{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":82,"total_is_capped":false,"direct_labels_cover":7,"predictions_cover":82,"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":"f07e81d75e32","filters":{"venue":"PeerJ Computer Science"}},"results":[{"id":"W3182706339","doi":"10.7717/peerj-cs.623","title":"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation","year":2021,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":4871,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Mean squared error; Statistics; Mathematics; Linear regression; Regression analysis; Regression; Metric (unit); Mean absolute percentage error; Range (aeronautics); Binary number; Ground truth; Coefficient of determination; Artificial intelligence; Computer science; Arithmetic","retraction":null,"screen_n_in":null,"score":{"opus":0.09081828685409067,"gpt":0.4531866506908934,"spread":0.3623683638368028,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002288417,0.00009799087,0.0002133173,0.000182144,0.0001944552,0.00008048859,0.0002007822,0.00002996532,0.000005825399],"category_scores_gemma":[0.001054128,0.00006477263,0.00004219158,0.001405513,0.0002934458,0.000272113,0.0002071806,0.00008540945,4.815971e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006178125,"about_ca_system_score_gemma":0.00013051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000798255,"about_ca_topic_score_gemma":0.00002833935,"domain_scores_codex":[0.9982521,0.0001591708,0.0003613284,0.0002753538,0.0007630966,0.000188951],"domain_scores_gemma":[0.9977785,0.000888739,0.0002064961,0.0003230403,0.0007421922,0.00006098817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002960465,0.0001456324,0.001209731,0.00009116616,0.00003246633,0.000007659679,0.01784954,0.006444954,0.0009298566,0.02326996,0.00006705443,0.9499224],"study_design_scores_gemma":[0.0002308543,0.00003401391,0.01408549,0.00005639361,0.00005502595,0.000003183944,0.0003087116,0.936386,0.003697262,0.04504198,0.00002190944,0.0000791675],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1937144,0.00005058266,0.805635,0.0002457032,0.00007284033,0.000168246,0.000008513293,0.000007861368,0.00009682302],"genre_scores_gemma":[0.5732072,0.00001447711,0.4266694,0.00004929525,0.000007392372,0.00001125132,0.000003047212,0.000002798553,0.0000351933],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9498432,"threshold_uncertainty_score":0.2641351,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386373863","doi":"10.7717/peerj-cs.1516","title":"PyMC: a modern, and comprehensive probabilistic programming framework in Python","year":2023,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":792,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institute for Clinical Evaluative Sciences; SickKids Foundation; Hospital for Sick Children; Public Health Ontario; University of Toronto","funders":"","keywords":"Computer science; Python (programming language); Probabilistic logic; Programming language; Syntax; Theoretical computer science; Statistical model; Variety (cybernetics); Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.02204423488414791,"gpt":0.2755833600401185,"spread":0.2535391251559705,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006312973,0.0002270898,0.0002635695,0.0005055296,0.0003081853,0.001026295,0.001779491,0.00007111495,0.000001792776],"category_scores_gemma":[0.0001208038,0.0002041468,0.00003598937,0.004257549,0.0005030578,0.001119702,0.001448314,0.0002834666,0.00004774491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006125245,"about_ca_system_score_gemma":0.0002610378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002477483,"about_ca_topic_score_gemma":0.000008696206,"domain_scores_codex":[0.9970675,0.00004354387,0.0003133848,0.001107894,0.0006667744,0.0008009801],"domain_scores_gemma":[0.9985405,0.0002189619,0.00009527702,0.0006918828,0.0002229637,0.0002304015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005171526,0.00009265203,0.004258906,0.0001828547,0.000004940455,0.0001539364,0.006038675,0.002953338,0.000417299,0.174134,0.0001375874,0.8116206],"study_design_scores_gemma":[0.0001650309,0.0001049896,0.04534252,0.0001570589,0.000001664468,0.00005193633,0.00002288204,0.8381396,0.00008674058,0.1152141,0.0004338651,0.0002796372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1004951,0.0001048004,0.8961822,0.001830198,0.0004517282,0.0003252849,9.095494e-7,0.0005106573,0.00009912422],"genre_scores_gemma":[0.7018499,0.00001583297,0.2977507,0.0002678005,0.00005323398,0.00003466573,6.453917e-7,0.000008296925,0.00001887358],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8351862,"threshold_uncertainty_score":0.9896591,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2470289662","doi":"10.7717/peerj-cs.86","title":"Software citation principles","year":2016,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":269,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Science and Technology Facilities Council; Engineering and Physical Sciences Research Council; University of California, Los Angeles; University of California, Santa Barbara; University of Illinois at Urbana-Champaign; National Institute of Standards and Technology; National Institutes of Health; University of Manchester; University of St Andrews; Louisiana State University; Institute for Quantitative Social Science, Harvard University; City University of New York; Oregon State University; Cardiff Metropolitan University; Johns Hopkins University; National Science Foundation; TRIUMF; University of California, Davis; Graduate Center; Harvard University; CERN; Smithsonian Institution","keywords":"Citation; Acknowledgement; Computer science; Software; Software peer review; Set (abstract data type); Working group; Work (physics); Data science; Knowledge management; Software development; Software engineering; World Wide Web; Software construction; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.2010224114758447,"gpt":0.3785119673439535,"spread":0.1774895558681088,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01030636,0.0001172251,0.0001449276,0.0006447301,0.0005033998,0.001298427,0.003162401,0.00001997397,0.0001581775],"category_scores_gemma":[0.004502901,0.00006378196,0.00006303198,0.002541181,0.0006075376,0.001224558,0.001603831,0.00004252581,0.001700923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007071002,"about_ca_system_score_gemma":0.0001452062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007829435,"about_ca_topic_score_gemma":0.000006714883,"domain_scores_codex":[0.9946552,0.00007837229,0.0004668764,0.001277816,0.003092043,0.0004297029],"domain_scores_gemma":[0.996183,0.001108137,0.0001863771,0.001641735,0.0006841779,0.0001965979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002586772,0.00002581053,0.007407844,0.000001099178,0.000001742208,0.000004069297,0.0002073352,0.0005208305,0.0006130699,0.007695381,0.02524293,0.9582773],"study_design_scores_gemma":[0.0006105856,0.000142608,0.3975246,0.00007810073,0.000006280874,0.00002341149,0.00007542448,0.1475416,0.001822134,0.05090122,0.4007456,0.0005284845],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1158914,0.00001085425,0.8765469,0.003234718,0.002887297,0.00009840711,0.00000749462,0.000161099,0.001161861],"genre_scores_gemma":[0.8461369,0.000001423736,0.1454227,0.0004642559,0.0002431078,0.000005042497,0.000001228963,0.000005926831,0.00771946],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9577488,"threshold_uncertainty_score":0.9997383,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2734665458","doi":"10.7717/peerj-cs.142","title":"Sustainable computational science: the ReScience initiative","year":2017,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":127,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Wilfrid Laurier University; University of Ottawa; Université de Montréal","funders":"","keywords":"Political science; Computer science; Environmental planning; Environmental science","retraction":null,"screen_n_in":null,"score":{"opus":0.1720765735388619,"gpt":0.4334810130783564,"spread":0.2614044395394945,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication","open_science"],"consensus_categories":["metaresearch","sts","open_science"],"category_scores_codex":[0.04645304,0.0002153466,0.0002434957,0.0009657423,0.01882052,0.02314151,0.02221928,0.0000258874,0.0000798452],"category_scores_gemma":[0.01260854,0.0001294233,0.00009504386,0.004761856,0.01591577,0.005812092,0.01082859,0.0002427157,0.0005161003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002125966,"about_ca_system_score_gemma":0.001812908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001598499,"about_ca_topic_score_gemma":0.00001289786,"domain_scores_codex":[0.9875078,0.0001296931,0.0006318749,0.002192443,0.008313158,0.00122501],"domain_scores_gemma":[0.9893201,0.001135758,0.0006593343,0.005001235,0.003511799,0.0003717995],"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":[0.00002271197,0.0002206358,0.0146081,0.00000917701,0.00001149933,0.0001045603,0.007754668,0.05880602,0.0003584454,0.4876059,0.08524322,0.345255],"study_design_scores_gemma":[0.0002246459,0.00006760009,0.266613,0.00001316783,0.000004122255,0.00002384927,0.001066169,0.6212096,0.0002537471,0.08938779,0.02089179,0.0002445174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2887597,0.00003689177,0.637459,0.01947738,0.007216597,0.0006958073,0.00001488414,0.0001781919,0.04616149],"genre_scores_gemma":[0.9739089,8.270417e-7,0.02143545,0.001079717,0.000230535,0.000006114953,9.735879e-7,0.000005310044,0.003332147],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6851493,"threshold_uncertainty_score":0.9971716,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2802494476","doi":"10.7717/peerj-cs.154","title":"Supervised deep learning embeddings for the prediction of cervical cancer diagnosis","year":2018,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"AI in cancer detection","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Princess Margaret Cancer Centre","funders":"European Regional Development Fund","keywords":"Machine learning; Artificial intelligence; Computer science; Deep learning; Dimensionality reduction; Cervical cancer; Supervised learning; Embedding; Population; Cancer; Medicine; Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.02399263484056004,"gpt":0.2799082927756323,"spread":0.2559156579350723,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009734308,0.0001399278,0.000158841,0.0001588623,0.0008118585,0.0002664924,0.002087623,0.00005048409,0.00003105256],"category_scores_gemma":[0.00009998571,0.0001055875,0.00008467063,0.001413703,0.0006983176,0.0009566864,0.0006076006,0.0001528283,0.000009367564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001547573,"about_ca_system_score_gemma":0.0001667671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001590543,"about_ca_topic_score_gemma":0.00004257217,"domain_scores_codex":[0.9979133,0.00004256903,0.0002771619,0.0006401314,0.0007056095,0.0004211983],"domain_scores_gemma":[0.998015,0.0003966088,0.0001531524,0.0005783819,0.0007568931,0.00009999522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002474748,0.00005523857,0.01489723,0.00004033297,0.00002539504,5.27213e-7,0.004847244,0.006996901,0.002557742,0.003300216,0.001736653,0.9655178],"study_design_scores_gemma":[0.0002425048,0.000369776,0.03118159,0.00002967859,0.00001077661,0.000006303905,0.0000178943,0.9422522,0.0195298,0.0004511095,0.005800092,0.0001083271],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03297585,0.0001347865,0.9617395,0.001601731,0.002987118,0.0003108485,0.000004581168,0.0001811512,0.00006445648],"genre_scores_gemma":[0.9115713,0.00004654744,0.0868746,0.0003518465,0.0008253533,0.0002889478,4.714581e-7,0.000009572605,0.00003134308],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9654095,"threshold_uncertainty_score":0.6244242,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4390913672","doi":"10.7717/peerj-cs.1793","title":"Evaluating deep learning variants for cyber-attacks detection and multi-class classification in IoT networks","year":2024,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"King Khalid University","keywords":"Computer science; Deep learning; Artificial intelligence; Machine learning; Recurrent neural network; Convolutional neural network; Computer security; Categorical variable; The Internet; Artificial neural network; Big data; Data mining; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.0498496368555807,"gpt":0.329751001898999,"spread":0.2799013650434183,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002923609,0.0001684773,0.0001600667,0.0004293621,0.0006069339,0.00116409,0.0006280385,0.00009986164,0.000002564904],"category_scores_gemma":[0.0001286514,0.0001666635,0.00004837338,0.00181136,0.0001489031,0.001128135,0.0003432338,0.0003888234,0.00000932448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001654396,"about_ca_system_score_gemma":0.00008852636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002967818,"about_ca_topic_score_gemma":0.00008085098,"domain_scores_codex":[0.9976392,0.0001350234,0.0003368255,0.0009886045,0.0004302762,0.0004701],"domain_scores_gemma":[0.9989604,0.0003249244,0.00009336683,0.0003133979,0.0001913605,0.0001165942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000562629,0.00002309779,0.00008512237,0.00002114771,0.00000328925,0.000002647716,0.000813546,0.08979217,0.005015279,0.00264143,0.0000113202,0.9015853],"study_design_scores_gemma":[0.0002866847,0.0002320848,0.007772491,0.00008679811,0.000004615362,0.00003765907,0.0000131643,0.9893234,0.0004518747,0.0007048365,0.0008867821,0.0001996099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07358555,0.0003436361,0.9230068,0.0003754835,0.002033755,0.0003350328,2.168952e-7,0.000284953,0.00003451434],"genre_scores_gemma":[0.8574448,0.00003212811,0.14202,0.0001266471,0.000284359,0.00004869107,9.058377e-7,0.000009920143,0.00003255511],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9013857,"threshold_uncertainty_score":0.9998728,"prediction_status":"machine_predicted_unvalidated"},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"split"},{"id":"W4398242747","doi":"10.7717/peerj-cs.2000","title":"A review on cultivating effective learning: synthesizing educational theories and virtual reality for enhanced educational experiences","year":2024,"lang":"en","type":"review","venue":"PeerJ Computer Science","topic":"Engineering Education and Technology","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Moncton","funders":"","keywords":"Virtual reality; Mathematics education; Computer science; Psychology; Pedagogy; Human–computer interaction","retraction":null,"screen_n_in":null,"score":{"opus":0.02813410832904851,"gpt":0.3730462931480209,"spread":0.3449121848189724,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001514832,0.0004176122,0.0007831889,0.0004963318,0.0004850217,0.0005405823,0.001673386,0.0001149799,0.000008720448],"category_scores_gemma":[0.001764784,0.0003222521,0.0001829555,0.001524396,0.0004814642,0.0004572296,0.0005316048,0.0004619942,0.00003205274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002272882,"about_ca_system_score_gemma":0.001250916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003866227,"about_ca_topic_score_gemma":4.179263e-7,"domain_scores_codex":[0.9972086,0.0001397735,0.0004909106,0.001326046,0.0004201104,0.0004145541],"domain_scores_gemma":[0.9964107,0.002287639,0.0002893918,0.0005818352,0.0002894272,0.0001409885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[3.380128e-7,0.00003471625,2.246017e-7,0.005545167,0.00001712383,1.825532e-7,0.001281825,0.00001041823,0.000003176522,0.2513096,0.0009267725,0.7408704],"study_design_scores_gemma":[0.0001059098,0.0004498224,0.00001989534,0.07461447,0.0001655438,0.0001475715,0.0001189532,0.008034084,0.0001201068,0.01169479,0.9033343,0.001194537],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0000128546,0.7490302,0.2457131,0.001812543,0.00214466,0.0009050189,0.00000369082,0.0002119491,0.0001660633],"genre_scores_gemma":[0.001113292,0.951813,0.04288365,0.0003636681,0.0005956469,0.002834013,0.00001852683,0.00002963531,0.0003486041],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9024075,"threshold_uncertainty_score":0.9999229,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4391647410","doi":"10.7717/peerj-cs.1776","title":"Applications, challenges, and solutions of unmanned aerial vehicles in smart city using blockchain","year":2024,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Moncton","funders":"","keywords":"Drone; Smart city; Computer science; Geospatial analysis; Computer security; Blockchain; Data science; Internet of Things; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.02771090858003606,"gpt":0.2414904201672448,"spread":0.2137795115872088,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003016801,0.00006011037,0.0000749371,0.0001863331,0.0000709652,0.00004807044,0.0001349099,0.00002639459,0.000001740456],"category_scores_gemma":[0.000002952188,0.00006336672,0.00001195504,0.0005316701,0.0001235961,0.0001074094,0.0000706568,0.00005373646,0.000001632368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004531454,"about_ca_system_score_gemma":0.00003616181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002968661,"about_ca_topic_score_gemma":0.00002344112,"domain_scores_codex":[0.9994116,0.000006876151,0.0001384879,0.0001905749,0.0001121504,0.0001403062],"domain_scores_gemma":[0.9997384,0.00002675804,0.00001159303,0.0001474142,0.00003892319,0.00003693224],"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":[0.000002284374,0.00006739646,0.000291611,0.0002433877,0.00001239864,0.000001423739,0.00164998,0.5110547,0.01393919,0.06184126,0.00006354135,0.4108328],"study_design_scores_gemma":[0.00005256639,0.000006207909,0.002620507,0.00003076806,0.000003148483,0.000004413374,0.000009888268,0.9952012,0.0004021711,0.0009234002,0.0006790347,0.00006668968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1995517,0.00432431,0.7952631,0.0001853338,0.0001664806,0.0001992087,0.000005669617,0.0001378909,0.0001663259],"genre_scores_gemma":[0.9558206,0.0002184837,0.04385573,0.000005146606,0.00006680736,0.00002312992,0.000001481938,0.000005904137,0.00000273596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7562689,"threshold_uncertainty_score":0.258402,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4389391334","doi":"10.7717/peerj-cs.1657","title":"Integration of federated learning with IoT for smart cities applications, challenges, and solutions","year":2023,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Internet of Things; Big data; Computer security; Data science; Work (physics); Artificial intelligence; World Wide Web; Engineering; Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.07156154561711862,"gpt":0.2863425768698028,"spread":0.2147810312526842,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.00112943,0.0001119802,0.0001421627,0.0003497771,0.0005535706,0.0002362063,0.006106615,0.00004285508,3.523854e-7],"category_scores_gemma":[0.001129129,0.00009607351,0.00001815777,0.001290974,0.0005150491,0.0005908576,0.01420684,0.0001263385,0.000004481026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003805235,"about_ca_system_score_gemma":0.0001399167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000134317,"about_ca_topic_score_gemma":0.00001896189,"domain_scores_codex":[0.9985273,0.00002927158,0.0001814505,0.0005676059,0.0003549868,0.0003394167],"domain_scores_gemma":[0.9976258,0.0002374143,0.0001111837,0.001653606,0.0003195613,0.00005241417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006567003,0.00003922051,0.000202346,0.00008803282,0.00001422667,0.000001568876,0.0008449898,0.0005100006,0.003427126,0.09823812,0.004649412,0.8919784],"study_design_scores_gemma":[0.0001553859,0.0001707131,0.00425274,0.00005094838,0.000002765746,0.00001206145,0.00009361267,0.9469343,0.001938739,0.04438507,0.001868489,0.0001351586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006920693,0.0003049474,0.9811411,0.01020118,0.0001112453,0.0004043478,0.00000594631,0.0008171877,0.00009336432],"genre_scores_gemma":[0.2978952,0.0001923334,0.7015992,0.0000398779,0.00002903808,0.0001923127,0.00001168625,0.000007040383,0.00003331065],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9464243,"threshold_uncertainty_score":0.9992708,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3161499990","doi":"10.7717/peerj-cs.502","title":"Augmenting the technology acceptance model with trust model for the initial adoption of a blockchain-based system","year":2021,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":55,"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":"Technology acceptance model; Structural equation modeling; Blockchain; Computer science; Test (biology); Latent variable; Empirical research; Data sharing; Knowledge management; Data science; Computer security; Usability; Human–computer interaction; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.02204306694216132,"gpt":0.2597133560236394,"spread":0.2376702890814781,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001010207,0.0001592184,0.0001980205,0.0001778859,0.0009817157,0.0001521602,0.003085868,0.00009308511,3.527094e-7],"category_scores_gemma":[0.00003680878,0.00009755071,0.00006815764,0.002182043,0.0009784732,0.000101458,0.0006556129,0.0002286733,0.0000011811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006871509,"about_ca_system_score_gemma":0.0006626582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004113075,"about_ca_topic_score_gemma":0.00001323465,"domain_scores_codex":[0.9981519,0.00002515459,0.0002970386,0.0006581844,0.0004596281,0.0004080961],"domain_scores_gemma":[0.9972537,0.0001784546,0.0002282258,0.001532088,0.0007651515,0.00004238497],"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":[0.000005492032,0.00005927759,0.0000586005,0.00002699466,0.00001012135,0.000001717492,0.0003743574,0.5493251,0.0009307745,0.420594,0.00003721286,0.02857634],"study_design_scores_gemma":[0.0003058979,0.00004697996,0.00006521847,0.00003472439,0.00001436124,0.00003806464,0.00009555771,0.986751,0.008167513,0.004317889,0.00003791144,0.0001249377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06547357,0.0001194654,0.926041,0.007489423,0.0001166178,0.0004484669,0.000006635657,0.0002605724,0.00004423597],"genre_scores_gemma":[0.6808462,0.000001010717,0.3187155,0.0002226431,0.00002261509,0.0001695338,4.995575e-7,0.000005168497,0.00001683349],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6153727,"threshold_uncertainty_score":0.7550663,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2988380278","doi":"10.7717/peerj-cs.230","title":"Motivational strategies and approaches for single and multi-player exergames: a social perspective","year":2019,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Educational Games and Gamification","field":"Psychology","cited_by":51,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University; Carleton University","funders":"","keywords":"Social connectedness; Psychology; Perspective (graphical); Physical activity; Psychological intervention; Situational ethics; Applied psychology; Computer science; Social psychology; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.09866783473492735,"gpt":0.3442628987711208,"spread":0.2455950640361935,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000230293,0.00007156303,0.0000787815,0.00007321916,0.0001451309,0.000195363,0.0001201852,0.00003129409,0.00002010476],"category_scores_gemma":[0.00001299889,0.00006524405,0.00001635174,0.0001369529,0.0002851692,0.0003288274,0.00004232717,0.00004412161,0.000008212885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003697762,"about_ca_system_score_gemma":0.00008227392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002383336,"about_ca_topic_score_gemma":0.00000246268,"domain_scores_codex":[0.9992363,0.00001525835,0.00008444765,0.0003846199,0.000130486,0.0001488451],"domain_scores_gemma":[0.9995679,0.0000907592,0.00005021339,0.00009027885,0.0001634694,0.00003739087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00002312543,0.0002932638,0.005944349,0.00002250548,0.00002104802,1.825878e-7,0.05707823,0.00009380502,0.002857772,0.8928813,0.0003689445,0.04041549],"study_design_scores_gemma":[0.0007294868,0.00018477,0.9247793,0.000009134823,0.000007972748,0.00002047165,0.02627483,0.03766105,0.000095852,0.00914053,0.0008896463,0.0002069348],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8395347,0.0002065159,0.1549198,0.002468029,0.0004587506,0.0003341781,0.000007896054,0.00002642824,0.002043688],"genre_scores_gemma":[0.9711905,6.544824e-7,0.02801493,0.0001213781,0.0001593347,0.00002939118,0.000004411796,0.000004558221,0.0004748985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.918835,"threshold_uncertainty_score":0.2660575,"prediction_status":"machine_predicted_unvalidated"},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"qualitative","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W2945163429","doi":"10.7717/peerj-cs.194","title":"The k conditional nearest neighbor algorithm for classification and class probability estimation","year":2019,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; Western University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"k-nearest neighbors algorithm; Nonparametric statistics; Pattern recognition (psychology); Posterior probability; Computer science; Naive Bayes classifier; Benchmark (surveying); Algorithm; Artificial intelligence; Classifier (UML); Mathematics; Bayesian probability; Statistics; Support vector machine","retraction":null,"screen_n_in":null,"score":{"opus":0.01833268062500404,"gpt":0.2739356002516186,"spread":0.2556029196266146,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001709978,0.0001186874,0.0001047896,0.00008167111,0.0008171412,0.001052339,0.001125265,0.00003865477,0.000002249382],"category_scores_gemma":[0.0001486451,0.00008718733,0.0000320408,0.0004796811,0.0003686189,0.001188962,0.0002824228,0.0001253256,0.0000445729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007407946,"about_ca_system_score_gemma":0.0002142841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009757418,"about_ca_topic_score_gemma":0.000002894956,"domain_scores_codex":[0.9982074,0.00006765746,0.0002357491,0.0007005546,0.0004981109,0.0002905388],"domain_scores_gemma":[0.9981267,0.0004903812,0.0001608269,0.0007790727,0.0003402563,0.0001027506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000243423,0.00002585024,0.001430208,0.000011118,0.000002423648,9.617201e-8,0.0001057623,0.0006635652,0.0003647642,0.2735528,0.0003654408,0.7234756],"study_design_scores_gemma":[0.000185441,0.00007841911,0.1442086,0.000005894579,0.000001872117,0.000008785947,0.000004685796,0.8333433,0.00005909189,0.01505371,0.006949361,0.0001008435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007737174,0.00002742212,0.9843647,0.00640761,0.0005913663,0.000539531,0.00001078156,0.0001349576,0.0001863861],"genre_scores_gemma":[0.5014479,0.00000448724,0.4980343,0.0002060281,0.00008697593,0.00006405531,0.00004104739,0.000004761543,0.0001104217],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8326797,"threshold_uncertainty_score":0.9999847,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2191249398","doi":"10.7717/peerj-cs.37","title":"Semantic representation of scientific literature: bringing claims, contributions and named entities onto the Linked Open Data cloud","year":2015,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":31,"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":"Computer science; Linked data; Workflow; Entity linking; Information retrieval; RDF; Semantic Web; Semantic search; Knowledge base; World Wide Web; Pipeline (software); Named-entity recognition; Cloud computing; SPARQL; Data science; Database; Task (project management)","retraction":null,"screen_n_in":null,"score":{"opus":0.09285361448683829,"gpt":0.3465087549040279,"spread":0.2536551404171896,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.003881242,0.0001466377,0.0002589115,0.0002144888,0.0007452409,0.004724496,0.007653061,0.00004259787,0.000001449251],"category_scores_gemma":[0.0005624796,0.0001046258,0.00003155366,0.001946761,0.001287113,0.004207159,0.008339882,0.0001565788,0.000007941722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004898792,"about_ca_system_score_gemma":0.00049559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003161794,"about_ca_topic_score_gemma":0.00009517625,"domain_scores_codex":[0.9973115,0.0001527687,0.0003483866,0.0009405953,0.0008387822,0.0004079208],"domain_scores_gemma":[0.9963309,0.0002876723,0.0001978668,0.002235671,0.0007844998,0.0001633665],"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":[0.0000620523,0.0004780154,0.02180061,0.000184724,0.0001416782,0.0001069245,0.06082826,0.001546784,0.01129689,0.6955162,0.05917961,0.1488582],"study_design_scores_gemma":[0.0007381192,0.00009461369,0.02624903,0.0001761798,0.00002195442,0.0001392461,0.0003223926,0.9408184,0.003089249,0.02487187,0.003194928,0.0002840695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.196172,0.001073576,0.7922249,0.005777831,0.003719635,0.0005239774,0.0000237935,0.0001396293,0.0003446769],"genre_scores_gemma":[0.9487894,0.00001748235,0.05064382,0.0001782557,0.0001305287,0.000007121493,0.0000186312,0.00000394887,0.0002108036],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9392716,"threshold_uncertainty_score":0.9996805,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4210353672","doi":"10.7717/peerj-cs.861","title":"Generative adversarial network based adaptive data augmentation for handwritten Arabic text recognition","year":2022,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Cambrian College","funders":"King Fahd University of Petroleum and Minerals","keywords":"Computer science; Artificial intelligence; Generative grammar; Arabic; Domain (mathematical analysis); Class (philosophy); Pattern recognition (psychology); Natural language processing; Machine learning; Speech recognition","retraction":null,"screen_n_in":null,"score":{"opus":0.06651338339506535,"gpt":0.2917767324694792,"spread":0.2252633490744138,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00297171,0.0002527583,0.0002637008,0.0003684984,0.001829559,0.0006129874,0.003961866,0.00004532751,0.00007545162],"category_scores_gemma":[0.00008701237,0.0002698308,0.00009006613,0.001824399,0.000266652,0.002534024,0.00235208,0.0002570177,0.00003032638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003167443,"about_ca_system_score_gemma":0.000690194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003745611,"about_ca_topic_score_gemma":0.00001338087,"domain_scores_codex":[0.9960083,0.0002805478,0.0004273855,0.001514643,0.00111215,0.0006569728],"domain_scores_gemma":[0.99739,0.0003485466,0.0002904117,0.001242229,0.0005511906,0.000177584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001332701,0.0002962555,0.00006302776,0.00001725355,0.00003620965,0.0000175972,0.0008662214,0.01309638,0.001973071,0.00534327,0.04549721,0.9326602],"study_design_scores_gemma":[0.001028362,0.0007215282,0.0002430987,0.00002207078,0.00001719715,0.00002446429,0.00002988987,0.9744828,0.005331137,0.01368586,0.004011657,0.0004019679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001181627,0.00004036014,0.993127,0.001744169,0.001905828,0.001093986,0.0002020191,0.0004989452,0.0002060836],"genre_scores_gemma":[0.1403143,0.000003084736,0.8553906,0.002712093,0.0006446063,0.0004914349,0.0003785752,0.00001744614,0.00004783692],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9613864,"threshold_uncertainty_score":0.9999754,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3048404532","doi":"10.7717/peerj-cs.284","title":"Lean thinking by integrating with discrete event simulation and design of experiments: an emergency department expansion","year":2020,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":29,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Emergency department; Triage; Discrete event simulation; Health care; Operations management; Data collection; Event (particle physics); Computer science; Lean manufacturing; Quality (philosophy); Operations research; Plan (archaeology); Medical emergency; Medicine; Engineering; Simulation; Nursing; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.1000619029727869,"gpt":0.434035489207735,"spread":0.3339735862349481,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082489,0.0001027183,0.0001396138,0.00004912457,0.0009617558,0.00002809893,0.0001661423,0.00003833294,0.00002471184],"category_scores_gemma":[0.00006955281,0.00007619099,0.00001038266,0.0003500264,0.00006346007,0.0005406024,0.00009657061,0.0001426526,0.000001895448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005532933,"about_ca_system_score_gemma":0.0002311467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007128771,"about_ca_topic_score_gemma":0.000009251368,"domain_scores_codex":[0.9984432,0.0001964439,0.000393929,0.0003514885,0.0003873655,0.0002275832],"domain_scores_gemma":[0.999102,0.00007205152,0.0001677998,0.0001586919,0.0003127512,0.000186694],"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":[0.00005228347,0.00005906596,0.01117347,0.0000726067,0.000005892432,9.001399e-7,0.04425794,0.9231018,0.006504666,0.00089331,0.0001275106,0.01375055],"study_design_scores_gemma":[0.0002131054,0.0004608845,0.001042046,0.00009134183,0.00000370326,2.474883e-7,0.0009150402,0.9966141,0.0004891068,0.00004576765,0.00003389734,0.00009075775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.295077,0.00004172381,0.7036451,0.0006503091,0.0001145469,0.0004222734,0.000002621806,0.00003377659,0.00001270149],"genre_scores_gemma":[0.7744989,0.00001013699,0.2250562,0.0003320148,0.00005397273,0.00002031347,0.00001460014,0.000007165256,0.000006644302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4794219,"threshold_uncertainty_score":0.7397145,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4225274980","doi":"10.7717/peerj-cs.951","title":"Deep learning methods for inverse problems","year":2022,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Inverse problem; Outlier; Computer science; Robustness (evolution); Artificial intelligence; Inverse; Rendering (computer graphics); Machine learning; Pattern recognition (psychology); Mathematical optimization; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02891769631331021,"gpt":0.2974497916580822,"spread":0.268532095344772,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007293797,0.00008555115,0.0001006576,0.0001401067,0.0004256082,0.0000802364,0.0004555346,0.00001253177,0.00001380038],"category_scores_gemma":[0.00002057631,0.00009120041,0.00004043649,0.0004385208,0.00007790313,0.0001397991,0.000309071,0.0001672154,0.000002563125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000739625,"about_ca_system_score_gemma":0.00002081792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006186279,"about_ca_topic_score_gemma":9.227273e-7,"domain_scores_codex":[0.99918,0.00004012802,0.0001053259,0.0002294428,0.0001820915,0.0002629677],"domain_scores_gemma":[0.9995936,0.00007132655,0.00002390967,0.000193454,0.00006401355,0.00005372068],"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":[0.000001024299,0.000006937903,0.00003597106,0.000006667286,0.000004098144,0.00000123156,0.0005358555,0.7799267,0.01663623,0.0004801459,0.0009220081,0.2014431],"study_design_scores_gemma":[0.00006866067,0.00007067496,0.00007372699,0.000004761402,0.000002965888,0.00001495908,0.00001640229,0.9457094,0.008429709,0.001792487,0.04369605,0.0001201986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007565645,0.0001122835,0.9900901,0.00005149714,0.0007105451,0.000164768,3.85108e-7,0.0007940101,0.0005107822],"genre_scores_gemma":[0.3004906,0.000004032941,0.6992118,0.0001163026,0.00006215626,0.00005361087,0.000001371797,0.000013602,0.00004651777],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2929249,"threshold_uncertainty_score":0.3719045,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385581085","doi":"10.7717/peerj-cs.1431","title":"A new metaphor-less simple algorithm based on Rao algorithms: a Fully Informed Search Algorithm (FISA)","year":2023,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Algorithm; Computer science; Benchmark (surveying); Simplicity; MATLAB; Simple (philosophy)","retraction":null,"screen_n_in":null,"score":{"opus":0.03195693581331657,"gpt":0.3106109909444785,"spread":0.2786540551311619,"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":[0.002324954,0.0007683376,0.00073024,0.002332573,0.001205488,0.001610316,0.005599944,0.0001780016,0.00004938657],"category_scores_gemma":[0.0002965104,0.0007373911,0.0002859474,0.01281123,0.0006471507,0.003286673,0.002274866,0.0006744385,0.0007618616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006653502,"about_ca_system_score_gemma":0.002981523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001838492,"about_ca_topic_score_gemma":0.00001123689,"domain_scores_codex":[0.9905891,0.0002098696,0.0008473478,0.002474431,0.003695635,0.002183558],"domain_scores_gemma":[0.9939848,0.000824581,0.0002994438,0.002397907,0.001271527,0.001221715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006762221,0.0001007434,0.00002594159,0.00001199534,0.0000190568,0.0001134629,0.0009123253,0.1603171,0.00006976654,0.0006399712,0.002765506,0.8350174],"study_design_scores_gemma":[0.001790278,0.0004437954,0.001686226,0.00005127038,0.00001014693,0.00005773986,0.00009388707,0.9865534,0.002942429,0.001046324,0.004436382,0.000888093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002047469,0.00002954368,0.9923505,0.001582369,0.002399822,0.0009354822,0.00004910752,0.001969619,0.0004788033],"genre_scores_gemma":[0.001647752,0.00002504659,0.9948838,0.001874241,0.0005744018,0.00006450271,0.00004980688,0.00006583759,0.000814603],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8341293,"threshold_uncertainty_score":0.9997802,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2997348301","doi":"10.7717/peerj-cs.243","title":"HACSim: an R package to estimate intraspecific sample sizes for genetic diversity assessment using haplotype accumulation curves","year":2020,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"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":"Sampling (signal processing); Barcode; Haplotype; Sample size determination; DNA barcoding; Sample (material); Biology; Statistics; Genetic diversity; Computer science; Estimator; Evolutionary biology; Mathematics; Genetics; Population; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.08286917328849763,"gpt":0.3542387032832539,"spread":0.2713695299947563,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001543222,0.0001113061,0.0001069728,0.00003768874,0.000463359,0.0001095046,0.0004537899,0.0000420127,0.00001712022],"category_scores_gemma":[0.00006343683,0.0001160117,0.00003907997,0.0002211336,0.00008641962,0.00002365309,0.0006040843,0.00003859971,0.000002714477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001910717,"about_ca_system_score_gemma":0.00009062557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003095313,"about_ca_topic_score_gemma":0.00001475039,"domain_scores_codex":[0.998903,0.00002659698,0.0001178611,0.0004791726,0.0002612758,0.0002121056],"domain_scores_gemma":[0.999281,0.00001415631,0.0000571158,0.0002336617,0.0002021819,0.0002118758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001373566,0.00009990377,0.1841599,0.0001806009,0.00004801498,0.000004556859,0.00203281,0.2936253,0.4725527,0.0004037725,0.003960447,0.04279461],"study_design_scores_gemma":[0.000467779,0.000656714,0.6716238,0.00002100931,0.0000357476,0.000004820589,0.00003633621,0.3100605,0.01379025,0.0002802791,0.002649406,0.0003733509],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5074296,0.00001782448,0.4918804,0.0003084647,0.0001519636,0.0001606982,0.00003116958,0.00001437177,0.000005557652],"genre_scores_gemma":[0.6889127,0.00000380898,0.3099526,0.0009181942,0.0001503095,0.000001178805,0.00005313958,0.000004564631,0.000003479088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4874639,"threshold_uncertainty_score":0.4730819,"prediction_status":"machine_predicted_unvalidated"},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"software","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"split"},{"id":"W4399240157","doi":"10.7717/peerj-cs.2033","title":"Analysis of the performance of Faster R-CNN and YOLOv8 in detecting fishing vessels and fishes in real time","year":2024,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Moncton","funders":"","keywords":"Fishing; Context (archaeology); Relevance (law); Computer science; Object detection; Fishery; Focus (optics); Data science; Marine fisheries; Fish <Actinopterygii>; Artificial intelligence; Geography; Pattern recognition (psychology); Political science; Archaeology","retraction":null,"screen_n_in":null,"score":{"opus":0.01382748090345307,"gpt":0.2546168142399983,"spread":0.2407893333365453,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006362405,0.00008461493,0.0001853058,0.0003987674,0.00007889861,0.0001295471,0.0007833438,0.0000218733,7.020957e-7],"category_scores_gemma":[0.00002123337,0.00006455138,0.00002746273,0.004400519,0.0002844194,0.0008686658,0.0007705469,0.0001248325,4.980496e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002714984,"about_ca_system_score_gemma":0.00004365488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000362888,"about_ca_topic_score_gemma":0.0000463957,"domain_scores_codex":[0.9987985,0.0000351091,0.0002641863,0.0004452219,0.0002568893,0.0002000215],"domain_scores_gemma":[0.9991876,0.0002840429,0.00007801658,0.0003708883,0.00004430346,0.00003508887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006735001,0.0000579472,0.2990261,0.0001617714,0.00004182363,0.00000838307,0.0112688,0.1433772,0.04833313,0.002575429,0.00001953277,0.4951231],"study_design_scores_gemma":[0.00003399433,0.00001804496,0.2639947,0.00006008287,0.000006287421,0.00000389597,0.000004667634,0.7332392,0.002414885,0.0001670868,0.00000707671,0.00005002566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9551973,0.00007552774,0.04406688,0.0004119352,0.00007117933,0.00009883246,9.527365e-7,0.00002625475,0.00005116023],"genre_scores_gemma":[0.9771354,0.00002584018,0.02276994,0.00003732948,0.00001171894,0.000004915103,1.38482e-7,0.000002700478,0.00001197074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.589862,"threshold_uncertainty_score":0.2632329,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3009458272","doi":"10.7717/peerj-cs.261","title":"An evolutionary decomposition-based multi-objective feature selection for multi-label classification","year":2020,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":26,"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":"Computer science; Feature selection; Multi-label classification; Artificial intelligence; Feature (linguistics); Data mining; Selection (genetic algorithm); Field (mathematics); Machine learning; Evolutionary algorithm; Genetic algorithm; Pattern recognition (psychology); Optimization problem; Mathematics; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.0639818680707189,"gpt":0.3394460651024501,"spread":0.2754641970317312,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003752646,0.0002262307,0.0001836983,0.0003060418,0.0008421026,0.0005839603,0.002138096,0.0001177598,0.000003105057],"category_scores_gemma":[0.0001114997,0.0002207839,0.00007169111,0.001922263,0.000323073,0.002167139,0.000189955,0.0002073007,0.00003361106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002374463,"about_ca_system_score_gemma":0.0004140735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008670199,"about_ca_topic_score_gemma":0.000004649314,"domain_scores_codex":[0.9975435,0.00006344998,0.0002605851,0.001185334,0.0005177491,0.0004293905],"domain_scores_gemma":[0.9981498,0.00009847568,0.0002055604,0.0005769397,0.0007371023,0.0002321798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001122891,0.002148129,0.0129816,0.00009700658,0.00004045316,0.000003773885,0.002778848,0.01115243,0.3210153,0.1287898,0.008124213,0.5127561],"study_design_scores_gemma":[0.0009078244,0.0003928248,0.03554131,0.000009347048,0.000005629405,0.000004388469,0.00003245917,0.9475176,0.0144527,0.0004104572,0.0004528293,0.0002725922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005797497,0.0000424049,0.9820167,0.009660914,0.0004218147,0.0006151589,0.000009452894,0.001423598,0.00001245969],"genre_scores_gemma":[0.4275707,0.000001299969,0.57139,0.0008397424,0.00007439216,0.00008560067,0.00001506099,0.000007122189,0.00001604827],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9363652,"threshold_uncertainty_score":0.9003305,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1943673337","doi":"10.7717/peerj-cs.25","title":"Mining known attack patterns from security-related events","year":2015,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Concordia University; McGill University","funders":"","keywords":"Computer security; Malware; Asset (computer security); Order (exchange); Hacker; Computer science; Denial-of-service attack; Attack patterns; Security service; Quality (philosophy); Information security; Business; The Internet; World Wide Web; Intrusion detection system","retraction":null,"screen_n_in":null,"score":{"opus":0.03510844297880161,"gpt":0.2699499274382047,"spread":0.2348414844594031,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001183236,0.0002035444,0.000200998,0.0002396308,0.000298944,0.0006075655,0.002656264,0.00007703294,0.00001184604],"category_scores_gemma":[0.00007924163,0.0001939189,0.00006757913,0.001135316,0.0001255781,0.001879901,0.001103334,0.0002148435,0.0002553203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001439657,"about_ca_system_score_gemma":0.0002404374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000303275,"about_ca_topic_score_gemma":0.00002556858,"domain_scores_codex":[0.9970739,0.00009713411,0.0003047888,0.0009304793,0.001073008,0.0005207265],"domain_scores_gemma":[0.998128,0.0001007738,0.0001414639,0.0009126469,0.000298059,0.0004190893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004238763,0.001029306,0.1839989,0.00004864289,0.0001441083,0.0003017864,0.1043832,0.0164569,0.00207098,0.0141906,0.03416098,0.6431722],"study_design_scores_gemma":[0.0006453355,0.0001946161,0.03313969,0.00006779702,0.000006617553,0.0000477889,0.0000308245,0.9512714,0.001663723,0.00883267,0.003679317,0.000420244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5595872,0.00005147804,0.4347396,0.0006217996,0.004204663,0.00008426031,0.00000262786,0.0003136595,0.0003947551],"genre_scores_gemma":[0.9532422,0.000002232987,0.04596648,0.0003802162,0.0003112548,0.000005374175,0.000003900581,0.000009168351,0.00007909958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9348145,"threshold_uncertainty_score":0.7907785,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4389340115","doi":"10.7717/peerj-cs.1651","title":"A deep learning approach for the detection and counting of colon cancer cells (HT-29 cells) bunches and impurities","year":2023,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"AI in cancer detection","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Moncton","funders":"","keywords":"Deep learning; Artificial intelligence; Colorectal cancer; Computer science; Pattern recognition (psychology); Cancer cell; Cancer; Machine learning; Medicine; Internal medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.01881575428924857,"gpt":0.2453289860026231,"spread":0.2265132317133745,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00147125,0.0001381707,0.0001665632,0.0002005975,0.0008292056,0.0004173006,0.0006750575,0.0000465036,5.345106e-7],"category_scores_gemma":[0.00002799058,0.0001117364,0.00003620745,0.001156187,0.0005093088,0.0006343332,0.0006015281,0.0001582765,9.169589e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007303944,"about_ca_system_score_gemma":0.00008298049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002088017,"about_ca_topic_score_gemma":0.00004065672,"domain_scores_codex":[0.9983542,0.00004446499,0.0002040306,0.0005862041,0.0004431122,0.0003679937],"domain_scores_gemma":[0.9988626,0.0004151881,0.0001651273,0.0002718553,0.0002220714,0.00006317568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001600782,0.00001772612,0.0005488247,0.0002290732,0.00001999267,9.435665e-7,0.006638397,0.09958635,0.1238732,0.0004722702,0.0001028871,0.7684943],"study_design_scores_gemma":[0.0001746801,0.0001335827,0.002625102,0.00001823535,0.000008757621,0.00000945608,0.0001415616,0.9399213,0.05609362,0.0002289278,0.000516864,0.0001278938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1053801,0.000470912,0.8925881,0.0001603736,0.0009090231,0.0003107623,0.00000129304,0.0001546423,0.00002474409],"genre_scores_gemma":[0.9440735,0.000215114,0.05532947,0.00006387904,0.000169823,0.00009019166,2.453272e-7,0.000009459727,0.00004834985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.840335,"threshold_uncertainty_score":0.6377663,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2725428559","doi":"10.7717/peerj-cs.121","title":"ScholarLens: extracting competences from research publications for the automatic generation of semantic user profiles","year":2017,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":18,"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":"Computer science; Linked data; RDF; Semantic Web; Workflow; World Wide Web; Information retrieval; Semantic data model; Data science; Semantic technology; Semantic computing; Database","retraction":null,"screen_n_in":null,"score":{"opus":0.2624818257962674,"gpt":0.4093294126286107,"spread":0.1468475868323433,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.004271472,0.0001190002,0.0001825928,0.000220698,0.003568627,0.003567832,0.006427431,0.00004370925,0.000005391545],"category_scores_gemma":[0.001868956,0.00007958485,0.00006478019,0.0004828496,0.001169982,0.003248745,0.001215268,0.0001969861,0.00001426543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003522986,"about_ca_system_score_gemma":0.0004011572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003998678,"about_ca_topic_score_gemma":0.00009853068,"domain_scores_codex":[0.9974702,0.0001032843,0.0003365994,0.0006295741,0.001010659,0.0004496567],"domain_scores_gemma":[0.9948348,0.001547792,0.0003370829,0.00204967,0.001157979,0.00007261563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004929512,0.0002779564,0.03992656,0.00009616982,0.00007240647,0.000003915409,0.006091171,0.001207756,0.04546332,0.2658702,0.007045053,0.6339406],"study_design_scores_gemma":[0.000116376,0.00004533061,0.1840509,0.00004155966,0.000005540735,0.000004515269,0.00008087432,0.8038875,0.00809666,0.002898133,0.0006780396,0.00009463329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.192299,0.0001809384,0.7966812,0.009085764,0.001044394,0.0004607544,0.000003673006,0.0001190531,0.0001252952],"genre_scores_gemma":[0.6755523,0.000009820352,0.3240612,0.00005953225,0.0002034572,0.00005286888,0.000001168525,0.000003419037,0.00005621652],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8026797,"threshold_uncertainty_score":0.9989483,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2603442026","doi":"10.7717/peerj-cs.109","title":"Isolated guitar transcription using a deep belief network","year":2017,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Music and Audio Processing","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Guitar; Transcription (linguistics); Computer science; Polyphony; Speech recognition; Musical notation; Deep learning; Notation; Audio signal; Artificial intelligence; Speech coding; Acoustics; Mathematics; Arithmetic; Musical","retraction":null,"screen_n_in":null,"score":{"opus":0.03355993295865235,"gpt":0.2763902682384667,"spread":0.2428303352798143,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00109871,0.0002037843,0.0002200594,0.0001271501,0.003231687,0.002833928,0.004462136,0.00005866447,0.000009502017],"category_scores_gemma":[0.000035398,0.0001883674,0.00007796683,0.000589517,0.0005278467,0.003625823,0.0009525199,0.0001775067,0.00003358395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007746611,"about_ca_system_score_gemma":0.0002465367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005882487,"about_ca_topic_score_gemma":0.000009914481,"domain_scores_codex":[0.99739,0.00003397668,0.0002809433,0.0008508209,0.0006840897,0.0007602238],"domain_scores_gemma":[0.9977996,0.00002344792,0.0002616364,0.001425938,0.0002731002,0.0002162195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009202134,0.00009963875,0.001624238,0.00003949241,0.00001821986,0.00007139975,0.003194474,0.02563784,0.01837536,0.01979649,0.001274733,0.9298589],"study_design_scores_gemma":[0.0002185673,0.00005476331,0.006834653,0.00007235294,0.000005588934,0.0000606157,0.000002193393,0.9867783,0.0005875236,0.003913315,0.001206977,0.0002651687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0316161,0.0001492734,0.9631863,0.001554006,0.002424984,0.0001165755,3.645975e-7,0.0002082465,0.0007441241],"genre_scores_gemma":[0.6376256,0.000003736238,0.3609358,0.0009619905,0.0004237757,0.000002022154,3.001769e-7,0.000006552796,0.00004020771],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9611405,"threshold_uncertainty_score":0.9982013,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4416455941","doi":"10.7717/peerj-cs.3309","title":"The Silhouette coefficient and the Davies-Bouldin index are more informative than Dunn index, Calinski-Harabasz index, Shannon entropy, and Gap statistic for unsupervised clustering internal evaluation of two convex clusters","year":2025,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Silhouette; Cluster analysis; Rand index; Pattern recognition (psychology); Ground truth; Consensus clustering; Euclidean distance; Correlation clustering","retraction":null,"screen_n_in":null,"score":{"opus":0.0262903710357365,"gpt":0.3489306135391119,"spread":0.3226402425033754,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005510658,0.000321251,0.0004314577,0.0004220268,0.001093618,0.00108187,0.002359494,0.00006137125,0.000001260569],"category_scores_gemma":[0.0005196544,0.0002122211,0.00007342413,0.001228431,0.002483773,0.001005859,0.003192935,0.0004039452,9.752703e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002783602,"about_ca_system_score_gemma":0.0004863967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001693457,"about_ca_topic_score_gemma":0.0001205099,"domain_scores_codex":[0.9955573,0.0003475819,0.0006857439,0.0007849578,0.001896881,0.0007275491],"domain_scores_gemma":[0.9957978,0.001482012,0.0004086071,0.000841974,0.001295061,0.0001745732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006152953,0.0001070965,0.007529434,0.0003988074,0.0001626694,0.000008967187,0.02046039,0.402385,0.00030904,0.01136353,0.0001522117,0.5565076],"study_design_scores_gemma":[0.004060003,0.0001164844,0.02687488,0.0002289656,0.00002153014,0.0000245505,0.0005193836,0.964752,0.0003373733,0.002775573,0.00007492906,0.0002143312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0819464,0.0002704879,0.9131182,0.0023276,0.0007179963,0.001498084,0.00002549475,0.00005384738,0.00004187218],"genre_scores_gemma":[0.9855249,0.00003389925,0.01374683,0.0004146438,0.00005406095,0.0001538978,0.000002783655,0.00001168501,0.00005731246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9035785,"threshold_uncertainty_score":0.9999551,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388533824","doi":"10.7717/peerj-cs.1557","title":"An improved hybrid whale optimization algorithm for global optimization and engineering design problems","year":2023,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Benchmark (surveying); Computer science; Differential evolution; Particle swarm optimization; Mathematical optimization; Evolutionary algorithm; Engineering optimization; Metaheuristic; Population; Multi-objective optimization; Algorithm; Optimization problem; Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02608407197188254,"gpt":0.2807383630520047,"spread":0.2546542910801222,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002313466,0.0002380692,0.000231193,0.0004427867,0.0004857193,0.001634867,0.001639251,0.00005259487,0.000004965875],"category_scores_gemma":[0.0001974218,0.0002418425,0.00003992849,0.002978551,0.0001704027,0.002380859,0.0005856535,0.00009972134,0.000007007164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001308179,"about_ca_system_score_gemma":0.0003185819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008985164,"about_ca_topic_score_gemma":2.42797e-7,"domain_scores_codex":[0.9969376,0.00008375131,0.000363287,0.001102039,0.0007742628,0.0007390488],"domain_scores_gemma":[0.9978259,0.0001819253,0.0001179263,0.0006967415,0.0007904451,0.0003870501],"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":[0.000001177751,0.00002852606,0.000005771661,0.00001523687,0.000004818649,0.000003203317,0.00008849384,0.8871977,0.0001076315,0.0005051747,0.00008959169,0.1119527],"study_design_scores_gemma":[0.000460939,0.0002496029,0.00008033342,0.00001581991,0.000004715054,0.00003717436,0.000003408298,0.9981863,0.0004098626,0.0002125653,0.00005750167,0.0002817632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005677911,0.00003076867,0.9970413,0.0003610995,0.0007610505,0.000966155,0.00001405206,0.000757659,0.00001116853],"genre_scores_gemma":[0.001959222,0.00003723835,0.9975791,0.00008314655,0.0001333737,0.0001172812,0.0000303193,0.00001974661,0.00004056524],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.111671,"threshold_uncertainty_score":0.9994015,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4394930958","doi":"10.7717/peerj-cs.1986","title":"Design of load-aware resource allocation for heterogeneous fog computing systems","year":2024,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Computer science; Cloud computing; Distributed computing; Edge computing; Load balancing (electrical power); Enhanced Data Rates for GSM Evolution; Heuristic; Reduction (mathematics); Architecture; Resource allocation; Edge device; Utility computing; Fog computing; Computer network; Operating system; Cloud computing security","retraction":null,"screen_n_in":null,"score":{"opus":0.03533311199576857,"gpt":0.2726005120224942,"spread":0.2372674000267256,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003102405,0.0002670066,0.0003351006,0.0003937854,0.0004885121,0.001255626,0.002527089,0.00007557334,3.529996e-7],"category_scores_gemma":[0.00006213271,0.0002496872,0.0001233466,0.001647081,0.0002382488,0.0007462985,0.0008819692,0.0001588385,0.00002223004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002746156,"about_ca_system_score_gemma":0.00070968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002937938,"about_ca_topic_score_gemma":2.327652e-7,"domain_scores_codex":[0.9964578,0.0001065937,0.0005848681,0.001107774,0.001003161,0.0007398604],"domain_scores_gemma":[0.9975135,0.0006684315,0.0001775808,0.0008208245,0.0006456144,0.0001740826],"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":[0.00001673757,0.0001093867,0.00006295049,0.0007338548,0.00006079586,0.0000514311,0.007485364,0.6004146,0.006711537,0.0104863,0.01580538,0.3580616],"study_design_scores_gemma":[0.0001551364,0.0002093704,0.00004428136,0.0002887824,0.00000890427,0.0001287917,0.000007403893,0.9879751,0.003916202,0.0004369896,0.006544295,0.000284786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00395782,0.0008041335,0.9804924,0.0004388112,0.01306255,0.0005900733,6.855145e-7,0.0005667059,0.00008689451],"genre_scores_gemma":[0.7474741,0.000003063623,0.2503338,0.0001752626,0.001887041,0.00001634019,0.000002653036,0.00002473248,0.00008311414],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7435162,"threshold_uncertainty_score":0.9999955,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4402200785","doi":"10.7717/peerj-cs.2256","title":"Ten quick tips for clinical electroencephalographic (EEG) data acquisition and signal processing","year":2024,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Electroencephalography; Computer science; Signal processing; SIGNAL (programming language); Software; Artificial intelligence; Digital signal processing; Psychology; Neuroscience; Computer hardware","retraction":null,"screen_n_in":null,"score":{"opus":0.09042846953877715,"gpt":0.3772123340653016,"spread":0.2867838645265245,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001923997,0.0001886516,0.0002105058,0.0002760056,0.0004812315,0.00151599,0.001718195,0.00005689438,0.000007513246],"category_scores_gemma":[0.00009106267,0.0001508027,0.00005912647,0.001028722,0.001048746,0.001756434,0.0008383614,0.0002460461,0.00001359118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000215073,"about_ca_system_score_gemma":0.0002491088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003339608,"about_ca_topic_score_gemma":0.000001744097,"domain_scores_codex":[0.9968346,0.00008753662,0.0003734335,0.001619252,0.0005253743,0.0005597951],"domain_scores_gemma":[0.9985304,0.0006178078,0.00007646477,0.0004885021,0.000105006,0.0001818498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004088242,0.0001435059,0.001678492,0.0002207066,0.000009563211,0.00004870355,0.0006617761,0.00006179457,0.06685108,0.002853079,0.0103205,0.9171099],"study_design_scores_gemma":[0.0002131043,0.0004635449,0.003484986,0.0001590697,0.00001389075,0.0002538739,0.000007519431,0.976636,0.007399093,0.002440028,0.008677192,0.000251715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2803364,0.0008733237,0.7143993,0.002298082,0.001205401,0.0003708093,0.00003776954,0.0003707721,0.0001081118],"genre_scores_gemma":[0.9734319,0.00006133271,0.02441921,0.001444443,0.0005554755,0.00001191103,0.000006962576,0.00001445242,0.00005434494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9765742,"threshold_uncertainty_score":0.9995205,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4289711940","doi":"10.7717/peerj-cs.1066","title":"Causal graph extraction from news: a comparative study of time-series causality learning techniques","year":2022,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Topic Modeling","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"Agencia Nacional de Promoción Científica y Tecnológica; Universidad Nacional del Sur; Consejo Nacional de Investigaciones Científicas y Técnicas; Compute Canada","keywords":"Causal structure; Computer science; Causality (physics); Machine learning; Graph; Artificial intelligence; Time series; Causal inference; Event (particle physics); Natural language processing; Theoretical computer science; Data mining; Information retrieval; Data science; Econometrics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.04088231282822022,"gpt":0.3120596989749664,"spread":0.2711773861467461,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001215742,0.0001828345,0.0003264239,0.0003200333,0.0008556932,0.0002488465,0.001987604,0.00002367577,0.00003042434],"category_scores_gemma":[0.00002387524,0.0001870976,0.00005428075,0.001418283,0.0002095309,0.001427931,0.001999939,0.0004008009,0.000007080579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001347416,"about_ca_system_score_gemma":0.0001946733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001821831,"about_ca_topic_score_gemma":0.00007398872,"domain_scores_codex":[0.9967501,0.0003640691,0.0004243195,0.0009121704,0.001209768,0.0003396173],"domain_scores_gemma":[0.9984453,0.0001486454,0.0002926099,0.0008167594,0.0001955735,0.0001011568],"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":[0.0001796054,0.004914843,0.08138537,0.00004699116,0.0002196651,0.0002333308,0.2007128,0.36122,0.1102791,0.02150598,0.001605619,0.2176966],"study_design_scores_gemma":[0.0005372689,0.002102281,0.02107839,0.00002141076,0.00001868482,0.00005560193,0.002088786,0.9511283,0.01599785,0.005151666,0.001234877,0.0005849035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4421037,0.00001834866,0.5567247,0.0001656482,0.0003793428,0.0002350289,0.000002321354,0.0002399802,0.0001310193],"genre_scores_gemma":[0.8324881,0.000001365933,0.1672665,0.0000552774,0.00007133409,0.00004294981,0.000002093275,0.000005166454,0.00006727073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5899082,"threshold_uncertainty_score":0.7629619,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4409235847","doi":"10.7717/peerj-cs.2799","title":"Two-stage object detection in low-light environments using deep learning image enhancement","year":2025,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Lakehead University","funders":"","keywords":"Stage (stratigraphy); Artificial intelligence; Computer vision; Computer science; Image (mathematics); Object detection; Object (grammar); Pattern recognition (psychology); Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.007814860085145968,"gpt":0.2672527838775381,"spread":0.2594379237923921,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001553769,0.0002778107,0.0002527804,0.0008457908,0.0004941905,0.0006369616,0.002036959,0.00005022022,0.000015056],"category_scores_gemma":[0.00005462971,0.0002928864,0.00006565963,0.002209626,0.0002520297,0.002252341,0.001683135,0.000347484,0.00004474513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007429473,"about_ca_system_score_gemma":0.0001505686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008391914,"about_ca_topic_score_gemma":0.00002380515,"domain_scores_codex":[0.9966387,0.0001404176,0.0004603916,0.001150838,0.0008335095,0.0007762103],"domain_scores_gemma":[0.9987571,0.00005685183,0.0001753707,0.0008338565,0.00008162422,0.00009516504],"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":[0.000005731921,0.0001431101,0.0004398416,0.00002958392,0.000006645997,0.00003487199,0.0006671895,0.005465596,0.8680826,0.001049944,0.00001066453,0.1240642],"study_design_scores_gemma":[0.0002312158,0.00005380695,0.001050125,0.0000694571,0.000002039412,0.000003339741,0.000008630359,0.5010939,0.4967536,0.0001924432,0.000363709,0.0001776945],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07201681,0.00006192557,0.925181,0.0001377898,0.0008334802,0.0003682469,1.807e-7,0.0002187422,0.001181873],"genre_scores_gemma":[0.7274747,0.0000122434,0.2718154,0.0002575803,0.00003948119,0.00003023131,5.387063e-7,0.000008158651,0.0003616281],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6554579,"threshold_uncertainty_score":0.9999523,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4223965482","doi":"10.7717/peerj-cs.947","title":"(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment","year":2022,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Lakehead University","funders":"","keywords":"Impeachment; Politics; Framing (construction); Persuasion; Rhetoric; Narrative; Public opinion; Social media; Sentiment analysis; Media studies; Political communication; Political science; Computer science; Law; Sociology; Artificial intelligence; Psychology; History; Social psychology; Linguistics; Literature; Art","retraction":null,"screen_n_in":null,"score":{"opus":0.03937009277240332,"gpt":0.3089195381351041,"spread":0.2695494453627008,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00227541,0.0001035968,0.0001386713,0.0001376491,0.002823717,0.0002201204,0.00130482,0.00001655159,0.0004916134],"category_scores_gemma":[0.0001127112,0.00007932939,0.00005912132,0.0006719758,0.0005251986,0.0008117209,0.0006958011,0.0001638102,0.000005712114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002833133,"about_ca_system_score_gemma":0.0004026685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004008029,"about_ca_topic_score_gemma":0.0003422863,"domain_scores_codex":[0.9973285,0.0001427172,0.000314648,0.0002469702,0.001562603,0.0004046191],"domain_scores_gemma":[0.9991118,0.00005934806,0.0002368269,0.0003173441,0.0001272582,0.0001474545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001154243,0.0001301611,0.0003453361,0.0000308231,0.00001227734,0.000004473115,0.9787062,0.004107727,0.0005035315,0.002532513,0.003565147,0.01005027],"study_design_scores_gemma":[0.00305397,0.001139377,0.2178527,0.0004238719,0.00004472383,0.00005217615,0.3548803,0.1858353,0.007276812,0.005333723,0.2224167,0.001690268],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765362,0.0001014157,0.003254958,0.01582116,0.0006277626,0.0005138778,0.00001465892,0.00007634003,0.00305367],"genre_scores_gemma":[0.9955605,0.00001489384,0.003178264,0.0005388295,0.0001174443,0.000007353156,0.000002056331,0.00000438081,0.00057634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6238258,"threshold_uncertainty_score":0.9984745,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392238058","doi":"10.7717/peerj-cs.1888","title":"exKidneyBERT: a language model for kidney transplant pathology reports and the crucial role of extended vocabularies","year":2024,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Vocabulary; Natural language processing; Artificial intelligence; Information extraction; Information retrieval; Language model; Domain (mathematical analysis); Field (mathematics); Pathology; Medicine; Linguistics","retraction":null,"screen_n_in":null,"score":{"opus":0.007218420904766878,"gpt":0.2577834305421606,"spread":0.2505650096373938,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006859644,0.00008395188,0.0001346753,0.00003983043,0.00009789358,0.00005153277,0.0002114214,0.00006888739,9.834527e-7],"category_scores_gemma":[0.0001741715,0.00005182964,0.00005836945,0.00009165894,0.00128346,0.000004748262,0.0001230041,0.00005446297,2.18303e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003212745,"about_ca_system_score_gemma":0.0002309266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001304714,"about_ca_topic_score_gemma":0.000005961197,"domain_scores_codex":[0.9991339,0.00002231481,0.0001624697,0.0003615943,0.0001352627,0.0001844401],"domain_scores_gemma":[0.9995713,0.00003809448,0.00003622002,0.0002260377,0.00004859026,0.00007973931],"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":[0.0002755105,0.00006938349,0.0001719847,0.0001826384,0.00006776215,0.0001012098,0.01414408,0.0002029742,0.6381007,0.007608771,0.00376258,0.3353123],"study_design_scores_gemma":[0.0007636515,0.0003192687,0.0005244365,0.00008118515,0.0000500193,0.0007048093,0.0001409752,0.8985221,0.08163239,0.008698129,0.008334966,0.000228087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3108101,0.004409286,0.6818917,0.001951755,0.000523565,0.0002113285,0.00004722253,0.00003854558,0.0001165661],"genre_scores_gemma":[0.975688,0.00004658667,0.02356744,0.0004172666,0.0001227406,0.00002071881,0.00001265023,0.000005283952,0.0001192436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8983191,"threshold_uncertainty_score":0.4728962,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4308630605","doi":"10.7717/peerj-cs.1091","title":"Feature selection enhancement and feature space visualization for speech-based emotion recognition","year":2022,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Computer science; Speech recognition; Feature (linguistics); Set (abstract data type); Emotion recognition; Feature selection; Pattern recognition (psychology); Artificial intelligence; Feature vector; Speech processing; Visualization","retraction":null,"screen_n_in":null,"score":{"opus":0.02845440566603689,"gpt":0.3187630918992309,"spread":0.290308686233194,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007853823,0.0001312964,0.0001124263,0.0003130307,0.0008141019,0.0001147121,0.0001383437,0.00006221598,0.000242103],"category_scores_gemma":[0.00002606488,0.0001400659,0.0000458981,0.0007999215,0.00009339766,0.0002228852,0.00006539241,0.0001697426,0.00001961557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001540806,"about_ca_system_score_gemma":0.00007239468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001052805,"about_ca_topic_score_gemma":0.000007872842,"domain_scores_codex":[0.99851,0.0001244148,0.0001136555,0.0005767525,0.0003974289,0.0002777565],"domain_scores_gemma":[0.9993051,0.00004634656,0.0001360161,0.0001366619,0.0002928427,0.00008306396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003502343,0.0008582899,0.0008508429,0.0001121895,0.00003812919,0.000006271068,0.003153433,0.0008138021,0.03832557,0.004736854,0.104038,0.8467163],"study_design_scores_gemma":[0.00813087,0.006123499,0.03952892,0.0001562657,0.0001683176,0.0005265267,0.001048682,0.7186159,0.07649776,0.005271207,0.1423294,0.001602637],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.178175,0.00005230684,0.8128912,0.004559007,0.002786732,0.0007959621,0.00002957992,0.0001550441,0.000555116],"genre_scores_gemma":[0.9312012,0.000006716055,0.06188444,0.002265829,0.0005176676,0.000280111,0.0004630758,0.0000264491,0.003354507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8451138,"threshold_uncertainty_score":0.6261496,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3197333275","doi":"10.7717/peerj-cs.672","title":"Classification of high-voltage power line structures in low density ALS data acquired over broad non-urban areas","year":2021,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Remote Sensing and LiDAR Applications","field":"Environmental 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":"Université Laval","funders":"Ministère des Forêts, de la Faune et des Parcs","keywords":"Electric power transmission; Computer science; Segmentation; Point cloud; Line (geometry); Transmission line; Electrical conductor; Filter (signal processing); Transmission (telecommunications); Raw data; Network topology; Remote sensing; Geography; Electrical engineering; Artificial intelligence; Engineering; Telecommunications; Computer vision; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01783425947860427,"gpt":0.2621613271951883,"spread":0.2443270677165841,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004863589,0.0001205523,0.000176206,0.00006691744,0.0001413991,0.00009566602,0.0009607383,0.0000495573,0.0001166742],"category_scores_gemma":[0.00006837298,0.0001126797,0.00002693484,0.001020097,0.0005770764,0.0004199866,0.0009664883,0.00008513217,0.00004253297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000972769,"about_ca_system_score_gemma":0.0000844886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000697695,"about_ca_topic_score_gemma":0.000260962,"domain_scores_codex":[0.9980695,0.00003607501,0.0002713724,0.0007677859,0.0005904617,0.0002648081],"domain_scores_gemma":[0.9982383,0.00005279382,0.0001239954,0.001422928,0.0000554365,0.0001064993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001890875,0.0004219927,0.08883709,0.00002389415,0.00001184494,0.00003096244,0.001667215,0.01053127,0.8121213,0.001285534,0.009090334,0.07595961],"study_design_scores_gemma":[0.0001503321,0.00001554121,0.7996693,0.00001705556,0.000003677582,0.00001005412,0.00001426103,0.1811515,0.01792095,0.000541885,0.0003908571,0.0001145657],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.894377,0.00001192837,0.1044003,0.0003509065,0.0002269258,0.0001159557,0.00001666017,0.00002304823,0.0004773346],"genre_scores_gemma":[0.9737691,0.000003665054,0.02582144,0.0002015602,0.00004840147,5.773486e-7,0.00004321268,0.000006320702,0.0001056726],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7942004,"threshold_uncertainty_score":0.4594944,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392160170","doi":"10.7717/peerj-cs.1896","title":"Ensemble machine learning reveals key features for diabetes duration from electronic health records","year":2024,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Diabetes Management and Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Ministero dell'Università e della Ricerca; European Commission; Dipartimenti di Eccellenza","keywords":"Diabetes mellitus; Random forest; Context (archaeology); Type 2 diabetes; Medicine; Health records; Kidney disease; Disease; Retinopathy; Artificial intelligence; Diabetic retinopathy; Computer science; Machine learning; Internal medicine; Geography; Health care; Endocrinology","retraction":null,"screen_n_in":null,"score":{"opus":0.01751837860891434,"gpt":0.3138604138712033,"spread":0.296342035262289,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001698072,0.0001144005,0.0002053739,0.0002783641,0.0002923814,0.0003645771,0.000246651,0.00002904902,0.00002792963],"category_scores_gemma":[0.0001171902,0.0000881845,0.00006920728,0.0006599777,0.0001007914,0.0002679655,0.0001433817,0.0002487267,0.00002964651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001665418,"about_ca_system_score_gemma":0.000305856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003752115,"about_ca_topic_score_gemma":0.00002712581,"domain_scores_codex":[0.9980996,0.00004493117,0.0001914239,0.0005000073,0.0005123309,0.0006517384],"domain_scores_gemma":[0.9993101,0.0001763823,0.00004389731,0.0002104434,0.0001159878,0.0001431927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004138111,0.00009615856,0.01382131,0.0006542777,0.0000959163,0.000008561213,0.0008611697,0.0002357989,0.01932245,0.00411219,0.05196102,0.9087898],"study_design_scores_gemma":[0.0005812593,0.001722432,0.03127518,0.0004445904,0.00003367006,0.00000224324,0.00001237218,0.8571264,0.003497266,0.004758678,0.1003213,0.0002246444],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7236286,0.009859072,0.2078154,0.05206734,0.002339295,0.001935511,0.00001900864,0.0006913734,0.001644343],"genre_scores_gemma":[0.9729807,0.00009908364,0.01661393,0.001184115,0.0006491411,0.00003801587,0.00009384802,0.00001966953,0.008321487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9085651,"threshold_uncertainty_score":0.3596059,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392852573","doi":"10.7717/peerj-cs.1928","title":"Architecting an enterprise financial management model: leveraging multi-head attention mechanism-transformer for user information transformation","year":2024,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Knowledge management; Process management; Finance; Business","retraction":null,"screen_n_in":null,"score":{"opus":0.1042559495177365,"gpt":0.3976716681856701,"spread":0.2934157186679335,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01148264,0.0002344975,0.0002280238,0.001371908,0.000728148,0.001988429,0.001184368,0.00006311097,0.00001133895],"category_scores_gemma":[0.0003422534,0.0001974775,0.0001826663,0.001745753,0.0001230777,0.006582053,0.0001303926,0.0001964875,0.00004171773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001741736,"about_ca_system_score_gemma":0.0001823699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000134468,"about_ca_topic_score_gemma":0.00001464411,"domain_scores_codex":[0.9958467,0.0001231365,0.00088153,0.0007393484,0.001834714,0.000574516],"domain_scores_gemma":[0.9983742,0.0003838754,0.0001457911,0.0004673351,0.0004597341,0.0001690636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004100508,0.00002882425,0.00006145976,0.00007078378,0.000005146819,0.000001270498,0.006824727,0.03470623,0.0008458309,0.01145898,0.00006993376,0.9458858],"study_design_scores_gemma":[0.0004172205,0.0001114248,0.002530455,0.0001122603,0.00001820116,0.00002082349,0.0001468334,0.9687379,0.0007599773,0.0253178,0.001590542,0.0002365408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1568962,0.000007606753,0.8394931,0.0002694225,0.001933086,0.0007405783,0.00001593713,0.0002385136,0.000405551],"genre_scores_gemma":[0.5322,0.00000145867,0.4673804,0.0001622242,0.00007460968,0.00005623193,0.000007383743,0.000009429869,0.0001082257],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9456493,"threshold_uncertainty_score":0.9990476,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3157029782","doi":"10.7717/peerj-cs.786","title":"AdCOFE: Advanced Contextual Feature Extraction in conversations for emotion classification","year":2021,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Lakehead University","funders":"","keywords":"Feature extraction; Computer science; Feature (linguistics); Artificial intelligence; Extraction (chemistry); Pattern recognition (psychology); Natural language processing; Psychology; Linguistics; Chemistry; Chromatography","retraction":null,"screen_n_in":null,"score":{"opus":0.03654557621958029,"gpt":0.3188550560258584,"spread":0.2823094798062781,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006696561,0.0001073015,0.0001471121,0.0001965698,0.0002810631,0.0004203841,0.0005692319,0.00004623232,0.000009128084],"category_scores_gemma":[0.0001078034,0.000110797,0.00007464558,0.001558305,0.00008176322,0.001575265,0.0001422182,0.0001064509,0.00001350819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001285742,"about_ca_system_score_gemma":0.0002106256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000316445,"about_ca_topic_score_gemma":0.00001589967,"domain_scores_codex":[0.9982818,0.00004790837,0.0002364974,0.0006644718,0.000494536,0.0002747228],"domain_scores_gemma":[0.9987544,0.0001424565,0.0001366879,0.0004383003,0.0004487527,0.00007938853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001492061,0.0002824967,0.004167628,0.00002373772,0.00001953402,0.00001200166,0.003113328,0.005669245,0.08685956,0.1128343,0.002886184,0.784117],"study_design_scores_gemma":[0.0004988443,0.0000382114,0.06127805,0.00002561679,0.000004342311,0.00001103316,0.0001812932,0.9274984,0.006728386,0.0004926518,0.003099713,0.0001434727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04007497,0.00005514963,0.9544929,0.003759528,0.001197405,0.0001633637,0.000001966226,0.00006800707,0.0001866584],"genre_scores_gemma":[0.7476867,0.000009807185,0.2515947,0.0002973532,0.00009660664,0.00001929735,0.00001654381,0.000003641471,0.0002753886],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9218292,"threshold_uncertainty_score":0.451817,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3041467857","doi":"10.7717/peerj-cs.280","title":"A novel fully convolutional network for visual saliency prediction","year":2020,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"Centre For Cold Ocean Resources Engineering; Okanagan University College; Memorial University of Newfoundland; University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"Memorial University of Newfoundland; Ministry of Higher Education and Scientific Research","keywords":"Computer science; Benchmark (surveying); Artificial intelligence; Convolutional neural network; Deep learning; Task (project management); Human visual system model; Machine learning; Scratch; Pattern recognition (psychology); Image (mathematics); Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03169068776208844,"gpt":0.2784433478589776,"spread":0.2467526600968892,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000778107,0.0001811027,0.0001808021,0.000122282,0.0007267203,0.000459126,0.001304695,0.00005616526,0.00001006022],"category_scores_gemma":[0.00008455973,0.0001758618,0.0001215662,0.001755861,0.0002394259,0.001381463,0.0004583485,0.0001362225,0.00006039884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008219449,"about_ca_system_score_gemma":0.0002885859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007318459,"about_ca_topic_score_gemma":0.000001865727,"domain_scores_codex":[0.9972575,0.00003077111,0.0003640087,0.0009370718,0.0008352695,0.0005753192],"domain_scores_gemma":[0.9987291,0.00006844882,0.0001345469,0.0002790682,0.0004498486,0.0003389527],"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":[0.0001471933,0.0009132043,0.004768354,0.0001415956,0.00006610089,0.00001106047,0.003774913,0.08426829,0.09723461,0.511955,0.03539225,0.2613275],"study_design_scores_gemma":[0.0005551145,0.000666571,0.01344812,0.00001210048,0.000004839761,0.00003281636,0.000006497232,0.977753,0.0006252857,0.001143709,0.005548431,0.0002034421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00907206,0.00002451167,0.9836786,0.003547274,0.002662722,0.0003535902,0.00001115635,0.0005093974,0.000140665],"genre_scores_gemma":[0.6869559,0.000001984009,0.3086348,0.003088938,0.001206651,0.00004386664,0.000008172444,0.000009240132,0.0000504268],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8934848,"threshold_uncertainty_score":0.7171438,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2620887933","doi":"10.7717/peerj-cs.991","title":"A longitudinal study of topic classification on Twitter","year":2022,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Classifier (UML); Computer science; Artificial intelligence; Machine learning; Generalization; Social media; Feature (linguistics); Entertainment; Natural language processing; Information retrieval; World Wide Web; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0619292373093529,"gpt":0.3235566165200028,"spread":0.2616273792106499,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003464068,0.00006657164,0.0001138953,0.0001482756,0.0002723959,0.00004150037,0.000536904,0.000002988787,0.0002006208],"category_scores_gemma":[8.701331e-7,0.00006297943,0.00004151596,0.000697072,0.00006532915,0.00006932732,0.000376581,0.0001011077,0.000003731051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003752821,"about_ca_system_score_gemma":0.0000344287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001190981,"about_ca_topic_score_gemma":0.000002114069,"domain_scores_codex":[0.9988912,0.00004666595,0.0001563803,0.0003048333,0.0004646327,0.0001362688],"domain_scores_gemma":[0.9993872,0.0000279325,0.00009483797,0.0003899052,0.00007009403,0.0000300229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001237388,0.001838194,0.8694375,0.000002565076,0.00003903527,0.000002098519,0.002403754,0.006329845,0.0007709352,0.02494631,0.002980737,0.09123665],"study_design_scores_gemma":[0.0003121873,0.0008307557,0.8122804,0.000004642922,0.00002064224,9.11561e-7,0.0004137069,0.1811357,0.0004080438,0.00325061,0.001169325,0.0001731439],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9054469,0.000001622156,0.09315315,0.0001160791,0.000108017,0.0001409348,0.000001006092,0.00002678261,0.001005498],"genre_scores_gemma":[0.9975954,2.752385e-8,0.002139061,0.00003808706,0.0001102286,0.00003748411,0.000002011001,0.000003003112,0.00007466532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1748058,"threshold_uncertainty_score":0.2568226,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4297900902","doi":"10.7717/peerj-cs.1081","title":"Minimizing features while maintaining performance in data classification problems","year":2022,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feature selection; Feature (linguistics); Computer science; Artificial intelligence; Principal component analysis; Pattern recognition (psychology); Machine learning; Selection (genetic algorithm); Data mining; Reduction (mathematics); Dimensionality reduction; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.07281756164724634,"gpt":0.2739164239555219,"spread":0.2010988623082756,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002029583,0.0001258329,0.0001295871,0.0003956711,0.00080293,0.0004241705,0.004371025,0.00002261725,0.00001841774],"category_scores_gemma":[0.00002733758,0.0001239451,0.00001994685,0.001792616,0.0001153265,0.00245892,0.004040695,0.0003154904,0.00002424227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001304042,"about_ca_system_score_gemma":0.0002369008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002187946,"about_ca_topic_score_gemma":0.00000855876,"domain_scores_codex":[0.9973835,0.00009291973,0.0002587256,0.0009506982,0.0008595742,0.0004546341],"domain_scores_gemma":[0.9984203,0.00006881542,0.0001238732,0.001212294,0.00008285182,0.00009186192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002053857,0.0003906916,0.01262339,0.00005916552,0.00000746698,0.00003644307,0.009400315,0.08733613,0.01156837,0.009457512,0.01302625,0.8560737],"study_design_scores_gemma":[0.0002109116,0.0000670363,0.03475063,0.00003749095,0.000001033557,0.00004834793,0.0001112903,0.9574881,0.000422633,0.0003080722,0.006369765,0.0001846621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2326741,0.0001624428,0.7559265,0.00552458,0.00233281,0.0005398008,0.00001580762,0.000396056,0.002427876],"genre_scores_gemma":[0.9004148,0.000009296375,0.09882585,0.0005315412,0.00005845213,0.00004818604,0.00003105361,0.000005579745,0.00007522286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.870152,"threshold_uncertainty_score":0.8122524,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4393227387","doi":"10.7717/peerj-cs.1955","title":"Structural health monitoring of aircraft through prediction of delamination using machine learning","year":2024,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Delamination (geology); Structural health monitoring; Computer science; Artificial intelligence; Materials science; Engineering; Structural engineering; Geology; Seismology","retraction":null,"screen_n_in":null,"score":{"opus":0.0396921917182652,"gpt":0.3252613243574885,"spread":0.2855691326392233,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004020561,0.0001122477,0.0001696289,0.0002233042,0.000119651,0.00004021639,0.0002345889,0.00003623262,0.000001541709],"category_scores_gemma":[0.00001662101,0.000107742,0.00003376671,0.0007359778,0.0001153397,0.0005804483,0.0000792494,0.0002060812,3.451493e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002915643,"about_ca_system_score_gemma":0.00008793028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002562741,"about_ca_topic_score_gemma":8.898384e-7,"domain_scores_codex":[0.99875,0.00002344317,0.0003437621,0.0002156916,0.0004185839,0.0002484907],"domain_scores_gemma":[0.9995681,0.00004837041,0.00006450383,0.0001550775,0.000112951,0.00005101594],"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":[0.000005993756,0.000007601829,0.06656098,0.002498918,0.0000278839,0.000004378427,0.007738259,0.442584,0.05323569,0.001012284,0.00003262036,0.4262913],"study_design_scores_gemma":[0.00003755811,0.00008378198,0.06818346,0.0003337497,0.00000333353,0.00001852675,0.0000134795,0.886789,0.04413758,0.0003074748,0.00002779636,0.00006431901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8166415,0.000932992,0.1791941,0.00002307388,0.002556396,0.0001065276,0.00000689205,0.0005288935,0.000009568976],"genre_scores_gemma":[0.8205484,0.00004956214,0.1791654,0.000001712858,0.0002170638,0.000001694339,0.000001767307,0.00001262782,0.000001751751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4442049,"threshold_uncertainty_score":0.4393593,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400927488","doi":"10.7717/peerj-cs.2174","title":"Developing a tablet-based brain-computer interface and robotic prototype for upper limb rehabilitation","year":2024,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"College Ahuntsic","funders":"","keywords":"Brain–computer interface; Rehabilitation; Motor imagery; Physical medicine and rehabilitation; Motor function; Interface (matter); Stroke (engine); Computer science; Upper limb; Human–computer interaction; Simulation; Medicine; Psychology; Engineering; Physical therapy; Neuroscience; Electroencephalography; Mechanical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03088198790261929,"gpt":0.3127281939260159,"spread":0.2818462060233966,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001082203,0.0003034786,0.000280022,0.0005178469,0.000433997,0.001465444,0.0008620251,0.00006783285,0.000008774087],"category_scores_gemma":[0.0003888804,0.0002555926,0.0001066792,0.001104673,0.0006668128,0.0009791809,0.0004241043,0.0002157462,0.00004123886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001533666,"about_ca_system_score_gemma":0.0004205398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007403591,"about_ca_topic_score_gemma":0.000002829451,"domain_scores_codex":[0.9968421,0.0001012201,0.000396015,0.001467925,0.0005305408,0.0006621393],"domain_scores_gemma":[0.9968366,0.002333108,0.00007397979,0.0003978281,0.0001933799,0.0001650924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003033491,0.0003815556,0.0007569089,0.003123889,0.00004174958,0.000071306,0.01047576,0.06591371,0.274299,0.08605219,0.02051829,0.5380623],"study_design_scores_gemma":[0.0002944509,0.001186605,0.0006753594,0.0004729911,0.000006923856,0.00005984027,0.00001106028,0.9173314,0.0645212,0.002170293,0.01288952,0.0003803334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1224809,0.0001253778,0.8595634,0.01410837,0.002125744,0.001161386,0.000005914168,0.000380951,0.00004793338],"genre_scores_gemma":[0.6674358,0.000001901555,0.3292866,0.002716676,0.0002841269,0.0001182373,0.00000118291,0.00002733645,0.0001281639],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8514177,"threshold_uncertainty_score":0.9999896,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4206551694","doi":"10.7717/peerj-cs.788","title":"Ambient intelligence governance review: from service-oriented to self-service","year":2022,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Ambient intelligence; Computer science; Interoperability; Service-oriented architecture; Service (business); Context (archaeology); Context awareness; Adaptation (eye); Architecture; Ubiquitous computing; Process (computing); Corporate governance; Knowledge management; Process management; Data science; World Wide Web; Artificial intelligence; Web service; Human–computer interaction; Engineering; Business","retraction":null,"screen_n_in":null,"score":{"opus":0.02219663327169951,"gpt":0.2595324949954096,"spread":0.2373358617237101,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002019259,0.0003729755,0.0005011879,0.000240473,0.0009927923,0.0004990541,0.007012855,0.00003436508,0.0001773533],"category_scores_gemma":[0.00009494166,0.0003992162,0.0001178911,0.008345349,0.00006732844,0.001765292,0.007112932,0.0004259361,0.0009536089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000685066,"about_ca_system_score_gemma":0.0006139324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001328485,"about_ca_topic_score_gemma":0.0002049725,"domain_scores_codex":[0.9934795,0.0003013404,0.0007058162,0.001952135,0.002721366,0.0008398051],"domain_scores_gemma":[0.995401,0.0002869703,0.0004000729,0.002221749,0.001169223,0.0005210025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004849946,0.001619151,0.001696045,0.0006317341,0.0001197954,0.0002369212,0.02270841,0.007847195,0.006918808,0.01435252,0.05331181,0.8905091],"study_design_scores_gemma":[0.0003784645,0.0003463973,0.007926668,0.0006972339,0.00002357892,0.0002032073,0.0001002556,0.6356167,0.003766565,0.0006425683,0.3490693,0.00122912],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02182811,0.001061759,0.9423037,0.02767768,0.004774601,0.001077847,0.00007523822,0.0008131132,0.0003880286],"genre_scores_gemma":[0.709693,0.0001622472,0.1855122,0.1034099,0.0004228542,0.0005706851,0.00001986397,0.00004210396,0.0001672224],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.88928,"threshold_uncertainty_score":0.999846,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2528053997","doi":"10.7717/peerj-cs.89","title":"TCP adaptation with network coding and opportunistic data forwarding in multi-hop wireless networks","year":2016,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":7,"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":"Computer network; Computer science; Linear network coding; Network packet; Wireless network; Packet forwarding; TCP Westwood plus; TCP Friendly Rate Control; TCP tuning; Transmission Control Protocol; Wireless; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.1217875355662183,"gpt":0.3048338790954295,"spread":0.1830463435292112,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002050109,0.0001961033,0.0002356611,0.0001859878,0.0005426265,0.0005303091,0.003019782,0.00003927626,0.00000323541],"category_scores_gemma":[0.00006240498,0.0001384435,0.00001603333,0.001455479,0.000395579,0.002208958,0.00291855,0.0001636726,0.000004236101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008881382,"about_ca_system_score_gemma":0.0002060096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001225798,"about_ca_topic_score_gemma":0.0001883365,"domain_scores_codex":[0.9976324,0.0001403898,0.0003197948,0.0009322533,0.0004062476,0.0005688951],"domain_scores_gemma":[0.9974951,0.0003977694,0.0001504036,0.001528768,0.0002228226,0.0002051449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008514034,0.00003633213,0.004557189,0.0000058942,0.000007348038,0.0000190878,0.000603733,0.01211835,0.000269291,0.03523592,0.0002857806,0.9468526],"study_design_scores_gemma":[0.0005675697,0.00005226058,0.01177969,0.0002572284,0.000003154677,0.00002885162,0.00001267228,0.9863541,0.00001353114,0.00009303178,0.0005928701,0.0002450898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005425648,0.0001949915,0.9920867,0.001507583,0.000362633,0.0002043437,0.000001627902,0.0001288852,0.00008760138],"genre_scores_gemma":[0.8046257,0.0004153916,0.1945039,0.0003040726,0.00008944356,0.000009641131,0.000003576206,0.00000833933,0.00003994584],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9742357,"threshold_uncertainty_score":0.5645564,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3155042034","doi":"10.7717/peerj-cs.464","title":"Optimal 1-NN prototypes for pathological geometries","year":2021,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Machine Learning and Data Classification","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":"University of Waterloo","funders":"","keywords":"Computer science; Classifier (UML); Parametric statistics; Heuristic; Artificial intelligence; Machine learning; Algorithm; Mathematical optimization; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02670095511211819,"gpt":0.2941873908895498,"spread":0.2674864357774316,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001140384,0.0001250623,0.0001552355,0.0001671882,0.0004612718,0.0008488743,0.001585867,0.0000428489,0.000009998751],"category_scores_gemma":[0.0004898719,0.0001050066,0.00006168555,0.001478091,0.000247348,0.0008386753,0.0007690943,0.0001331359,0.00004461256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003332807,"about_ca_system_score_gemma":0.0003097226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003178382,"about_ca_topic_score_gemma":9.414264e-7,"domain_scores_codex":[0.9980014,0.00004853737,0.0002017077,0.000855939,0.0004914842,0.0004009434],"domain_scores_gemma":[0.9983269,0.0001860657,0.00008163516,0.0008042341,0.0004674319,0.0001337236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001474664,0.0002818736,0.003077858,0.00005837123,0.000009907266,0.00005885163,0.0008859427,0.004412364,0.01393019,0.2496679,0.003064462,0.7245375],"study_design_scores_gemma":[0.0003528956,0.0002927245,0.03538818,0.00001693785,0.000004195702,0.0001382826,0.00001196676,0.8904296,0.01517614,0.002475706,0.0553934,0.0003200138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01998896,0.00008507646,0.9744681,0.004066127,0.0005675342,0.0002403038,0.000005191946,0.0002519668,0.0003267772],"genre_scores_gemma":[0.3000933,0.000005121659,0.698957,0.0004737646,0.0001482914,0.00006212396,0.00001244376,0.000004307168,0.0002436774],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8860172,"threshold_uncertainty_score":0.8185714,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410325598","doi":"10.7717/peerj-cs.2885","title":"Hyperdimensional computing in biomedical sciences: a brief review","year":2025,"lang":"en","type":"review","venue":"PeerJ Computer Science","topic":"Ferroelectric and Negative Capacitance Devices","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Data science; Engineering ethics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03202193163737898,"gpt":0.3232248926661552,"spread":0.2912029610287762,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001788123,0.0004196346,0.001468678,0.0009208429,0.0002093669,0.0001138137,0.001537515,0.0001193147,0.00001702767],"category_scores_gemma":[0.0001698543,0.0003362406,0.000211382,0.007269707,0.0009106268,0.0002576382,0.0003320568,0.0005454723,0.00004545858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002998779,"about_ca_system_score_gemma":0.001029014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000420909,"about_ca_topic_score_gemma":0.00000146426,"domain_scores_codex":[0.996793,0.00009163062,0.0007292297,0.0008248857,0.000852351,0.0007088504],"domain_scores_gemma":[0.9987146,0.0005698257,0.0001110466,0.0003501304,0.0001035003,0.0001508787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[1.067627e-7,0.00001544138,0.000001052256,0.02621874,0.000009065826,0.00001872047,0.00001991969,0.00008655476,4.970055e-7,0.0004599985,0.003740443,0.9694295],"study_design_scores_gemma":[0.00008642961,0.00004003802,0.000009621115,0.1609914,0.00005892003,0.00008778441,0.000001060991,0.07726132,0.000001099466,0.00005865101,0.7609273,0.0004763651],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000003635658,0.9779983,0.0184228,0.00005210163,0.001173747,0.0004720083,0.000009697394,0.0001808538,0.001686828],"genre_scores_gemma":[0.00002508682,0.989445,0.009848271,0.0004638408,0.0001466588,0.00002236429,0.00001050351,0.00001474223,0.00002348127],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9689531,"threshold_uncertainty_score":0.999909,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2968249981","doi":"10.7717/peerj-cs.210","title":"Pay attention and you won’t lose it: a deep learning approach to sequence imputation","year":2019,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Advanced Neural Network Applications","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":"University of British Columbia, Okanagan Campus; University of British Columbia; University of Waterloo","funders":"","keywords":"Bottleneck; Computer science; Inference; Artificial intelligence; Machine learning; Imputation (statistics); Deep learning; Sequence (biology); Data mining; Missing data","retraction":null,"screen_n_in":null,"score":{"opus":0.02097689253950328,"gpt":0.2766219643984715,"spread":0.2556450718589682,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005230926,0.00019105,0.0001743043,0.0002369427,0.0004249253,0.0005190249,0.001420348,0.000040134,0.000002102147],"category_scores_gemma":[0.00003338245,0.0001626145,0.00003778435,0.001976463,0.000174337,0.00172481,0.0008050203,0.0002386202,0.0001818386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001056991,"about_ca_system_score_gemma":0.00007089388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001023511,"about_ca_topic_score_gemma":0.000001455195,"domain_scores_codex":[0.9973409,0.00005718182,0.0002573881,0.00119676,0.000629017,0.0005187918],"domain_scores_gemma":[0.998613,0.00008894137,0.0001319408,0.0006762919,0.0002279209,0.0002618899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000518156,0.0000828774,0.004672309,0.00002892852,0.000005293348,0.000003550062,0.002170348,0.2687717,0.01676881,0.03467415,0.0001337605,0.6726831],"study_design_scores_gemma":[0.0001600364,0.0001080158,0.01749166,0.00001776278,0.00000236343,0.0000592145,0.0000207576,0.9787702,0.0002436845,0.001633904,0.001242014,0.0002504237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09211152,0.00002524857,0.9045819,0.001718825,0.0003221621,0.0004692312,4.935354e-7,0.000238602,0.0005320344],"genre_scores_gemma":[0.6043111,0.000006222273,0.3947909,0.0005910893,0.00006288758,0.0000300651,0.000002457826,0.000007347963,0.0001979122],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7099984,"threshold_uncertainty_score":0.6631226,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4402173216","doi":"10.7717/peerj-cs.2295","title":"Ten quick tips for electrocardiogram (ECG) signal processing","year":2024,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"ECG Monitoring and Analysis","field":"Medicine","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 Toronto","funders":"","keywords":"Computer science; Medicine; Cardiology; Speech recognition","retraction":null,"screen_n_in":null,"score":{"opus":0.01800572351959235,"gpt":0.3088921582091156,"spread":0.2908864346895232,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006288883,0.0001047511,0.0001880931,0.0002731952,0.0002242644,0.0003360659,0.0002121298,0.00002977512,0.000004814795],"category_scores_gemma":[0.0000186744,0.0000801845,0.0001441068,0.001212229,0.000146482,0.0002021724,0.00005736902,0.000130683,0.00002231621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006772632,"about_ca_system_score_gemma":0.0003428658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008325309,"about_ca_topic_score_gemma":3.668375e-7,"domain_scores_codex":[0.9985597,0.00000792558,0.0001464924,0.0004602338,0.0004562773,0.0003693917],"domain_scores_gemma":[0.9994032,0.00005132339,0.00002211628,0.0001731664,0.0002046373,0.0001455752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001024792,0.00003533639,0.002803685,0.0001813722,0.00003882506,0.00002486231,0.0002792148,0.0001342544,0.006882094,0.00007110693,0.002207118,0.9873319],"study_design_scores_gemma":[0.0001676579,0.0002904894,0.00163587,0.0002385426,0.000093479,0.0001034369,0.00001544464,0.9779707,0.004770828,0.0002257062,0.01435672,0.0001310917],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0297259,0.001069921,0.9662461,0.001633032,0.0004201312,0.0001525041,0.00000135911,0.0003094215,0.0004416697],"genre_scores_gemma":[0.9110557,0.000007425464,0.0868146,0.0001657203,0.001228001,0.0000171302,0.000003099637,0.00001155261,0.0006968085],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9872008,"threshold_uncertainty_score":0.3269829,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4409819785","doi":"10.7717/peerj-cs.2767","title":"WG-Storm: a resource-aware scheduler for distributed stream processing engines","year":2025,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Cloud Computing and Resource Management","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":"Carleton University","funders":"","keywords":"Storm; Computer science; Stream processing; Distributed computing; Scheduling (production processes); Meteorology; Engineering; Operations management; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.01274351090679161,"gpt":0.2577161046440395,"spread":0.2449725937372479,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001150374,0.0002933598,0.0002946797,0.0004993285,0.001058088,0.001156438,0.003383671,0.00006573211,0.000001499367],"category_scores_gemma":[0.00009251675,0.0002564052,0.000132095,0.002739407,0.0003084022,0.0001877081,0.001898447,0.0001849977,0.000009771589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001730431,"about_ca_system_score_gemma":0.0003544544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001764212,"about_ca_topic_score_gemma":0.000003822133,"domain_scores_codex":[0.9968825,0.00004183424,0.0003876634,0.001187282,0.0006744577,0.0008262424],"domain_scores_gemma":[0.9980518,0.000157394,0.0001436198,0.001031262,0.0004333419,0.0001825591],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001352571,0.0002382021,0.0007541724,0.000229143,0.00003438008,0.00001348458,0.001015566,0.08069011,0.0001786518,0.02764638,0.01308256,0.8761038],"study_design_scores_gemma":[0.0004485984,0.00008179522,0.002785944,0.0001758028,0.00001169337,0.000007842153,0.00003760961,0.9504113,0.000608858,0.001110035,0.04402015,0.0003004042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03613164,0.0001780917,0.9568837,0.004339683,0.000896317,0.0004167706,0.000005722633,0.0006982471,0.0004498845],"genre_scores_gemma":[0.8186366,9.350109e-7,0.179369,0.0009850245,0.0002787166,0.00005140254,0.000006628139,0.00001240942,0.0006593086],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8758034,"threshold_uncertainty_score":0.9999888,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4396897807","doi":"10.7717/peerj-cs.2003","title":"Applying a deep learning pipeline to classify land cover from low-quality historical RGB imagery","year":2024,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"Environment and Climate Change Canada","keywords":"Land cover; Satellite imagery; Cover (algebra); Remote sensing; Land use; Convolutional neural network; Computer science; Deep learning; Artificial intelligence; Physical geography; Geography; Ecology; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02246771798820442,"gpt":0.2646689674354333,"spread":0.2422012494472289,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007320772,0.0001857521,0.0002169341,0.0002439504,0.0001595808,0.0006072811,0.0003865779,0.00006046869,0.00002239031],"category_scores_gemma":[0.0001715833,0.0001837641,0.0000642422,0.0009400753,0.00009253358,0.0004669971,0.0001657354,0.0003759998,0.000663876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008485711,"about_ca_system_score_gemma":0.00006409834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008603513,"about_ca_topic_score_gemma":0.000005780099,"domain_scores_codex":[0.9980317,0.00004598103,0.0003278372,0.0006148539,0.0005886458,0.0003909512],"domain_scores_gemma":[0.9989977,0.0002524448,0.00003057997,0.0003931535,0.0001226763,0.000203463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009109926,0.00003137047,0.0009108019,0.0001332002,0.00001640105,0.0000813786,0.001411123,0.2174081,0.1866379,0.00008467373,0.01814779,0.5751282],"study_design_scores_gemma":[0.00007221517,0.00001152558,0.003903702,0.00006436399,0.000006614787,0.000009015083,0.000003056389,0.9291649,0.001772626,0.00005469597,0.06470704,0.0002302879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04746174,0.0003315673,0.9472027,0.0006637833,0.002544145,0.0001759767,0.000002856414,0.0009244064,0.0006927756],"genre_scores_gemma":[0.9072531,0.00001154701,0.09108693,0.0002490801,0.0007586046,0.0000133296,0.0000104191,0.00003938323,0.0005775495],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8597914,"threshold_uncertainty_score":0.8533004,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}