070638554,0.00011948615,0.00016197051,0.000067955625,0.00012684062,0.00015634691,0.00023252833],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015765043,0.000059954975,0.00024489014,0.000002850953,0.000053545198,0.000002907522,0.00032437086,0.0036228437,0.000009147747,0.98894477,0.0027503252,0.003968652],"study_design_scores_gemma":[0.00020224671,0.00010848549,0.0059736082,0.00002257701,0.0000097240345,0.000008969092,0.00006289923,0.7121881,0.000023901888,0.26957253,0.01172161,0.000105337735],"about_ca_topic_score_codex":0.000019282294,"about_ca_topic_score_gemma":0.00004292819,"teacher_disagreement_score":0.8192403,"about_ca_system_score_codex":0.00007334429,"about_ca_system_score_gemma":0.00026485522,"threshold_uncertainty_score":0.64147854},"labels":[],"label_agreement":null},{"id":"W2865911429","doi":"","title":"Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Automatic summarization; Computer science; Sentence; Natural language processing; Artificial intelligence; Set (abstract data type); Machine translation; Word embedding; Multi-document summarization; Word (group theory); Information retrieval; Embedding; Linguistics","score_opus":0.09255504555690312,"score_gpt":0.35067864217008043,"score_spread":0.2581235966131773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2865911429","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019227285,0.0000055608693,0.9714387,0.00037144776,0.0032860602,0.00016540384,0.00001919588,0.0001233481,0.005362998],"genre_scores_gemma":[0.74055874,0.00000444199,0.2583115,0.00035521196,0.0006255122,0.0000051722964,0.000050740782,0.00001008818,0.0000785606],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788076,0.00006362153,0.000448594,0.00055856205,0.00080871646,0.00023975187],"domain_scores_gemma":[0.9965663,0.0002238833,0.00023066219,0.00026954882,0.0025988095,0.000110789944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021380687,0.00021325622,0.00015391248,0.00023755271,0.00023289512,0.000314883,0.0008110492,0.00007673706,0.00011245847],"category_scores_gemma":[0.0008346924,0.00022523694,0.00005828557,0.00020359861,0.00010738713,0.00017256255,0.00022565099,0.00019150038,0.00013074625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003916254,0.0001713156,0.0007992749,0.000008153049,0.00005159957,0.000023358145,0.0005311417,0.15378876,0.00056674995,0.83995277,0.000050495804,0.0040171975],"study_design_scores_gemma":[0.00045467512,0.00007731343,0.0020216324,0.00009380146,0.00000749658,0.000008743067,0.000046987858,0.9449942,0.00031332675,0.0514223,0.00033968795,0.00021981719],"about_ca_topic_score_codex":0.00006779522,"about_ca_topic_score_gemma":0.00000999946,"teacher_disagreement_score":0.79120547,"about_ca_system_score_codex":0.0001965213,"about_ca_system_score_gemma":0.00029152076,"threshold_uncertainty_score":0.91848963},"labels":[],"label_agreement":null},{"id":"W2875408189","doi":"","title":"The APVA-TURBO Approach To Question Answering in Knowledge Base","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Question answering; Computer science; Correctness; Bottleneck; Knowledge base; Object (grammar); Artificial intelligence; Base (topology); Subject (documents); Information retrieval; Theoretical computer science; Machine learning; Programming language; World Wide Web","score_opus":0.06531592434647242,"score_gpt":0.3406081913215932,"score_spread":0.27529226697512077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2875408189","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002513832,0.0000131933,0.8715593,0.0012661086,0.0028357229,0.0001533294,0.000006369505,0.000083975814,0.12156812],"genre_scores_gemma":[0.862905,0.0000028951317,0.13540085,0.00038542543,0.0009915937,0.000021051508,0.000013761576,0.000007966835,0.0002714179],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998434,0.000077441735,0.00035216106,0.00042753207,0.00046668787,0.00024216816],"domain_scores_gemma":[0.9978457,0.00032768655,0.000085603904,0.0002792889,0.0013626661,0.00009904717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005605672,0.00014558565,0.000106291605,0.0001962695,0.00018614256,0.00034523258,0.0011010083,0.00004968825,0.000008368925],"category_scores_gemma":[0.0014253221,0.00012732402,0.000033782715,0.00024727962,0.00007021613,0.00006853541,0.00024132628,0.00019664342,0.00014637575],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000138998885,0.00007478104,0.00015860703,0.0000027894296,0.0000077558125,0.0000028122367,0.00048089566,0.03289331,0.000010552972,0.95546263,0.00022891592,0.010663023],"study_design_scores_gemma":[0.00017743811,0.00005555214,0.0013246665,0.000054500295,0.0000011771339,0.0000039503375,0.000030942847,0.90677595,0.00005108608,0.08408241,0.0073035727,0.00013876296],"about_ca_topic_score_codex":0.000031416617,"about_ca_topic_score_gemma":0.00004688915,"teacher_disagreement_score":0.87388265,"about_ca_system_score_codex":0.00017628819,"about_ca_system_score_gemma":0.00022928895,"threshold_uncertainty_score":0.5192123},"labels":[],"label_agreement":null},{"id":"W2888880755","doi":"","title":"Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Principle of compositionality; Computer science; Artificial intelligence; Natural language processing; Character (mathematics); Treebank; Expression (computer science); Artificial neural network; Programming language; Annotation","score_opus":0.06360377069358782,"score_gpt":0.3540654245744966,"score_spread":0.2904616538809088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888880755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004524056,0.00027146935,0.9754065,0.00052888505,0.0024313803,0.00020223219,0.00021049814,0.00031391106,0.016111063],"genre_scores_gemma":[0.6591848,0.000002725429,0.33923593,0.0003069456,0.000988926,0.000008171136,0.00014362068,0.000011672359,0.00011718159],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804926,0.000099249795,0.0004895937,0.00048072462,0.0006337372,0.00024741178],"domain_scores_gemma":[0.9958454,0.0003326113,0.0003551041,0.00032582445,0.0030431547,0.000097947406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026735256,0.00024611276,0.00024242974,0.00019022946,0.00015680482,0.00020496741,0.001331492,0.00012531353,0.0000723284],"category_scores_gemma":[0.00037909386,0.00023026748,0.00008957244,0.0002442898,0.00017657179,0.00020793862,0.00038136943,0.00031591894,0.00002997656],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005849254,0.00017074423,0.00007324354,0.000019401788,0.000029441368,0.000013064134,0.0011671126,0.006863066,0.0009801631,0.98539937,0.0009114216,0.004314499],"study_design_scores_gemma":[0.00028464242,0.0000907592,0.00028034463,0.0002803422,0.0000057185384,0.00001241873,0.0000267294,0.717918,0.0020621172,0.2783196,0.00048227597,0.0002370549],"about_ca_topic_score_codex":0.00002857141,"about_ca_topic_score_gemma":0.000008522462,"teacher_disagreement_score":0.7110549,"about_ca_system_score_codex":0.00008352491,"about_ca_system_score_gemma":0.00018120841,"threshold_uncertainty_score":0.9390035},"labels":[],"label_agreement":null},{"id":"W2889229100","doi":"","title":"NLP for Conversations: Sentiment, Summarization, and Group Dynamics","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of the Fraser Valley","funders":"","keywords":"Automatic summarization; Computer science; Natural language processing; Sentiment analysis; Artificial intelligence; Dynamics (music); Information retrieval; Group (periodic table); Psychology","score_opus":0.029641051308958492,"score_gpt":0.33400512286884554,"score_spread":0.30436407155988704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889229100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022873981,0.0000059121244,0.98510706,0.0013288442,0.0008410563,0.00020433999,0.00006772694,0.00018254119,0.012033785],"genre_scores_gemma":[0.65475446,0.000009998177,0.34360698,0.0005893228,0.00037410198,0.000025323567,0.00033500182,0.000009663789,0.0002951428],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869466,0.000027507103,0.00032474627,0.00041571647,0.00038352763,0.00015383662],"domain_scores_gemma":[0.99665546,0.0003312222,0.00020108136,0.00019566862,0.0025463144,0.00007025339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018226441,0.00015513077,0.0001368855,0.0002340172,0.00020372542,0.00026942889,0.00052764855,0.000058399735,0.00003166263],"category_scores_gemma":[0.0008596759,0.00016914503,0.00004835144,0.00017620935,0.00015853466,0.00014379172,0.00014838426,0.00008526821,0.000023379867],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012493152,0.000053810403,0.00046468852,0.0000042665556,0.000045242447,0.000001169833,0.00007284122,0.0004951333,0.000017612201,0.99399006,0.0006902702,0.0041523976],"study_design_scores_gemma":[0.00020775186,0.000094772724,0.0003544072,0.000017917757,0.000007680129,0.0000019088413,0.000012703083,0.6216614,0.00006746326,0.3717184,0.0057367496,0.0001187825],"about_ca_topic_score_codex":0.000009104454,"about_ca_topic_score_gemma":0.000026833899,"teacher_disagreement_score":0.6545257,"about_ca_system_score_codex":0.00012969351,"about_ca_system_score_gemma":0.00008732676,"threshold_uncertainty_score":0.6897534},"labels":[],"label_agreement":null},{"id":"W2914220664","doi":"","title":"Deep Models for Arabic Dialect Identification on Benchmarked Data","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Benchmark (surveying); Computer science; Artificial intelligence; Task (project management); Deep learning; Arabic; Natural language processing; Binary classification; Deep neural networks; Identification (biology); Machine learning; Artificial neural network; Recurrent neural network; Test data; Binary number; Speech recognition; Support vector machine; Linguistics; Mathematics; Geography","score_opus":0.10115388688345246,"score_gpt":0.3765717506027052,"score_spread":0.27541786371925275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914220664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009287752,0.00003137437,0.98580986,0.0011503412,0.0021741772,0.00025371724,0.00013872211,0.00026703114,0.010081911],"genre_scores_gemma":[0.62919503,0.000003921534,0.36861864,0.0005881207,0.000850204,0.000022285976,0.0005925079,0.000010708423,0.00011854203],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797714,0.000045725727,0.0003982279,0.0006981606,0.0006784282,0.00020230356],"domain_scores_gemma":[0.9956573,0.00052609446,0.0002619635,0.0006476,0.0028367208,0.00007028241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004256117,0.00018967163,0.00014424206,0.00024298718,0.00020661365,0.00047888214,0.0026044133,0.00008198056,0.000031793563],"category_scores_gemma":[0.0026168681,0.00018658256,0.0000456692,0.00018074797,0.000105652776,0.0002641137,0.00031181247,0.00017205605,0.000059308568],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004994828,0.00008310348,0.0000070191954,0.0000074498844,0.000026034339,0.0000028959012,0.00009448813,0.00352941,0.00005483354,0.9814138,0.0019423721,0.012788677],"study_design_scores_gemma":[0.00013315675,0.00009371698,0.00003693794,0.000038031587,0.000004027241,0.0000019199458,0.000002484114,0.5473148,0.00028629342,0.4509433,0.0010273019,0.0001180229],"about_ca_topic_score_codex":0.000009185227,"about_ca_topic_score_gemma":0.00000984462,"teacher_disagreement_score":0.6291022,"about_ca_system_score_codex":0.000108290544,"about_ca_system_score_gemma":0.00021029137,"threshold_uncertainty_score":0.76086164},"labels":[],"label_agreement":null},{"id":"W2914752849","doi":"","title":"Cyberbullying Intervention Based on Convolutional Neural Networks","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Bullying, Victimization, and Aggression","field":"Psychology","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Flagging; Computer science; Convolutional neural network; Intervention (counseling); Interface (matter); Process (computing); User interface; Mechanism (biology); Service (business); Human–computer interaction; Natural (archaeology); World Wide Web; Artificial intelligence; Multimedia; Psychology","score_opus":0.048301103511600885,"score_gpt":0.3533375149440351,"score_spread":0.3050364114324342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914752849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012785319,0.000035411318,0.70356345,0.0015211711,0.024003752,0.00039813085,0.00019438786,0.00032846522,0.25716993],"genre_scores_gemma":[0.988922,0.000002018124,0.0034049759,0.0019766004,0.0036396028,0.000030720137,0.00082696293,0.000032673044,0.0011644586],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785537,0.00015559334,0.00051160814,0.0005145031,0.0006765516,0.0002864036],"domain_scores_gemma":[0.9969382,0.00041583655,0.0003384972,0.00021176375,0.0019777638,0.00011790622],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00028025982,0.0002584594,0.00017904668,0.00029091683,0.000316982,0.00014550395,0.00038685458,0.0001557235,0.006128346],"category_scores_gemma":[0.00068377145,0.00026180552,0.00012603305,0.00017281689,0.00021655374,0.00003956066,0.000053711956,0.00033984572,0.00046947433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055963174,0.00050693634,0.0036672382,0.000005390662,0.000077148405,0.000024081606,0.00015524586,0.2940863,0.0000037286343,0.68963206,0.006753276,0.0045289686],"study_design_scores_gemma":[0.0012230524,0.00041258754,0.010917351,0.00020374038,0.000016367956,0.000007839983,0.00005772,0.97122204,0.0000068030704,0.008588685,0.0070833038,0.0002604815],"about_ca_topic_score_codex":0.00004540383,"about_ca_topic_score_gemma":0.000012272481,"teacher_disagreement_score":0.9761367,"about_ca_system_score_codex":0.0001329983,"about_ca_system_score_gemma":0.000084425454,"threshold_uncertainty_score":0.99998343},"labels":[],"label_agreement":null},{"id":"W2962696263","doi":"","title":"Extracting Parallel Sentences with Bidirectional Recurrent Neural Networks to Improve Machine Translation","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Machine translation; Sentence; Artificial intelligence; Task (project management); Natural language processing; Translation (biology); Feature engineering; Recurrent neural network; Parallel corpora; Baseline (sea); Artificial neural network; Feature (linguistics); Feature extraction; Speech recognition; Deep learning","score_opus":0.03912998338648059,"score_gpt":0.3326886547462771,"score_spread":0.2935586713597965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962696263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007078235,0.000065881555,0.9895089,0.0022221026,0.0024882588,0.00020910104,0.000028562372,0.0003485367,0.004420802],"genre_scores_gemma":[0.5793703,0.000002614429,0.41918373,0.0004972018,0.0008184182,0.000015929263,0.00005044606,0.000009473332,0.00005188713],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980262,0.000053633637,0.00035344803,0.00055049424,0.00076807506,0.0002481303],"domain_scores_gemma":[0.99714875,0.00028181996,0.00021665786,0.00018642293,0.0020419536,0.00012438552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023019321,0.00023210059,0.00015608815,0.00023959983,0.00022457316,0.00040038736,0.0007900103,0.00006957733,0.000050189195],"category_scores_gemma":[0.00044459422,0.0002006908,0.00004822792,0.0003123464,0.000091193295,0.00017900273,0.00010796589,0.00031828333,0.000024173822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032778239,0.00021296073,0.0006983272,0.000012420132,0.0000843692,0.000042141797,0.00045359362,0.054174982,0.00014781338,0.7279155,0.0004939099,0.21543615],"study_design_scores_gemma":[0.00025702713,0.00040993752,0.00026838912,0.00008841777,0.0000062583354,0.000024604244,0.000011833147,0.9714423,0.00014771541,0.025839144,0.0012460483,0.00025834783],"about_ca_topic_score_codex":0.00003921289,"about_ca_topic_score_gemma":0.000042252563,"teacher_disagreement_score":0.9172673,"about_ca_system_score_codex":0.00009703132,"about_ca_system_score_gemma":0.00012712061,"threshold_uncertainty_score":0.81839335},"labels":[],"label_agreement":null}]}