{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":51,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":51,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"4dfd76f1328d","filters":{"venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference"}},"results":[{"id":"W3160543801","doi":"10.32473/flairs.v34i1.128339","title":"An Exploration On-demand Article Recommender System for Cancer Patients Information Provisioning","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Provisioning; Recommender system; Baseline (sea); Cancer; Knowledge management; World Wide Web; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.158861643164401,"gpt":0.3893944126586932,"spread":0.2305327694942922,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001717301,0.0001427386,0.0001707025,0.00009640665,0.0004461825,0.0009862044,0.001524222,0.00009708464,0.00001462948],"category_scores_gemma":[0.0004176612,0.0001129124,0.0001522905,0.000535586,0.00008268312,0.003554046,0.000393425,0.000297817,0.000009141509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003204659,"about_ca_system_score_gemma":0.0002461209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008440596,"about_ca_topic_score_gemma":0.00001285299,"domain_scores_codex":[0.9975582,0.00004555496,0.0006014374,0.0003776753,0.001085543,0.0003315942],"domain_scores_gemma":[0.9935175,0.0001925222,0.0002843402,0.0002401842,0.005674896,0.00009061609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004959373,0.000223868,0.0009941212,0.0001793336,0.00006563751,1.192808e-7,0.00620298,0.0001785312,0.01723299,0.8978759,0.004046939,0.07294997],"study_design_scores_gemma":[0.00008661881,0.0001942381,0.0001431636,0.0003780502,0.000004823206,0.000001151138,0.006859144,0.2456588,0.6927763,0.05162973,0.002100978,0.0001669604],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.140224,0.00003160902,0.8347767,0.01534532,0.00285159,0.001792313,0.00006195949,0.0002515856,0.004664911],"genre_scores_gemma":[0.9907334,0.00006125857,0.008418844,0.0001420381,0.0001983371,0.0003729784,0.00001004558,0.000009013685,0.00005402687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8505095,"threshold_uncertainty_score":0.9509991,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3160310472","doi":"10.32473/flairs.v34i1.128502","title":"Multilingual Automatic Term Extraction in Low-Resource Domains","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Task (project management); Term (time); Artificial intelligence; Resource (disambiguation); Raw data; Sequence labeling; Sequence (biology); Domain (mathematical analysis); Artificial neural network; Natural language processing; Deep learning; Information extraction; Machine learning; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.08710697055732607,"gpt":0.4004800386628963,"spread":0.3133730681055702,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002078337,0.0002041935,0.0002744965,0.0002221246,0.0002646479,0.0005162154,0.003189277,0.0001410629,0.00008844407],"category_scores_gemma":[0.001762214,0.0001790473,0.0003041015,0.001793862,0.000421635,0.001061137,0.001454756,0.0008989691,0.00002369183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004439155,"about_ca_system_score_gemma":0.0003974242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006845812,"about_ca_topic_score_gemma":0.00007803932,"domain_scores_codex":[0.9961391,0.00005852997,0.0007726431,0.0007010722,0.001793482,0.0005351656],"domain_scores_gemma":[0.9961059,0.000489856,0.0002878123,0.0003944755,0.002619711,0.0001022488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002526952,0.0004802595,0.002578113,0.0001311717,0.0001084078,0.00001141271,0.006400238,0.0003392325,0.5017235,0.3444209,0.0005724651,0.1432091],"study_design_scores_gemma":[0.00003771513,0.00002352843,0.0005776777,0.0002894874,0.0000044572,0.00001096844,0.002612766,0.1910461,0.715722,0.08925138,0.0002695278,0.0001544012],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8505075,0.00008193103,0.1353222,0.006354816,0.0005591424,0.0005470475,0.0000080201,0.0002353244,0.006384049],"genre_scores_gemma":[0.9739368,0.000130608,0.02518836,0.00008563096,0.0001645121,0.00006448942,0.000002828893,0.00001411733,0.0004126522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2551695,"threshold_uncertainty_score":0.7301338,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3162322285","doi":"10.32473/flairs.v34i1.128427","title":"Ensemble-based Semi-Supervised Learning for Hate Speech Detection","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"","keywords":"Leverage (statistics); Computer science; Ensemble learning; Artificial intelligence; Labeled data; Voice activity detection; Machine learning; Supervised learning; Natural language processing; Speech recognition; Speech processing; Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.1015021418685871,"gpt":0.3376627144692195,"spread":0.2361605726006324,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002227236,0.0001973452,0.0002142535,0.0001322447,0.0007053282,0.0008230943,0.001946072,0.0001622678,0.00005546758],"category_scores_gemma":[0.001869183,0.0001760554,0.000389223,0.001210534,0.0002356389,0.0006266777,0.0005757969,0.0007671627,0.00003178647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002551182,"about_ca_system_score_gemma":0.0004476213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000852781,"about_ca_topic_score_gemma":0.00004035001,"domain_scores_codex":[0.9967236,0.00005457744,0.0005132316,0.000681288,0.001457823,0.0005695153],"domain_scores_gemma":[0.9931443,0.0004502333,0.0002049895,0.0002563128,0.005820449,0.0001237485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007353141,0.00009520501,0.000139367,0.00009288476,0.00006677448,0.000001382427,0.0009067326,0.0006212663,0.7946385,0.06590591,0.0003404059,0.137118],"study_design_scores_gemma":[0.00005462334,0.00008437897,0.00002432478,0.00008992251,0.000005227126,0.0000076928,0.001051961,0.3283182,0.6384166,0.03037995,0.001448454,0.0001186241],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2261693,0.00004960882,0.7598013,0.007484033,0.001790504,0.0007203536,0.000007419265,0.0001819721,0.003795493],"genre_scores_gemma":[0.9817951,0.00008121198,0.01641374,0.000112642,0.0003562587,0.0001262974,0.000004369744,0.00001858727,0.001091782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7556258,"threshold_uncertainty_score":0.7937117,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4225399182","doi":"10.32473/flairs.v35i.130545","title":"Preliminary Thoughts on Defining f(x) for Ethical Machines","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Normative; Ethical issues; Ethical theories; Engineering ethics; Ethical theory; Computer science; Epistemology; Management science; Philosophy; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.241515362300319,"gpt":0.4686553522257582,"spread":0.2271399899254392,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.009834615,0.0001580886,0.0002123283,0.0001113083,0.003888577,0.0004875049,0.002521407,0.000237731,0.000241109],"category_scores_gemma":[0.008336889,0.0001373988,0.0003831033,0.0007108013,0.00133441,0.0003131971,0.0008821791,0.002291492,0.00001246085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004914873,"about_ca_system_score_gemma":0.0009565392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008326048,"about_ca_topic_score_gemma":0.0001258034,"domain_scores_codex":[0.9952151,0.0001477323,0.0004687186,0.0004462819,0.003085371,0.0006368616],"domain_scores_gemma":[0.9935161,0.002257832,0.0002420057,0.0001403727,0.003689399,0.0001542602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002487157,0.0001235983,0.0002007783,0.0000267386,0.000053621,2.674832e-7,0.03149434,0.0001095255,0.002831117,0.9468579,0.0113146,0.006738835],"study_design_scores_gemma":[0.00006801974,0.0005032509,0.0001115566,0.0001123031,0.00001457138,0.00000114099,0.06988233,0.00921574,0.01816751,0.8666965,0.03498803,0.0002390086],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.4135774,0.0001462965,0.001373366,0.4392466,0.006749617,0.002901104,0.000345925,0.0001986711,0.135461],"genre_scores_gemma":[0.9942763,0.0001813943,0.0009902975,0.0009669255,0.001282186,0.0002556297,0.00000600617,0.00002139009,0.002019907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5806989,"threshold_uncertainty_score":0.9980636,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4376958607","doi":"10.32473/flairs.36.133328","title":"Towards a multi-modal Deep Learning Architecture for User Modeling","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal; École de Technologie Supérieure","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Modal; Convolutional neural network; User modeling; Representation (politics); Feature (linguistics); Feature learning; Machine learning; Architecture; Human–computer interaction; User interface","retraction":null,"screen_n_in":null,"score":{"opus":0.1617934655474933,"gpt":0.4043254720886784,"spread":0.242532006541185,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002873693,0.0002281693,0.0002344444,0.0002442583,0.0007988706,0.0006153047,0.004436553,0.0001477999,0.00002432277],"category_scores_gemma":[0.002624923,0.0001888185,0.0003685913,0.00162538,0.0002990125,0.0004725218,0.001714098,0.001177574,0.00006694995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001846839,"about_ca_system_score_gemma":0.0002510227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000425548,"about_ca_topic_score_gemma":0.00002860366,"domain_scores_codex":[0.9964392,0.00004212186,0.0005637996,0.0007536059,0.00149364,0.0007076304],"domain_scores_gemma":[0.9956569,0.000510332,0.0002039103,0.0003047177,0.00318382,0.0001402997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000513515,0.000118319,0.0009440446,0.0001176528,0.0001203207,3.521524e-7,0.009741803,0.1044118,0.07089645,0.7246037,0.0004254219,0.08856871],"study_design_scores_gemma":[0.00005837372,0.00004985723,0.0002550077,0.00007525666,0.000005036617,0.000002341631,0.001794868,0.8807309,0.02308929,0.09296522,0.0008043615,0.0001695287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1208836,0.00001851446,0.862153,0.01450374,0.000463457,0.0008040696,0.00001222656,0.000302125,0.0008593277],"genre_scores_gemma":[0.9251984,0.00007259184,0.07347722,0.00006500505,0.0002930049,0.0003863071,0.000007698131,0.00002735933,0.0004724064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8043149,"threshold_uncertainty_score":0.8244294,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3160036765","doi":"10.32473/flairs.v34i1.128474","title":"Confusion detection using cognitive ability tests","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Confusion; Memorization; Cognition; Computer science; Support vector machine; Artificial intelligence; Cognitive psychology; Orientation (vector space); Psychology; Pattern recognition (psychology); Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.2393936907274938,"gpt":0.4125526449569652,"spread":0.1731589542294714,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001462647,0.0002016066,0.0002238238,0.00009178733,0.0005810941,0.0004900055,0.00128028,0.0001367929,0.0002708333],"category_scores_gemma":[0.006148138,0.0001658744,0.0002806322,0.001003955,0.001004759,0.0005515,0.00109268,0.0008301278,0.00003349117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002487193,"about_ca_system_score_gemma":0.0003740937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001195499,"about_ca_topic_score_gemma":0.00002968973,"domain_scores_codex":[0.9964277,0.00009509473,0.0005708677,0.0007710458,0.001644272,0.0004910523],"domain_scores_gemma":[0.9939618,0.001129142,0.0002411019,0.0001768575,0.004375758,0.0001153051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007054405,0.0001440157,0.000562349,0.00004847317,0.00002332785,0.000001534484,0.001626423,0.00004489794,0.9656116,0.02052252,0.00007923728,0.01126506],"study_design_scores_gemma":[0.0000449418,0.00006039767,0.0002638085,0.0002596281,0.00000876575,0.00002681557,0.004525518,0.06571788,0.8934524,0.03530254,0.0001899151,0.0001473938],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879504,0.00002339105,0.004309603,0.001848832,0.001171958,0.0003706221,0.00003480903,0.0000519427,0.004238419],"genre_scores_gemma":[0.9984805,0.00009098292,0.0005316973,0.000190284,0.0003549084,0.00002494513,0.000001390832,0.00001639066,0.0003089158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07215922,"threshold_uncertainty_score":0.736034,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3160789541","doi":"10.32473/flairs.v34i1.128367","title":"Using Deep Learning algorithms to detect the success or failure of the Electroconvulsive Therapy (ECT) sessions","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Blood Pressure and Hypertension Studies","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Electroconvulsive therapy; Major depressive disorder; Electroencephalography; Mental health; Depression (economics); Health professionals; Session (web analytics); Psychology; Psychiatry; Magnetic resonance imaging; Mental healthcare; Health care; Medicine; Computer science; Cognition","retraction":null,"screen_n_in":null,"score":{"opus":0.2126229742069513,"gpt":0.4176508145072,"spread":0.2050278403002487,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001250771,0.0001875006,0.0003733968,0.00006703609,0.0008077985,0.00014963,0.001163126,0.000113367,0.0002326935],"category_scores_gemma":[0.002445376,0.00009141127,0.000363276,0.001275647,0.0006107995,0.0001510274,0.0009545566,0.001102298,0.00000617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007492217,"about_ca_system_score_gemma":0.0006247148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001926878,"about_ca_topic_score_gemma":0.0001019613,"domain_scores_codex":[0.996989,0.00008196821,0.0005049828,0.0003970661,0.001599897,0.0004271384],"domain_scores_gemma":[0.9919586,0.0007039324,0.0002252723,0.0002443455,0.006774592,0.00009330479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004135763,0.0001391244,0.003371721,0.00009711983,0.0008382006,0.000003135173,0.008423677,0.0001389175,0.9544232,0.008394202,0.002290874,0.02146628],"study_design_scores_gemma":[0.00009937648,0.0001528131,0.0007995661,0.0004542391,0.00009267467,0.00004705759,0.02128927,0.008401048,0.9603813,0.003738236,0.004425522,0.0001188553],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9372151,0.001374676,0.003299762,0.0534221,0.0008904951,0.001556168,0.000020073,0.0000459397,0.002175681],"genre_scores_gemma":[0.9952501,0.000832719,0.001929774,0.0006305681,0.0004020143,0.00006098731,0.000001094402,0.00001996805,0.0008727646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05803501,"threshold_uncertainty_score":0.6213014,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4225401927","doi":"10.32473/flairs.v35i.130667","title":"Integration of Multivariate Beta-based Hidden Markov Models and Support Vector Machines with Medical Applications","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hidden Markov model; Discriminative model; Support vector machine; Artificial intelligence; Computer science; Fisher kernel; Pattern recognition (psychology); Kernel (algebra); Generative model; Machine learning; Multivariate statistics; Decision boundary; Kernel method; Generative grammar; Mathematics; Kernel Fisher discriminant analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.1128507009302343,"gpt":0.3615773986869301,"spread":0.2487266977566958,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001732246,0.0001381938,0.000182102,0.0001627035,0.0003985953,0.000188755,0.002957416,0.00007144608,0.000157424],"category_scores_gemma":[0.0003420957,0.0001017286,0.0001044532,0.0008781509,0.0006705998,0.0005244341,0.001287343,0.0005766175,0.000002145106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001316642,"about_ca_system_score_gemma":0.0004376687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001214476,"about_ca_topic_score_gemma":0.00001396057,"domain_scores_codex":[0.9967593,0.00003630712,0.0004761584,0.0004369571,0.002043535,0.0002477556],"domain_scores_gemma":[0.9974758,0.0003683706,0.0002962102,0.0002574911,0.001526135,0.00007599542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004502509,0.0001272505,0.0002735077,0.0000283021,0.00003959802,1.949978e-7,0.00109727,0.00007223694,0.02177595,0.9019112,0.0003030611,0.0743264],"study_design_scores_gemma":[0.00008659988,0.0001936301,0.0003178394,0.00006609406,0.000008637675,0.000005572848,0.002588348,0.6694635,0.1836186,0.1429707,0.0005275344,0.0001530177],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06164354,0.00005804109,0.8946281,0.03783727,0.0003371715,0.001321726,0.00006491088,0.0002041622,0.003905063],"genre_scores_gemma":[0.980639,0.00004833671,0.01854276,0.00006423431,0.00004166911,0.0004159072,0.000007669147,0.00000852583,0.0002319223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9189954,"threshold_uncertainty_score":0.5495663,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4225428113","doi":"10.32473/flairs.v35i.130688","title":"Unsupervised Neural Network for Data-Driven Corrosion Detection of a Mining Pipeline","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institute of Mining, Metallurgy and Petroleum","keywords":"Corrosion; Pipeline transport; Artificial neural network; Pipeline (software); Computer science; Representation (politics); Data mining; Artificial intelligence; Engineering; Materials science; Environmental engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1806987928359528,"gpt":0.3621233291969468,"spread":0.181424536360994,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002002137,0.0001515421,0.0002097892,0.00009521437,0.0003741837,0.00008002794,0.002303368,0.00006076828,0.00005699031],"category_scores_gemma":[0.0009552969,0.0001438636,0.000147514,0.0006878252,0.0002856137,0.0003075325,0.00129389,0.0005629003,9.122792e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002668736,"about_ca_system_score_gemma":0.0001013909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008963145,"about_ca_topic_score_gemma":0.0000185661,"domain_scores_codex":[0.9977199,0.00002587384,0.0005468135,0.0003487145,0.0009906208,0.0003680553],"domain_scores_gemma":[0.9974185,0.0005080413,0.0001729751,0.0002451211,0.00160378,0.00005157948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001863631,0.00006547577,0.001729199,0.0001908897,0.00006087378,2.028266e-7,0.001683819,0.009299682,0.9326341,0.03861792,0.004311988,0.01121948],"study_design_scores_gemma":[0.00003682606,0.0001087916,0.00006945171,0.00008438626,0.00001117665,0.000003859802,0.002151216,0.7414069,0.1943997,0.06146883,0.0001466677,0.0001122858],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9110462,0.00007396514,0.08395809,0.000607351,0.001481376,0.001079586,0.0002332983,0.0002545477,0.001265567],"genre_scores_gemma":[0.9502038,0.00004051734,0.04921114,0.00001381112,0.0002866647,0.0001692322,0.00002250742,0.00002957925,0.00002269735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7382345,"threshold_uncertainty_score":0.5866589,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400282938","doi":"10.32473/flairs.37.1.135537","title":"Fluid Path Detection Model for Lab on a Chip Images Using Deep Learning-Based Segmentation Approach","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Artificial intelligence; Computer science; Path (computing); Chip; Deep learning; Lab-on-a-chip; Computer vision; Pattern recognition (psychology); Machine learning; Materials science; Nanotechnology; Microfluidics; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.118825085077113,"gpt":0.370022375763588,"spread":0.251197290686475,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002061486,0.0001920008,0.0001574532,0.0002582668,0.00051848,0.001011837,0.001425795,0.0001200054,0.000009892499],"category_scores_gemma":[0.0005037791,0.0001573403,0.0002995117,0.0008781452,0.0002316149,0.0008472433,0.0003016172,0.0006490982,0.000007644645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004283835,"about_ca_system_score_gemma":0.0002615597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007035665,"about_ca_topic_score_gemma":0.000003124066,"domain_scores_codex":[0.9973726,0.00003638411,0.0004168548,0.0006101958,0.001174931,0.000389038],"domain_scores_gemma":[0.9973893,0.0002670289,0.0001332222,0.0001654475,0.001979954,0.00006509278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000123916,0.0001513051,0.00001374043,0.0003201595,0.00008660529,4.589389e-7,0.0040092,0.01642884,0.7619864,0.08218358,0.0004711413,0.1342247],"study_design_scores_gemma":[0.00001693619,0.00008668847,0.00000183201,0.00007956258,0.000004506542,0.000001711226,0.0004907486,0.5020635,0.4716254,0.02549775,0.00005188674,0.00007957833],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01232625,0.00004701966,0.9846647,0.000888064,0.0004586243,0.0006478482,0.00001031701,0.0002401106,0.0007170891],"genre_scores_gemma":[0.9467382,0.00006281193,0.05238917,0.00007700177,0.0002352118,0.0002246216,0.000003330564,0.00002119542,0.0002483958],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.934412,"threshold_uncertainty_score":0.9757167,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410398281","doi":"10.32473/flairs.38.1.138970","title":"Flexible Dirichlet Mixture Model for Multi-modal data Clustering","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Modal; Cluster analysis; Latent Dirichlet allocation; Mixture model; Computer science; Dirichlet distribution; Data mining; Mathematics; Artificial intelligence; Topic model; Materials science","retraction":null,"screen_n_in":null,"score":{"opus":0.3601967099388323,"gpt":0.4684816825311803,"spread":0.108284972592348,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.003151299,0.0002593183,0.0002959745,0.0002374071,0.0006591455,0.0008450337,0.01223186,0.0001723754,0.00001242061],"category_scores_gemma":[0.002324894,0.0002179005,0.0002310585,0.001365381,0.000582255,0.001431383,0.008736842,0.0009134717,0.00001067561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003694937,"about_ca_system_score_gemma":0.0007431544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007856959,"about_ca_topic_score_gemma":0.00003609779,"domain_scores_codex":[0.9956667,0.00002795092,0.000676033,0.001104355,0.001685244,0.0008396778],"domain_scores_gemma":[0.9939089,0.0005311086,0.0001957937,0.000901266,0.004328982,0.0001339567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002071021,0.0003752105,0.0001908339,0.0005111713,0.0003100208,8.1816e-7,0.003900715,0.01616905,0.1041877,0.7441871,0.01179224,0.1181681],"study_design_scores_gemma":[0.00008235581,0.00003162257,0.0000187613,0.0002001036,0.000005706021,0.00000185703,0.0007792484,0.8340309,0.08647263,0.07717104,0.00104549,0.0001602829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001510459,0.00007421227,0.9850252,0.009911838,0.0008840231,0.0008777473,0.00009887792,0.0001169069,0.001500796],"genre_scores_gemma":[0.5616374,0.0002532886,0.4297535,0.0002425456,0.0003234917,0.0002906595,0.00001669657,0.00003164512,0.0074507],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8178619,"threshold_uncertainty_score":0.9992803,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4225383538","doi":"10.32473/flairs.v35i.130643","title":"Learning to Rank with BERT for Argument Quality Evaluation","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Software Engineering Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Argument (complex analysis); Leverage (statistics); Ranking (information retrieval); Rank (graph theory); Computer science; Learning to rank; Pairwise comparison; Artificial intelligence; Quality (philosophy); Machine learning; Representation (politics); Task (project management); Mathematics; Epistemology; Political science; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1733581635769031,"gpt":0.4124483342358819,"spread":0.2390901706589788,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01070053,0.000161822,0.0001929893,0.0001919284,0.0008429987,0.0004419312,0.003850947,0.00004507584,0.0001292876],"category_scores_gemma":[0.004979629,0.0001346914,0.0001800458,0.001418039,0.000178859,0.0004222614,0.002017629,0.0008378072,0.00001204803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008449698,"about_ca_system_score_gemma":0.0005887208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001424329,"about_ca_topic_score_gemma":0.000008892461,"domain_scores_codex":[0.9937664,0.00009144918,0.000474417,0.0006544385,0.004441972,0.0005712755],"domain_scores_gemma":[0.9927074,0.001428033,0.0001683558,0.0002881402,0.005266827,0.0001412031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006095431,0.0004200523,0.009612994,0.0001988931,0.000314855,7.003683e-7,0.02631562,0.04691332,0.1375668,0.670898,0.006011865,0.1011373],"study_design_scores_gemma":[0.0002323584,0.001057488,0.002247653,0.0001383416,0.00001435323,0.000008590004,0.0129781,0.7026005,0.2128633,0.06035595,0.007051974,0.0004513247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6925938,0.00003934548,0.2813055,0.0203471,0.001201408,0.002986661,0.00002853369,0.0001751283,0.001322605],"genre_scores_gemma":[0.9849531,0.00001195313,0.0128946,0.00008088593,0.0001609482,0.001322938,0.000004408199,0.00001749992,0.0005536423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6556872,"threshold_uncertainty_score":0.7156082,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4225373816","doi":"10.32473/flairs.v35i.130660","title":"Protein-Protein Interaction Extraction using Attention-based Tree-Structured Neural Network Models","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Tree (set theory); Artificial intelligence; Task (project management); Artificial neural network; Machine learning; Natural language processing; Phrase; Recurrent neural network; Tree structure; Data structure; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1538659671948852,"gpt":0.382034202859431,"spread":0.2281682356645457,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001282805,0.0001653503,0.0001522856,0.00006863941,0.0006683978,0.0001341555,0.001064491,0.0001280494,0.0001193219],"category_scores_gemma":[0.0003980929,0.0001431576,0.0002633297,0.0004308269,0.0004358038,0.0000361083,0.0006528759,0.0007209345,0.000001898267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001910848,"about_ca_system_score_gemma":0.0002482816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001584005,"about_ca_topic_score_gemma":0.00002512412,"domain_scores_codex":[0.9975178,0.00007362838,0.0004312598,0.0004661468,0.001112803,0.0003983247],"domain_scores_gemma":[0.9983742,0.0000509034,0.0002728646,0.0001658762,0.001064026,0.00007210027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003219965,0.00008075459,0.0002606747,0.00002885048,0.00006687319,3.891707e-7,0.0001613991,0.0194884,0.962221,0.007210732,0.000714816,0.009444155],"study_design_scores_gemma":[0.00007298899,0.0002292806,0.00007792407,0.00008003163,0.00001032424,0.000008640844,0.003676583,0.4964831,0.4774544,0.02041063,0.001325206,0.0001709386],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835955,0.00006593693,0.01237326,0.002033229,0.0008225169,0.0005143457,0.00002628045,0.00002299711,0.0005459741],"genre_scores_gemma":[0.9949263,0.0000111236,0.003848374,0.00006621629,0.000553626,0.0001599224,0.00002428184,0.00001780392,0.0003923941],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4847666,"threshold_uncertainty_score":0.5837798,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4375858571","doi":"10.32473/flairs.36.133256","title":"Identifying Protein-Protein Interaction using Tree-Transformers and Heterogeneous Graph Neural Network","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial neural network; Transformer; Graph; Artificial intelligence; Theoretical computer science; Engineering; Electrical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1735366575085686,"gpt":0.3955412738069915,"spread":0.2220046162984229,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001183944,0.000154601,0.0001487146,0.0000813055,0.0003495089,0.0001867174,0.000665562,0.0001592828,0.00001947157],"category_scores_gemma":[0.000451918,0.0001255751,0.0001856879,0.0005297845,0.0006863251,0.00002986781,0.0005071223,0.0003917931,0.0000054461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004644376,"about_ca_system_score_gemma":0.00008517911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001078004,"about_ca_topic_score_gemma":0.00003647337,"domain_scores_codex":[0.9980675,0.00003227305,0.0003608882,0.0004179623,0.0006712938,0.0004500667],"domain_scores_gemma":[0.9989368,0.00004785102,0.0001310186,0.0001013479,0.0006957811,0.00008723792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000126188,0.00002364392,0.0004614107,0.00006792822,0.00009856176,7.514979e-7,0.0004544057,0.0002515037,0.9669683,0.002910907,0.0004013979,0.02823502],"study_design_scores_gemma":[0.00006241524,0.0001884231,0.0001884229,0.000290609,0.00001172549,0.00001477906,0.0053326,0.05059383,0.9203092,0.02198047,0.0008266749,0.0002007943],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950312,0.0001012711,0.002444308,0.001275017,0.0004329923,0.000360213,0.000009754471,0.00003332501,0.0003118611],"genre_scores_gemma":[0.9976125,0.0002084379,0.001388796,0.0000320926,0.0004103614,0.00005400016,0.000008460977,0.00001588923,0.0002695001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05034233,"threshold_uncertainty_score":0.5120804,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4225384347","doi":"10.32473/flairs.v35i.130629","title":"Estimating Automobile Crash Characteristics from Images using Deep Learning","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Acadia University","funders":"","keywords":"Crash; Collision; Artificial intelligence; Deep learning; Computer science; Machine learning; Simulation; Engineering; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.06565464327735158,"gpt":0.3190429977632977,"spread":0.2533883544859461,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001358322,0.0001768714,0.0002237132,0.0001026744,0.0008931436,0.000157146,0.001620851,0.0001058731,0.0006304391],"category_scores_gemma":[0.0005178555,0.0001735012,0.0001710123,0.0005210078,0.0004307879,0.0002745835,0.00108646,0.001686086,0.00001755314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004736838,"about_ca_system_score_gemma":0.000105744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001160867,"about_ca_topic_score_gemma":0.000003565608,"domain_scores_codex":[0.9977151,0.00003023507,0.0005289076,0.0003347397,0.000946175,0.0004449075],"domain_scores_gemma":[0.9986176,0.0002705334,0.0001651464,0.0001427518,0.0007437642,0.00006013228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007214647,0.0001651054,0.01328645,0.0001381969,0.0004164957,0.000004101414,0.008513815,0.1083799,0.707839,0.05435707,0.0008288834,0.1059989],"study_design_scores_gemma":[0.00002346569,0.00003231558,0.0005001618,0.00004957359,0.00001078913,0.000005127429,0.004534036,0.8535967,0.114211,0.02645815,0.0004216828,0.0001569735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9708507,0.00007054833,0.02474676,0.0006021052,0.001225451,0.0002851167,0.00007153165,0.0002977272,0.001850027],"genre_scores_gemma":[0.9917303,0.00006005796,0.007660598,0.0000193327,0.0002892696,0.00007510767,0.00001086853,0.00003088155,0.000123624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7452168,"threshold_uncertainty_score":0.7325299,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3163104826","doi":"10.32473/flairs.v34i1.128490","title":"Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pairwise comparison; Mixture model; Robustness (evolution); Cluster analysis; Class (philosophy); Computer science; Artificial intelligence; Synthetic data; Machine learning; Pattern recognition (psychology); Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.164536875868728,"gpt":0.3525063937222555,"spread":0.1879695178535275,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001999428,0.000248361,0.000261724,0.0001263019,0.0005923365,0.0009731233,0.002639614,0.0001248928,0.0002328593],"category_scores_gemma":[0.001401996,0.0001995084,0.0002509023,0.001250282,0.0008029864,0.0008235262,0.0007856609,0.001307893,0.00003349361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001976354,"about_ca_system_score_gemma":0.001352792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003742746,"about_ca_topic_score_gemma":0.00002040976,"domain_scores_codex":[0.9957889,0.00007680299,0.0005286589,0.0007446961,0.002251402,0.0006095794],"domain_scores_gemma":[0.9925889,0.0004609161,0.0002359412,0.0002837951,0.006241781,0.0001886265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009392417,0.0001899876,0.001110189,0.00008553512,0.0001450611,0.000006600214,0.006294235,0.04268352,0.2277171,0.6946458,0.0007883244,0.02623983],"study_design_scores_gemma":[0.0001084374,0.0000501061,0.00007136372,0.0002026325,0.000006381948,0.00001178245,0.004779119,0.7820833,0.1941757,0.01792157,0.0003941704,0.0001954554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04836265,0.00004033152,0.9182959,0.01384834,0.0004333985,0.0003730794,0.00001923555,0.0001406217,0.01848644],"genre_scores_gemma":[0.9303437,0.00004794402,0.06723307,0.0003326883,0.0001308357,0.0000403293,0.000007236869,0.00002005928,0.001844101],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8819811,"threshold_uncertainty_score":0.9383851,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4377018759","doi":"10.32473/flairs.36.133326","title":"Improving Word Embedding Using Variational Dropout","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Dropout (neural networks); Word (group theory); Computer science; Word embedding; Artificial intelligence; Overfitting; Orthogonality; Natural language processing; Embedding; Curse of dimensionality; Inference; Machine learning; Mathematics; Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.1990856946383329,"gpt":0.3984730303623926,"spread":0.1993873357240597,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003455847,0.0001938136,0.0002053183,0.0002582893,0.0006139872,0.0008061012,0.004125287,0.0001270171,0.0000657112],"category_scores_gemma":[0.001488146,0.0001678292,0.000258524,0.001849985,0.0003065518,0.001138562,0.0024391,0.0006988443,0.0000664264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003636732,"about_ca_system_score_gemma":0.0005077656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002745563,"about_ca_topic_score_gemma":0.000006455823,"domain_scores_codex":[0.9956858,0.00003266134,0.0006569643,0.0006868811,0.00226242,0.0006753307],"domain_scores_gemma":[0.9960703,0.0004141316,0.0002797954,0.0003151488,0.002799976,0.0001206595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001375613,0.00004150502,0.0007072125,0.00005660216,0.00006638886,0.00000100113,0.003320102,0.005313184,0.1588532,0.8031308,0.0005694106,0.02792683],"study_design_scores_gemma":[0.00002465488,0.0000143209,0.00007469109,0.0001047648,0.00000387208,0.000004232504,0.001965705,0.8147879,0.05835484,0.1243828,0.0001444237,0.0001378164],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2484116,0.000022422,0.7389196,0.007011036,0.002358871,0.0004880824,0.00001252377,0.0002630567,0.002512876],"genre_scores_gemma":[0.9528213,0.00004321376,0.04595847,0.00006658696,0.0005772361,0.00003551517,0.000002047158,0.00001744114,0.0004782521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8094747,"threshold_uncertainty_score":0.7773253,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400282308","doi":"10.32473/flairs.37.1.135277","title":"Embedding Ethics Into Artificial Intelligence: Understanding What Can Be Done, What Can't, and What Is Done","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Université du Québec à Trois-Rivières","keywords":"Embedding; Engineering ethics; Ethics of technology; Computer science; Ethical issues; Ethical decision; Management science; Sociology; Artificial intelligence; Information ethics; Engineering; Meta-ethics","retraction":null,"screen_n_in":null,"score":{"opus":0.3507927044911351,"gpt":0.4780498345077304,"spread":0.1272571300165953,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["sts"],"category_scores_codex":[0.01215105,0.0004261018,0.0004700627,0.0003472331,0.003168846,0.02342567,0.002347853,0.0007741717,0.0003338082],"category_scores_gemma":[0.004386782,0.0003805735,0.0004466683,0.001784107,0.004718537,0.009303156,0.001087613,0.003515783,0.00002124531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001700307,"about_ca_system_score_gemma":0.002348491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005714443,"about_ca_topic_score_gemma":0.005242493,"domain_scores_codex":[0.9920825,0.0002077129,0.00103219,0.001055121,0.004412143,0.001210376],"domain_scores_gemma":[0.9911359,0.002889532,0.000294921,0.0002665085,0.004918869,0.000494268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00004117005,0.00005235848,0.00002486189,0.0001779398,0.0001608025,0.000002472514,0.2914402,0.00002162924,0.005548189,0.6681601,0.0007323507,0.03363796],"study_design_scores_gemma":[0.00001276379,0.00005805468,0.00000215054,0.001751724,0.00002429333,0.000002002437,0.4805503,0.006875568,0.0316104,0.4771325,0.001723189,0.0002570793],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2065359,0.005838727,0.006758669,0.7623395,0.01201232,0.001807877,0.00006295666,0.0002964419,0.004347607],"genre_scores_gemma":[0.8938046,0.1020177,0.0005429793,0.001504142,0.001412967,0.00005324231,0.000007172032,0.00004851753,0.0006086636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7608353,"threshold_uncertainty_score":0.9998646,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400282035","doi":"10.32473/flairs.37.1.135283","title":"Assessing the Impact of Sequence Length Learning on Classification Tasks for Transformer Encoder Models","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Encoder; Transformer; Computer science; Artificial intelligence; Sequence (biology); Sequence learning; Speech recognition; Pattern recognition (psychology); Engineering; Electrical engineering; Biology; Voltage","retraction":null,"screen_n_in":null,"score":{"opus":0.374587593761272,"gpt":0.4799399593753982,"spread":0.1053523656141263,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00208555,0.0001629576,0.0001608576,0.00009303696,0.0004746091,0.0009678479,0.002452902,0.00008772501,0.00001767136],"category_scores_gemma":[0.0002625549,0.00009757494,0.0004181863,0.0009372841,0.0004457025,0.001237554,0.0001917477,0.0007461051,0.000006441869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002099006,"about_ca_system_score_gemma":0.0003939722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001060958,"about_ca_topic_score_gemma":0.000002672721,"domain_scores_codex":[0.9976094,0.00003188832,0.0004933538,0.0004787107,0.001015567,0.0003710139],"domain_scores_gemma":[0.9967507,0.000954045,0.0001687793,0.0002180656,0.001846607,0.00006187438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001389988,0.00005166067,0.00003785261,0.0000547225,0.00007143321,6.782889e-8,0.002210837,0.01102598,0.1306832,0.8116741,0.0008370632,0.04333926],"study_design_scores_gemma":[0.00001323113,0.00008213828,0.00005802335,0.0001818878,0.000005376303,0.000001829806,0.001222176,0.7430351,0.06830662,0.1867673,0.0002478466,0.00007853064],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1620198,0.0001406825,0.8076555,0.01907271,0.0006578078,0.001358357,0.00003923637,0.000125086,0.008930911],"genre_scores_gemma":[0.9966999,0.000266736,0.002390859,0.0000297281,0.0002240423,0.0001752175,0.000003426202,0.00001357556,0.0001965254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8346801,"threshold_uncertainty_score":0.933298,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400281194","doi":"10.32473/flairs.37.1.135320","title":"Developing a predictive model using multivariate analysis and Long Short-Term Memory (LSTM) to assess corrosion degradation in mining pipeline thickness.","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières; Innovation and Economic Development Trois Rivières","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Pipeline (software); Degradation (telecommunications); Multivariate statistics; Long short term memory; Term (time); Corrosion; Computer science; Multivariate analysis; Artificial intelligence; Data mining; Machine learning; Materials science; Metallurgy; Artificial neural network; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.2239269259411425,"gpt":0.4080404702413655,"spread":0.184113544300223,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002012017,0.0002156011,0.0002650839,0.0005473765,0.0001588203,0.0004009174,0.0007302464,0.0001316375,0.00000902665],"category_scores_gemma":[0.000808786,0.000194848,0.0001254304,0.001931518,0.0002221869,0.0006305833,0.0005339247,0.0006330801,0.00000126894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007463173,"about_ca_system_score_gemma":0.0002152579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001685442,"about_ca_topic_score_gemma":0.00009677897,"domain_scores_codex":[0.9976457,0.00002501686,0.0005671269,0.0004907142,0.0008946731,0.0003767593],"domain_scores_gemma":[0.9979534,0.0003922081,0.00005969294,0.0001169596,0.001394139,0.00008359423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006601617,0.00003461557,0.01997724,0.0003833774,0.000236768,0.000002766876,0.008392128,0.03019529,0.8810175,0.05080876,0.00005400988,0.008831551],"study_design_scores_gemma":[0.0000126311,0.00001471511,0.001510873,0.0008164751,0.00003797548,0.000003761358,0.001469671,0.7735584,0.1809029,0.04152027,4.51493e-7,0.000151905],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5300804,0.00002845811,0.4688495,0.0001794511,0.000166843,0.0002762897,0.00001377764,0.0001136997,0.0002916595],"genre_scores_gemma":[0.8508206,0.00007998972,0.1488969,0.000009225691,0.00008341395,0.00006232275,0.000005071372,0.00002653351,0.00001593889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7433631,"threshold_uncertainty_score":0.7945672,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4375858679","doi":"10.32473/flairs.36.133203","title":"Further Thoughts on Defining f(x) for Ethical Machines: Ethics, Rational Choice, and Risk Analysis","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Consequentialism; Utilitarianism; Deontological ethics; Normative; Perspective (graphical); Ethical theory; Epistemology; Rational agent; Normative ethics; Management science; Engineering ethics; Computer science; Risk analysis (engineering); Sociology; Economics; Artificial intelligence; Philosophy; Business; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.2636580989225152,"gpt":0.4931448676318907,"spread":0.2294867687093755,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01471845,0.0001696768,0.0002680573,0.000253541,0.002303334,0.0007846283,0.001241998,0.0005539086,0.00007744353],"category_scores_gemma":[0.02881416,0.000137669,0.0004114905,0.001894628,0.00182795,0.0003615937,0.0003259065,0.002428978,0.00002592098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001805692,"about_ca_system_score_gemma":0.0007269451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002576009,"about_ca_topic_score_gemma":0.002706788,"domain_scores_codex":[0.9957007,0.0001841859,0.0004826782,0.0004930333,0.002560135,0.0005793071],"domain_scores_gemma":[0.987034,0.007065677,0.0002698413,0.0001291505,0.005317797,0.0001835749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005559688,0.00004573986,0.008949739,0.00003269175,0.0004336964,1.061246e-7,0.0505867,0.0001663382,0.0008840735,0.9320668,0.002948043,0.003830505],"study_design_scores_gemma":[0.00008996724,0.0001296983,0.005616405,0.0001562384,0.0001296045,1.8152e-7,0.04516143,0.04584571,0.004823224,0.8883893,0.009362099,0.0002961737],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6799138,0.00006009201,0.002634471,0.2978515,0.001467989,0.001224911,0.0002551669,0.0001750072,0.01641713],"genre_scores_gemma":[0.9941342,0.001929439,0.0008822619,0.0005555645,0.00125869,0.00008189428,0.0000133926,0.00001823296,0.001126288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3142205,"threshold_uncertainty_score":0.9998724,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3163694547","doi":"10.32473/flairs.v34i1.128506","title":"Covid-19 News Clustering using MCMC-Based Learing of finite EMSD Mixture Models","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Markov chain Monte Carlo; Cluster analysis; Computer science; Mixture model; Multinomial distribution; Artificial intelligence; Bayesian probability; Machine learning; Generative model; Flexibility (engineering); Task (project management); Dirichlet distribution; Statistical model; Data mining; Generative grammar; Mathematics; Statistics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.2869876506097959,"gpt":0.4139066479645853,"spread":0.1269189973547895,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002633718,0.0002354202,0.0003533245,0.0001655109,0.0003923948,0.0004660565,0.003197721,0.0001859592,0.00007660125],"category_scores_gemma":[0.002630155,0.0002033447,0.0004074457,0.001313945,0.0004629721,0.0008331793,0.00175898,0.0007945975,0.000003224197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003362471,"about_ca_system_score_gemma":0.001863494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004262797,"about_ca_topic_score_gemma":0.00005312014,"domain_scores_codex":[0.9961603,0.0000967507,0.0007611437,0.0007261382,0.001702287,0.0005533959],"domain_scores_gemma":[0.9945044,0.0007019187,0.0003604879,0.0004356831,0.003716592,0.0002808969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000599197,0.000146006,0.0002303587,0.0003444693,0.0001116279,0.000004080975,0.005261139,0.02864238,0.2483098,0.6968657,0.0004942166,0.01953029],"study_design_scores_gemma":[0.00003970315,0.00002326621,0.000003949169,0.0001645565,0.000006341972,0.000005958861,0.0008005698,0.577907,0.2241479,0.196613,0.0001689203,0.0001188603],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01303248,0.0001077558,0.9767389,0.006628909,0.0006120264,0.0002735477,0.00001665029,0.00004592222,0.002543858],"genre_scores_gemma":[0.737291,0.0001326183,0.2616813,0.0004519995,0.0001862293,0.00001894816,0.000002004617,0.00001687732,0.0002190057],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7242585,"threshold_uncertainty_score":0.8292157,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400281282","doi":"10.32473/flairs.37.1.135043","title":"Latent Beta-Liouville Probabilistic Modeling for Bursty Topic Discovery in Textual Data","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Latent Dirichlet allocation; Burstiness; Perplexity; Computer science; Topic model; Natural language processing; Language model; Word (group theory); Probabilistic logic; Dirichlet distribution; Artificial intelligence; Range (aeronautics); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.312632176089768,"gpt":0.4053127273422721,"spread":0.09268055125250407,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.003142123,0.0001829019,0.0002204546,0.0001660733,0.0001949708,0.001225096,0.005549003,0.0001076647,0.00001596333],"category_scores_gemma":[0.0009565125,0.0001419386,0.0001885461,0.0007648363,0.0002272503,0.001776091,0.002578678,0.0006386988,0.00001034233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003128054,"about_ca_system_score_gemma":0.0005189169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003185786,"about_ca_topic_score_gemma":0.000078407,"domain_scores_codex":[0.9965472,0.00002205514,0.0006833407,0.0009447889,0.001272038,0.0005306039],"domain_scores_gemma":[0.9976671,0.000459392,0.0000836039,0.0005144729,0.001200746,0.00007470678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001907495,0.00006642524,0.0001723045,0.0001940899,0.00005303942,7.760107e-7,0.002008849,0.005353256,0.005118276,0.9666145,0.0005144654,0.01988494],"study_design_scores_gemma":[0.0000248913,0.00003015866,0.00001269038,0.0003087107,0.000005351504,0.000002257806,0.001020272,0.7982248,0.007001175,0.1929307,0.0003196842,0.0001193319],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1313938,0.0002585897,0.8482869,0.01547656,0.001745381,0.001103062,0.00008164008,0.0001083274,0.001545716],"genre_scores_gemma":[0.9875429,0.0001400643,0.0112946,0.00004332557,0.0004225125,0.0001148155,0.000009648156,0.00001416519,0.0004179628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8561491,"threshold_uncertainty_score":0.9998314,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3161484751","doi":"10.32473/flairs.v34i1.128508","title":"Representing Time Series Data in Intelligent Training Systems","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Dynamic time warping; Computer science; Embedding; Simple (philosophy); Euclidean distance; Time series; Representation (politics); Series (stratigraphy); Artificial intelligence; Machine learning; Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.2591728554641427,"gpt":0.3711537784040096,"spread":0.1119809229398669,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.004541498,0.0001962709,0.0003458339,0.0001482718,0.0003520856,0.001308961,0.005832138,0.0001074225,0.0001235133],"category_scores_gemma":[0.002702839,0.0001666456,0.0002083471,0.001902932,0.0003797983,0.001774584,0.005170526,0.0007229229,0.00003897439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000178463,"about_ca_system_score_gemma":0.0004906919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002873778,"about_ca_topic_score_gemma":0.00005348145,"domain_scores_codex":[0.995683,0.00006723838,0.0009384752,0.0009303768,0.001752643,0.000628339],"domain_scores_gemma":[0.9954568,0.0003930282,0.0003185553,0.0007469473,0.002976546,0.0001080986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003498024,0.0001644977,0.001878616,0.0001324106,0.0002304607,0.00001106374,0.01136743,0.001329284,0.08760439,0.8506708,0.001821707,0.04475442],"study_design_scores_gemma":[0.0000302382,0.00003215824,0.0001144073,0.0004562918,0.000008991299,0.00003326341,0.02257841,0.8319176,0.1087215,0.03298568,0.002886629,0.0002347894],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.505497,0.002316581,0.2966237,0.06817406,0.008689521,0.002739484,0.0002416033,0.0005938913,0.1151241],"genre_scores_gemma":[0.9876186,0.0003088958,0.009354768,0.00003768831,0.000376677,0.00002812286,0.00001405863,0.00001637857,0.002244815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8305883,"threshold_uncertainty_score":0.9997278,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4229048432","doi":"10.32473/flairs.v35i.130724","title":"Vehicle Traffic Estimation Using Deep Learning","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Acadia University","funders":"","keywords":"Mean absolute percentage error; Traffic flow (computer networking); Computer science; Artificial neural network; Convolutional neural network; Mean squared error; Deep learning; Word error rate; Statistics; Meteorology; Artificial intelligence; Geography; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.08944595839567647,"gpt":0.3304626479577519,"spread":0.2410166895620754,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001272842,0.0001207176,0.0001187116,0.000138966,0.0005565907,0.0001606947,0.001041907,0.00004773445,0.0001808529],"category_scores_gemma":[0.0001702366,0.0001181507,0.0001487764,0.000655469,0.000188669,0.0003128199,0.0005098165,0.000823539,0.000008361297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004076099,"about_ca_system_score_gemma":0.00004660071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002672543,"about_ca_topic_score_gemma":0.000003532392,"domain_scores_codex":[0.9979321,0.00002260693,0.0003547547,0.0002292302,0.001156677,0.0003046703],"domain_scores_gemma":[0.9991685,0.00007979255,0.00008176338,0.00009055698,0.0005268423,0.00005256138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003546423,0.00008671193,0.000171765,0.0000951582,0.0001213527,5.013037e-7,0.003909544,0.7009143,0.1022903,0.08328079,0.005145025,0.1039491],"study_design_scores_gemma":[0.00002258907,0.00003917869,0.00004009322,0.00003191109,0.000007531261,0.000002897581,0.006490177,0.9457253,0.04201769,0.003771271,0.001747534,0.0001038133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9091262,0.0000905745,0.07746136,0.001300022,0.001471679,0.0006648109,0.00001567985,0.001391913,0.008477762],"genre_scores_gemma":[0.9974538,0.0001205007,0.002043962,0.00002235789,0.0001140386,0.00008899602,0.000004201755,0.00002069054,0.0001314629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.244811,"threshold_uncertainty_score":0.4818046,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4375858784","doi":"10.32473/flairs.36.133365","title":"Towards binary encoding in Bidirectional Associative Memories","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Encoding (memory); Computer science; Associative property; Binary number; Cognition; Bidirectional associative memory; Task (project management); Recall; Artificial intelligence; Content-addressable memory; Transmission (telecommunications); Function (biology); Artificial neural network; Pattern recognition (psychology); Cognitive psychology; Psychology; Neuroscience; Arithmetic; Mathematics; Biology; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1746696167332556,"gpt":0.3893567369444212,"spread":0.2146871202111656,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002304469,0.0001474457,0.0001809504,0.0002145767,0.000407686,0.0003884975,0.002791543,0.00009598213,0.00004361278],"category_scores_gemma":[0.0007169853,0.0001211621,0.0001931631,0.003259886,0.0003575579,0.0007178244,0.001407047,0.0006457077,0.00006084253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002843112,"about_ca_system_score_gemma":0.0002815663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001511199,"about_ca_topic_score_gemma":0.00003081949,"domain_scores_codex":[0.997034,0.00003215352,0.000473072,0.0004949331,0.001466746,0.0004991707],"domain_scores_gemma":[0.9974306,0.0004670087,0.0001739041,0.0001740022,0.001676628,0.00007786888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001478836,0.00008303605,0.001862945,0.00002240315,0.0000443492,0.000001078527,0.003808505,0.0004851157,0.05965068,0.9126794,0.006180195,0.01516751],"study_design_scores_gemma":[0.00004269408,0.0000532532,0.003867244,0.000167384,0.000003051327,0.000002790945,0.004178975,0.3703371,0.2209332,0.3985571,0.001645401,0.0002118259],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8664156,0.00007756673,0.01275352,0.08088115,0.003372313,0.001257123,0.00005329814,0.000455254,0.03473419],"genre_scores_gemma":[0.9963397,0.0003224253,0.002013271,0.00007492171,0.0003236888,0.0001403127,0.00000310179,0.000009719342,0.0007728509],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5141223,"threshold_uncertainty_score":0.5187428,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4229048469","doi":"10.32473/flairs.v35i.130722","title":"Generative Adversarial learning with Negative Data Augmentation for Semi-supervised Text Classification","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Discriminator; Generator (circuit theory); Computer science; Generative grammar; Manifold (fluid mechanics); Representation (politics); Boundary (topology); Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Mode (computer interface); Generative model; Decision boundary; Matching (statistics); Mixing (physics); Key (lock); Power (physics); Machine learning; Mathematics; Statistics; Physics; Support vector machine","retraction":null,"screen_n_in":null,"score":{"opus":0.214596084131797,"gpt":0.3702213578985494,"spread":0.1556252737667524,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002310546,0.0001709781,0.000173313,0.0001371919,0.0009387408,0.0005804647,0.003909872,0.00005369719,0.00005583677],"category_scores_gemma":[0.001403612,0.0001421145,0.0001150053,0.001085778,0.0004256674,0.001757391,0.002073715,0.0006851821,0.000007876832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004939458,"about_ca_system_score_gemma":0.0004941854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009635271,"about_ca_topic_score_gemma":0.000021775,"domain_scores_codex":[0.996314,0.00006590647,0.0004515367,0.0007581469,0.002029925,0.0003804849],"domain_scores_gemma":[0.9959128,0.0006059495,0.0003482544,0.000342202,0.002708345,0.00008245148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006901989,0.0002757422,0.0004444006,0.00006820293,0.0002953563,5.90768e-7,0.017294,0.003646613,0.1459032,0.696938,0.005481457,0.1289623],"study_design_scores_gemma":[0.0001459198,0.0003957065,0.00007785138,0.00004340792,0.00001135345,0.000005278398,0.0195698,0.778241,0.1462484,0.05356883,0.001522594,0.0001699051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1670853,0.00002589302,0.7971298,0.02076861,0.003649272,0.00313509,0.0002385147,0.0002229156,0.007744633],"genre_scores_gemma":[0.9807264,0.00002140578,0.01786701,0.000076306,0.0003284056,0.0004170365,0.00005134074,0.00001681221,0.0004953416],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8136411,"threshold_uncertainty_score":0.726558,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400281600","doi":"10.32473/flairs.37.1.135596","title":"Decoding Complexity: A Mathematical Framework for Enhanced Translation Comprehension","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Decoding methods; Translation (biology); Computer science; Comprehension; Natural language processing; Theoretical computer science; Artificial intelligence; Cognitive science; Programming language; Psychology; Algorithm; Biology; Genetics","retraction":null,"screen_n_in":null,"score":{"opus":0.27049055119903,"gpt":0.4406814160020632,"spread":0.1701908648030332,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001656484,0.0001889187,0.0002232907,0.0001582511,0.0003531616,0.001133761,0.002845182,0.0001679262,0.00005438843],"category_scores_gemma":[0.001049962,0.0001435012,0.0003124582,0.0009575826,0.0004730049,0.0009015316,0.0006322134,0.0007665457,0.00001952985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001940685,"about_ca_system_score_gemma":0.0002048594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000145296,"about_ca_topic_score_gemma":0.000002796582,"domain_scores_codex":[0.9970865,0.00002254565,0.0005497728,0.0005877402,0.001315693,0.0004377047],"domain_scores_gemma":[0.9963104,0.001244531,0.0001270927,0.0002212212,0.00201465,0.00008207007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002008247,0.0000324726,0.000002771858,0.0002186053,0.00003437128,2.793972e-7,0.002680149,0.000004228081,0.1362192,0.8019593,0.0003626945,0.05846582],"study_design_scores_gemma":[0.000008702438,0.00002527454,6.853369e-7,0.0005044111,0.000003751695,0.000002932359,0.0003615149,0.217899,0.3095219,0.4714417,0.0001557568,0.00007440745],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005175868,0.0003578137,0.9803994,0.01143502,0.0007031218,0.0006363724,0.00001289811,0.000299841,0.0009796264],"genre_scores_gemma":[0.5671213,0.00005899345,0.4324068,0.00004369886,0.0002182551,0.00007919828,0.000001796959,0.00001183189,0.00005812044],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5619454,"threshold_uncertainty_score":0.9999031,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4375858861","doi":"10.32473/flairs.36.133230","title":"Biogeography-based optimization for feature selection","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"","keywords":"Cluster analysis; Data mining; Computer science; Cluster (spacecraft); Selection (genetic algorithm); Feature selection; Biogeography; Feature (linguistics); Machine learning; Artificial intelligence; Ecology; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.1306504308282535,"gpt":0.3887174398760385,"spread":0.258067009047785,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002415673,0.0001823212,0.0001837143,0.0003903291,0.0006222587,0.0006036139,0.00334316,0.000147894,0.00002570233],"category_scores_gemma":[0.001298057,0.000153489,0.0003141644,0.003711357,0.0003962902,0.0007351465,0.0008564305,0.0005657099,0.00002044931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002299908,"about_ca_system_score_gemma":0.0003421664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004548665,"about_ca_topic_score_gemma":0.000005800894,"domain_scores_codex":[0.9964576,0.00002824467,0.0003919281,0.0006402828,0.001813005,0.0006688824],"domain_scores_gemma":[0.9936957,0.000523844,0.0001820778,0.0002341943,0.00525048,0.0001136575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002200101,0.0002543676,0.001346904,0.0003557919,0.000241763,9.339437e-7,0.002328169,0.1523001,0.1648825,0.5705727,0.0167161,0.09078072],"study_design_scores_gemma":[0.00004634686,0.00008612353,0.00008269148,0.0000707466,0.000002643204,0.000001178143,0.0004667092,0.7603301,0.1991411,0.03895126,0.000702559,0.0001185114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009455058,0.0000177188,0.9703578,0.01714246,0.0009523315,0.001015369,0.00005063096,0.0003057502,0.0007028244],"genre_scores_gemma":[0.7509341,0.0001701915,0.2466924,0.0001373516,0.0005785044,0.0005112389,0.0000295751,0.00004181376,0.0009047543],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7414791,"threshold_uncertainty_score":0.62591,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4375858707","doi":"10.32473/flairs.36.133373","title":"Using Knowledge Graph Embedding for Fault Detection","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Automotive industry; Fault detection and isolation; Automotive engineering; Fault (geology); Electric vehicle; Computer science; Engineering; Business; Artificial intelligence; Power (physics); Actuator","retraction":null,"screen_n_in":null,"score":{"opus":0.2955318834200215,"gpt":0.4257532962685253,"spread":0.1302214128485037,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002164543,0.0001584634,0.0001875597,0.000264674,0.0004450458,0.0002499104,0.0006769999,0.0001840808,0.00002591335],"category_scores_gemma":[0.0005017546,0.000135776,0.0003105529,0.001478509,0.0001667455,0.0003232768,0.0001946564,0.000474994,0.00003809201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002800384,"about_ca_system_score_gemma":0.00006904382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005843582,"about_ca_topic_score_gemma":0.00001201839,"domain_scores_codex":[0.9980057,0.00001570254,0.0004997854,0.0002930491,0.0007454865,0.0004402918],"domain_scores_gemma":[0.99736,0.0002802955,0.00009633737,0.0001105205,0.00208105,0.00007183096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005775874,0.00002220172,0.00003985631,0.0001615633,0.0001031398,1.232834e-7,0.001994202,0.01554651,0.9253557,0.01831469,0.003400014,0.03500428],"study_design_scores_gemma":[0.00003167615,0.0000298826,0.00000983718,0.0001219436,0.000004722878,0.000001937492,0.002874973,0.509641,0.4723226,0.01263367,0.002232002,0.00009583322],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9062651,0.00006854981,0.08279327,0.0002371353,0.005656752,0.001071725,0.00004240559,0.0003654058,0.003499593],"genre_scores_gemma":[0.9981376,0.0001275274,0.0004807806,0.000004162642,0.0008834288,0.0001162779,0.000002349629,0.00003029789,0.0002175602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4940945,"threshold_uncertainty_score":0.5536785,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4375858688","doi":"10.32473/flairs.36.133140","title":"Using Bidirectional Associative Memory Neural Networks to Solve the N-bit Task","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial neural network; Content-addressable memory; Task (project management); Bidirectional associative memory; Identifier; Associative property; Artificial intelligence; Arithmetic; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.2190795194833,"gpt":0.3981573109817071,"spread":0.1790777914984071,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002422927,0.0001899387,0.0001865173,0.0001228066,0.001054339,0.0006937868,0.004022941,0.000100198,0.0000334684],"category_scores_gemma":[0.0005831482,0.000131614,0.0002847319,0.003061316,0.0004154409,0.0005199907,0.001991885,0.0008177789,0.00005520564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002541463,"about_ca_system_score_gemma":0.0001817526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001954693,"about_ca_topic_score_gemma":0.00002713094,"domain_scores_codex":[0.9965881,0.00003722873,0.0004877983,0.0005746651,0.001660293,0.0006519394],"domain_scores_gemma":[0.9961051,0.000771793,0.0002220871,0.0002855189,0.002477962,0.0001375388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003816669,0.0001061784,0.0005956988,0.00001872698,0.0001850314,0.000001046457,0.005464219,0.04436342,0.06565739,0.815604,0.03408302,0.03388304],"study_design_scores_gemma":[0.00001895113,0.00003310069,0.0004334081,0.00005224193,0.000006123827,0.000003773713,0.002021812,0.9300273,0.01970898,0.04650579,0.001042107,0.0001463915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4334846,0.00009824832,0.4169874,0.1313966,0.006282804,0.002232412,0.00007531956,0.0005696067,0.008872968],"genre_scores_gemma":[0.994809,0.00008285536,0.002833076,0.0004414433,0.0009293069,0.0001518764,0.000002869323,0.00001669836,0.000732876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8856639,"threshold_uncertainty_score":0.8109227,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410398250","doi":"10.32473/flairs.38.1.138756","title":"Incorporating Wave-ViT for Breast Cancer Diagnosis Using MRI Imaging","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Breast cancer; Medicine; Radiology; Magnetic resonance imaging; Breast MRI; Cancer; Mammography; Medical physics; Internal medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.1759041074243973,"gpt":0.4583605814808804,"spread":0.2824564740564831,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009461621,0.0001586946,0.0002300904,0.0001273731,0.000457154,0.0001449211,0.0006564585,0.00008336147,0.00008551037],"category_scores_gemma":[0.0005955307,0.0001291953,0.0002659188,0.0007894636,0.000512762,0.0002638446,0.0004511192,0.0004631138,0.000001768774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004489603,"about_ca_system_score_gemma":0.0004615338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000258825,"about_ca_topic_score_gemma":0.00001244696,"domain_scores_codex":[0.9980364,0.000007583356,0.0005065153,0.0004195935,0.0006657731,0.0003640609],"domain_scores_gemma":[0.9949428,0.0003650081,0.0002138132,0.0001633552,0.004235607,0.00007940207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002064585,0.00025621,0.01486863,0.0003298163,0.0001335143,4.590881e-7,0.0005547637,0.000248439,0.245235,0.6019484,0.009118062,0.1271002],"study_design_scores_gemma":[0.00009593255,0.00003115346,0.0006827652,0.001300121,0.00005845351,0.000009852768,0.003723953,0.2218582,0.567064,0.2019459,0.003060049,0.0001695904],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1829133,0.0004238459,0.6547824,0.1423501,0.001268351,0.00531679,0.0007054361,0.0002670799,0.01197266],"genre_scores_gemma":[0.9580874,0.0004812013,0.0390338,0.0003769191,0.0004004148,0.001076698,0.000005500798,0.00002090461,0.0005172049],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.775174,"threshold_uncertainty_score":0.5268431,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4248507868","doi":"10.32473/flairs.v34i1.128751","title":"Committee Listings","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Nuclear Physics; Russian Academy of Sciences; Philips Research Americas; University of North Carolina at Charlotte; University of Colorado Boulder; Slovenská technická univerzita v Bratislave; University of Nicosia; Lomonosov Moscow State University; University of Texas at El Paso; International Institute for Applied Systems Analysis; Technological University Dublin; Nanjing University; Agricultural University of Athens; Los Alamos National Laboratory; National and Kapodistrian University of Athens; Universidade Federal do Rio Grande do Sul; Centre National de la Recherche Scientifique; Simon Fraser University; Université de Bretagne Occidentale; Radford University; Tsinghua University; Universität Wien; Università degli Studi di Milano-Bicocca; Université du Québec en Outaouais; University College Cork; Illinois State University; Aalborg Universitet; Indiana University Bloomington; International Science and Technology Center; Cardiff University; Tennessee Tech University; Office of Naval Research; Universität Trier; Florida Institute of Technology; Université du Québec à Trois-Rivières; Montana State University; Universitetet i Bergen; University of Miami; University of Texas at Arlington; University of South Carolina; Cyprus University of Technology; University of Regina; University of Louisiana at Lafayette; Middlesex University; Army Research Laboratory; Bradley University; Drexel University; Washington State University; Universidade Federal de São João del-Rei; University of Ottawa; University of Maryland, Baltimore County; DePaul University; Kennesaw State University; Dana-Farber/Harvard Cancer Center; University of Cyprus; Samsung; University of Southern California; University of Memphis; University of Manchester; University of Hartford; Universidade do Porto; Northwestern University; University of Central Florida; Tulane University; Concordia University; Central Connecticut State University; Université du Québec à Montréal; Korea University; Univerzita Karlova v Praze; Universiteit Gent; Universidade Federal de Pelotas; University of Pittsburgh; Clemson University; Massachusetts Institute of Technology","keywords":"Business","retraction":null,"screen_n_in":null,"score":{"opus":0.2526136666280347,"gpt":0.3352411544128727,"spread":0.08262748778483797,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00220029,0.0001361606,0.0002828738,0.00009116087,0.0004064784,0.0004256103,0.001414556,0.00008651198,0.0045693],"category_scores_gemma":[0.001551823,0.0001312111,0.0003099915,0.0006164217,0.0006966458,0.0003721643,0.00103554,0.0004315271,0.001627689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002041683,"about_ca_system_score_gemma":0.0001087344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000235164,"about_ca_topic_score_gemma":0.00001698947,"domain_scores_codex":[0.998013,0.000007559551,0.0006958573,0.0005584502,0.0003040684,0.0004210223],"domain_scores_gemma":[0.9975904,0.0001683218,0.0002966943,0.0002071667,0.001656763,0.00008064539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001130267,0.00007307698,0.003412803,0.00002993498,0.0001048075,4.685077e-7,0.002631246,0.0000137413,0.002773641,0.9147744,0.07525516,0.0009193812],"study_design_scores_gemma":[0.00009514121,0.00003916552,0.0008277181,0.0001567894,0.000009293025,0.000007067194,0.03206221,0.0147682,0.1173019,0.6823062,0.1520465,0.0003797964],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1710923,0.0004781172,0.0008413898,0.01509792,0.005755348,0.0003464987,0.0003522657,0.00005533735,0.8059807],"genre_scores_gemma":[0.922357,0.0004429702,0.001205157,0.0001845361,0.000346369,0.00002883867,0.000004107405,0.00001383868,0.07541721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7512646,"threshold_uncertainty_score":0.9991497,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3163052523","doi":"10.32473/flairs.v34i1.128478","title":"One game show, two boys, two aces, three prisoners - what’s an AI to do?","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Counterintuitive; Simple (philosophy); Representation (politics); Pearl; Dice; Mathematical economics; Psychology; Computer science; Epistemology; Mathematics; Statistics; Philosophy; Law","retraction":null,"screen_n_in":null,"score":{"opus":0.1688177085273532,"gpt":0.4016206498379394,"spread":0.2328029413105862,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.004065016,0.0003724451,0.0004654596,0.0002119912,0.0004741407,0.003767861,0.007669947,0.0001539173,0.0002308861],"category_scores_gemma":[0.001726227,0.0003462702,0.0004026231,0.002340733,0.0007154187,0.003221259,0.004807475,0.001313609,0.0001095509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006094086,"about_ca_system_score_gemma":0.001098748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004207806,"about_ca_topic_score_gemma":0.0003508186,"domain_scores_codex":[0.9926272,0.0001064658,0.0009657848,0.001559527,0.003680316,0.001060722],"domain_scores_gemma":[0.9889233,0.0005229386,0.0002647094,0.0008863557,0.008942704,0.0004599951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006778554,0.0006414169,0.001415923,0.00006947954,0.0001663348,0.000004287863,0.008854099,0.001239657,0.07595581,0.7024374,0.001361618,0.2077862],"study_design_scores_gemma":[0.0001141247,0.0002146945,0.0006549959,0.0003062355,0.00001201062,0.00001621946,0.00534704,0.3036405,0.280531,0.407221,0.001487754,0.0004543087],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3774294,0.0003669448,0.5385489,0.07347769,0.004902626,0.001705978,0.00003555271,0.0003280009,0.003204887],"genre_scores_gemma":[0.9525094,0.0002010881,0.04520676,0.0007361076,0.00085167,0.0001292491,0.000005778938,0.00003037113,0.0003295619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.57508,"threshold_uncertainty_score":0.9998989,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4225387407","doi":"10.32473/flairs.v35i.130696","title":"The Place of Quasi Topological Structure in the Mathematical Theory of Categorization","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Fuzzy and Soft Set Theory","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Mathematical structure; Mathematical theory; Categorization; Category theory; Computer science; Point (geometry); Frame (networking); Topological space; Bridging (networking); Mathematics; Topology (electrical circuits); Artificial intelligence; Pure mathematics; Physics; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.2642478696166664,"gpt":0.435318484020279,"spread":0.1710706144036126,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.02181356,0.0001303156,0.0002651301,0.0001229393,0.0006459994,0.0002000013,0.005697739,0.00008495004,0.0007349793],"category_scores_gemma":[0.0145585,0.00006420879,0.0002626987,0.001533692,0.001934772,0.0002137781,0.001223454,0.0009392921,0.000006309324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001184665,"about_ca_system_score_gemma":0.0002822138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004484498,"about_ca_topic_score_gemma":0.00001931029,"domain_scores_codex":[0.9931527,0.0005109599,0.001129028,0.0003634237,0.004505427,0.0003384507],"domain_scores_gemma":[0.9870319,0.009708677,0.0005301069,0.0003558589,0.002331265,0.00004220041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002432319,0.0001380599,0.0007698226,0.00001658692,0.00002555846,2.335663e-7,0.01273611,0.0003266248,0.01057929,0.9698045,0.0006801128,0.004679893],"study_design_scores_gemma":[0.0000294657,0.0001166531,0.000361453,0.00002176288,0.000004281269,0.000004547326,0.1030493,0.009093813,0.03443889,0.8524564,0.0003660521,0.00005743873],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9786208,0.00009549296,0.002620685,0.01068633,0.0006159513,0.000645586,0.00007263142,0.000009113598,0.006633437],"genre_scores_gemma":[0.9991191,0.00006000559,0.0001551391,0.00006542694,0.00009220031,0.00004982335,0.000001788864,0.000006529735,0.0004499609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1173481,"threshold_uncertainty_score":0.9996819,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4376958591","doi":"10.32473/flairs.36.133320","title":"Multi-hop Question Generation without Supporting Fact Information","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Lethbridge","keywords":"Hop (telecommunications); Computer science; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.2237400688409685,"gpt":0.4126515714519567,"spread":0.1889115026109882,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004013039,0.00016971,0.0001704313,0.0002349278,0.0004629986,0.0008782198,0.002500711,0.0001256978,0.00002937846],"category_scores_gemma":[0.001835107,0.0001427932,0.0001789001,0.00115791,0.0002036825,0.0025942,0.001075973,0.0005551967,0.0001509611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002482462,"about_ca_system_score_gemma":0.0002907942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002199159,"about_ca_topic_score_gemma":0.00001712486,"domain_scores_codex":[0.9964641,0.0000383783,0.0007678946,0.0004288607,0.0017816,0.0005191471],"domain_scores_gemma":[0.9957394,0.0001436357,0.0003484643,0.0002619855,0.003406553,0.0000999625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002058355,0.00007016547,0.004298848,0.00009962611,0.00007270377,5.022133e-7,0.0127652,0.003198225,0.2556584,0.6302838,0.002818596,0.0907134],"study_design_scores_gemma":[0.00003464278,0.0000262968,0.0004162962,0.00007810497,0.00000279299,0.00000242615,0.002257249,0.8332623,0.144471,0.01889546,0.0004265726,0.000126906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3779352,0.000009289995,0.6083063,0.009529697,0.00173472,0.0006460946,0.00001281042,0.0002885532,0.001537215],"genre_scores_gemma":[0.977041,0.00008986005,0.0219585,0.0000935608,0.0003524378,0.00007161538,0.00001278435,0.000009601054,0.0003706352],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8300641,"threshold_uncertainty_score":0.8468693,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4377018773","doi":"10.32473/flairs.36.133317","title":"Evaluation of Techniques for Sim2Real Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"","keywords":"Reinforcement learning; Computer science; Bridging (networking); Bridge (graph theory); Generalization; Noise (video); Domain (mathematical analysis); Human–computer interaction; Transfer of learning; Process (computing); Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.2601722272290781,"gpt":0.4271582699048349,"spread":0.1669860426757567,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01198714,0.000153167,0.0002006287,0.0002352001,0.0003384382,0.0002606954,0.002935859,0.0001078452,0.00004510781],"category_scores_gemma":[0.004364729,0.000129172,0.0002602759,0.001312617,0.0003557846,0.0005899029,0.001169859,0.0004708591,0.00001903057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003086101,"about_ca_system_score_gemma":0.0004924182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005510717,"about_ca_topic_score_gemma":0.000002431155,"domain_scores_codex":[0.9944324,0.00005535708,0.0007108808,0.0004081477,0.003930742,0.0004624391],"domain_scores_gemma":[0.9883778,0.0005529157,0.0004215579,0.0002397228,0.01034383,0.00006416006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003817395,0.0000420232,0.0003727943,0.0001391192,0.0001646087,8.629897e-8,0.004408824,0.1983682,0.08783665,0.6414411,0.002633905,0.06455453],"study_design_scores_gemma":[0.00003354614,0.0001211914,0.00004775445,0.0001152546,0.00001181921,4.474741e-7,0.001441645,0.6604279,0.2939756,0.0431373,0.0006055086,0.00008213164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03162128,0.00001972297,0.9435728,0.004976011,0.001013546,0.002012042,0.000005650845,0.0002668033,0.01651212],"genre_scores_gemma":[0.9914294,0.000132091,0.007120717,0.00002130541,0.0001769791,0.00022323,0.000006317567,0.00001362988,0.0008763621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9598081,"threshold_uncertainty_score":0.5455606,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410397730","doi":"10.32473/flairs.38.1.138888","title":"Leveraging Faithfulness in Abstractive Text Summarization with Elementary Discourse Units","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Lethbridge","funders":"","keywords":"Automatic summarization; Natural language processing; Computer science; Linguistics; Artificial intelligence; Psychology; Philosophy","retraction":null,"screen_n_in":null,"score":{"opus":0.1112441505932301,"gpt":0.3668951031233309,"spread":0.2556509525301008,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001606682,0.0001648653,0.0001768488,0.0002402109,0.0002664026,0.0004361379,0.002719695,0.0000758537,0.0000288632],"category_scores_gemma":[0.0004187314,0.000129226,0.00007185792,0.001858876,0.0003461353,0.001026676,0.0009708255,0.0006918586,0.000005251298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003699488,"about_ca_system_score_gemma":0.0006154001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005815012,"about_ca_topic_score_gemma":0.00009720201,"domain_scores_codex":[0.9973012,0.00003095003,0.0004938237,0.0005473628,0.001202847,0.0004238685],"domain_scores_gemma":[0.9967806,0.0002746264,0.0001684451,0.0002338104,0.002487459,0.00005507415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007150171,0.0001343613,0.01071378,0.0000749874,0.00009213285,0.000001350128,0.008271636,0.002278022,0.01504585,0.9103408,0.0003782343,0.05259737],"study_design_scores_gemma":[0.0001254759,0.0000553867,0.002318262,0.0008090549,0.000008907963,0.000002752918,0.03295359,0.4922805,0.2999325,0.1709994,0.0002581671,0.0002559582],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5863619,0.00002607604,0.3893688,0.01367913,0.0006383571,0.0006066224,0.000006122457,0.0000515512,0.009261389],"genre_scores_gemma":[0.9941651,0.00004175921,0.005121474,0.0001065542,0.00008490559,0.00006936634,0.000002773869,0.000007637285,0.000400412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7393413,"threshold_uncertainty_score":0.5269683,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3160487084","doi":"10.32473/flairs.v34i1.128379","title":"Entropy-based Variational Learning of Finite Inverted Beta-Liouville Mixture Model","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Agence Nationale de la Recherche; Equipex","keywords":"Cluster analysis; Inference; Artificial intelligence; Entropy (arrow of time); Mixture model; Unsupervised learning; Computer science; Categorization; Pattern recognition (psychology); BETA (programming language); Kullback–Leibler divergence; Machine learning; Algorithm; Mathematics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.113545119647876,"gpt":0.3514613912485156,"spread":0.2379162716006397,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002369007,0.0002050426,0.0003066976,0.0001422098,0.0003190063,0.0003166469,0.002637355,0.0001766151,0.0001229714],"category_scores_gemma":[0.001821652,0.0001708375,0.0003946414,0.001255084,0.0003925098,0.0005306484,0.001005578,0.0009256417,0.000008976924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001412873,"about_ca_system_score_gemma":0.001121323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004267574,"about_ca_topic_score_gemma":0.000005380864,"domain_scores_codex":[0.9961953,0.00009940042,0.0006793527,0.0006078454,0.001968468,0.0004495977],"domain_scores_gemma":[0.9922773,0.0006638592,0.0003476537,0.0002957641,0.006290858,0.0001245807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000259817,0.0001149205,0.0002088823,0.00005985945,0.00008075988,6.408471e-7,0.001572908,0.005860019,0.1253497,0.8593895,0.0007101502,0.006626724],"study_design_scores_gemma":[0.0000355788,0.00002478917,0.00002282783,0.00008333425,0.000006040612,0.000001351718,0.0002043235,0.5163112,0.2732626,0.2097981,0.0001641962,0.00008567033],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007208618,0.00005978679,0.9799408,0.008205066,0.000424072,0.0002092225,0.0000216896,0.0000417167,0.003889047],"genre_scores_gemma":[0.75647,0.00009456096,0.2424214,0.0001824384,0.0001404792,0.00002888932,0.000007026157,0.0000128727,0.0006423179],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7492614,"threshold_uncertainty_score":0.696655,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3160648873","doi":"10.32473/flairs.v34i1.128479","title":"Performance Metrics for State-Based Imitation Learning","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Perceptron; Artificial neural network; Machine learning; Imitation; Domain (mathematical analysis); State (computer science); Multilayer perceptron; Long short term memory; Layer (electronics); Recurrent neural network; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.1510561623617243,"gpt":0.3704275475126838,"spread":0.2193713851509595,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001569532,0.0001186304,0.0001335608,0.0001237869,0.000543406,0.000438141,0.001704203,0.00007552828,0.00002767452],"category_scores_gemma":[0.001079267,0.0001042707,0.0002189398,0.001530664,0.0002103882,0.0005108336,0.0004567765,0.0004441258,0.00001126699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001823782,"about_ca_system_score_gemma":0.0004098047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002117128,"about_ca_topic_score_gemma":0.000003883073,"domain_scores_codex":[0.9978343,0.00002046672,0.0004154211,0.0004325763,0.0009734915,0.0003237627],"domain_scores_gemma":[0.9931788,0.0004336369,0.0002068685,0.0001875555,0.005924916,0.00006815349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003789375,0.000167883,0.00175123,0.0001317697,0.00006191135,1.992506e-7,0.001361612,0.001721919,0.1761647,0.6794975,0.001639007,0.1374644],"study_design_scores_gemma":[0.00002052604,0.00005691036,0.0001235045,0.0000382547,0.000002562124,0.000001149064,0.0005783896,0.4304607,0.5330801,0.03337839,0.002184386,0.00007512879],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1116012,0.00002515782,0.8782082,0.006559315,0.0003490965,0.0004549766,0.00001034022,0.0001173407,0.002674405],"genre_scores_gemma":[0.9489668,0.0001597947,0.04962302,0.00008221294,0.0001027131,0.0002051208,0.000004228782,0.00001004011,0.0008460616],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8373656,"threshold_uncertainty_score":0.4252036,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410397769","doi":"10.32473/flairs.38.1.139141","title":"Online Community Modeling and Moderation","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Social Media and Politics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Moderation; Psychology; Computer science; Social psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.2771773974238197,"gpt":0.4584976435238617,"spread":0.181320246100042,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002267594,0.0001028859,0.0001491262,0.00009237752,0.001573737,0.0003327544,0.001137371,0.0001462654,0.00004039201],"category_scores_gemma":[0.003465677,0.00009137503,0.0001138828,0.0005749448,0.00119279,0.0003309709,0.0004590219,0.0008753469,0.000003448122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000246627,"about_ca_system_score_gemma":0.0005520133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006222969,"about_ca_topic_score_gemma":0.0008896319,"domain_scores_codex":[0.9979759,0.0001084028,0.0003606346,0.0001957872,0.0009959944,0.000363256],"domain_scores_gemma":[0.9961746,0.0006270722,0.000085672,0.000104996,0.002917002,0.00009068107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002241433,0.0001143871,0.001511133,0.00004214473,0.00004574163,4.152993e-8,0.03739844,0.00007823544,0.007547593,0.9450001,0.0006528706,0.007586947],"study_design_scores_gemma":[0.00003314582,0.00002323941,0.00009757467,0.0002031748,0.00001299394,2.196631e-7,0.2296154,0.1329801,0.02356054,0.6119435,0.001422822,0.0001072403],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9557815,0.00005480591,0.003037238,0.02008085,0.0008847783,0.0003615375,0.0000200736,0.00003931035,0.01973994],"genre_scores_gemma":[0.9973945,0.0005826451,0.0006426833,0.0001956716,0.0003905352,0.0000257143,0.00000323147,0.000006166988,0.0007588684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3330566,"threshold_uncertainty_score":0.9997261,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4225371371","doi":"10.32473/flairs.v35i.130731","title":"Pedestrian Traffic Prediction using Deep Learning","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Pedestrian; Artificial intelligence; Artificial neural network; Event (particle physics); Traffic flow (computer networking); Deep learning; Dual (grammatical number); Pedestrian detection; Machine learning; Engineering; Transport engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1045126509968553,"gpt":0.3240030754447231,"spread":0.2194904244478678,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001500587,0.0001432337,0.000137303,0.0001752982,0.0006532845,0.0001763053,0.001171027,0.00006236983,0.000218098],"category_scores_gemma":[0.0001749633,0.0001397872,0.0001899248,0.0007423238,0.0002206638,0.0003203352,0.0005537919,0.001048111,0.000006312105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004835922,"about_ca_system_score_gemma":0.00006389717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003577635,"about_ca_topic_score_gemma":0.00000594626,"domain_scores_codex":[0.9975892,0.00002863538,0.0004253849,0.0002779689,0.001322786,0.0003559786],"domain_scores_gemma":[0.9990597,0.00007775074,0.00009237944,0.0001049062,0.0005973525,0.00006793278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000120355,0.0002452445,0.001280704,0.000269375,0.0004060077,0.000001568973,0.009042284,0.5532947,0.1753619,0.1227939,0.01977574,0.1174083],"study_design_scores_gemma":[0.00003340991,0.00006782755,0.00007885779,0.00004117029,0.00001240241,0.000006152821,0.01117401,0.9536886,0.02571581,0.002674024,0.006387146,0.0001206493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9273607,0.0001519219,0.04883696,0.001350625,0.003121365,0.001013185,0.00005585801,0.002183998,0.0159254],"genre_scores_gemma":[0.99833,0.0003030052,0.0007836301,0.00001851378,0.000247553,0.000111441,0.00000689576,0.00002465375,0.0001743287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4003939,"threshold_uncertainty_score":0.5700358,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4225425583","doi":"10.32473/flairs.v35i.130850","title":"Learning Automata with Artificial Reflecting Barriers in Games with Limited Information","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Nash equilibrium; Computer science; Game theory; Reinforcement learning; Fictitious play; Complete information; Perfect information; Learning automata; Mathematical economics; Point (geometry); Saddle point; Artificial intelligence; Automaton; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.09331720338213315,"gpt":0.3458034193125344,"spread":0.2524862159304012,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003353915,0.0001768504,0.0001918754,0.0003605072,0.0009492207,0.0008205108,0.002582936,0.00005825795,0.0001388173],"category_scores_gemma":[0.0009345421,0.0001361168,0.00008933832,0.002636265,0.0003722953,0.00188465,0.001346254,0.001400404,0.0000100834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004226262,"about_ca_system_score_gemma":0.000732037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002183584,"about_ca_topic_score_gemma":0.00003426434,"domain_scores_codex":[0.9958211,0.00009778232,0.0005827454,0.0004334958,0.002508427,0.000556518],"domain_scores_gemma":[0.9968515,0.0002667761,0.0003139089,0.0002109846,0.002227584,0.0001292354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000575042,0.000213485,0.008321437,0.0001103421,0.0001381561,0.000003470503,0.04743377,0.1109505,0.01334517,0.7679871,0.0007758148,0.05014572],"study_design_scores_gemma":[0.0001280906,0.0005612353,0.0002162333,0.0001404487,0.000003891304,0.00001675952,0.04064172,0.9116417,0.0283918,0.01624228,0.001766927,0.0002489836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7212794,0.00002813904,0.2107804,0.03778448,0.001059549,0.002666706,0.00003166358,0.0005220464,0.02584759],"genre_scores_gemma":[0.9908398,0.00003777818,0.008495878,0.0001318882,0.00005631296,0.0001884088,0.000006545221,0.00001258404,0.0002308006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8006911,"threshold_uncertainty_score":0.7912205,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410432471","doi":"10.32473/flairs.38.1.138855","title":"AI Governance in Academia: Guidelines for Generative AI","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Université du Québec à Trois-Rivières","keywords":"Generative grammar; Corporate governance; Engineering ethics; Artificial intelligence; Computer science; Psychology; Engineering; Management; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.3286494932545765,"gpt":0.4636849476764018,"spread":0.1350354544218253,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00249802,0.000247059,0.0003011013,0.0002249098,0.000365882,0.0007109527,0.00272006,0.0003793542,0.0002401827],"category_scores_gemma":[0.005069472,0.0001959439,0.0002487803,0.001832367,0.000575193,0.001757616,0.001182054,0.001519039,0.00003863804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002273377,"about_ca_system_score_gemma":0.0003066749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006891386,"about_ca_topic_score_gemma":0.0002107233,"domain_scores_codex":[0.9967763,0.00000737357,0.0009033216,0.0006156117,0.001142662,0.0005546905],"domain_scores_gemma":[0.9890611,0.0002432746,0.0003082135,0.0002057809,0.01016186,0.00001979569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00012518,0.00008099663,0.002656698,0.0002209545,0.00004722082,1.705105e-7,0.0001443433,0.0001665227,0.01532341,0.8406019,0.1235443,0.01708831],"study_design_scores_gemma":[0.0001049164,0.0000151674,0.00069846,0.0009033917,0.00002146792,7.153719e-7,0.002185384,0.1733437,0.1400384,0.5647613,0.1176351,0.0002919309],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0927541,0.0006360677,0.06762525,0.7892684,0.007043088,0.003583145,0.0001870062,0.0001795263,0.03872336],"genre_scores_gemma":[0.9829962,0.0004391311,0.001598403,0.01045788,0.001851389,0.0003008182,0.00001638379,0.00002332662,0.002316419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8902422,"threshold_uncertainty_score":0.799036,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410397895","doi":"10.32473/flairs.38.1.138971","title":"RQPool: A Novel Multi-Branch Graph-Level Anomaly Detection","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Anomaly detection; Computer science; Graph; Anomaly (physics); Artificial intelligence; Theoretical computer science; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.1594303350550822,"gpt":0.3608686463202636,"spread":0.2014383112651813,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002175469,0.0002178326,0.000226201,0.0003080336,0.0007071213,0.0006635115,0.003564681,0.0001966343,0.00003632758],"category_scores_gemma":[0.0007304618,0.0001848998,0.0003520835,0.002406581,0.0005220319,0.001016431,0.001121995,0.0009410945,0.00002371309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002568794,"about_ca_system_score_gemma":0.0002848523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004074993,"about_ca_topic_score_gemma":0.0001632371,"domain_scores_codex":[0.9967509,0.00003391614,0.0006484382,0.0006820508,0.001359208,0.0005255122],"domain_scores_gemma":[0.9955316,0.000277569,0.0002316158,0.0003191158,0.003544092,0.00009603176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007115697,0.0002506942,0.0002454468,0.00007034846,0.0001172213,1.964961e-7,0.001530902,0.0001562398,0.4351363,0.4821166,0.001391871,0.07891301],"study_design_scores_gemma":[0.0000738981,0.00006226601,0.0006271079,0.0001899421,0.000006644265,0.000004246798,0.0006028599,0.2681652,0.6351876,0.0935308,0.001395266,0.0001541895],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1303236,0.00006133164,0.8578454,0.005013309,0.003200538,0.0005486458,0.0000137575,0.0001261101,0.002867225],"genre_scores_gemma":[0.9903886,0.000212431,0.00795364,0.0001870746,0.0002638527,0.00008741393,8.598809e-7,0.0000100639,0.0008960463],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.860065,"threshold_uncertainty_score":0.7539996,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400281151","doi":"10.32473/flairs.37.1.135275","title":"Ethics of AI Explained","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Université du Québec à Trois-Rivières","keywords":"Information ethics; Applied ethics; Meta-ethics; Engineering ethics; Ethics of technology; Normative ethics; Psychology; Sociology; Epistemology; Philosophy; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.3141774611558331,"gpt":0.5028181561763003,"spread":0.1886406950204673,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.01053518,0.0001449111,0.0002210779,0.0001426893,0.0008560605,0.0008014906,0.002095604,0.0003603727,0.000379076],"category_scores_gemma":[0.0097721,0.0001181941,0.0003775574,0.001197897,0.002773218,0.0008902274,0.0004739759,0.00227461,0.00003154858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002927912,"about_ca_system_score_gemma":0.00215324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002690785,"about_ca_topic_score_gemma":0.0004582227,"domain_scores_codex":[0.9952731,0.000101286,0.0005809503,0.0003654063,0.00315694,0.0005223029],"domain_scores_gemma":[0.9896577,0.001728468,0.0001451306,0.0001361697,0.008178348,0.0001541659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002237352,0.00004824516,0.0001807608,0.0001170373,0.00008308178,3.760227e-7,0.06780086,0.000007073907,0.02241388,0.9015433,0.004539761,0.003243226],"study_design_scores_gemma":[0.00001849148,0.00006631303,0.00004440706,0.0006213262,0.00001473265,6.086516e-7,0.067619,0.003879901,0.1243352,0.7867009,0.01654293,0.0001561902],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2569106,0.0005031054,0.002833078,0.5084128,0.006043038,0.001278433,0.0001136629,0.0002301201,0.2236752],"genre_scores_gemma":[0.9949499,0.001398788,0.0004469838,0.00027059,0.0008201601,0.00002395759,0.000001658762,0.00001700423,0.00207096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7380393,"threshold_uncertainty_score":0.9999406,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410398730","doi":"10.32473/flairs.38.1.139110","title":"Preface","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Historical Geography and Geographical Thought","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Philosophy","retraction":null,"screen_n_in":null,"score":{"opus":0.1302723172466035,"gpt":0.418289214212581,"spread":0.2880168969659775,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00360714,0.0001375456,0.0001914918,0.0001871906,0.001250459,0.0003121331,0.002591623,0.0001948688,0.0003429691],"category_scores_gemma":[0.002617407,0.0001114287,0.0004241246,0.003787477,0.002248332,0.000339043,0.0005318556,0.0007893419,0.00003206806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002408546,"about_ca_system_score_gemma":0.0004636933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00111139,"about_ca_topic_score_gemma":0.0001862135,"domain_scores_codex":[0.9966572,0.00006888291,0.0004670151,0.0004173622,0.001819587,0.0005699314],"domain_scores_gemma":[0.99554,0.0005348765,0.0001301677,0.0001644366,0.003494748,0.0001358188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003910253,0.00009888445,0.002232812,0.00002757335,0.00007189707,1.164405e-7,0.004252562,0.000006054641,0.004437289,0.9630659,0.009131743,0.01663602],"study_design_scores_gemma":[0.00003643855,0.00003767552,0.0005580237,0.0002345478,0.00001681156,2.147747e-7,0.02066386,0.0008221494,0.04178981,0.6438168,0.2918432,0.0001805179],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.09881697,0.0004521089,0.003348448,0.0724717,0.004046714,0.001198124,0.00002932968,0.0001750278,0.8194616],"genre_scores_gemma":[0.9878365,0.0009840337,0.0007153693,0.0001880871,0.0003775432,0.00006357128,9.441276e-7,0.000006797442,0.00982713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8890195,"threshold_uncertainty_score":0.9617649,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410432646","doi":"10.32473/flairs.38.1.138913","title":"Creating Domain-Specific Datasets for Intelligent Environmental Feature Comparison","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; University of Windsor","funders":"","keywords":"Domain (mathematical analysis); Feature (linguistics); Computer science; Artificial intelligence; Data mining; Pattern recognition (psychology); Information retrieval; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1059942501670421,"gpt":0.4030994945546468,"spread":0.2971052443876047,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001095412,0.0001917349,0.0002159806,0.000127842,0.0006742913,0.0004440063,0.003636539,0.0001077004,0.00002211401],"category_scores_gemma":[0.0001392079,0.0001625329,0.0002469761,0.0007415857,0.0004347525,0.0004705934,0.001362964,0.000531006,0.00001093969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003295514,"about_ca_system_score_gemma":0.0001607252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001109728,"about_ca_topic_score_gemma":0.000002314999,"domain_scores_codex":[0.997427,0.00001803029,0.0005600469,0.0006288398,0.0009619218,0.0004041738],"domain_scores_gemma":[0.9979302,0.0005327617,0.0002400026,0.0003288052,0.0008918,0.00007639679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002458643,0.0001270958,0.0001389784,0.00002785701,0.00004559653,7.40694e-8,0.0008003162,0.0002302572,0.03228854,0.9268279,0.01339401,0.02609477],"study_design_scores_gemma":[0.00004187101,0.00005248084,0.000099203,0.0001471752,0.000004796098,0.000001840936,0.002591807,0.09444045,0.2509519,0.5582559,0.09325191,0.0001606728],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005900546,0.0001222124,0.9772092,0.01236621,0.0004068751,0.0009094731,0.0001247387,0.00007732661,0.002883452],"genre_scores_gemma":[0.7721577,0.0002459454,0.226286,0.0001975104,0.0001928429,0.0003543994,0.00004441152,0.00001210383,0.0005090891],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7662571,"threshold_uncertainty_score":0.6757655,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400280941","doi":"10.32473/flairs.37.1.135597","title":"Exploration of Word Embeddings with Graph-Based Context Adaptation for Enhanced Word Vectors","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Word embedding; Word (group theory); Natural language understanding; Embedding; Context (archaeology); Natural language; Graph; Representation (politics); Semantic similarity; Linguistics; Theoretical computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.191747697463749,"gpt":0.3730239915487573,"spread":0.1812762940850084,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001667448,0.0001820722,0.0002172462,0.0002255874,0.0002022271,0.0005042288,0.001910309,0.00009558711,0.00002176321],"category_scores_gemma":[0.0005619655,0.0001394985,0.0002441439,0.001149911,0.0003791135,0.001471006,0.0002570636,0.0003843404,0.000005464385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000178404,"about_ca_system_score_gemma":0.0005127429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001320763,"about_ca_topic_score_gemma":0.00005297595,"domain_scores_codex":[0.9970235,0.00002173974,0.0005933457,0.0005858463,0.001409446,0.0003661497],"domain_scores_gemma":[0.9952078,0.0005898859,0.0002237621,0.000216615,0.00368837,0.00007359424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001521025,0.00007253882,0.00004805827,0.000266339,0.0001090373,2.429194e-7,0.01129603,0.004121238,0.1143662,0.7641556,0.000329609,0.105083],"study_design_scores_gemma":[0.00003797227,0.00009467425,0.000007601979,0.0004351128,0.000006739878,4.784782e-7,0.003645548,0.479183,0.4305399,0.08575032,0.0002002473,0.00009829547],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1069634,0.00005852064,0.8866716,0.004427753,0.0007203185,0.0006516665,0.00001205363,0.00008051118,0.0004141606],"genre_scores_gemma":[0.9680585,0.00004410308,0.03127746,0.0000419084,0.000180773,0.0002075418,0.000003803777,0.00001647028,0.0001693898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8610951,"threshold_uncertainty_score":0.5688583,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4225424639","doi":"10.32473/flairs.v35i.130561","title":"Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic Discretization","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"","keywords":"Inference; Nat; Bayesian network; Discretization; Dynamic Bayesian network; Variable elimination; Computer science; Approximate inference; Focus (optics); Tree (set theory); Treewidth; Fiducial inference; Bayesian inference; Exponential family; Algorithm; Frequentist inference; Mathematics; Artificial intelligence; Bayesian probability; Theoretical computer science; Machine learning; Combinatorics; Graph; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.06755913222578512,"gpt":0.3435113616906051,"spread":0.27595222946482,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00255018,0.0002774626,0.0002963814,0.0001667394,0.001247475,0.0007399946,0.004743763,0.00008066689,0.00008696275],"category_scores_gemma":[0.0004508233,0.0002255197,0.0002495675,0.001236506,0.0004290636,0.0009709169,0.001461234,0.001063889,0.000003723518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004206352,"about_ca_system_score_gemma":0.0006331647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000115109,"about_ca_topic_score_gemma":0.00002396506,"domain_scores_codex":[0.9956999,0.00005979077,0.0006548997,0.0008452093,0.001988447,0.0007516908],"domain_scores_gemma":[0.9954492,0.0005079967,0.0003641613,0.0003747747,0.003151804,0.0001521058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002319881,0.0002659282,0.0003218779,0.00007119438,0.0001298535,0.000001048313,0.002255102,0.06429785,0.01293472,0.8928898,0.001195502,0.02540515],"study_design_scores_gemma":[0.0000745966,0.0002425071,0.00002527579,0.00008588098,0.00001005075,0.00001085075,0.001486045,0.8481603,0.01756166,0.1317931,0.0003123735,0.0002373244],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0222044,0.00004991257,0.9689181,0.005949042,0.0006151302,0.0008204617,0.00004818733,0.0001049664,0.001289812],"genre_scores_gemma":[0.9795471,0.0001031978,0.01901427,0.0001433841,0.0001309765,0.0005151844,0.00001858535,0.00002709444,0.0005002313],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9573427,"threshold_uncertainty_score":0.9594697,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}