{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":106,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":106,"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":"64a5714dfe6d","filters":{"venue":"Computer Science and Software Engineering"}},"results":[{"id":"W2202473840","doi":"","title":"Monitoring sentiment in open source mailing lists: exploratory study on the apache ecosystem","year":2014,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Software Engineering Research","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Sentiment analysis; Software; World Wide Web; Open source; Happiness; Software engineering; Data science; Artificial intelligence; Operating system; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.02911306773837615,"gpt":0.2620063775089251,"spread":0.232893309770549,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005358235,0.0002494477,0.0002523319,0.0004235433,0.0003336766,0.001534451,0.00330926,0.00004203365,8.207971e-7],"category_scores_gemma":[0.001249755,0.0001996424,0.00002913438,0.001441947,0.00004528361,0.001044699,0.00250148,0.0003901125,0.0000174788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002416137,"about_ca_system_score_gemma":0.00009826537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001317012,"about_ca_topic_score_gemma":0.000002568929,"domain_scores_codex":[0.9972557,0.0001129132,0.0002927459,0.0008227268,0.0008877395,0.0006281984],"domain_scores_gemma":[0.9969408,0.001625633,0.00004830764,0.001055411,0.000128286,0.0002015615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001077869,0.0004972882,0.3939492,0.0001814549,0.0000641999,0.0001151008,0.02890139,0.3881134,0.001112934,0.003071371,0.0001834091,0.1837995],"study_design_scores_gemma":[0.0005067777,0.0002570011,0.1007039,0.0004010703,0.000002868046,0.00001617569,0.0003106131,0.8949029,0.00171559,0.00003325129,0.000685535,0.0004643025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4729272,0.00003790597,0.5258358,0.00006848885,0.0005550151,0.0003283823,1.701834e-7,0.0002434639,0.00000359797],"genre_scores_gemma":[0.9694012,0.000003191059,0.03022903,0.00005240868,0.0001838522,0.00009786538,1.054655e-7,0.00002247433,0.000009915807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5067895,"threshold_uncertainty_score":0.9995021,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2402498022","doi":"","title":"Cardinality estimation using neural networks","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"IBM (Canada); York University; University of Waterloo","funders":"","keywords":"Cardinality (data modeling); Estimator; Computer science; Bounded function; Artificial neural network; Range (aeronautics); Column (typography); Algorithm; Data mining; Theoretical computer science; Artificial intelligence; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.031090544868678,"gpt":0.2570836142534774,"spread":0.2259930693847994,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001098497,0.0001249635,0.0001282827,0.0001585912,0.0001269086,0.0005023266,0.0008553343,0.00003436782,1.207435e-7],"category_scores_gemma":[0.0002739784,0.0001231951,0.00001818909,0.0007217527,0.00009410084,0.001709538,0.000823183,0.000106594,7.767732e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001002296,"about_ca_system_score_gemma":0.00009237914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002401856,"about_ca_topic_score_gemma":2.094444e-7,"domain_scores_codex":[0.9987708,0.00001670416,0.0001403284,0.0003951544,0.0003726766,0.0003043632],"domain_scores_gemma":[0.999041,0.00006246389,0.00003871514,0.0004704103,0.0001766827,0.0002107773],"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":[6.9267e-7,0.000008494232,0.00211587,0.00001045655,0.00000354939,0.00001755741,0.0003749687,0.7384527,0.00003405179,0.001957014,0.0002497735,0.2567748],"study_design_scores_gemma":[0.00005960838,0.00003404263,0.001589322,0.00002098825,0.000002121414,0.00007372801,0.000001505168,0.9977146,0.0001314348,0.0001387488,0.00008738355,0.0001464558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06685996,0.00008140155,0.9316941,0.00002964312,0.0005871691,0.00006396235,6.100063e-7,0.0006791942,0.000003992298],"genre_scores_gemma":[0.4374268,7.237877e-7,0.5624659,0.00004499072,0.00005493762,0.000001841312,6.858579e-7,0.000003653411,4.331169e-7],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3705669,"threshold_uncertainty_score":0.5023751,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2990180950","doi":"","title":"Interpreting financial time series with SHAP values","year":2019,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":55,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Machine learning; Process (computing); Series (stratigraphy); Artificial intelligence; Predictive modelling; Class (philosophy); Feature (linguistics); Time series; Finance; Cluster (spacecraft); Econometrics; Data mining; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01702989056758429,"gpt":0.2778311453588767,"spread":0.2608012547912924,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00518721,0.0001620514,0.000251229,0.0003396343,0.0001899263,0.0005711566,0.0008094567,0.00003930878,0.00004451258],"category_scores_gemma":[0.006796211,0.0001164942,0.00003283808,0.001214111,0.0002026758,0.000992163,0.0005284536,0.0001441593,0.00007086431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003895397,"about_ca_system_score_gemma":0.0001468943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001897096,"about_ca_topic_score_gemma":3.042612e-7,"domain_scores_codex":[0.9976582,0.00004745,0.000260871,0.0006131704,0.001054686,0.0003656581],"domain_scores_gemma":[0.9974859,0.001536412,0.00007949542,0.0004218351,0.0003427058,0.000133621],"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":[0.00005465031,0.00001836391,0.1052741,0.00004082527,0.00001150588,0.00002901598,0.003299511,0.03109835,0.002233996,0.0006906294,0.001135277,0.8561138],"study_design_scores_gemma":[0.0003154704,0.0004638118,0.1317484,0.0003289974,0.000006633094,0.0002091784,0.00004618499,0.859087,0.001496618,0.001538479,0.00414509,0.0006141381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4695294,0.00002937845,0.5295527,0.00003758302,0.0005236455,0.00008248621,7.861205e-7,0.0001236256,0.0001204017],"genre_scores_gemma":[0.3778277,6.725557e-7,0.6215461,0.000129484,0.0001279025,0.000004198448,2.409083e-7,0.00001362731,0.0003500519],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8554997,"threshold_uncertainty_score":0.813619,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2242441826","doi":"","title":"Analyzing auto-scaling issues in cloud environments","year":2014,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":21,"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":"Cloud computing; Provisioning; Computer science; Scaling; Data science; Software; Open research; Utility computing; Distributed computing; Data mining; Cloud computing security; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.005731023992808735,"gpt":0.1986388838704528,"spread":0.192907859877644,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001543852,0.0001672414,0.000185758,0.0003632073,0.0001983963,0.0003664849,0.0009866428,0.00003563723,6.216736e-7],"category_scores_gemma":[0.0001417476,0.0001610522,0.00003094004,0.0008060916,0.00008080806,0.000144083,0.00103127,0.0001540636,0.000008856266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007594698,"about_ca_system_score_gemma":0.00001832738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000157261,"about_ca_topic_score_gemma":5.196189e-7,"domain_scores_codex":[0.9982943,0.00002968603,0.0002216365,0.0005968746,0.000390358,0.0004671269],"domain_scores_gemma":[0.999203,0.0001357073,0.00004233973,0.0004600379,0.00002085521,0.0001380332],"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":[6.001558e-7,0.00003266301,0.006781738,0.00003658429,0.000007564329,0.0000167159,0.001400527,0.5293515,0.0003181571,0.00522764,0.0000645365,0.4567618],"study_design_scores_gemma":[0.000141893,0.00002785034,0.01720222,0.00007545295,0.000001600771,0.000007250968,0.000004331871,0.9768292,0.0001656088,0.0001172986,0.005220493,0.0002067384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2117599,0.0001599986,0.7871376,0.0001579498,0.0005120996,0.00005572166,5.996015e-8,0.0002004355,0.00001625865],"genre_scores_gemma":[0.7890972,0.000008681774,0.2105019,0.0001380522,0.0002131991,0.000003184791,1.809571e-7,0.000008343202,0.00002919186],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5773374,"threshold_uncertainty_score":0.6567518,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2782709646","doi":"","title":"Foodie fooderson a conversational agent for the smart kitchen","year":2017,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"AI in Service Interactions","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"IBM (Canada); University of Victoria","funders":"","keywords":"Watson; Context (archaeology); Computer science; IBM; Recipe; Cognitive computing; Dialog system; Architecture; Recommender system; Human–computer interaction; World Wide Web; Cognition; Multimedia; Artificial intelligence; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.02727232036484973,"gpt":0.2532925382559698,"spread":0.2260202178911201,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005988475,0.0001102272,0.00009299233,0.00007462135,0.001271166,0.00115877,0.001852288,0.00002624901,0.000001578341],"category_scores_gemma":[0.0003769227,0.00008682448,0.00004401918,0.0001142266,0.0001438469,0.00131694,0.0006763999,0.00009698689,0.000006475171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005807845,"about_ca_system_score_gemma":0.0001075291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002301987,"about_ca_topic_score_gemma":0.000005657128,"domain_scores_codex":[0.9989484,0.000004830777,0.0001156744,0.0003527527,0.0003025814,0.0002757676],"domain_scores_gemma":[0.9984163,0.0004940382,0.00006922746,0.0007262576,0.000213257,0.00008089039],"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":[0.00001332463,0.000103126,0.01140945,0.0002169282,0.0001894274,0.00002182607,0.01110337,0.02937078,0.001024716,0.1073037,0.007325393,0.831918],"study_design_scores_gemma":[0.0001503026,0.00003961005,0.02210967,0.00002681143,0.000005153452,0.00001784086,0.00001257491,0.9652528,0.0002652925,0.0002708376,0.01171708,0.0001320508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004752384,0.00007562782,0.9901698,0.002600813,0.002057221,0.000186005,0.000001883089,0.0001403354,0.00001591333],"genre_scores_gemma":[0.5432244,0.000008713012,0.4557995,0.0006489898,0.0002324423,0.00004964359,5.443317e-7,0.000007580236,0.00002811838],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.935882,"threshold_uncertainty_score":0.9998781,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2269703771","doi":"","title":"Enterprise application development in the cloud with IBM Bluemix","year":2014,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Cloud Computing and Resource Management","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":"IBM (Canada)","funders":"","keywords":"IBM; Cloud computing; Computer science; Java; Software deployment; Downtime; Variety (cybernetics); Software engineering; Service (business); Operating system; Development environment; Process (computing); Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.00399086311560279,"gpt":0.1764764035175291,"spread":0.1724855404019263,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001462692,0.0001234568,0.00009884018,0.0001618749,0.0002059827,0.0003014098,0.001166456,0.00001889554,1.027144e-7],"category_scores_gemma":[0.00004211444,0.00008206683,0.00001214889,0.0007700858,0.00006462728,0.00007438298,0.000435228,0.0001144426,0.000004430966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004532671,"about_ca_system_score_gemma":0.00004083181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005352464,"about_ca_topic_score_gemma":0.00000241108,"domain_scores_codex":[0.9986975,0.00002311685,0.0001455255,0.0004093229,0.0004297927,0.0002947793],"domain_scores_gemma":[0.9992685,0.0001362485,0.00003543746,0.0004421395,0.00005176459,0.00006586897],"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":[0.000001411536,0.0000434158,0.004980659,0.000038707,0.000005216918,0.000007919747,0.006172187,0.08012698,0.00004628901,0.009922459,0.00004549549,0.8986093],"study_design_scores_gemma":[0.0001618852,0.00004510095,0.03413657,0.0000558628,0.00000133595,0.00002310639,0.00001399113,0.9490279,0.00008760256,0.00003846194,0.01623936,0.0001688278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2455056,0.00003613238,0.7538868,0.0001777127,0.0001337383,0.0001036388,2.025711e-8,0.0001283927,0.00002798254],"genre_scores_gemma":[0.8143188,0.000001125511,0.1852424,0.0003260416,0.0000859904,0.0000167589,1.862585e-7,0.000004135093,0.000004637958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8984404,"threshold_uncertainty_score":0.3346588,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2920267096","doi":"","title":"A context-aware machine learning-based approach","year":2018,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Data Stream Mining Techniques","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 Waterloo","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Context (archaeology); Artificial neural network; Set (abstract data type); Context model; Control (management)","retraction":null,"screen_n_in":null,"score":{"opus":0.01083987578725308,"gpt":0.211665959954587,"spread":0.200826084167334,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008630335,0.0001988399,0.0001787869,0.0003235704,0.0003361598,0.0005088143,0.001565734,0.00005104811,0.000002083506],"category_scores_gemma":[0.0002733296,0.0001858512,0.00002944812,0.0009659621,0.0003361128,0.0008781533,0.0008439545,0.0002019635,0.00001043653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005528193,"about_ca_system_score_gemma":0.0001375485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001985995,"about_ca_topic_score_gemma":0.000001242609,"domain_scores_codex":[0.9982619,0.00002211257,0.0001680524,0.0006765058,0.0004332568,0.0004381572],"domain_scores_gemma":[0.9987812,0.0001234821,0.00005073813,0.0006143551,0.0002426258,0.0001876078],"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":[0.000007894541,0.0001479015,0.01109621,0.0001387154,0.00002679743,0.0000523452,0.002448644,0.008538254,0.0005378277,0.01207585,0.002298852,0.9626307],"study_design_scores_gemma":[0.0001378123,0.0001869799,0.001160086,0.00004277944,0.000001999075,0.00003387258,0.000002537468,0.9910986,0.001812426,0.00003823743,0.005243346,0.0002413046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00445451,0.00008164454,0.9933879,0.00007098457,0.0002921363,0.0001058391,0.000002597613,0.001577663,0.00002666522],"genre_scores_gemma":[0.5003529,0.000001711989,0.4993271,0.0002111979,0.00008039361,0.000008730105,0.000002639812,0.000008449204,0.000006878937],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9825604,"threshold_uncertainty_score":0.757879,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2401568214","doi":"","title":"Combining static analysis and targeted symbolic execution for scalable bug-finding in application binaries","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Software Testing and Debugging Techniques","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":"University of Waterloo","funders":"","keywords":"Symbolic execution; Computer science; Concolic testing; Program analysis; Static analysis; Statement (logic); Programming language; Pruning; Process (computing); Program slicing; Scalability; Code (set theory); Debugging; Software; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.009698424595459712,"gpt":0.2327454978405086,"spread":0.2230470732450489,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001052292,0.0001373366,0.0002074737,0.0007675769,0.0003061853,0.000222774,0.0003879239,0.0000417172,1.486026e-7],"category_scores_gemma":[0.0006029964,0.0001133422,0.00002559788,0.001757989,0.0001123881,0.0007577514,0.0003391658,0.00005700262,4.134237e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007820714,"about_ca_system_score_gemma":0.00005626722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003507767,"about_ca_topic_score_gemma":0.000002483029,"domain_scores_codex":[0.9987053,0.00001435231,0.0002044044,0.0005132507,0.0002132215,0.000349434],"domain_scores_gemma":[0.9987367,0.000679037,0.00006087861,0.000295513,0.0001272418,0.000100663],"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":[0.000006975503,0.00005949639,0.32081,0.0001827303,0.000058654,0.000007855931,0.003910672,0.005956926,0.005811926,0.0153613,0.0001630399,0.6476704],"study_design_scores_gemma":[0.0002193656,0.00006159708,0.06885753,0.0001168389,0.00001387315,0.000008271134,0.000002418778,0.9250665,0.001315541,0.004064573,0.00005807992,0.000215406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1887185,0.00008394691,0.809449,0.0001105339,0.00008331757,0.0001437,0.000001106257,0.001409251,6.184848e-7],"genre_scores_gemma":[0.6102147,0.000008294181,0.3896838,0.00003517921,0.000013423,0.00003731618,7.220218e-7,0.00000468868,0.000001985962],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9191096,"threshold_uncertainty_score":0.4621959,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2252116325","doi":"","title":"Reverse engineering of object-oriented code into Umple using an incremental and rule-based approach","year":2014,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Software Engineering Research","field":"Computer Science","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 Ottawa","funders":"","keywords":"Computer science; Programming language; Unified Modeling Language; Java; Reverse engineering; Code (set theory); Object-oriented programming; Code generation; Source code; Metamodeling; KPI-driven code analysis; Software engineering; Theoretical computer science; Static program analysis; Software development; Software; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.01591619199514458,"gpt":0.2391135352928475,"spread":0.2231973432977029,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00167551,0.0002761649,0.0003135229,0.0006459114,0.000208227,0.0002566089,0.0009270883,0.00007552013,7.484948e-7],"category_scores_gemma":[0.0009452294,0.0002904937,0.00003776714,0.001255925,0.0001901315,0.001158384,0.0007252534,0.0002364689,7.060984e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001317072,"about_ca_system_score_gemma":0.0001472988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006709057,"about_ca_topic_score_gemma":0.000001001548,"domain_scores_codex":[0.9975799,0.00003433627,0.0003041622,0.0007669941,0.0007458319,0.0005687621],"domain_scores_gemma":[0.998202,0.0004574766,0.00006252294,0.0006748186,0.0002343184,0.0003689204],"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":[0.00001023123,0.0001772111,0.03155772,0.0008495232,0.00003831541,0.00001722819,0.002619061,0.8768844,0.04531053,0.003911197,0.00002700474,0.03859759],"study_design_scores_gemma":[0.0003589124,0.0001207861,0.009951453,0.0000927884,0.000005089541,0.00003268658,0.00000775535,0.9831638,0.005791557,0.00001517638,0.0001462342,0.0003137668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4025334,0.00005866892,0.5968015,0.000007859602,0.0002100563,0.0001048863,0.000001298178,0.0002814144,8.993568e-7],"genre_scores_gemma":[0.5063183,0.000001807434,0.4935794,0.0000251972,0.00005185731,0.000005832795,0.000001635951,0.0000153548,5.624697e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1062794,"threshold_uncertainty_score":0.9999547,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2294277500","doi":"","title":"The effect of a collaborative game on group work","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Team Dynamics and Performance","field":"Psychology","cited_by":9,"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":"Task (project management); Work (physics); Video game; Computer science; Group work; Task group; Post hoc; Face (sociological concept); Discipline; Group (periodic table); Human–computer interaction; Multimedia; Engineering; Psychology; Engineering management; Political science; Sociology","retraction":null,"screen_n_in":null,"score":{"opus":0.007273266881848159,"gpt":0.2446962439212119,"spread":0.2374229770393638,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000815231,0.00007344076,0.00009019423,0.00005601467,0.00007031651,0.0000501053,0.0002095375,0.00002263353,7.850359e-7],"category_scores_gemma":[0.0001005542,0.00004678591,0.00001194746,0.0005179583,0.0001262857,0.00007078165,0.00006231372,0.00008109895,0.000006340541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000211361,"about_ca_system_score_gemma":0.00002104525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003134331,"about_ca_topic_score_gemma":4.732742e-7,"domain_scores_codex":[0.9994126,0.00001831011,0.00008004915,0.0001475441,0.0001693923,0.0001721196],"domain_scores_gemma":[0.9993582,0.0002984371,0.00002637027,0.0001798745,0.00006404941,0.00007304906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002463203,0.00004417487,0.1231643,0.00005400151,0.00005907658,0.00002241995,0.0145136,0.03462201,0.00007865908,0.01370119,0.00252963,0.8109646],"study_design_scores_gemma":[0.003043694,0.006355866,0.532148,0.0003931797,0.00002527128,0.00004550123,0.0001904412,0.4093347,0.0005135995,0.0001512606,0.04691525,0.0008832488],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8886372,0.0003499154,0.1095285,0.00002901972,0.001254898,0.00009758712,0.000001241863,0.00004461976,0.00005701416],"genre_scores_gemma":[0.9974867,0.000004714451,0.002376151,0.00002390386,0.00007186877,0.00001023352,3.113848e-7,0.000004808164,0.00002133498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8100813,"threshold_uncertainty_score":0.1907874,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2920295506","doi":"","title":"Ischemic stroke detection using EEG signals","year":2018,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":9,"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":"Electroencephalography; Computer science; Stroke (engine); Gold standard (test); Decision tree; Wearable computer; Artificial intelligence; Multilayer perceptron; Ischemic stroke; Pattern recognition (psychology); Magnetic resonance imaging; Artificial neural network; Medicine; Cardiology; Ischemia; Internal medicine; Radiology; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02155998204348062,"gpt":0.2448809757655708,"spread":0.2233209937220902,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003518525,0.0001433739,0.0001202391,0.0002113267,0.0003342371,0.0002886452,0.0003843214,0.00003911869,0.000005502115],"category_scores_gemma":[0.0002967073,0.0001330387,0.00002670177,0.0005150562,0.0002966327,0.000624163,0.0002704035,0.0001232013,0.00001198263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004724961,"about_ca_system_score_gemma":0.00004527251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004937667,"about_ca_topic_score_gemma":5.051419e-7,"domain_scores_codex":[0.9986574,0.00001460279,0.000146327,0.0004915919,0.0003230052,0.0003670436],"domain_scores_gemma":[0.9993824,0.0001573761,0.00003912503,0.0001964338,0.0001001697,0.0001244981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001777081,0.0000067694,0.0001442307,0.00001172317,0.000001387875,0.000004416117,0.000264165,0.002598959,0.9632024,0.00001761411,0.00004322566,0.03370335],"study_design_scores_gemma":[0.00006037045,0.00006181878,0.0003534788,0.00003013154,0.000001733098,0.0000597933,0.000002851742,0.4446994,0.5534327,0.000004894161,0.001175054,0.0001177862],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5219391,0.00001933554,0.4770192,0.000008325412,0.0008131027,0.00004193726,9.55908e-7,0.0001428428,0.00001519921],"genre_scores_gemma":[0.9621862,0.000002900274,0.03708564,0.0002492832,0.0004457143,0.000001868727,6.854534e-8,0.00001084071,0.00001754295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4421004,"threshold_uncertainty_score":0.5425162,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2592746543","doi":"","title":"Performance analysis roundtrip: automatic generation of performance models and results feedback using cross-model trace links","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Unified Modeling Language; TRACE (psycholinguistics); Model transformation; Applications of UML; Traceability; Model-driven architecture; Programming language; Software engineering; Software; Distributed computing; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.03104075142311767,"gpt":0.2484347456524853,"spread":0.2173939942293676,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001926118,0.0002407152,0.0003729328,0.0004974096,0.0003519144,0.0002974484,0.0006913098,0.0001359404,5.348456e-7],"category_scores_gemma":[0.0001382475,0.0001744427,0.00006499544,0.001533649,0.0002795678,0.003912528,0.0004017685,0.000143342,0.000001193056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000113932,"about_ca_system_score_gemma":0.0002035988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007640368,"about_ca_topic_score_gemma":7.619624e-7,"domain_scores_codex":[0.9975744,0.00002044776,0.0006355204,0.0007315621,0.0005988943,0.0004391431],"domain_scores_gemma":[0.9983639,0.000148646,0.0001936951,0.0007019572,0.0004219416,0.0001698971],"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":[0.000004601354,0.00001823847,0.02257536,0.0001790749,0.00003256473,7.98143e-7,0.000719989,0.8573527,0.001573077,0.0001161458,0.00000451085,0.1174229],"study_design_scores_gemma":[0.0003731785,0.00007514704,0.03131115,0.0001484486,0.00002562402,0.00001963943,0.000001302822,0.9658127,0.00195694,0.00001469885,0.000009335484,0.0002518748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4971741,0.00008525662,0.5023544,0.00001501308,0.0001702292,0.00008171673,0.000002331187,0.0001144872,0.000002405162],"genre_scores_gemma":[0.7587908,0.00008957125,0.241018,0.00001633295,0.0000580079,0.000005270205,6.094051e-7,0.000007316367,0.00001410492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2616166,"threshold_uncertainty_score":0.7113567,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2295745251","doi":"","title":"CrashAutomata: an approach for the detection of duplicate crash reports based on generalizable automata","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Software Engineering Research","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":"Concordia University","funders":"","keywords":"Crash; Computer science; False positive paradox; Software; Automaton; Precision and recall; Data mining; Generalization; Machine learning; Artificial intelligence; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.03035857657399654,"gpt":0.2547864409965724,"spread":0.2244278644225758,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002756174,0.0001891184,0.0001957823,0.000332736,0.0002202225,0.0003707532,0.001214075,0.00005803748,2.115017e-7],"category_scores_gemma":[0.001376354,0.0001483518,0.00004432176,0.001106602,0.00012844,0.0008028573,0.0003563333,0.0001414697,7.007258e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001034676,"about_ca_system_score_gemma":0.0002455795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001742454,"about_ca_topic_score_gemma":3.323034e-7,"domain_scores_codex":[0.9977776,0.00002088686,0.0002631338,0.0006672026,0.0008097211,0.0004615011],"domain_scores_gemma":[0.9973975,0.0006152729,0.00007581503,0.001218486,0.0004186938,0.0002741654],"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":[0.000003471408,0.00005529566,0.001531943,0.00007061306,0.0000077392,0.000007023139,0.000189948,0.960867,0.0009686429,0.0002641397,0.0001199994,0.03591416],"study_design_scores_gemma":[0.0002098295,0.0001910823,0.01016585,0.00002036382,0.000003839991,0.00004630334,0.000002718788,0.9852555,0.003420146,0.00003972712,0.0004674662,0.0001771414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01970883,0.0001005075,0.9784009,0.00005330988,0.0006665945,0.0003507676,0.000001803317,0.0007145505,0.00000269469],"genre_scores_gemma":[0.562071,0.000002011077,0.4376893,0.00005372981,0.00009228857,0.00007031887,0.000001836884,0.0000151015,0.000004321756],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5423622,"threshold_uncertainty_score":0.6049613,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2591607085","doi":"","title":"An effective method for detecting duplicate crash reports using crash traces and hidden Markov models","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Software Engineering Research","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":"Concordia University","funders":"","keywords":"Crash; Computer science; Hidden Markov model; Software; Task (project management); Eclipse; Data mining; Machine learning; Artificial intelligence; Operating system; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01780779090722437,"gpt":0.2837781660799824,"spread":0.265970375172758,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003056245,0.0002951439,0.000314987,0.0004752794,0.0004033275,0.0006477735,0.0008151901,0.00009223498,5.115131e-7],"category_scores_gemma":[0.001449807,0.0002404086,0.00005047526,0.0007651905,0.0001396319,0.002614991,0.0006268475,0.0001589377,4.498556e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000154048,"about_ca_system_score_gemma":0.0001123219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001529872,"about_ca_topic_score_gemma":9.477096e-7,"domain_scores_codex":[0.9971328,0.00004581096,0.0003087853,0.001211375,0.0005364469,0.0007647292],"domain_scores_gemma":[0.9959912,0.00243751,0.00008775656,0.0007478597,0.0003425948,0.0003930875],"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":[0.000003513757,0.00001924863,0.007604685,0.0001154788,0.0000214664,0.00004306495,0.0006283243,0.01712945,0.02740375,0.0003100451,0.00001053459,0.9467104],"study_design_scores_gemma":[0.0002397575,0.0001368483,0.02086201,0.0001424276,0.000007388299,0.0003242747,0.0000036253,0.9697462,0.007287606,0.0008314645,0.00003606258,0.0003823143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.316582,0.0001264933,0.6820308,0.00004071217,0.0003575401,0.0003546217,0.00000159864,0.000505715,4.792581e-7],"genre_scores_gemma":[0.4576065,0.00000647978,0.5422007,0.0000197802,0.00009741756,0.00004616619,1.601109e-7,0.00002023062,0.000002587509],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9526168,"threshold_uncertainty_score":0.9803576,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2594649443","doi":"","title":"Microservices in the modern software world","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Software System Performance and Reliability","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":"University of Toronto","funders":"","keywords":"Microservices; Computer science; Software architecture; Software engineering; Service-oriented architecture; Flexibility (engineering); Scalability; Resource-oriented architecture; Architectural style; Architecture; Software; Software development; Reference architecture; Computer architecture; Operating system; Component-based software engineering; World Wide Web; Web service; Cloud computing","retraction":null,"screen_n_in":null,"score":{"opus":0.007591136296314276,"gpt":0.2033649713391033,"spread":0.1957738350427891,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001759446,0.0001932322,0.0001836259,0.0003392521,0.000227385,0.0003790948,0.002004476,0.00004447181,0.000001444457],"category_scores_gemma":[0.0001991141,0.000104711,0.00004080817,0.001491906,0.0001673165,0.001584138,0.0005507314,0.000134931,0.00001610221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008429469,"about_ca_system_score_gemma":0.0001186442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001667346,"about_ca_topic_score_gemma":0.00001469144,"domain_scores_codex":[0.998033,0.00003392356,0.0002623325,0.0006034231,0.0005455077,0.0005217872],"domain_scores_gemma":[0.9983918,0.0005176105,0.00004902129,0.0007938164,0.0001402525,0.0001074736],"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":[0.000003751766,0.00006497207,0.1078349,0.0001637905,0.000009031859,0.00003949297,0.005245384,0.003158704,0.0007916087,0.002105251,0.0004016678,0.8801815],"study_design_scores_gemma":[0.00131362,0.0001829805,0.4395282,0.0009481022,0.000008356818,0.0002505741,0.00002629829,0.5402443,0.001656139,0.004417166,0.01008396,0.001340306],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1649022,0.0002554323,0.8332326,0.0005403039,0.0006072236,0.0001514257,9.943938e-7,0.0003025787,0.000007165051],"genre_scores_gemma":[0.847459,0.00001891537,0.1517584,0.0005644318,0.000135394,0.00002791686,2.278396e-7,0.000008924431,0.00002670701],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8788412,"threshold_uncertainty_score":0.4269991,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2594194814","doi":"","title":"From relations to multi-dimensional maps: a SQL-to-HBase transformation methodology","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Advanced Database Systems and Queries","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":"University of Alberta","funders":"","keywords":"Computer science; SQL; Schema (genetic algorithms); Relational database; Database; Transformation (genetics); Data transformation; Information retrieval; Data mining; Data warehouse; Chemistry","retraction":null,"screen_n_in":null,"score":{"opus":0.03077973482452679,"gpt":0.2652913606888613,"spread":0.2345116258643345,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006904997,0.0001358947,0.0001570595,0.0002811993,0.0001958226,0.00007239881,0.0003859768,0.00003389269,0.00000338538],"category_scores_gemma":[0.0004428797,0.0001004355,0.00002432587,0.0006273987,0.00005147911,0.001454679,0.0003359847,0.00005908471,0.00005363459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000631709,"about_ca_system_score_gemma":0.00008031282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003133015,"about_ca_topic_score_gemma":0.000006044455,"domain_scores_codex":[0.9986885,0.00003137044,0.0002133504,0.000480417,0.0002824189,0.0003039472],"domain_scores_gemma":[0.9987298,0.0004303044,0.0000288405,0.0003833468,0.0001514196,0.0002763487],"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":[0.00001420858,0.00005367131,0.0006538668,0.0000362145,0.00002598481,0.0000351129,0.01359632,0.08551191,0.05400035,0.07706295,0.001216466,0.7677929],"study_design_scores_gemma":[0.001249818,0.0003573525,0.02544789,0.0007661353,0.00001279742,0.0001172609,0.00006788135,0.8788732,0.02053188,0.001151033,0.06994965,0.001475152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02628307,0.00003885396,0.971471,0.001002689,0.0007572701,0.0001661454,0.00003246628,0.0002470469,0.000001498513],"genre_scores_gemma":[0.04382932,0.000001415246,0.9556668,0.0003585439,0.00009503883,0.00002124265,0.0000026352,0.000006317672,0.00001863853],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7933612,"threshold_uncertainty_score":0.409564,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2396547321","doi":"","title":"An empirical study on change recommendation","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Programmer; Computer science; Reuse; Ranking (information retrieval); Focus (optics); Repetition (rhetorical device); Sensitivity (control systems); Empirical research; Recommender system; Software engineering; Information retrieval; Programming language; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.0910226896818921,"gpt":0.3403271222810527,"spread":0.2493044325991606,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002080568,0.0001851642,0.0001606384,0.000502237,0.0001508942,0.0005558813,0.001149438,0.00004249391,8.665778e-7],"category_scores_gemma":[0.0008945041,0.0001744997,0.00001765783,0.001305743,0.00005530279,0.001798586,0.0004921725,0.0002228172,0.00002092785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001650755,"about_ca_system_score_gemma":0.0001295292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001075013,"about_ca_topic_score_gemma":5.993824e-7,"domain_scores_codex":[0.9978585,0.00004500831,0.0001667106,0.0006958644,0.0007709141,0.0004629637],"domain_scores_gemma":[0.9981847,0.0003324218,0.00002502946,0.0006589129,0.0002741754,0.0005247446],"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":[0.00001030683,0.0006757701,0.384897,0.00002963617,0.00002144178,0.0001074187,0.01865379,0.01353061,0.0001103034,0.0004336332,0.001006731,0.5805234],"study_design_scores_gemma":[0.0003807136,0.001073373,0.3369509,0.00001961502,0.000001779229,0.00002547442,0.00003934051,0.6600345,0.0001186925,0.00003155199,0.001021975,0.0003020348],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3415497,0.00001523303,0.6566648,0.0001595784,0.0007824306,0.0001948419,4.49086e-7,0.0006305365,0.000002411999],"genre_scores_gemma":[0.8925629,0.000001291692,0.106788,0.0002348508,0.0003521453,0.00004309494,0.000001261467,0.00001433609,0.000002125274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6465039,"threshold_uncertainty_score":0.711589,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2593837619","doi":"","title":"Proactive auto-scaling of resources for stream processing engines in the cloud","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Data Stream Mining Techniques","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":"Queen's University","funders":"","keywords":"Computer science; Stream processing; Workload; Cloud computing; Scaling; IBM; Process (computing); Real-time computing; Distributed computing; Database; Data science; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.01243848851139623,"gpt":0.2335593557633601,"spread":0.2211208672519639,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001257556,0.0001270877,0.0001473522,0.0002428719,0.0001071754,0.0001917278,0.001345587,0.00003313972,1.275428e-7],"category_scores_gemma":[0.0004293188,0.0000761911,0.00002396194,0.000676753,0.000146075,0.0009537944,0.0003368447,0.00006438232,1.81528e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000358904,"about_ca_system_score_gemma":0.0000812413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005583928,"about_ca_topic_score_gemma":6.477094e-7,"domain_scores_codex":[0.9987984,0.00001589504,0.0001966748,0.0003869709,0.000304468,0.0002976479],"domain_scores_gemma":[0.9988658,0.0005073493,0.00006628579,0.0003771598,0.0001378124,0.00004557669],"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":[0.000002781667,0.00002941012,0.001446943,0.00007379529,0.000004417275,0.000003397345,0.004655398,0.0002996707,0.00137371,0.004273884,0.00006730441,0.9877693],"study_design_scores_gemma":[0.0005914515,0.0003447057,0.03607931,0.001089968,0.00001013471,0.00005900067,0.00009228504,0.9363637,0.01971408,0.002028846,0.003027639,0.0005988747],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09602905,0.00008653518,0.9031999,0.0001505936,0.0001204943,0.0001890415,0.000003723662,0.0002165229,0.000004106637],"genre_scores_gemma":[0.5182975,0.000004077587,0.4815806,0.00002530789,0.00005971681,0.00002624418,1.976755e-7,0.000004907958,0.000001399603],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9871704,"threshold_uncertainty_score":0.3106983,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2782779765","doi":"","title":"Using IBM watson cloud services to build natural language processing solutions to leverage chat tools","year":2017,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Speech and dialogue systems","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":"IBM (Canada)","funders":"","keywords":"IBM; Watson; Computer science; Leverage (statistics); Cloud computing; World Wide Web; Cognitive computing; Multimedia; Data science; Artificial intelligence; Cognition; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.02687267921664701,"gpt":0.265047471642919,"spread":0.238174792426272,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007114661,0.0001999998,0.0002044011,0.0002645878,0.001211292,0.003036265,0.001926445,0.00004551967,5.70977e-7],"category_scores_gemma":[0.000235848,0.0001864741,0.00003238016,0.0005341269,0.00005572621,0.002494337,0.001508714,0.0001303764,0.00001435072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001029281,"about_ca_system_score_gemma":0.0001331734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001105829,"about_ca_topic_score_gemma":0.00001897999,"domain_scores_codex":[0.9980628,0.00001148704,0.0001832662,0.0006197382,0.0004663233,0.0006564003],"domain_scores_gemma":[0.9985374,0.00004869217,0.00006649928,0.00077928,0.0002058223,0.0003623388],"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":[0.000007480985,0.00003700335,0.002175169,0.0003267872,0.00001569612,0.000115236,0.01602316,0.03794833,0.03335243,0.001391101,0.0001796707,0.908428],"study_design_scores_gemma":[0.0002042202,0.00006219093,0.01679966,0.0004450787,0.000004373971,0.00008816146,0.00005108142,0.9771942,0.002957608,0.00002831621,0.001636877,0.000528201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2106897,0.0006066493,0.786063,0.0002438184,0.001943478,0.0001833173,0.000001982237,0.0002582173,0.000009821737],"genre_scores_gemma":[0.6903285,0.000001883593,0.3089399,0.0002881629,0.0004131412,0.000006835111,5.793788e-7,0.000008617507,0.00001237149],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9392459,"threshold_uncertainty_score":0.9979987,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2271865972","doi":"","title":"MOTL: a textual language for trace specification of state machines and associations","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; TRACE (psycholinguistics); Tracing; Programming language; Construct (python library); Language construct; Semantics (computer science); Syntax; Abstraction; State (computer science); Modeling language; Finite-state machine; Theoretical computer science; Natural language processing; Software","retraction":null,"screen_n_in":null,"score":{"opus":0.01906141550957943,"gpt":0.2481286145721092,"spread":0.2290671990625297,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001151572,0.0001238348,0.000161979,0.0002593646,0.00006891746,0.0001326718,0.0004456103,0.00003715925,9.588769e-8],"category_scores_gemma":[0.0001558615,0.0001235683,0.00002196792,0.0004691122,0.00007291097,0.0006093632,0.0002265074,0.00007744034,3.115385e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006000885,"about_ca_system_score_gemma":0.00007529278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001369194,"about_ca_topic_score_gemma":0.000001405408,"domain_scores_codex":[0.9989213,0.000008327923,0.0002016412,0.0003213626,0.0003102916,0.0002371138],"domain_scores_gemma":[0.9990462,0.0001663649,0.00006713042,0.0002802986,0.0002883767,0.0001515937],"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":[0.000004147317,0.00005033961,0.003591852,0.0001151114,0.00002009595,0.000005516072,0.01000163,0.02365894,0.003883304,0.05608988,0.0002994899,0.9022797],"study_design_scores_gemma":[0.0001947831,0.00008574456,0.01116024,0.00003002912,0.00000335777,0.00001252414,0.000006338179,0.984296,0.002589014,0.0002839186,0.001155422,0.0001826527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06938618,0.0002403748,0.9295509,0.00004317503,0.0001593164,0.0001736938,0.000009832577,0.0004342271,0.000002283766],"genre_scores_gemma":[0.246319,0.000006783458,0.7535907,0.00001683746,0.00004016466,0.00001077825,0.000001645055,0.000008214352,0.000005871572],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.960637,"threshold_uncertainty_score":0.5038969,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2203597119","doi":"","title":"Mining common morphological fragments from process event logs","year":2014,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University; Toronto Metropolitan University","funders":"","keywords":"Computer science; Process mining; Code refactoring; Process (computing); Event (particle physics); Data mining; Business process discovery; Process modeling; Work in process; Software engineering; Artificial intelligence; Business process; Business process management; Business process modeling; Programming language; Software; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01096041938625573,"gpt":0.2123986259174516,"spread":0.2014382065311958,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005567396,0.000172166,0.0002215028,0.0002360477,0.0002782427,0.0004855126,0.0004170171,0.00004974519,0.00001039222],"category_scores_gemma":[0.0002360276,0.0001484166,0.00003483016,0.0007111115,0.0000887597,0.0009731905,0.0002854882,0.0001089185,0.00001659857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000170092,"about_ca_system_score_gemma":0.0000162412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000746743,"about_ca_topic_score_gemma":0.000002049522,"domain_scores_codex":[0.9986507,0.000003284516,0.0001972291,0.00044731,0.0003886808,0.000312823],"domain_scores_gemma":[0.9994312,0.00006145117,0.00007707583,0.0001923434,0.0002053216,0.00003263704],"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":[0.00001061058,0.0001245177,0.1603937,0.0003646885,0.00004062592,0.00003242144,0.000365666,0.4051216,0.0006101068,0.0007098166,0.000274849,0.4319514],"study_design_scores_gemma":[0.0001573531,0.000005906417,0.01437966,0.00009956228,0.00002021941,0.000002373434,0.00001364005,0.9839931,0.00004181858,0.0003921383,0.0006729606,0.0002212326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5208383,0.00004626934,0.4786552,0.00006273181,0.0002026664,0.00002628178,2.66058e-7,0.0001558797,0.00001240211],"genre_scores_gemma":[0.9826416,0.000002974301,0.0158332,0.0008297077,0.0006605259,0.00000763878,0.000007763697,0.00001286666,0.000003760608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5788715,"threshold_uncertainty_score":0.6052254,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2266551045","doi":"","title":"Mitigating dynamic attacks using multi-agent game-theoretic techniques","year":2014,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Exploit; Computer security; Computer science; Intrusion detection system; Game theory; Adaptation (eye); Cloud computing; Risk analysis (engineering)","retraction":null,"screen_n_in":null,"score":{"opus":0.009074059306976186,"gpt":0.230730793441785,"spread":0.2216567341348088,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001066065,0.0001709169,0.0001587587,0.0002467076,0.0003237157,0.000431235,0.0006989061,0.00005910543,0.000001391321],"category_scores_gemma":[0.0001921559,0.0001680325,0.00003761727,0.0007287929,0.0001674113,0.0008550841,0.0005714521,0.0001895641,0.000003739111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009256115,"about_ca_system_score_gemma":0.00004593827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006478162,"about_ca_topic_score_gemma":8.803862e-7,"domain_scores_codex":[0.9984984,0.0000330799,0.0002083838,0.0005153134,0.0003446921,0.0004001202],"domain_scores_gemma":[0.9991343,0.0001201518,0.00006125999,0.0003913004,0.0001335931,0.0001594216],"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":[0.000001417874,0.00004107733,0.0006528401,0.0001153883,0.00000927172,0.0000115499,0.001522355,0.03584268,0.01185265,0.01158425,0.00001690358,0.9383496],"study_design_scores_gemma":[0.0000816933,0.00005750647,0.0009470532,0.0001157288,0.000002496966,0.00005527424,0.000002192111,0.9935911,0.003950701,0.0002747158,0.0007007843,0.0002207142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1639424,0.00008128685,0.8347647,0.00003051863,0.000536429,0.00009173455,2.060366e-7,0.0005470073,0.000005722909],"genre_scores_gemma":[0.5207464,0.0000111476,0.4790458,0.0001191889,0.0000648365,0.000003571022,1.852022e-7,0.000006946131,0.000001987715],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9577485,"threshold_uncertainty_score":0.6852167,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2782766517","doi":"","title":"The first workshop on blockchain & eHealth: towards provable privacy & security in data intensive health research","year":2017,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"eHealth; Blockchain; Health care; Wearable computer; Computer science; Healthcare delivery; Knowledge management; Internet privacy; Data science; Information privacy; Business; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.07906675048345717,"gpt":0.3455651598797686,"spread":0.2664984093963114,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.007231021,0.0001609652,0.0002075764,0.0003067802,0.00326943,0.001036567,0.007413659,0.0000848872,2.272311e-7],"category_scores_gemma":[0.002856229,0.0001270903,0.00001703903,0.0009091754,0.0006955049,0.0004814184,0.005835957,0.0008082123,0.000004399963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001865525,"about_ca_system_score_gemma":0.0005178984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001735394,"about_ca_topic_score_gemma":0.000307704,"domain_scores_codex":[0.9972534,0.0000439711,0.0002750524,0.0009713954,0.0006106576,0.0008455862],"domain_scores_gemma":[0.99485,0.0005382154,0.0001066131,0.003893432,0.0004265681,0.0001851114],"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":[0.000007533515,0.0001218129,0.001992706,0.00009999936,0.00001098556,0.00002961191,0.005870041,0.001220199,0.000005943999,0.1027158,0.005705561,0.8822198],"study_design_scores_gemma":[0.0001940466,0.0000765904,0.01669142,0.0001912548,5.19687e-7,0.00001835771,0.0000487622,0.9553649,0.00005127334,0.003721247,0.02346024,0.0001813896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1186138,0.001192016,0.7986949,0.07910603,0.0009166997,0.0009867747,0.000006196867,0.0004556554,0.00002788403],"genre_scores_gemma":[0.951056,0.0002564745,0.04788641,0.0006157621,0.0001036855,0.00006080961,0.000001043837,0.000009711392,0.00001007846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9541447,"threshold_uncertainty_score":0.999564,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2406521162","doi":"","title":"Multitenancy benefits in application servers","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"IBM (Canada); University of New Brunswick","funders":"","keywords":"Computer science; Server; Memory footprint; Cloud computing; Java; Operating system; Service (business); Application server; Software; Footprint; Shared resource; Resource (disambiguation); Distributed computing; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.01203554119444132,"gpt":0.2006244745191748,"spread":0.1885889333247334,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008668158,0.0001084247,0.0001045816,0.0002690295,0.00007848095,0.0001950702,0.0007614747,0.0000262284,7.392603e-8],"category_scores_gemma":[0.0001013255,0.0001018514,0.00001541873,0.001052371,0.00004165691,0.0001510463,0.0006920042,0.00008963661,0.000007000638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009827037,"about_ca_system_score_gemma":0.00005781448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003367517,"about_ca_topic_score_gemma":0.000002918206,"domain_scores_codex":[0.9987627,0.000009053803,0.0001419882,0.0004273997,0.0003578097,0.0003010053],"domain_scores_gemma":[0.9992958,0.00004756785,0.00002884691,0.000353084,0.0001052874,0.0001693987],"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":[5.316158e-7,0.00002145079,0.005941336,0.00001703966,0.000001957504,0.000008829722,0.001112982,0.5601364,0.00001918984,0.006377096,0.00005926474,0.4263039],"study_design_scores_gemma":[0.0002054233,0.00002512225,0.02302562,0.00003797189,6.618871e-7,0.000009324077,0.000006920471,0.9752766,0.00004756966,0.0000889583,0.001137095,0.0001387291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.277048,0.0001601508,0.722061,0.0001217629,0.0002944152,0.00007998925,1.013364e-7,0.0002201423,0.00001446921],"genre_scores_gemma":[0.7673499,0.00000271929,0.2324211,0.0001376176,0.00006646337,0.000008024775,2.341406e-7,0.000004992422,0.000008919772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4903019,"threshold_uncertainty_score":0.4153379,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2294136539","doi":"","title":"Towards improved performance and compliance in healthcare using wearables and bluetooth technologies","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Wearable computer; Bluetooth; Smartwatch; Computer science; Wearable technology; Health care; Domain (mathematical analysis); Corporate governance; Compliance (psychology); Human–computer interaction; Data science; Embedded system; Risk analysis (engineering); Wireless; Business; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.02597620681379306,"gpt":0.2307053926303006,"spread":0.2047291858165076,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002824083,0.0001380531,0.0001607108,0.0002677414,0.00008679798,0.0001129084,0.0001648776,0.0000773252,7.783586e-8],"category_scores_gemma":[0.0001140522,0.0001334803,0.000005792942,0.0005029087,0.0002170196,0.000424493,0.0001966156,0.0001525289,2.393246e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007789717,"about_ca_system_score_gemma":0.00004230764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002974133,"about_ca_topic_score_gemma":0.000004182735,"domain_scores_codex":[0.9992072,0.000003225793,0.0001354643,0.0002296683,0.0001286299,0.0002958299],"domain_scores_gemma":[0.9996868,0.00002052282,0.00001418301,0.0001387389,0.00007206036,0.00006767849],"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":[0.000005701463,0.00001084506,0.06485713,0.0009395698,0.000009838679,0.00001438938,0.001494698,0.1382204,0.001771171,0.001085148,0.00003330919,0.7915578],"study_design_scores_gemma":[0.0001723386,0.00003878587,0.00767112,0.0001340343,0.000001370921,0.00003164014,0.00008011352,0.9892882,0.002176827,0.0000915861,0.0001383824,0.000175597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7050047,0.002203946,0.2916535,0.00005954853,0.0001799383,0.00008667757,0.000001231758,0.0008071266,0.000003330669],"genre_scores_gemma":[0.9247997,0.0003605695,0.07478991,0.00001933339,0.00001341675,0.000005158094,3.249191e-7,0.0000107613,7.966142e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8510678,"threshold_uncertainty_score":0.5443169,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2991648864","doi":"","title":"Decentralized and secure delivery network of IoT update files based on ethereum smart contracts and blockchain technology","year":2019,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Blockchain; Computer science; Computer security; Cloud computing; Smart contract; Exploit; Incentive; Peer-to-peer; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.00331352538155073,"gpt":0.1818586758453894,"spread":0.1785451504638386,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005290319,0.0001576981,0.0002343846,0.0002276541,0.000141832,0.00007637621,0.0005279694,0.000138334,0.000001605193],"category_scores_gemma":[0.00007146948,0.0001476821,0.00001750892,0.0007626935,0.000274533,0.00008708808,0.0003905123,0.0002069583,0.000001308307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001830865,"about_ca_system_score_gemma":0.00006870791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005022973,"about_ca_topic_score_gemma":0.000002000287,"domain_scores_codex":[0.998779,0.00001409467,0.0001749691,0.0005061266,0.0001820122,0.0003438104],"domain_scores_gemma":[0.9990348,0.0002158252,0.00006555796,0.0004678703,0.0001193728,0.00009653441],"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":[0.00003086511,0.0001893398,0.0772832,0.0002935523,0.00006724449,0.00003507539,0.0008436241,0.09705395,0.004217315,0.2893896,0.0003750053,0.5302212],"study_design_scores_gemma":[0.000371136,0.0001096963,0.008455144,0.00009144531,0.000003911343,0.00002229013,0.000004417155,0.9870268,0.001131727,0.001309609,0.001287455,0.0001863483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5618233,0.0005878636,0.436732,0.0004203013,0.0001087983,0.0001408118,0.000001705269,0.0001829694,0.000002282517],"genre_scores_gemma":[0.8147093,0.00004995775,0.1849772,0.0002353614,0.00001257252,0.000008492114,4.271486e-7,0.000005585126,0.000001126243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8899729,"threshold_uncertainty_score":0.6022301,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2396873128","doi":"","title":"Context extraction in recommendation systems in software engineering: a preliminary survey","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Context (archaeology); Task (project management); Software; Key (lock); Focus (optics); Software engineering; Data science; Recommender system; Software system; World Wide Web; Systems engineering; Engineering; Computer security; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.0331688464224023,"gpt":0.247467988867994,"spread":0.2142991424455917,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003033911,0.0001835383,0.0002527201,0.0007906481,0.00005997259,0.0004428033,0.0006569066,0.00006748772,5.224257e-7],"category_scores_gemma":[0.001454624,0.0001923742,0.00002153819,0.001838688,0.00003410798,0.001882674,0.0003628449,0.0002204306,0.000005998562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00021794,"about_ca_system_score_gemma":0.0001705496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005169074,"about_ca_topic_score_gemma":0.00003878144,"domain_scores_codex":[0.9982647,0.00006012025,0.0003394203,0.0005834867,0.0003533726,0.0003989192],"domain_scores_gemma":[0.9987838,0.000401333,0.00006594913,0.000371075,0.0001780338,0.0001998616],"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":[0.00001619846,0.0001418115,0.1685062,0.0001291471,0.00001789254,0.000103001,0.003345685,0.5563297,0.0001236648,0.0006175116,0.0007191501,0.26995],"study_design_scores_gemma":[0.0002464267,0.00006504726,0.1353526,0.0001129621,0.000001564101,0.00003263854,0.00002632896,0.8633642,0.00002577811,0.000003937446,0.0005612922,0.0002072523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1590526,0.0002596621,0.8395322,0.00004965471,0.0008051515,0.0000979594,0.000003541587,0.0001979805,0.000001322023],"genre_scores_gemma":[0.8989885,0.00001219583,0.1008484,0.00004055073,0.00006031111,0.00001848091,0.00001537266,0.0000102758,0.00000587775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7399359,"threshold_uncertainty_score":0.7844791,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2594215239","doi":"","title":"Local versus global models for effort-aware defect prediction","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Context (archaeology); Set (abstract data type); Machine learning; Training set; Data mining; Predictive modelling; Artificial intelligence; Data set; Software","retraction":null,"screen_n_in":null,"score":{"opus":0.01854812757945171,"gpt":0.2451937850721587,"spread":0.226645657492707,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008769109,0.0002257429,0.000188385,0.0002127628,0.000229335,0.000277526,0.001126262,0.00008179331,8.785858e-7],"category_scores_gemma":[0.0006300434,0.0001787116,0.00008151272,0.0008762568,0.0001819399,0.001698409,0.0006141711,0.00009349651,0.000006814242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003785658,"about_ca_system_score_gemma":0.0001971614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004898093,"about_ca_topic_score_gemma":8.899823e-7,"domain_scores_codex":[0.9976229,0.000009234963,0.0002063859,0.0007769184,0.0006611143,0.0007234306],"domain_scores_gemma":[0.99795,0.0009247294,0.00003045357,0.0004889238,0.0002865861,0.0003193352],"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":[0.00003911202,0.00004327097,0.007638492,0.0001491562,0.00006316222,0.00002391503,0.000243928,0.2062478,0.0002983192,0.01871846,0.0008257895,0.7657086],"study_design_scores_gemma":[0.0008706439,0.0002406427,0.01177886,0.00009283392,0.000005362932,0.00003157541,0.000001541683,0.9852489,0.0004323809,0.0004700795,0.0005558617,0.0002713428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.022112,0.0001474572,0.9747833,0.00009358884,0.00152789,0.0002502927,0.00001511003,0.001067222,0.000003150858],"genre_scores_gemma":[0.7903369,0.00001289403,0.2093948,0.00002709996,0.0001514037,0.00005492418,9.202387e-7,0.00001463479,0.000006339686],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7790011,"threshold_uncertainty_score":0.728765,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2594206172","doi":"","title":"IBM 2016 community hackathon","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"IBM; General partnership; Management; Business; Computer science; Political science; Economics; Finance","retraction":null,"screen_n_in":null,"score":{"opus":0.007798049523057883,"gpt":0.1853686060664849,"spread":0.1775705565434271,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005026511,0.0001259886,0.0001039654,0.0001508326,0.0001229051,0.00005852379,0.000272044,0.00004890206,0.000006551259],"category_scores_gemma":[0.0001477611,0.00009031506,0.00001771345,0.0003343743,0.0001256959,0.0003457803,0.00009790349,0.0001434138,0.00002372568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007987881,"about_ca_system_score_gemma":0.0000308479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003892113,"about_ca_topic_score_gemma":4.268444e-7,"domain_scores_codex":[0.9992394,0.000007193373,0.000117804,0.0001427894,0.0001929316,0.0002998408],"domain_scores_gemma":[0.9993634,0.0001417044,0.000007750061,0.0002456933,0.00005404208,0.0001874209],"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":[0.000001129217,0.00002669109,0.0008013519,0.0003195965,0.00001652567,0.000003931198,0.0007994229,0.004767199,0.0301713,0.0003952433,0.004457456,0.9582402],"study_design_scores_gemma":[0.001307304,0.0002535031,0.1790097,0.001856811,0.00002806002,0.000170931,0.00004446994,0.6801017,0.02624468,0.0006759235,0.108096,0.00221097],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2118379,0.0002704584,0.785771,0.00008032942,0.001253874,0.00003919862,0.000002191355,0.0007219783,0.00002307865],"genre_scores_gemma":[0.9611629,0.0001201477,0.03835404,0.00004931962,0.0002485952,0.000009662172,0.000001062097,0.00002103447,0.00003329695],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9560292,"threshold_uncertainty_score":0.3682941,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2395207450","doi":"","title":"Towards convenient management of software clone codes in practice: an integrated approach","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":3,"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":"clone (Java method); Software maintenance; Software development; Cloning (programming); Software engineering; Computer science; Software; Software system; Software evolution; Software construction; Programming language; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.02622532678153187,"gpt":0.2756197119736059,"spread":0.249394385192074,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003147811,0.0002596629,0.0003331667,0.0006706381,0.0000729802,0.00031342,0.001682703,0.00007683485,5.615838e-7],"category_scores_gemma":[0.001324699,0.0002498795,0.00003183406,0.002495257,0.0001988134,0.001958031,0.001062502,0.0003095889,0.000003142478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002658765,"about_ca_system_score_gemma":0.0003550737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006443116,"about_ca_topic_score_gemma":6.25931e-7,"domain_scores_codex":[0.9969183,0.00005754822,0.0003746385,0.0007798016,0.001251479,0.0006182266],"domain_scores_gemma":[0.9976978,0.0003174189,0.00007319625,0.0008039132,0.0006666993,0.0004410314],"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":[0.00004831854,0.001069408,0.02825201,0.0009396353,0.0001508045,0.0004954657,0.01068203,0.4237388,0.0002228414,0.01767361,0.0002599568,0.5164671],"study_design_scores_gemma":[0.0009029348,0.000343094,0.03068674,0.0002739378,0.00001227298,0.0001184747,0.0002319306,0.9652264,0.000764551,0.0001017082,0.0008011415,0.0005368404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1000277,0.0003481603,0.8984282,0.00004011729,0.0004121867,0.0002636931,0.000002220782,0.0004619065,0.000015826],"genre_scores_gemma":[0.3207818,0.00002519867,0.6790819,0.00003341496,0.00002914314,0.00002600332,0.000003118084,0.0000142655,0.000005231911],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5414876,"threshold_uncertainty_score":0.9999954,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2255008176","doi":"","title":"Towards understanding digital information discovery and curation","year":2014,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Information seeking; World Wide Web; Variety (cybernetics); Data science; Digital curation; Digital library; Data curation; Task (project management); Knowledge management; Information retrieval; Engineering; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.01335092478697681,"gpt":0.2072612776891846,"spread":0.1939103529022078,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0004919377,0.00008543474,0.00007473023,0.0002145356,0.0002239575,0.002381407,0.0002663824,0.000024357,2.702157e-7],"category_scores_gemma":[0.0002033529,0.00007431689,0.00001249429,0.0004157536,0.00007765349,0.01704636,0.0003001391,0.00007233344,0.000003825839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006571099,"about_ca_system_score_gemma":0.00005830593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001967455,"about_ca_topic_score_gemma":8.807791e-8,"domain_scores_codex":[0.9991403,0.000004820763,0.0001436248,0.0001418489,0.0003704825,0.0001989702],"domain_scores_gemma":[0.9995661,0.00005941795,0.00003262308,0.0001415818,0.0000945139,0.000105765],"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":[0.000001858132,0.000007564637,0.001491079,0.00006408813,0.000003253063,0.000001073026,0.002576947,0.002353763,0.00007716787,0.2904669,0.00005023114,0.7029061],"study_design_scores_gemma":[0.0001334127,0.00005865348,0.009712266,0.0000232312,0.000001008691,0.00002137224,0.0000193234,0.9879392,0.0001586038,0.0007356149,0.001051087,0.0001462285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04139985,0.000007984243,0.9579445,0.0001109867,0.0002724361,0.00006416893,0.000001114516,0.0001498281,0.00004916898],"genre_scores_gemma":[0.9427686,0.000005377235,0.05704951,0.000129146,0.00003706795,0.000002253649,0.000002312682,0.000001933579,0.000003741135],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9855855,"threshold_uncertainty_score":0.9986542,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2920574268","doi":"","title":"Predictive analytics in healthcare epileptic seizure recognition","year":2018,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Epilepsy; Epileptic seizure; Electroencephalography; Computer science; Artificial intelligence; Binary classification; Machine learning; Random forest; Predictive analytics; Pattern recognition (psychology); Support vector machine; Psychology; Psychiatry","retraction":null,"screen_n_in":null,"score":{"opus":0.01942698543849088,"gpt":0.2532644094628805,"spread":0.2338374240243896,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001253408,0.0001879704,0.000194239,0.0007477609,0.0001786315,0.0003082019,0.0009310642,0.00007765947,0.000002091121],"category_scores_gemma":[0.001330809,0.0001947429,0.00002648125,0.002502507,0.0002227387,0.00106931,0.0005855397,0.0003096988,0.00002498999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002302934,"about_ca_system_score_gemma":0.0002423443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002932903,"about_ca_topic_score_gemma":0.000006119087,"domain_scores_codex":[0.9977537,0.00002659877,0.0002375062,0.000684903,0.0006401039,0.0006571523],"domain_scores_gemma":[0.9982297,0.0004776576,0.00003427987,0.0005026448,0.0004792018,0.0002765337],"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":[0.00001676175,0.0001286809,0.165509,0.0003626218,0.00003953758,0.0002373312,0.005331718,0.02323658,0.000516643,0.001968321,0.0006897846,0.801963],"study_design_scores_gemma":[0.0002015075,0.0002484045,0.1521309,0.0001431302,0.000001788007,0.00005382772,0.000004646266,0.8458749,0.0006095195,0.0003077903,0.0001616477,0.0002619599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1423631,0.0001028093,0.8559678,0.0002452758,0.000690747,0.0001548204,0.000002233069,0.0004694858,0.000003702535],"genre_scores_gemma":[0.7877612,0.00001785555,0.2118055,0.0001548905,0.0002269894,0.00001531026,0.000001365281,0.00001274621,0.000004228437],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8226383,"threshold_uncertainty_score":0.7941384,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2240090265","doi":"","title":"Derive: finding semantic concepts with property-values from natural language text","year":2014,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Property (philosophy); Computer science; Natural language; Natural language processing; Property value; Information retrieval; Artificial intelligence; Matching (statistics); Semantic compression; Semantic computing; Semantic matching; Value (mathematics); Natural (archaeology); Semantic technology; Semantic Web; Mathematics; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.0070147636511661,"gpt":0.2232628817727366,"spread":0.2162481181215705,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000404321,0.0001905207,0.0002114967,0.0001448241,0.0002463937,0.000522493,0.0009661277,0.00003628157,0.000001461004],"category_scores_gemma":[0.0002585269,0.0001206082,0.00002397648,0.0004750137,0.0002094436,0.0009011534,0.0004511955,0.000147242,0.000008976166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003136355,"about_ca_system_score_gemma":0.00006681625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005468292,"about_ca_topic_score_gemma":0.000005765309,"domain_scores_codex":[0.9984604,0.00002254146,0.0001380529,0.0005614455,0.0004019723,0.0004155927],"domain_scores_gemma":[0.9990266,0.0002796948,0.00004313817,0.0004351864,0.00009736499,0.0001180281],"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":[0.00000739285,0.00004034002,0.02067812,0.0001087164,0.00004815097,0.0001322981,0.01475584,0.007233896,0.006588086,0.003536107,0.0003146814,0.9465564],"study_design_scores_gemma":[0.000265339,0.00009180928,0.03949507,0.0001493693,0.00000582555,0.00005254701,0.00005478137,0.9561069,0.003012754,0.00006973092,0.0003648498,0.0003310246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3257885,0.0003483376,0.6728804,0.00009686583,0.0004554162,0.00006668569,3.05513e-7,0.0003513559,0.00001219333],"genre_scores_gemma":[0.7088639,0.000004148702,0.2908104,0.0001826306,0.0001080005,0.000003686485,6.542299e-7,0.000006541601,0.0000199852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.948873,"threshold_uncertainty_score":0.5038412,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2782642056","doi":"","title":"IoT for remote wireless electrophysiological monitoring: proof of concept","year":2017,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Upload; Proof of concept; Cloud computing; Wireless; Analytics; Cardiac monitoring; Big data; IBM; Real-time computing; Embedded system; Database; Telecommunications; Data mining; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.02249263054319061,"gpt":0.2777660592962912,"spread":0.2552734287531006,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002356096,0.00008717756,0.0002219628,0.00007063129,0.000235682,0.00005686941,0.0002147162,0.00003730628,3.150174e-7],"category_scores_gemma":[0.000369287,0.00006993385,0.0000516422,0.00009583088,0.0001786368,0.00007850947,0.00009562969,0.00008171069,1.304531e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002439159,"about_ca_system_score_gemma":0.00005481075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007848865,"about_ca_topic_score_gemma":4.594795e-8,"domain_scores_codex":[0.9992444,0.000002729239,0.0001178015,0.0002334114,0.000190507,0.0002111364],"domain_scores_gemma":[0.9992751,0.00006256547,0.00005800594,0.0003050903,0.0001993566,0.00009990067],"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":[0.00003789751,0.00005267066,0.01978599,0.0003056049,0.00005820947,0.00001442384,0.000271117,0.003613469,0.08968821,0.00008422869,0.00002689431,0.8860613],"study_design_scores_gemma":[0.0005650854,0.0007213675,0.07944702,0.0004471015,0.00004782719,0.00001601922,0.000007540021,0.5926173,0.3257323,0.00005171025,0.0001456168,0.0002011211],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5824577,0.00006869246,0.4171071,0.00003160361,0.0002121696,0.00008764736,5.519887e-7,0.00003313609,0.000001366553],"genre_scores_gemma":[0.8840044,0.000006524193,0.1155341,0.000006671329,0.0004176774,0.000003385794,4.316108e-7,0.000005724313,0.00002111638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8858601,"threshold_uncertainty_score":0.2851819,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2397549142","doi":"","title":"Automated classification of congestive heart failure severity using time domain, frequency domain and non-linear heart rate variability measures","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Lakeridge Health","funders":"","keywords":"Heart failure; Heart rate variability; Classifier (UML); Medicine; Binary classification; Failure rate; Frequency domain; Artificial intelligence; Heart disease; Time domain; Computer science; Internal medicine; Cardiology; Heart rate; Statistics; Mathematics; Blood pressure; Support vector machine","retraction":null,"screen_n_in":null,"score":{"opus":0.02057714870642484,"gpt":0.2520372712234916,"spread":0.2314601225170667,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003601976,0.0001624419,0.0003970229,0.0001272972,0.0001162598,0.0000529451,0.00008882788,0.00009436019,0.000002642309],"category_scores_gemma":[0.0008320963,0.0001490423,0.00003369331,0.0004006127,0.000352102,0.0003356093,0.00009081393,0.0001676791,0.000002794172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000153859,"about_ca_system_score_gemma":0.0006247164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004649584,"about_ca_topic_score_gemma":0.000001137202,"domain_scores_codex":[0.998648,0.00008747247,0.0003082451,0.0004330422,0.0002524922,0.0002707398],"domain_scores_gemma":[0.9986358,0.000235859,0.00005212484,0.0002951288,0.0004759754,0.0003051641],"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":[0.00004397296,0.0001690615,0.07759731,0.0003504587,0.0000651509,0.00001123702,0.003098833,0.006698628,0.9091808,0.0003449346,0.0001753268,0.002264353],"study_design_scores_gemma":[0.0006554388,0.0001150612,0.2133984,0.0001137514,0.00001892956,0.00005871852,0.00003182002,0.7836013,0.001258769,0.0002372839,0.0003491371,0.0001613369],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7173358,0.00003178755,0.2816705,0.0004237085,0.0001083396,0.0002643029,0.000005831014,0.0001570302,0.000002647869],"genre_scores_gemma":[0.7210225,7.487335e-7,0.2788219,0.00007958645,0.00006069792,0.000004135766,0.000002312664,0.000007285162,8.79912e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.907922,"threshold_uncertainty_score":0.6077767,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2593956379","doi":"","title":"A Bayesian game decision-making model for uncertain adversary types","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":3,"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":"Adversary; Computer science; Computer security; Bayesian game; Game theory; Adversary model; Countermeasure; Threat model; Harm; Attack model; Order (exchange); Sequential game; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.009164843191108328,"gpt":0.2462645773831413,"spread":0.237099734192033,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005026615,0.0001609997,0.0001500864,0.0003056925,0.0001736965,0.0001585914,0.000845634,0.00004805902,7.433117e-7],"category_scores_gemma":[0.0006149515,0.0001237557,0.00004268109,0.0005218025,0.00009410959,0.001416385,0.0004579572,0.00006369027,0.000001706712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001181843,"about_ca_system_score_gemma":0.0001201179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.987112e-7,"about_ca_topic_score_gemma":5.63731e-7,"domain_scores_codex":[0.9985422,0.0000056772,0.0001790446,0.0005739757,0.0003082953,0.0003907623],"domain_scores_gemma":[0.9986196,0.0005536207,0.00004817171,0.0004390126,0.0002142225,0.0001254233],"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":[0.000003505898,0.000006194449,0.00005484319,0.00001493999,0.000003203713,0.000004649903,0.0002024334,0.05224668,0.0007901564,0.00322908,0.00008514324,0.9433592],"study_design_scores_gemma":[0.0001296412,0.00004799938,0.0001588107,0.0001653414,0.0000016415,0.00002332858,0.000001245986,0.9885769,0.001075552,0.009168138,0.0004409132,0.0002104305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002046596,0.00007705473,0.9963311,0.00009235452,0.0003653971,0.0001775165,0.000002491293,0.0009047526,0.000002756941],"genre_scores_gemma":[0.4044365,0.000008281082,0.5953633,0.0001117228,0.00004178772,0.00002174101,5.980231e-8,0.000008177311,0.000008365906],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9431487,"threshold_uncertainty_score":0.5046612,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2294525553","doi":"","title":"Evolutionary analysis of access control models: a formal concept analysis method","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Access Control and Trust","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta; Polytechnique Montréal","funders":"","keywords":"Role-based access control; Computer science; Access control; Formal concept analysis; Pairwise comparison; Suite; Software engineering; Database; Distributed computing; Computer security; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.03678827920979394,"gpt":0.3110880546820225,"spread":0.2742997754722286,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002759728,0.0001114638,0.0004006748,0.0009485272,0.0002906994,0.0002088771,0.0006607973,0.00005406239,0.00001110822],"category_scores_gemma":[0.0004267665,0.0001022716,0.0001387986,0.005466796,0.0003117172,0.002169305,0.0001972868,0.00007919626,3.450585e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001116717,"about_ca_system_score_gemma":0.0003830423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009201065,"about_ca_topic_score_gemma":0.00008921143,"domain_scores_codex":[0.9980851,0.00007506167,0.0002314009,0.0003189408,0.0009086928,0.0003807945],"domain_scores_gemma":[0.9983613,0.0002786152,0.00009041598,0.0002030663,0.0007820793,0.0002845031],"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":[0.00000445269,0.00001423233,0.01762666,0.000003012872,0.0005766047,0.000001688827,0.003017387,0.9484713,0.00000162912,0.005376282,0.0000250166,0.0248817],"study_design_scores_gemma":[0.0002793806,0.00001438451,0.02775519,0.000004065093,0.001058447,3.684194e-7,0.0001302045,0.9702146,0.000003773934,0.0002400085,0.0001758546,0.0001237286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02207072,0.000577499,0.9767383,0.00007012874,0.0001871973,0.0001253143,0.00001287717,0.00008326419,0.0001346484],"genre_scores_gemma":[0.9518726,0.000008398705,0.0479304,0.00007503748,0.00008910981,0.00001121395,0.000003781576,0.000003487482,0.00000594622],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9298019,"threshold_uncertainty_score":0.4170514,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2256317631","doi":"","title":"Chorus: an interactive approach to incremental modeling and validation in clouds","year":2014,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"IBM (Canada); University of Toronto","funders":"","keywords":"Chorus; Computer science; Reuse; Workload; Adaptation (eye); Latency (audio); Distributed computing; Operating system; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01176378729865309,"gpt":0.2183714554991911,"spread":0.206607668200538,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001239998,0.0001335686,0.0001322201,0.0003351864,0.0001521007,0.0004423879,0.0005699434,0.00002514849,9.828724e-8],"category_scores_gemma":[0.00009060424,0.0001291955,0.00001225235,0.0006015602,0.00003228939,0.0002745376,0.0008902118,0.0001170494,0.000001127331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006743239,"about_ca_system_score_gemma":0.00001721287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003155153,"about_ca_topic_score_gemma":7.060681e-7,"domain_scores_codex":[0.9986502,0.00003044396,0.0001616891,0.0005741942,0.000291498,0.0002920064],"domain_scores_gemma":[0.9994068,0.00005798872,0.00002334111,0.0002879777,0.00005645991,0.0001674527],"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":[0.000001260612,0.00003000407,0.0006989157,0.00001754291,0.000002464685,0.000001015364,0.002813054,0.8659655,0.0001311281,0.002095669,0.000004436406,0.128239],"study_design_scores_gemma":[0.0001458296,0.00007116126,0.003104062,0.0000468242,0.000001176861,0.00001004898,0.00003214278,0.9961494,0.0001225195,0.00008153446,0.00006525105,0.0001700122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4547862,0.0000108716,0.5448546,0.00002955361,0.0001294551,0.00007056016,6.695587e-8,0.00009703942,0.00002166433],"genre_scores_gemma":[0.7254556,8.123627e-7,0.2743461,0.000115691,0.00006951512,0.000005466162,3.793188e-7,0.000004768865,0.000001665681],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2706694,"threshold_uncertainty_score":0.5268438,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2990277257","doi":"","title":"How can OpenShift accelerate your Kubernetes adoption: a workshop exploring OpenShift features","year":2019,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"IBM (Canada)","funders":"","keywords":"IBM; Software deployment; Cloud computing; Computer science; Cluster (spacecraft); Distribution (mathematics); Software engineering; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.03141398135787203,"gpt":0.2182789379308355,"spread":0.1868649565729634,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008448499,0.0003139768,0.0002895231,0.0003482988,0.0003763321,0.003007989,0.001965376,0.00005820797,0.000002035227],"category_scores_gemma":[0.0001279155,0.0002830075,0.00006438745,0.001201,0.00008676274,0.0005590454,0.002221126,0.0003134793,0.00001333108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008872345,"about_ca_system_score_gemma":0.00008134636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001507608,"about_ca_topic_score_gemma":0.000002386545,"domain_scores_codex":[0.9973915,0.00002760468,0.0002016392,0.0009766468,0.0006847503,0.0007178879],"domain_scores_gemma":[0.9984831,0.0001771652,0.00007442637,0.00083127,0.0001631744,0.0002708959],"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":[0.00000743232,0.0000693554,0.008584905,0.0002629826,0.00007474089,0.0001417337,0.008214002,0.3851496,0.0005545949,0.02241202,0.001205833,0.5733228],"study_design_scores_gemma":[0.0005701117,0.0001402435,0.04536526,0.0003927673,0.00001055422,0.00009000371,0.0001729063,0.9423766,0.0008102668,0.000131681,0.008961155,0.0009784377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5262724,0.0003281621,0.4694913,0.001344794,0.001676588,0.0002605763,5.939413e-7,0.0005727797,0.0000528667],"genre_scores_gemma":[0.8786342,0.00001701926,0.120244,0.0003748627,0.0003022317,0.00002062742,7.729548e-7,0.00001952286,0.0003866975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5723444,"threshold_uncertainty_score":0.9999622,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2783639327","doi":"","title":"Transfer learning in neural networks: an experience report","year":2017,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Music and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Computer science; Task (project management); Transfer of learning; Spectrogram; Artificial neural network; Reuse; Artificial intelligence; Machine learning; Domain (mathematical analysis); Deep learning; Task analysis; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01575283095875398,"gpt":0.2438392234490922,"spread":0.2280863924903382,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008666579,0.0001478234,0.0001597411,0.0001443827,0.0007560612,0.001486542,0.001461322,0.00004062737,9.386568e-7],"category_scores_gemma":[0.0002262184,0.0001418581,0.00002107797,0.0002982162,0.0001953077,0.004840375,0.0004471566,0.0002289099,5.169294e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000304021,"about_ca_system_score_gemma":0.00007159851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001989153,"about_ca_topic_score_gemma":0.00000257629,"domain_scores_codex":[0.9983981,0.00001291003,0.0002026085,0.0006215103,0.0003235186,0.0004413871],"domain_scores_gemma":[0.9990137,0.00004338345,0.00004727421,0.0006373169,0.00008378153,0.0001744966],"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":[0.000001208843,0.00001760867,0.03077814,0.00002016321,0.000001662145,0.0003986831,0.003181816,0.3189258,0.0003571475,0.0008278471,0.000006871612,0.645483],"study_design_scores_gemma":[0.0001052232,0.00002831396,0.04699691,0.00004650042,6.243679e-7,0.0001468397,0.000008478869,0.9520258,0.0001805835,0.00001710398,0.0002537683,0.0001898452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3598185,0.00007256825,0.6393496,0.00009323271,0.0004616195,0.00003656191,2.896676e-8,0.0001594638,0.000008461767],"genre_scores_gemma":[0.922003,0.000006299919,0.07763229,0.0001988189,0.0001365548,0.000006678746,2.596925e-7,0.000006877579,0.000009232424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6452932,"threshold_uncertainty_score":0.99955,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2592247214","doi":"","title":"String deduplication during garbage collection in virtual machines","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Distributed systems and fault tolerance","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"IBM (Canada); University of New Brunswick","funders":"","keywords":"Garbage collection; Manual memory management; Computer science; Memory leak; Garbage; Heap (data structure); Copying; Memory management; Data deduplication; Operating system; Java; Virtual machine; Database; Virtual memory; Storage management; Python (programming language); Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.004434958098193664,"gpt":0.1909042989907788,"spread":0.1864693408925851,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004994373,0.0001238335,0.0001290357,0.0002796971,0.0001677585,0.0002257466,0.0005230774,0.00003627382,7.175814e-7],"category_scores_gemma":[0.0001340064,0.00009770577,0.00001888212,0.001019708,0.00005008521,0.001206311,0.0002575284,0.00007039218,0.000004462386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001373213,"about_ca_system_score_gemma":0.00005985242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001277732,"about_ca_topic_score_gemma":0.000003728534,"domain_scores_codex":[0.9987249,0.00001222309,0.0002014144,0.0004615647,0.0002809284,0.0003189546],"domain_scores_gemma":[0.999381,0.00008436466,0.00004226511,0.0002992209,0.00009062178,0.0001025228],"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":[0.000009818157,0.00008311266,0.05598118,0.0001156008,0.00001364359,0.00005817676,0.001795308,0.02198293,0.05381637,0.03300463,0.0001433511,0.8329959],"study_design_scores_gemma":[0.0005309205,0.0000465239,0.2369383,0.0002410713,9.87233e-7,0.00006196745,0.000003796062,0.7580689,0.003403346,0.00008411196,0.000320475,0.000299642],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3797188,0.00005483366,0.619629,0.00006786153,0.0003093785,0.00006419341,6.689801e-7,0.0001521152,0.0000032024],"genre_scores_gemma":[0.9695452,0.00001453544,0.03030145,0.00002301445,0.00006948152,0.00001537399,2.609276e-7,0.000005629859,0.00002504715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8326963,"threshold_uncertainty_score":0.3984326,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2592670932","doi":"","title":"Evaluation of Python-based tools for distributed computing on the Raspberry Pi","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"","keywords":"Python (programming language); Computer science; Factorization; Raspberry pi; Parallel computing; Algorithm; Mathematics; Operating system; Embedded system; Internet of Things","retraction":null,"screen_n_in":null,"score":{"opus":0.04019562558123467,"gpt":0.2567557795302319,"spread":0.2165601539489972,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005994907,0.0001721454,0.000198177,0.0001398102,0.0002752842,0.0003315834,0.001068544,0.00004345914,0.000001115558],"category_scores_gemma":[0.002358056,0.0001046105,0.00006455403,0.0006692698,0.0001275537,0.0004392204,0.0002201637,0.00006880984,0.000002481413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001164446,"about_ca_system_score_gemma":0.0003079595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000207371,"about_ca_topic_score_gemma":1.939691e-7,"domain_scores_codex":[0.9977712,0.00008333836,0.0002965432,0.0004697121,0.001000569,0.0003786077],"domain_scores_gemma":[0.9957305,0.002595064,0.0001321734,0.0005458451,0.0008990798,0.00009736865],"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":[0.00000651817,0.00004887102,0.0004823573,0.00006201328,0.00002683053,0.000001387739,0.0002924966,0.3620195,0.001195615,0.01470186,0.0005723222,0.6205902],"study_design_scores_gemma":[0.0004671672,0.000127448,0.004096487,0.0002729822,0.000009406735,0.000004306924,0.000003072762,0.9925226,0.00143957,0.0002535033,0.0006354832,0.000168002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0675676,0.00005683137,0.9306514,0.0004096479,0.0007190477,0.0003714778,0.00001811017,0.0001985993,0.000007239712],"genre_scores_gemma":[0.9502815,7.444687e-7,0.04947239,0.00009860573,0.0001163561,0.00001828321,0.000003114716,0.000007808979,0.000001206486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8827139,"threshold_uncertainty_score":0.4265892,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2296390200","doi":"","title":"GitHub's big data adaptor: an eclipse plugin","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Plug-in; Computer science; Eclipse; Parsing; World Wide Web; Source code; Variety (cybernetics); Database; Operating system; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.06667506117065905,"gpt":0.2417801940471921,"spread":0.1751051328765331,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020128,0.000203321,0.000182001,0.0002851133,0.000235977,0.0008171967,0.003565979,0.00004064141,3.211009e-7],"category_scores_gemma":[0.0002340783,0.0001843276,0.00001987462,0.000944559,0.0001286247,0.0004568253,0.004021823,0.0001583597,0.00001150324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007154254,"about_ca_system_score_gemma":0.0001933362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002208271,"about_ca_topic_score_gemma":0.000002241937,"domain_scores_codex":[0.9975857,0.0000286039,0.0002060605,0.0009473185,0.0007267515,0.0005055491],"domain_scores_gemma":[0.9974105,0.00008471492,0.00004881326,0.001784952,0.000164306,0.000506664],"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":[0.000001999461,0.00006514053,0.0008455233,0.00002910225,0.0000141961,0.00005993357,0.001931432,0.100403,0.00003225204,0.00329789,0.003896968,0.8894226],"study_design_scores_gemma":[0.0002043324,0.00008933235,0.001368991,0.00003275775,0.000003520035,0.00004320524,0.00001929876,0.9770683,0.00003450402,0.0001093078,0.02075279,0.0002736775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1057172,0.000148746,0.8908252,0.0001888572,0.002360176,0.00009106431,0.00000185061,0.0006162068,0.00005070685],"genre_scores_gemma":[0.5170368,0.000004459894,0.4803395,0.0003945853,0.002153162,0.000004100266,0.000003928663,0.00001805782,0.00004537471],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8891489,"threshold_uncertainty_score":0.7880247,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2911278647","doi":"","title":"2nd workshop on DevOps and software analytics for continuous engineering and improvement","year":2018,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; École de Technologie Supérieure; University of Victoria","funders":"","keywords":"DevOps; Software deployment; Software engineering; Computer science; IBM; Toolchain; Software analytics; Software development; Software; Analytics; Software system; Data science; Software construction; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.01444956978332487,"gpt":0.2449149988382957,"spread":0.2304654290549708,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001116741,0.0003572267,0.0003361078,0.0005591021,0.0003585714,0.0008300581,0.0008040776,0.0001015541,0.000001101141],"category_scores_gemma":[0.002462654,0.0003528644,0.00004018955,0.0009071865,0.0002560767,0.0007210264,0.0009286174,0.0002554568,0.000002366524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001248217,"about_ca_system_score_gemma":0.0001043733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000772729,"about_ca_topic_score_gemma":0.000002153094,"domain_scores_codex":[0.9972816,0.000008572469,0.0002871206,0.0009930829,0.0005750238,0.0008546289],"domain_scores_gemma":[0.9970288,0.00148047,0.00005108773,0.000608484,0.000390279,0.000440923],"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":[0.00002471028,0.00008875785,0.01595207,0.0006233936,0.0001169073,0.00005581501,0.002536127,0.02195855,0.003187291,0.006150365,0.0006624894,0.9486435],"study_design_scores_gemma":[0.0006156501,0.0005971808,0.02589053,0.0002642552,0.00001173638,0.00006339552,0.00001038807,0.9669466,0.001634253,0.00009830607,0.00322316,0.000644526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2108153,0.0003059066,0.7872738,0.00009814603,0.0006601593,0.0003091336,0.000003814869,0.0005325204,0.000001145372],"genre_scores_gemma":[0.471721,0.00005065876,0.5275801,0.0001796781,0.0003575061,0.00004710214,0.000001359077,0.00003850712,0.00002404338],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.947999,"threshold_uncertainty_score":0.9998924,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2591988823","doi":"","title":"TGDB: towards a benchmark for graph databases","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Computer science; Graph database; Wait-for graph; Graph; Theoretical computer science; Data mining; Database","retraction":null,"screen_n_in":null,"score":{"opus":0.01322364605018729,"gpt":0.2217163676464122,"spread":0.2084927215962249,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009405194,0.0001585499,0.0001463104,0.000270648,0.0002580357,0.0002065855,0.0009789782,0.00002374393,0.000001910063],"category_scores_gemma":[0.0003003413,0.0001104267,0.00005175005,0.0006819555,0.0001744799,0.001439896,0.0004799968,0.0000497799,0.000003049812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002381112,"about_ca_system_score_gemma":0.00009120815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002012273,"about_ca_topic_score_gemma":2.643883e-7,"domain_scores_codex":[0.9985439,0.0000107897,0.0001500235,0.0005600529,0.0002925657,0.000442664],"domain_scores_gemma":[0.9988312,0.0003106274,0.00003277336,0.0004791304,0.0001563001,0.0001900001],"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":[0.000002494918,0.00002315059,0.0004944,0.0000385922,0.00001103879,0.00000963266,0.0003078642,0.0002676735,0.001830239,0.1058448,0.0003088768,0.8908612],"study_design_scores_gemma":[0.002651265,0.0007919981,0.03355157,0.0008459975,0.00002902369,0.0002909874,0.00001937724,0.8518347,0.03072284,0.03762075,0.0390842,0.002557343],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01613142,0.0001424542,0.9821171,0.000193005,0.0009455078,0.0001303235,0.00001012346,0.0003241762,0.000005930298],"genre_scores_gemma":[0.2356519,0.00002213477,0.7639236,0.0001967133,0.0001521703,0.00002766486,9.517843e-7,0.000008626116,0.00001623363],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8883039,"threshold_uncertainty_score":0.4503071,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2782661736","doi":"","title":"A framework to extract personalized behavioural patterns of user's IoT devices data","year":2017,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Green IT and Sustainability","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"IBM (Canada); Queen's University","funders":"","keywords":"Computer science; Internet of Things; Big data; Embedded system; Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.03518693965766743,"gpt":0.276288500633791,"spread":0.2411015609761235,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004978812,0.0001435816,0.0001907265,0.0001072479,0.0002070482,0.0002713958,0.001270166,0.00004978135,0.000005533492],"category_scores_gemma":[0.0004899101,0.0001400758,0.00002468331,0.0001193204,0.00009706422,0.00053822,0.0006079813,0.0001438446,0.000001407206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004202903,"about_ca_system_score_gemma":0.00003768256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006073527,"about_ca_topic_score_gemma":0.000013766,"domain_scores_codex":[0.9989297,0.000004709395,0.0001596224,0.0003159474,0.0002761601,0.0003138582],"domain_scores_gemma":[0.9985788,0.0001093859,0.00002942809,0.001001589,0.0001077184,0.0001730903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004704834,0.00002605899,0.8660445,0.0004194104,0.00002555337,0.00003119721,0.002317409,0.01630194,0.0002817579,0.0003381784,0.0000948049,0.1141145],"study_design_scores_gemma":[0.0001078352,0.00002752041,0.7507831,0.0001538061,0.000009644957,0.000007674596,0.00003710793,0.2476409,0.0001465418,0.00001837249,0.0008370069,0.0002304652],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5592064,0.00006430789,0.4401896,0.00002267485,0.0003484464,0.00006301707,0.00001192446,0.00009249033,0.000001174829],"genre_scores_gemma":[0.9015859,0.000004726002,0.09829213,0.00001619807,0.00007893384,0.00000399708,0.000001993281,0.00001260012,0.000003541334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3423795,"threshold_uncertainty_score":0.5712127,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2594297796","doi":"","title":"An exploratory study on change suggestions for methods using clone detection","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Ranking (information retrieval); Computer science; Change detection; Precision and recall; Rank (graph theory); Change analysis; Information retrieval; Data mining; Complement (music); Software evolution; Recall; Data science; Software; Machine learning; Artificial intelligence; Software system; Cognitive psychology; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.08106462202636616,"gpt":0.3533319726558519,"spread":0.2722673506294858,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002498545,0.0002042841,0.0001846919,0.0006238005,0.0003510455,0.0003079949,0.000916043,0.00005207105,3.971822e-7],"category_scores_gemma":[0.001234958,0.0001638576,0.00003401406,0.001032176,0.00008408642,0.001859647,0.0003216733,0.0001208677,0.000002714781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001926936,"about_ca_system_score_gemma":0.00008973447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004730903,"about_ca_topic_score_gemma":0.000001274211,"domain_scores_codex":[0.9979937,0.00006555999,0.0001855115,0.000754388,0.0004756441,0.0005251847],"domain_scores_gemma":[0.9974067,0.001244368,0.0000362537,0.0007323254,0.0003030349,0.0002772898],"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":[0.000007224188,0.0001781727,0.005207914,0.00003740465,0.00001896663,0.00001375862,0.003346312,0.006168272,0.04846082,0.0004559901,0.000008076165,0.9360971],"study_design_scores_gemma":[0.000660621,0.001278504,0.06189634,0.000121619,0.000008264348,0.0000300249,0.00003582758,0.9186059,0.01649772,0.00009429998,0.0002331956,0.0005376767],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3124057,0.00003575586,0.6857545,0.00003012885,0.0008647245,0.0003563439,0.000001446249,0.0005512875,7.225994e-8],"genre_scores_gemma":[0.5851961,0.000002316867,0.4143984,0.00002697085,0.0002520008,0.0001071592,1.304799e-7,0.00001590115,0.000001003452],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9355594,"threshold_uncertainty_score":0.668192,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2394967315","doi":"","title":"Data-dependence profiling to enable safe thread level speculation","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Speculation; Compiler; Speculative execution; Parallel computing; Profiling (computer programming); Speculative multithreading; Thread (computing); Cache; Spec#; Supercomputer; Multithreading; Operating system; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.08294887851926361,"gpt":0.2845879590408981,"spread":0.2016390805216345,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001698208,0.0001555974,0.0001469557,0.0003522716,0.0001872727,0.0006762954,0.00191703,0.00003987114,4.509303e-7],"category_scores_gemma":[0.0005634849,0.0001524512,0.00001331169,0.001266421,0.00004775645,0.001697567,0.001841861,0.0001152893,0.00001512615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008585201,"about_ca_system_score_gemma":0.0002556063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000013537,"about_ca_topic_score_gemma":8.383399e-7,"domain_scores_codex":[0.9981006,0.00001716898,0.0002072801,0.0007121601,0.0005892989,0.0003734579],"domain_scores_gemma":[0.9983103,0.00007831235,0.00004700299,0.0008875988,0.0003578597,0.0003189413],"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":[0.000003181058,0.00003487253,0.002587849,0.00003315997,0.000008664645,0.00002097442,0.001106009,0.8511958,0.0005182254,0.021722,0.002364294,0.120405],"study_design_scores_gemma":[0.00009289249,0.00004726236,0.0008854506,0.00004067433,0.000001482744,0.00002917425,0.000003587081,0.9947913,0.0017565,0.0005026148,0.001624792,0.0002243045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004680281,0.00009744361,0.9935416,0.0001508486,0.00055203,0.0001622433,0.000003387259,0.0007585112,0.00005368546],"genre_scores_gemma":[0.1225315,0.000004019467,0.8771047,0.000201806,0.0001185178,0.000004986161,0.000004256284,0.000007629683,0.00002261152],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1435955,"threshold_uncertainty_score":0.6521532,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2400661058","doi":"","title":"Social computing and intelligence: exploring opportunities for the public and the enterprise","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Business intelligence; Collective intelligence; Crowds; Computer science; Social media analytics; Data science; Business analytics; Analytics; Social media; Knowledge management; Competitive intelligence; Intelligence cycle; World Wide Web; Military intelligence; Business model; Business; Computer security; Marketing; Electronic business; Political science","retraction":null,"screen_n_in":null,"score":{"opus":0.1029287736649534,"gpt":0.2824200417871031,"spread":0.1794912681221497,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001162705,0.00009664649,0.000126643,0.00006414631,0.0004881393,0.000447539,0.0002259106,0.000009237045,5.461176e-7],"category_scores_gemma":[0.0000417324,0.00006146247,0.0000275103,0.0001713968,0.0003581121,0.0002809154,0.0003885383,0.0000826289,7.680759e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001165893,"about_ca_system_score_gemma":0.00003673377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001137385,"about_ca_topic_score_gemma":4.919441e-7,"domain_scores_codex":[0.9993469,0.00001443351,0.0001204411,0.0001745267,0.00014446,0.0001992657],"domain_scores_gemma":[0.9992654,0.0003998344,0.00003594341,0.0001067416,0.000115824,0.00007628168],"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":[0.000004176529,0.000006614006,0.004028214,0.00001274762,0.00003554911,5.229894e-7,0.004982702,0.0009553741,0.000002674274,0.0643688,0.0002995503,0.9253031],"study_design_scores_gemma":[0.0001802987,0.00001795189,0.0007986011,0.00002177227,0.00001902346,0.000003797789,0.001124003,0.991511,0.00003086071,0.002293801,0.003866941,0.0001319175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07957435,0.0003155908,0.9192733,0.0005337601,0.0001109101,0.0001225534,7.764161e-7,0.00006078108,0.000007984961],"genre_scores_gemma":[0.985043,0.0000202725,0.01449625,0.00005265797,0.0003505881,0.00002652289,7.379535e-7,0.000006061066,0.00000391036],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9905556,"threshold_uncertainty_score":0.4315629,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2266492292","doi":"","title":"Engineering cyber physical systems","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University; University of Toronto; University of Victoria","funders":"","keywords":"Cyber-physical system; Transformative learning; Computer science; Context (archaeology); Wearable computer; Wearable technology; Systems engineering; Data science; Engineering; Embedded system","retraction":null,"screen_n_in":null,"score":{"opus":0.02132170453173842,"gpt":0.2197604505631107,"spread":0.1984387460313723,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001061569,0.0002221061,0.0002745263,0.0003234258,0.000115498,0.0008569551,0.0009099176,0.00004763495,3.352019e-7],"category_scores_gemma":[0.0004004637,0.0002170647,0.00004276833,0.001014221,0.00006613447,0.001906833,0.0006314756,0.0001798143,0.0000487207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001524618,"about_ca_system_score_gemma":0.000187962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000225404,"about_ca_topic_score_gemma":3.407979e-7,"domain_scores_codex":[0.9979481,0.00002261089,0.0002048827,0.0006102564,0.000736681,0.0004774605],"domain_scores_gemma":[0.998347,0.0002435997,0.00005200629,0.0005248312,0.0003837864,0.0004487273],"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":[0.000008850673,0.0003227654,0.005996758,0.0004999666,0.0001439824,0.0003417011,0.01679629,0.3698469,0.006034521,0.04232582,0.004255489,0.5534269],"study_design_scores_gemma":[0.0002054741,0.00004645634,0.001147204,0.00007665892,0.000002958373,0.000166485,0.00001154449,0.9929894,0.0003293353,0.0000232995,0.004697735,0.0003034418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08805965,0.0001752782,0.9086654,0.00005519818,0.002112041,0.0001503988,0.000001185337,0.0007524311,0.00002838115],"genre_scores_gemma":[0.9398137,0.00000178904,0.05961774,0.00005397664,0.0004492301,0.00002494453,5.877131e-7,0.00001566758,0.00002230154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8517541,"threshold_uncertainty_score":0.8851643,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}