{"meta":{"query_hash":"3ede270ed8e3","filters":{"venue":"The R Journal"},"cohort_total":21,"direct_labels_cover":0,"predictions_cover":21,"exported":21,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/3ede270ed8e3","api":"https://metacan.xera.ac/api/v1/cohort?venue=The+R+Journal"},"results":[{"id":"W1489432211","doi":"10.32614/rj-2012-009","title":"Who Did What? The Roles of R Package Authors and How to Refer to Them","year":2012,"lang":"en","type":"article","venue":"The R Journal","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; Western University","funders":"","keywords":"Disk formatting; R package; Computer science; Citation; Software engineering; Software package; Programming language; Data science; Software; World Wide Web; Operating system","score_opus":0.17612433853131018,"score_gpt":0.3762795612284366,"score_spread":0.2001552226971264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1489432211","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9364158,0.0004480969,0.012672863,0.04724409,0.0014765193,0.00012895168,0.0000052860023,0.000008330565,0.0016000612],"genre_scores_gemma":[0.9929477,0.000043777123,0.00046030185,0.00075621007,0.00027508137,8.354293e-7,1.445293e-7,0.0000044928347,0.005511471],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978814,0.00040204055,0.00024383771,0.0001543189,0.0010496553,0.00026872233],"domain_scores_gemma":[0.99790907,0.00080024556,0.00015536748,0.00080236053,0.000122059675,0.0002109194],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.018899342,0.00007886884,0.00013294589,0.00011119151,0.0004373943,0.0012777258,0.0012650044,0.000016762777,0.00014859546],"category_scores_gemma":[0.0015101868,0.000031037656,0.000048199618,0.0005132237,0.00007663302,0.000496661,0.0007058569,0.00015411561,0.00018206319],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038938775,0.000028306205,0.003391589,0.0000013838377,0.000023330456,0.0000011827217,0.021485327,0.00012098286,0.0005057171,0.0019537571,0.5240148,0.4484347],"study_design_scores_gemma":[0.0002438758,0.00010722056,0.13145536,0.000124981,0.0000521324,0.00015410599,0.06732829,0.00052772794,0.0015534892,0.0113652265,0.786872,0.00021557664],"about_ca_topic_score_codex":0.0000036265446,"about_ca_topic_score_gemma":0.000014718024,"teacher_disagreement_score":0.44821912,"about_ca_system_score_codex":0.000011369899,"about_ca_system_score_gemma":0.000012300725,"threshold_uncertainty_score":0.999759},"labels":[],"label_agreement":null},{"id":"W2133766087","doi":"10.32614/rj-2009-005","title":"expert: Modeling Without Data Using Expert Opinion","year":2009,"lang":"en","type":"article","venue":"The R Journal","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Expert opinion; Computer science; Casual; Selection (genetic algorithm); Section (typography); Artificial intelligence; Data science; Information retrieval; Operations research; Mathematics","score_opus":0.22595749478580054,"score_gpt":0.3198424119767767,"score_spread":0.09388491719097616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133766087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18805027,0.095863126,0.6972421,0.0065429816,0.0018444002,0.00022804922,0.000061176455,0.00006276847,0.010105124],"genre_scores_gemma":[0.9935584,0.002349906,0.0023227774,0.000409779,0.001182966,3.6810775e-7,0.0000063642688,0.00001398231,0.00015547521],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989784,0.000029994726,0.00052736676,0.00018293793,0.000053437823,0.0002278588],"domain_scores_gemma":[0.9989515,0.000013063532,0.00027720892,0.0006581136,0.000027859773,0.00007227744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009856167,0.0001099714,0.0002805022,0.00010614363,0.00042337715,0.00017458928,0.00064614567,0.00003678093,0.00042102652],"category_scores_gemma":[0.00003146853,0.00008450687,0.000086799235,0.00014707514,0.00001632842,0.00039974396,0.00009958365,0.00017687344,0.00006338733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008904121,0.0012343605,0.012791666,0.000058109712,0.0031591326,0.00007114504,0.05821739,0.45508325,0.0056285947,0.215051,0.07120185,0.17661309],"study_design_scores_gemma":[0.00022118146,0.000027413651,0.000102757476,0.000026685513,0.0000047126914,0.00015211644,0.00052358356,0.94522756,0.0000044486346,0.007959405,0.04559939,0.00015074296],"about_ca_topic_score_codex":0.0006751153,"about_ca_topic_score_gemma":0.000012231463,"teacher_disagreement_score":0.80550814,"about_ca_system_score_codex":0.00005897128,"about_ca_system_score_gemma":0.00001639461,"threshold_uncertainty_score":0.4609945},"labels":[],"label_agreement":null},{"id":"W2341066319","doi":"10.32614/rj-2013-004","title":"Hypothesis Tests for Multivariate Linear Models Using the car Package","year":2013,"lang":"en","type":"article","venue":"The R Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":259,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McMaster University","funders":"Social Sciences and Humanities Research Council of Canada; McMaster University","keywords":"Multivariate statistics; R package; Multivariate analysis; Econometrics; Computer science; Generalized linear model; Statistics; Mathematics","score_opus":0.08815247068049628,"score_gpt":0.3203991098254604,"score_spread":0.2322466391449641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2341066319","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94198537,0.000983177,0.05328503,0.0011626866,0.0000758547,0.00012858136,0.000015864323,0.000035537796,0.0023279146],"genre_scores_gemma":[0.9927639,0.00009755068,0.0046393746,0.00019348772,0.0006612406,0.000008319549,6.560444e-7,0.000027804746,0.0016076689],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991614,0.000035434543,0.00021977618,0.00009696068,0.00018354981,0.0003028705],"domain_scores_gemma":[0.9986661,0.00065704965,0.00017820192,0.0003051471,0.00011840021,0.000075085176],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032452377,0.00013449354,0.00016432037,0.000042117987,0.00057044125,0.000101262136,0.00044495735,0.000066538465,0.0009959303],"category_scores_gemma":[0.00022531819,0.00006517603,0.00015229943,0.0001774637,0.000067505265,0.00015198684,0.000043135835,0.000333208,0.000021209244],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005270351,0.000093169285,0.00033914755,0.000032581163,0.000512326,0.0000033923325,0.0013285544,0.0062379884,0.9852119,0.00014306753,0.0037813908,0.0022637476],"study_design_scores_gemma":[0.0010771743,0.000039283215,0.00025261755,0.000048812584,0.0010915332,0.00039943293,0.0034533872,0.27646393,0.68624717,0.02984944,0.0006893919,0.0003878079],"about_ca_topic_score_codex":0.00014664819,"about_ca_topic_score_gemma":0.0000021667618,"teacher_disagreement_score":0.29896474,"about_ca_system_score_codex":0.00006774131,"about_ca_system_score_gemma":0.00004526867,"threshold_uncertainty_score":0.99991727},"labels":[],"label_agreement":null},{"id":"W2485286041","doi":"10.32614/rj-2010-011","title":"dclone: Data Cloning in R","year":2010,"lang":"en","type":"article","venue":"The R Journal","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":105,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Directorate for Biological Sciences; University of Alberta; Alberta Biodiversity Monitoring Institute","keywords":"Cloning (programming); Biology; Computer science; Geography; Computational biology; Programming language","score_opus":0.2516510934466327,"score_gpt":0.4652947572976266,"score_spread":0.21364366385099387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2485286041","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7035792,0.000072199466,0.28179786,0.0018859089,0.0009198215,0.00009337359,0.000015777176,0.000021425945,0.011614457],"genre_scores_gemma":[0.5563877,0.000027990953,0.44303253,0.00011222248,0.00032175207,5.252172e-7,4.927645e-7,0.000009165649,0.00010761188],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993567,0.00014090839,0.00018040679,0.00005534342,0.00012623858,0.000140406],"domain_scores_gemma":[0.99798596,0.0014904366,0.000067297144,0.0003850731,0.000023971632,0.00004725095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027235204,0.000047338344,0.000087554494,0.000022011714,0.0000917273,0.000039212395,0.00053128746,0.000027936027,0.0005758943],"category_scores_gemma":[0.003972265,0.000026110301,0.000010297998,0.00006463807,0.00004622836,0.000061213395,0.00012535596,0.00077749,0.000020220237],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004692831,0.0001283722,0.0054689217,0.000023299164,0.00002763415,0.000063641,0.0014798931,0.0000027913927,0.014451438,0.78119195,0.013932274,0.18318284],"study_design_scores_gemma":[0.00019719056,0.000018986426,0.007532577,0.00002948227,0.000016141677,0.00035251377,0.00011060033,0.0037386795,0.00025880046,0.9855108,0.00216742,0.00006678004],"about_ca_topic_score_codex":0.000017287315,"about_ca_topic_score_gemma":0.000059488444,"teacher_disagreement_score":0.20431885,"about_ca_system_score_codex":0.000005108775,"about_ca_system_score_gemma":0.000032023177,"threshold_uncertainty_score":0.6305639},"labels":[],"label_agreement":null},{"id":"W2786358356","doi":"10.32614/rj-2017-066","title":"glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling","year":2017,"lang":"en","type":"article","venue":"The R Journal","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12550,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Zero (linguistics); Generalized linear mixed model; Mathematics; Flexibility (engineering); Applied mathematics; Statistics; Computer science","score_opus":0.03451425526076261,"score_gpt":0.2967759771678309,"score_spread":0.26226172190706826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786358356","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95209855,0.0010269932,0.045929287,0.0002787393,0.0002879237,0.00017228506,0.000012996781,0.000006066344,0.00018714709],"genre_scores_gemma":[0.98631984,0.00030643537,0.012251188,0.000061162566,0.00061231817,0.0000027038043,0.000009055867,0.0000152421835,0.00042204242],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9992323,0.00008307008,0.0002040349,0.00016912882,0.000091885806,0.00021956723],"domain_scores_gemma":[0.9992146,0.000019740166,0.00016838494,0.00040572186,0.000097908895,0.0000936567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059628603,0.00013086067,0.00014263602,0.000011593903,0.00076240086,0.00009468451,0.00036269825,0.00010222476,0.000008499624],"category_scores_gemma":[0.00015983875,0.00008633257,0.000074720796,0.00001043011,0.0001942374,0.000007522787,0.000099933684,0.00013805286,0.0000010824729],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033475182,0.00023142656,0.058819655,0.00013164882,0.0011217366,0.000004014881,0.0016749974,0.14738539,0.74123055,0.002304922,0.0066556963,0.037092436],"study_design_scores_gemma":[0.011716979,0.0020964867,0.52381146,0.00017920627,0.0006276531,0.00040961793,0.0007491278,0.16498291,0.18021479,0.1070776,0.006562098,0.0015720448],"about_ca_topic_score_codex":0.00002304066,"about_ca_topic_score_gemma":0.0000182613,"teacher_disagreement_score":0.5610158,"about_ca_system_score_codex":0.000004707965,"about_ca_system_score_gemma":0.000041338157,"threshold_uncertainty_score":0.5863848},"labels":[],"label_agreement":null},{"id":"W2786505165","doi":"10.32614/rj-2017-003","title":"OrthoPanels: An R Package for Estimating a Dynamic Panel Model with Fixed Effects Using the Orthogonal Reparameterization Approach","year":2017,"lang":"en","type":"article","venue":"The R Journal","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"R package; Applied mathematics; Econometrics; Computer science; Fixed effects model; Panel data; Mathematics; Statistics","score_opus":0.11724230814653214,"score_gpt":0.2898238387422776,"score_spread":0.17258153059574544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786505165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41012627,0.00006086748,0.5892775,0.000098810146,0.00008224751,0.00015642104,0.00006535814,0.0000075672774,0.00012496997],"genre_scores_gemma":[0.92112863,0.000026092162,0.07840342,0.00009029863,0.00017604863,0.000014366999,0.000047047317,0.000025965197,0.000088120134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990342,0.000060202332,0.00037374956,0.00021155122,0.00006616132,0.0002541327],"domain_scores_gemma":[0.9981468,0.00011835736,0.0009237384,0.00069170515,0.000050265156,0.00006913413],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0013080959,0.00014841383,0.0002819458,0.00007177141,0.0019088428,0.00052903057,0.0006282328,0.000052220606,0.000010587377],"category_scores_gemma":[0.00027542748,0.00008650313,0.00011204834,0.000079102225,0.00010287864,0.00051640644,0.00006203393,0.00022751046,0.000004236732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068006106,0.00030875523,0.036126122,0.00020726063,0.0009947831,0.000016494334,0.0054192105,0.918048,0.002156996,0.011041598,0.00009994573,0.024900768],"study_design_scores_gemma":[0.00041336176,0.00008227785,0.0050516464,0.00002369263,0.00010929692,0.00012061747,0.000091165006,0.9866018,0.000018492288,0.0073242397,0.000015095563,0.00014830196],"about_ca_topic_score_codex":0.00011412128,"about_ca_topic_score_gemma":0.000046700046,"teacher_disagreement_score":0.51100236,"about_ca_system_score_codex":0.000041027084,"about_ca_system_score_gemma":0.000034851862,"threshold_uncertainty_score":0.99939054},"labels":[],"label_agreement":null},{"id":"W2789370465","doi":"10.32614/rj-2016-045","title":"Ake: An R Package for Discrete and Continuous Associated Kernel Estimations","year":2016,"lang":"en","type":"article","venue":"The R Journal","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agence Universitaire de la Francophonie","keywords":"Kernel (algebra); R package; Computer science; Econometrics; Mathematics; Statistics; Discrete mathematics","score_opus":0.09035823148027411,"score_gpt":0.3961181074765041,"score_spread":0.30575987599623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789370465","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114331014,0.00002174318,0.8837079,0.0012381058,0.00007122719,0.00010964761,0.00007735157,0.000018017108,0.00042501724],"genre_scores_gemma":[0.7897516,0.00003229454,0.20922425,0.000083971674,0.00012007722,0.0000070187043,8.4452216e-7,0.0000166302,0.00076334295],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99929667,0.00019877742,0.00018667722,0.000057640507,0.00010060688,0.00015961377],"domain_scores_gemma":[0.9965084,0.0030519597,0.00013485558,0.00011792138,0.00009701732,0.00008985247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012096753,0.000068076304,0.00013359258,0.000014974377,0.00023132787,0.000056492612,0.000111985544,0.000032326487,0.000086963955],"category_scores_gemma":[0.005747816,0.000030067256,0.000027796215,0.000027848328,0.00007995455,0.00007984726,0.00001930199,0.000094468145,0.0000023545078],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102748214,0.00014206619,0.0014149183,0.000027223703,0.00015506479,0.00000666677,0.0019045972,4.6273337e-7,0.011870677,0.7389905,0.010198368,0.23518671],"study_design_scores_gemma":[0.0005944485,0.00017046745,0.005676632,0.00007433745,0.00008052877,0.000056951092,0.00011897165,0.0009373555,0.00033590008,0.9917308,0.00014292667,0.000080654085],"about_ca_topic_score_codex":0.0000026738621,"about_ca_topic_score_gemma":0.0000056799577,"teacher_disagreement_score":0.6754206,"about_ca_system_score_codex":0.00001752163,"about_ca_system_score_gemma":0.000017625007,"threshold_uncertainty_score":0.6881088},"labels":[],"label_agreement":null},{"id":"W2792088475","doi":"10.32614/rj-2013-010","title":"An Introduction to the EcoTroph R Package: Analyzing Aquatic Ecosystem Trophic Networks","year":2013,"lang":"en","type":"article","venue":"The R Journal","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Trophic level; Computer science; Interfacing; Ecosystem; R package; Software; Implementation; Environmental science; Fishery; Ecology; Software engineering; Biology; Operating system","score_opus":0.00846104806998206,"score_gpt":0.22592219448731368,"score_spread":0.2174611464173316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792088475","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94995666,0.000025540949,0.013664629,0.03143545,0.00038708758,0.00036691866,4.88231e-7,0.000018638462,0.0041445955],"genre_scores_gemma":[0.99653053,0.00007786637,0.00015164122,0.00026105955,0.001991037,0.000012462236,9.600725e-7,0.000012290974,0.00096212013],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856955,0.00043847843,0.00019962377,0.00012888758,0.0003255447,0.00033793863],"domain_scores_gemma":[0.99925303,0.00004889392,0.00007271845,0.00044330535,0.000013356511,0.00016868208],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0012103496,0.000090386064,0.000093178896,0.000027096798,0.0006058999,0.00027464627,0.0006129003,0.000028950357,0.012339742],"category_scores_gemma":[0.000051075007,0.000043773096,0.000041769956,0.00030021177,0.000056910936,0.00033575957,0.00015477459,0.0004832122,0.0009859803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011761147,0.00012601048,0.07526348,0.000008620631,0.000095593525,0.000016266838,0.002400902,0.12305285,0.008167825,0.00009677071,0.20908935,0.5815647],"study_design_scores_gemma":[0.0006291497,0.0011678779,0.17144926,0.000014195888,0.00010361443,0.001105813,0.0026151089,0.5007302,0.00024203841,0.001862044,0.31947383,0.0006068889],"about_ca_topic_score_codex":0.0003439421,"about_ca_topic_score_gemma":0.000345725,"teacher_disagreement_score":0.58095783,"about_ca_system_score_codex":0.0001265288,"about_ca_system_score_gemma":0.000007970743,"threshold_uncertainty_score":0.99979186},"labels":[],"label_agreement":null},{"id":"W2970689096","doi":"10.32614/rj-2019-029","title":"ipwErrorY: An R Package for Estimation of Average Treatment Effect with Misclassified Binary Outcome","year":2019,"lang":"en","type":"article","venue":"The R Journal","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"R package; Outcome (game theory); Statistics; Estimation; Binary number; Econometrics; Mathematics; Computer science; Economics; Arithmetic; Mathematical economics","score_opus":0.17842987736422145,"score_gpt":0.42649781193230446,"score_spread":0.24806793456808302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970689096","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8203621,0.000014306924,0.17842218,0.00008663712,0.000045773035,0.00065892923,0.000010741374,0.000057331777,0.00034201526],"genre_scores_gemma":[0.9578777,0.000008229432,0.04158721,0.00001668974,0.000032848297,0.00002357682,0.0000053140207,0.000028363122,0.00042007334],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99914974,0.00015475473,0.00026708795,0.00008607217,0.00017297379,0.00016935678],"domain_scores_gemma":[0.99858236,0.00062847196,0.00032367758,0.00035155285,0.00006112742,0.000052811472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072018977,0.00015584024,0.00030120256,0.000057395308,0.00009025097,0.00001884767,0.00017132083,0.000048183807,0.00007010842],"category_scores_gemma":[0.00009980337,0.000073874246,0.00007476724,0.00005793208,0.00004247867,0.00020011964,0.000014508868,0.00014310534,0.0000069789508],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.015816843,0.004378599,0.06762743,0.00287726,0.0022971313,0.00016246304,0.024731463,0.022161921,0.5322436,0.17840384,0.0026561888,0.14664324],"study_design_scores_gemma":[0.010612761,0.04598211,0.008654716,0.00093028264,0.0010990343,0.00097396935,0.0010413013,0.047522295,0.4314014,0.45031565,0.00038853122,0.0010779392],"about_ca_topic_score_codex":0.000003576248,"about_ca_topic_score_gemma":0.000004382721,"teacher_disagreement_score":0.27191183,"about_ca_system_score_codex":0.000111693895,"about_ca_system_score_gemma":0.000034215966,"threshold_uncertainty_score":0.30125043},"labels":[],"label_agreement":null},{"id":"W3088188126","doi":"10.32614/rj-2022-052","title":"casebase: An Alternative Framework for Survival Analysis and Comparison of Event Rates","year":2022,"lang":"en","type":"article","venue":"The R Journal","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Manitoba; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Proportional hazards model; Hazard; Statistics; Event (particle physics); Parametric statistics; Computer science; Econometrics; Matching (statistics); Survival analysis; Hazard ratio; Logistic regression; Data mining; Confidence interval; Mathematics","score_opus":0.19462156679100312,"score_gpt":0.5008535833507944,"score_spread":0.3062320165597913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088188126","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30985466,0.00007563355,0.6897071,0.00012570179,0.00008340289,0.00006132131,0.00006080542,0.0000024036046,0.000029006866],"genre_scores_gemma":[0.7813211,0.000009855864,0.21856579,0.000019818386,0.000055592856,0.0000053941285,0.0000012343363,0.0000052048313,0.000015986685],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99870545,0.0006358672,0.0002711092,0.00006375434,0.00021717479,0.0001066344],"domain_scores_gemma":[0.9949324,0.0045505306,0.0002590404,0.00012232894,0.000079215395,0.000056480705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020010895,0.00006098572,0.00025891277,0.000055232456,0.00029851025,0.000019474652,0.00015695432,0.000013558973,0.00044955497],"category_scores_gemma":[0.0010387433,0.000038816648,0.00006382342,0.00016836557,0.000051545245,0.000022193753,0.000056768964,0.0002736699,1.2931085e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004059752,0.0004989115,0.019279098,0.00004885027,0.0012194499,0.0000072924317,0.00925724,0.0015434774,0.00042132882,0.93249387,0.00039038013,0.034434102],"study_design_scores_gemma":[0.00018740896,0.00042637053,0.00327465,0.000009580439,0.0005701398,0.000019126386,0.0035046407,0.048851054,0.0009184957,0.94211143,0.000061503735,0.00006556762],"about_ca_topic_score_codex":0.000034218458,"about_ca_topic_score_gemma":0.0000146750335,"teacher_disagreement_score":0.47146648,"about_ca_system_score_codex":0.00001968761,"about_ca_system_score_gemma":0.00002028579,"threshold_uncertainty_score":0.49223116},"labels":[],"label_agreement":null},{"id":"W3168850057","doi":"10.32614/rj-2022-012","title":"RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests","year":2022,"lang":"en","type":"article","venue":"The R Journal","topic":"Data Analysis with R","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; HEC Montréal; Fondation HEC","keywords":"Random forest; R package; Computer science; Reliability (semiconductor); Predictive modelling; Prediction interval; Data mining; Point (geometry); Mean squared prediction error; Set (abstract data type); Statistics; Machine learning; Mathematics","score_opus":0.01908452781822515,"score_gpt":0.2593394716969587,"score_spread":0.24025494387873356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168850057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26857132,0.00014084813,0.72950006,0.000983455,0.0003372814,0.00028791933,0.000066080705,0.000064066786,0.000048962516],"genre_scores_gemma":[0.9965457,0.000014408966,0.0027286767,0.00025127377,0.00021204795,0.000045120803,0.000022560393,0.000017031378,0.00016319603],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984153,0.00037916296,0.00029143327,0.00021900823,0.0004247384,0.00027038634],"domain_scores_gemma":[0.99875325,0.00019339817,0.00024480314,0.0005261135,0.00014300464,0.00013940838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002168128,0.00013154822,0.00019571096,0.00013865913,0.00076964137,0.0003445004,0.0011893192,0.000018921157,0.000031185857],"category_scores_gemma":[0.000092715345,0.00007882922,0.00007690475,0.00024845792,0.000059370665,0.0009893012,0.0004089452,0.00030873012,0.0000015685267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.014635181,0.0015272642,0.31225857,0.0001876825,0.0035756947,0.00038819836,0.0448355,0.026616096,0.004189688,0.011604895,0.31495675,0.2652245],"study_design_scores_gemma":[0.016164927,0.0075099315,0.1802204,0.00014296464,0.00061881746,0.0061783744,0.0017620038,0.7483498,0.0013550596,0.02228006,0.01475355,0.00066413917],"about_ca_topic_score_codex":0.000013342915,"about_ca_topic_score_gemma":0.00023518509,"teacher_disagreement_score":0.72797436,"about_ca_system_score_codex":0.00005895826,"about_ca_system_score_gemma":0.000054653363,"threshold_uncertainty_score":0.5919537},"labels":[],"label_agreement":null},{"id":"W3171325567","doi":"10.32614/rj-2021-033","title":"Benchmarking R packages for Calculation of Persistent Homology","year":2021,"lang":"en","type":"article","venue":"The R Journal","topic":"Topological and Geometric Data Analysis","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Cancer Institute; National Institutes of Health","keywords":"Benchmarking; R package; Homology (biology); Computer science; Persistent homology; Software; Software package; Programming language; Software engineering; Computational biology; Theoretical computer science; Computational science; Biology; Algorithm; Genetics; Amino acid","score_opus":0.0257368699423724,"score_gpt":0.2636360315381434,"score_spread":0.237899161595771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171325567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078677244,0.0014206405,0.9152519,0.0041712984,0.00020025771,0.000024794648,0.0000028924617,0.0000057361376,0.00024523784],"genre_scores_gemma":[0.98522234,0.00011834998,0.014205481,0.00014130854,0.00010019178,6.222245e-7,0.0000026191028,0.0000010481309,0.00020801401],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994747,0.00009718286,0.00013857332,0.000068811365,0.00011598515,0.00010473315],"domain_scores_gemma":[0.9993499,0.00023329472,0.000096408534,0.00016807456,0.0001224382,0.000029869576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053166325,0.000034246088,0.00009161625,0.000057879446,0.00014886449,0.000034818746,0.00032983435,0.000020555823,0.000055158765],"category_scores_gemma":[0.00013891234,0.00001925405,0.000173951,0.00037652455,0.000030422943,0.000084303494,0.000101030106,0.000072228264,0.0000019226843],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050519542,0.0004221629,0.0074859606,0.000046195837,0.0013360902,0.00011391203,0.003854188,0.023295209,0.00602015,0.1249918,0.022914957,0.80946887],"study_design_scores_gemma":[0.0032611382,0.0023487438,0.19969054,0.000117603704,0.0015345912,0.0076612667,0.0027938443,0.5313537,0.014036701,0.16020975,0.07601925,0.0009729024],"about_ca_topic_score_codex":0.000005406827,"about_ca_topic_score_gemma":0.0000019819677,"teacher_disagreement_score":0.9065451,"about_ca_system_score_codex":0.000011112419,"about_ca_system_score_gemma":0.000027165297,"threshold_uncertainty_score":0.11449604},"labels":[],"label_agreement":null},{"id":"W3210658443","doi":"10.32614/rj-2021-093","title":"mgee2: An R package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates","year":2021,"lang":"en","type":"article","venue":"The R Journal","topic":"Data Analysis with R","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Covariate; R package; Statistics; Ordinal data; Longitudinal data; Econometrics; Mathematics; Computer science; Data mining","score_opus":0.07310279485164847,"score_gpt":0.325284774537262,"score_spread":0.2521819796856135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210658443","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22201239,0.00038366165,0.77555317,0.00162914,0.00005136181,0.00006106614,0.00024406973,0.000014983836,0.000050151823],"genre_scores_gemma":[0.8839216,0.00011153107,0.115316026,0.00012405042,0.00010241435,0.000002284263,0.0001408226,0.000011836772,0.00026943302],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980244,0.0004770552,0.000359862,0.00039932595,0.00045793393,0.00028140988],"domain_scores_gemma":[0.99680537,0.00057047355,0.0003419894,0.0017710906,0.00035700324,0.00015406501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024611768,0.00015562108,0.00041226117,0.00029112305,0.00035346497,0.0004501058,0.0019557728,0.00003499259,0.00005848467],"category_scores_gemma":[0.00025761066,0.00009320048,0.000084033876,0.0013823254,0.00015259048,0.001040396,0.0005336274,0.00021249156,0.000001319642],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011405539,0.002561245,0.63034004,0.00028042146,0.05485139,0.002597559,0.008635733,0.0044693626,0.06925701,0.11197885,0.023287032,0.08033582],"study_design_scores_gemma":[0.0024406319,0.00095833663,0.59187317,0.000100032856,0.01057704,0.003299283,0.0012643988,0.37892884,0.0045928736,0.0025225268,0.0028201914,0.00062269135],"about_ca_topic_score_codex":0.000037855403,"about_ca_topic_score_gemma":0.00037366277,"teacher_disagreement_score":0.6619092,"about_ca_system_score_codex":0.00002062951,"about_ca_system_score_gemma":0.00031246152,"threshold_uncertainty_score":0.43403807},"labels":[],"label_agreement":null},{"id":"W4290725785","doi":"10.32614/rj-2022-002","title":"tvReg: Time-varying Coefficients in Multi-Equation Regression in R","year":2022,"lang":"en","type":"article","venue":"The R Journal","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"European Regional Development Fund; Xunta de Galicia; Ministerio de Ciencia e Innovación; Ministerio de Economía y Competitividad; European Commission","keywords":"Autoregressive model; Computer science; Nonparametric statistics; Nonparametric regression; Econometrics; Covariance matrix; Impulse response; Covariance; Statistics; Mathematics; Algorithm","score_opus":0.24643162537281077,"score_gpt":0.4296362991041736,"score_spread":0.18320467373136284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290725785","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9435131,0.00014119672,0.05179601,0.002877357,0.00016519988,0.00025115727,0.0000048338375,0.000028272825,0.0012229041],"genre_scores_gemma":[0.9964456,0.000009082501,0.0025930372,0.00010917579,0.000021918428,0.000013875268,0.0000012804384,0.0000055794458,0.0008004674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808943,0.00046831914,0.00042524232,0.00012229841,0.0007416788,0.00015305402],"domain_scores_gemma":[0.99898285,0.0004529582,0.00023952314,0.00024114439,0.000051487605,0.0000320345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007345337,0.000055375822,0.00009501666,0.0002722407,0.0005111678,0.00007525838,0.0007098751,0.000018404193,0.0005252613],"category_scores_gemma":[0.000585787,0.000032503525,0.000034671655,0.0009936563,0.000030542276,0.00009451507,0.00021978421,0.00044978238,0.00006271029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033149013,0.0012666635,0.043435346,0.0000031468883,0.000008267297,0.00006772126,0.015043789,0.35895705,0.02066817,0.0017455785,0.047686983,0.5107858],"study_design_scores_gemma":[0.00088842935,0.00009790852,0.01655773,0.00006509357,0.00000388766,0.00024313078,0.0012624691,0.9390757,0.00051953126,0.029722214,0.011414721,0.00014916706],"about_ca_topic_score_codex":0.000032577158,"about_ca_topic_score_gemma":0.0000103313805,"teacher_disagreement_score":0.58011866,"about_ca_system_score_codex":0.000114831906,"about_ca_system_score_gemma":0.000043953572,"threshold_uncertainty_score":0.57512426},"labels":[],"label_agreement":null},{"id":"W4290725789","doi":"10.32614/rj-2022-020","title":"Palmer Archipelago Penguins Data in the palmerpenguins R Package - An Alternative to Anderson's Irises","year":2022,"lang":"en","type":"article","venue":"The R Journal","topic":"Avian ecology and behavior","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Archipelago; Geography; Environmental data; Documentation; Genealogy; Cartography; Computer science; History; Ecology; Archaeology; Biology","score_opus":0.057771083210203356,"score_gpt":0.31203008542616306,"score_spread":0.2542590022159597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290725789","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9943388,0.000030167454,0.0002002222,0.00328894,0.00021469791,0.00022599574,0.000051327115,0.000007171261,0.0016426449],"genre_scores_gemma":[0.99711317,0.000018106248,0.0001259964,0.0023324664,0.00012688711,0.000014750138,0.000011973827,0.00001038059,0.00024627638],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99785197,0.0010279835,0.00016943707,0.00020159931,0.0004287341,0.00032026257],"domain_scores_gemma":[0.9989886,0.00016260619,0.00009045325,0.00065803266,0.0000031037378,0.000097158576],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0022294377,0.00010934935,0.00010001652,0.000038194925,0.001007621,0.0000432138,0.0024072742,0.000019229745,0.007241996],"category_scores_gemma":[0.000058850554,0.00006186447,0.000028413378,0.00021445855,0.00017545477,0.0002419132,0.00094299106,0.0008406945,0.00019813894],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002125769,0.000775402,0.8696463,0.0000012031826,0.000027492366,0.00059716456,0.074008144,0.002629169,0.001340526,0.00004806522,0.027403148,0.023310818],"study_design_scores_gemma":[0.00035913006,0.00033548116,0.9638953,0.00000268174,0.00003619308,0.00067349547,0.02172947,0.00015128324,0.00004422322,0.00088104635,0.011749349,0.00014233128],"about_ca_topic_score_codex":0.00015920609,"about_ca_topic_score_gemma":0.0017370431,"teacher_disagreement_score":0.094249025,"about_ca_system_score_codex":0.000093718285,"about_ca_system_score_gemma":0.00002112775,"threshold_uncertainty_score":0.9936655},"labels":[],"label_agreement":null},{"id":"W4290725887","doi":"10.32614/rj-2022-022","title":"Power and Sample Size for Longitudinal Models in R -- The longpower Package and Shiny App","year":2022,"lang":"en","type":"article","venue":"The R Journal","topic":"Mental Health Research Topics","field":"Psychology","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Weston Brain Institute; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Sample size determination; Computer science; Neuroimaging; Statistical power; Alzheimer's Disease Neuroimaging Initiative; Sample (material); R package; Clinical study design; Longitudinal study; Longitudinal data; Clinical trial; Data science; Medical physics; Data mining; Alzheimer's disease; Statistics; Psychology; Medicine; Disease; Mathematics; Psychiatry","score_opus":0.09980713533691159,"score_gpt":0.41080061633821796,"score_spread":0.31099348100130636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290725887","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98203063,0.002126709,0.0017931641,0.009937095,0.00035583926,0.000640487,0.000063223546,0.000007267195,0.0030455661],"genre_scores_gemma":[0.9976188,0.0001038231,0.0002877798,0.0011337271,0.00011302667,0.00005913328,9.554038e-7,0.000012969569,0.000669813],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99831784,0.00066505605,0.00020319803,0.00013038631,0.00030103145,0.00038248702],"domain_scores_gemma":[0.99745965,0.0021349858,0.000070313894,0.00022269304,0.000020202393,0.000092184295],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003672341,0.000083619154,0.00011484264,0.000048015114,0.0006807794,0.00005862506,0.00031294857,0.000027658365,0.0018050863],"category_scores_gemma":[0.00014782873,0.00004842313,0.00003019315,0.000102560116,0.00009757902,0.00007546405,0.00014416993,0.00075992336,0.0000035553767],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011260433,0.0024187232,0.15539649,0.00032004077,0.00047451325,0.00091904664,0.3332135,0.00050760177,0.00032293607,0.15167159,0.25298044,0.090514675],"study_design_scores_gemma":[0.0073390757,0.0030272887,0.45631522,0.000040658906,0.00005981093,0.006184212,0.05033868,0.0015941671,0.000012898965,0.4154719,0.059163038,0.00045301867],"about_ca_topic_score_codex":0.00012069595,"about_ca_topic_score_gemma":0.00005415041,"teacher_disagreement_score":0.30091873,"about_ca_system_score_codex":0.00005861028,"about_ca_system_score_gemma":0.000037289927,"threshold_uncertainty_score":0.9991074},"labels":[],"label_agreement":null},{"id":"W4322619775","doi":"10.32614/rj-2023-003","title":"Making Provenance Work for You","year":2023,"lang":"en","type":"article","venue":"The R Journal","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Ministry of Education, India; Harvard University; National Science Foundation","keywords":"Provenance; Trustworthiness; Scripting language; Computer science; Debugging; Programming language; Computer security","score_opus":0.44606847275703776,"score_gpt":0.47825139590176036,"score_spread":0.0321829231447226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322619775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4207542,0.0005015712,0.500822,0.04124222,0.01723536,0.00083038973,0.000028778164,0.00029917478,0.01828632],"genre_scores_gemma":[0.9695391,0.00001096915,0.0019778064,0.000368859,0.00060620136,0.0000042765505,0.0000010150443,0.0000073907045,0.027484369],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984174,0.00010562823,0.00029512157,0.00017250805,0.0007660893,0.00024325134],"domain_scores_gemma":[0.9981232,0.0010272303,0.00017912952,0.0005221668,0.000111193025,0.000037057984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016649663,0.00004993809,0.00007851076,0.00013986575,0.00066252786,0.000681735,0.0012386731,0.000011281748,0.00007753241],"category_scores_gemma":[0.0022739447,0.000024823918,0.000069283335,0.0012236363,0.000041249037,0.00012268346,0.0002729239,0.00010679916,0.0006794711],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021689137,0.000005490705,0.0005938929,9.098374e-7,0.00000608127,0.000003499007,0.0005517189,0.0019718152,0.000016308213,0.00091217266,0.7025086,0.2934078],"study_design_scores_gemma":[0.00023233077,0.00002871285,0.01686127,0.000047957477,0.000011371195,0.000032813266,0.0016890271,0.01550969,0.000023706547,0.12426644,0.8412071,0.00008957144],"about_ca_topic_score_codex":6.090665e-7,"about_ca_topic_score_gemma":0.0000010244696,"teacher_disagreement_score":0.5487849,"about_ca_system_score_codex":0.000014288602,"about_ca_system_score_gemma":0.000024993133,"threshold_uncertainty_score":0.8733453},"labels":[],"label_agreement":null},{"id":"W4322628680","doi":"10.32614/rj-2023-012","title":"robslopes: Efficient Computation of the (Repeated) Median Slope","year":2023,"lang":"en","type":"article","venue":"The R Journal","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Estimator; Computation; Benchmark (surveying); Mathematics; R package; Parametric statistics; Statistics; Set (abstract data type); Algorithm; Computer science; Combinatorics; Geodesy","score_opus":0.011687805497591054,"score_gpt":0.24943032563297962,"score_spread":0.23774252013538857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322628680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39320007,0.00019077623,0.5792372,0.022517363,0.0024020832,0.0001417481,0.0000011860933,0.00016677426,0.002142777],"genre_scores_gemma":[0.99780035,0.000023295679,0.0015775145,0.00013343128,0.00014594053,5.644966e-7,3.0570618e-7,0.00000508234,0.00031350055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998919,0.00029509785,0.00018241367,0.00007498143,0.0003656525,0.00016283493],"domain_scores_gemma":[0.9993744,0.00013148753,0.00015593744,0.00022491737,0.00006798803,0.00004529688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013663607,0.00006110897,0.000076205564,0.00006230918,0.00035970713,0.00006740047,0.0009130976,0.000017863465,0.000009892718],"category_scores_gemma":[0.00008576088,0.000028747012,0.00006101777,0.0006489371,0.00004627747,0.000047664453,0.00020024522,0.00031606856,0.00006215008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004977112,0.000033258977,0.0006789099,0.000009788559,0.000032202133,0.000013730466,0.008575816,0.7714159,0.0006445486,0.0015244288,0.0048917984,0.21217465],"study_design_scores_gemma":[0.00019381405,0.000035666402,0.018557737,0.000049802056,0.0000072841685,0.00015643865,0.00015021613,0.9777418,0.00042153604,0.0017132311,0.00091425446,0.000058245205],"about_ca_topic_score_codex":0.000017687207,"about_ca_topic_score_gemma":0.0000019228387,"teacher_disagreement_score":0.6046003,"about_ca_system_score_codex":0.000014613802,"about_ca_system_score_gemma":0.000056258577,"threshold_uncertainty_score":0.27666128},"labels":[],"label_agreement":null},{"id":"W4322628704","doi":"10.32614/rj-2023-018","title":"pCODE: Estimating Parameters of ODE Models","year":2023,"lang":"en","type":"article","venue":"The R Journal","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Ode; Hessian matrix; Jacobian matrix and determinant; Ordinary differential equation; Computer science; Cascade; Applied mathematics; Mathematical optimization; Estimation theory; Dynamical systems theory; Algorithm; Mathematics; Differential equation; Mathematical analysis","score_opus":0.025500838565008076,"score_gpt":0.2606199628272761,"score_spread":0.23511912426226805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322628704","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97165626,0.00039945796,0.027444812,0.00018619077,0.0000991278,0.0000300778,0.0000015871477,0.000006616417,0.00017588772],"genre_scores_gemma":[0.99355084,0.00018818027,0.0057856794,0.00004411884,0.00017583117,0.0000010502081,0.000005569226,0.000012052324,0.00023669921],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993478,0.000096565585,0.00018176876,0.000073957766,0.00013580082,0.0001640766],"domain_scores_gemma":[0.99952745,0.000019267598,0.0001272101,0.00022403475,0.00005655353,0.000045479785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006405525,0.00006753515,0.000096164724,0.000039011906,0.00011116346,0.00001137578,0.0002168347,0.00004155699,0.000007584111],"category_scores_gemma":[0.00004004723,0.00004613035,0.00010677946,0.0001602037,0.000051666695,0.000002235813,0.00007253435,0.000088902016,0.000007505387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020605294,0.000007657997,0.00044448164,0.0000051969364,0.00015540993,0.0000020716382,0.000121463505,0.8824244,0.106743336,0.000018214247,0.0038417513,0.0062153875],"study_design_scores_gemma":[0.0004519156,0.00015254639,0.0013431652,0.000039099727,0.00022455373,0.00024589984,0.00039094206,0.90604544,0.083217606,0.0070879795,0.00057319424,0.00022766204],"about_ca_topic_score_codex":0.0000040994164,"about_ca_topic_score_gemma":0.0000037559396,"teacher_disagreement_score":0.023621015,"about_ca_system_score_codex":0.0000056050994,"about_ca_system_score_gemma":0.0000349532,"threshold_uncertainty_score":0.1881141},"labels":[],"label_agreement":null},{"id":"W4388213050","doi":"10.32614/rj-2023-034","title":"Fairness Audits and Debiasing Using \\pkg{mlr3fairness}","year":2023,"lang":"en","type":"article","venue":"The R Journal","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Debiasing; Computer science; Audit; Variety (cybernetics); Machine learning; Set (abstract data type); Artificial intelligence; Data science; Accounting","score_opus":0.07956026539987977,"score_gpt":0.3494817351495638,"score_spread":0.26992146974968406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388213050","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9873029,0.00043107153,0.0045888047,0.004909503,0.0009863545,0.00012078392,0.0000032396097,0.00009968902,0.0015576388],"genre_scores_gemma":[0.9976386,0.0009198242,0.0001515601,0.000078886944,0.0010541611,9.2953957e-7,8.20438e-7,0.000008526398,0.00014669866],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987819,0.00039985418,0.0001326244,0.00008632994,0.00030941993,0.00028985532],"domain_scores_gemma":[0.9994693,0.00013953117,0.00009711322,0.00012898025,0.00006129616,0.00010381264],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002706532,0.00006645099,0.00008505149,0.000083398714,0.002487953,0.0002476917,0.00033077152,0.000056601177,0.000052013715],"category_scores_gemma":[0.00060636114,0.00004828157,0.00002906401,0.00040929465,0.00018665053,0.00046707585,0.00017906382,0.0002705955,0.000036578615],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004079978,0.00020887388,0.029963253,0.00016613456,0.00031980284,0.00031130048,0.3557101,0.0009298576,0.020432895,0.038063403,0.053530645,0.49995577],"study_design_scores_gemma":[0.0023937954,0.00018078176,0.090348266,0.000581899,0.0003399492,0.001637702,0.12627381,0.015158125,0.001975296,0.5764105,0.18337408,0.0013258061],"about_ca_topic_score_codex":0.0010079845,"about_ca_topic_score_gemma":0.00046477842,"teacher_disagreement_score":0.53834707,"about_ca_system_score_codex":0.00007063381,"about_ca_system_score_gemma":0.00012264839,"threshold_uncertainty_score":0.99881065},"labels":[],"label_agreement":null},{"id":"W4406928098","doi":"10.32614/rj-2024-009","title":"shinymgr: A Framework for Building, Managing, and Stitching Shiny Modules into Reproducible Workflows","year":2025,"lang":"en","type":"article","venue":"The R Journal","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"U.S. Geological Survey; Colorado State University","keywords":"Image stitching; Workflow; Computer science; Software engineering; Systems engineering; Engineering; Database; Artificial intelligence","score_opus":0.00985385308360435,"score_gpt":0.26746296727028823,"score_spread":0.25760911418668386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406928098","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20001067,0.0066294796,0.7908993,0.0017011643,0.00030544773,0.00006312495,6.986131e-7,0.000099222896,0.0002909033],"genre_scores_gemma":[0.9250934,0.0011342376,0.0729554,0.00015683669,0.00035738238,0.0000062555587,5.2285725e-7,0.00002374973,0.0002722035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992215,0.00003521475,0.00023607942,0.00015970692,0.00011157707,0.00023592233],"domain_scores_gemma":[0.999388,0.00016257085,0.000041656443,0.0003127494,0.000037160273,0.00005790967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011260707,0.00013290526,0.00018095298,0.00015024586,0.00044927746,0.00020895814,0.00026343288,0.00006148391,0.000011431602],"category_scores_gemma":[0.0001736174,0.00009632083,0.00008727039,0.0002115319,0.000025861027,0.00011055787,0.000053555206,0.00049970776,0.0000021006895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003113853,0.000015098896,0.00055818877,0.00011939171,0.0004305035,0.0000039226634,0.0025272504,0.81213987,0.0008187581,0.008740384,0.0042349664,0.17038055],"study_design_scores_gemma":[0.0001549724,0.000011155096,0.00012566468,0.00037657772,0.00016066899,0.000016410613,0.00026375076,0.6709378,0.0003386669,0.32545355,0.0020246396,0.00013617036],"about_ca_topic_score_codex":0.000026554868,"about_ca_topic_score_gemma":0.000014434511,"teacher_disagreement_score":0.72508276,"about_ca_system_score_codex":0.00004442231,"about_ca_system_score_gemma":0.00001306704,"threshold_uncertainty_score":0.39278495},"labels":[],"label_agreement":null}]}