{"meta":{"query_hash":"5f78ad9233b1","filters":{"venue":"Journal of Statistical Software"},"cohort_total":24,"direct_labels_cover":0,"predictions_cover":24,"exported":24,"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/5f78ad9233b1","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Statistical+Software"},"results":[{"id":"W1486450196","doi":"10.18637/jss.v069.i04","title":"Parallel and Other Simulations in<i>R</i>Made Easy: An End-to-End Study","year":2016,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Data Analysis with R","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":"University of Waterloo","funders":"Eidgenössische Technische Hochschule Zürich","keywords":"Computer science; Graphics; Computation; Set (abstract data type); Table (database); Scale (ratio); Contrast (vision); Contingency table; Algorithm; Computational science; Data mining; Computer graphics (images); Artificial intelligence; Programming language; Machine learning","score_opus":0.026138770459304822,"score_gpt":0.3101626526354363,"score_spread":0.2840238821761315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486450196","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046863924,0.0005697152,0.9582276,0.0026658277,0.0006685382,0.00045305435,0.0028779644,0.016222684,0.013628228],"genre_scores_gemma":[0.037606288,0.0010071137,0.9289778,0.001995945,0.00032296058,0.002083768,0.0052231755,0.016021183,0.006761802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9847489,0.01021779,0.00082197547,0.0015188069,0.0022931113,0.00039943532],"domain_scores_gemma":[0.9234363,0.05172091,0.0017801429,0.016036732,0.006014254,0.0010117005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019363333,0.002010673,0.0018099896,0.0011006638,0.0011855948,0.0042372113,0.0039222534,0.0020881838,0.041121233],"category_scores_gemma":[0.10333714,0.0014067395,0.0031388332,0.0019386229,0.0014546439,0.005026298,0.004670346,0.00511322,0.022570744],"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":[0.0013817662,0.0006133857,0.0059952396,0.0021954963,0.0007405416,0.0010229503,0.001420893,0.18269168,0.006981722,0.24989198,0.2587219,0.2883425],"study_design_scores_gemma":[0.00050060893,0.00049055915,0.0019325786,0.0006955309,0.00019487449,0.000514462,0.0002993953,0.4262099,0.012773881,0.24921633,0.30693814,0.00023381237],"about_ca_topic_score_codex":0.002667231,"about_ca_topic_score_gemma":0.0030487406,"teacher_disagreement_score":0.041121233,"about_ca_system_score_codex":0.0011987215,"about_ca_system_score_gemma":0.0022458567,"threshold_uncertainty_score":0.13756424},"labels":[],"label_agreement":null},{"id":"W1630835083","doi":"10.18637/jss.v050.i12","title":"<b>nparLD</b>: An<i>R</i>Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments","year":2012,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":1065,"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; Deutsche Forschungsgemeinschaft; Deutscher Akademischer Austauschdienst","keywords":"Nonparametric statistics; Factorial; Outlier; Parametric statistics; Computer science; R package; Longitudinal data; Software; Econometrics; Rank (graph theory); Data science; Statistics; Data mining; Mathematics; Artificial intelligence","score_opus":0.243031611199427,"score_gpt":0.48486226510205405,"score_spread":0.24183065390262704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1630835083","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010697169,0.00017453366,0.9503642,0.00035953143,0.00020253695,0.0002770988,0.008296044,0.037035525,0.0022208805],"genre_scores_gemma":[0.011894289,0.00023189686,0.95112234,0.00044549577,0.00010678589,0.004089819,0.0063800085,0.023255397,0.002473911],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9859264,0.008318198,0.0014471936,0.0012124549,0.0026582615,0.00043760205],"domain_scores_gemma":[0.8864712,0.08897179,0.006272978,0.011540193,0.0058823363,0.000861449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01883005,0.002787469,0.0024309915,0.0037374313,0.0010490692,0.0027513823,0.004242149,0.002125585,0.072150566],"category_scores_gemma":[0.12284586,0.0019430325,0.0033262812,0.0041276305,0.0020145646,0.004081222,0.0041928263,0.0055153384,0.035644364],"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":[0.0009890427,0.00034290366,0.005547075,0.0040871133,0.0012494448,0.0008585449,0.0012382964,0.015103092,0.007879928,0.0862678,0.5571222,0.31931466],"study_design_scores_gemma":[0.000598862,0.0005234293,0.010220759,0.0010344507,0.0005040794,0.0019106776,0.00023962445,0.14822912,0.019983605,0.21162803,0.6045762,0.00055114564],"about_ca_topic_score_codex":0.0027674893,"about_ca_topic_score_gemma":0.0031367512,"teacher_disagreement_score":0.072150566,"about_ca_system_score_codex":0.000882225,"about_ca_system_score_gemma":0.0045722127,"threshold_uncertainty_score":0.24136764},"labels":[],"label_agreement":null},{"id":"W1868578820","doi":"10.18637/jss.v010.i01","title":"<b>MATCH</b>- A Software Package for Robust Profile Matching Using<i>S-PLUS</i>","year":2004,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":35,"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 Alberta","funders":"","keywords":"Listing (finance); Computer science; Software; Matching (statistics); Set (abstract data type); Graphical user interface; Programming language; Data mining; Software engineering; Mathematics; Statistics","score_opus":0.020547289171397226,"score_gpt":0.2583351193331463,"score_spread":0.23778783016174906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1868578820","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010756466,0.00025574188,0.61611676,0.00020368752,0.0003213314,0.000355141,0.041711494,0.33430973,0.0056505646],"genre_scores_gemma":[0.007932309,0.000446362,0.7801143,0.0004943532,0.00012679106,0.0021050863,0.058376193,0.13483612,0.015568537],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99811953,0.0004476273,0.00033139164,0.00033897403,0.0006344351,0.00012808456],"domain_scores_gemma":[0.9910324,0.0055278693,0.0005755482,0.0013640783,0.0013070907,0.00019298278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004065569,0.0027223898,0.0021396189,0.003715723,0.0009872688,0.002133046,0.003575612,0.0013471703,0.23445603],"category_scores_gemma":[0.016186727,0.0029794897,0.0025483754,0.0028413557,0.00058161083,0.0025633592,0.0028890562,0.0033207657,0.17084731],"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":[0.00023832494,0.000096603144,0.0011276664,0.0012923952,0.0002544023,0.00022949209,0.00013875529,0.0042794864,0.0048363497,0.007740946,0.7902331,0.18953246],"study_design_scores_gemma":[0.00037165257,0.00018086145,0.004646807,0.00040762266,0.00016291623,0.0009815651,0.00009778932,0.06070809,0.022817483,0.04224564,0.86701685,0.00036278574],"about_ca_topic_score_codex":0.0039702496,"about_ca_topic_score_gemma":0.0053397776,"teacher_disagreement_score":0.23445603,"about_ca_system_score_codex":0.0007947686,"about_ca_system_score_gemma":0.0020839046,"threshold_uncertainty_score":0.7843336},"labels":[],"label_agreement":null},{"id":"W1914588449","doi":"10.18637/jss.v019.i09","title":"<b>tgp</b>: An<i>R</i>Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models","year":2007,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":206,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Booth University College","funders":"","keywords":"Bayesian probability; Gaussian process; Semiparametric regression; Gaussian; Computer science; Nonlinear system; Bayesian inference; Inference; Dimension (graph theory); Mathematics; Applied mathematics; Algorithm; Regression; Artificial intelligence; Statistics","score_opus":0.022667416435603303,"score_gpt":0.3016473060079822,"score_spread":0.27897988957237885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1914588449","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005219552,0.00014149006,0.93608564,0.00026973934,0.0001498446,0.00016932772,0.01589873,0.043515924,0.0032473751],"genre_scores_gemma":[0.007965322,0.0003075983,0.9325506,0.00040811198,0.000111815374,0.0020618045,0.016825639,0.03166662,0.008102498],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980254,0.0009443271,0.00018682126,0.0002599888,0.00047523752,0.00010831829],"domain_scores_gemma":[0.98487955,0.0104541,0.0009007039,0.0019573474,0.0015650657,0.00024322288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005253334,0.0021973115,0.0016551693,0.0020246385,0.0006164,0.001698517,0.0026378867,0.0016275065,0.1729673],"category_scores_gemma":[0.028550629,0.0018421409,0.0020579582,0.0021527268,0.000650985,0.002261326,0.0019695556,0.003312712,0.09374729],"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":[0.00029054377,0.00012892611,0.0013167944,0.0016157184,0.00043252335,0.000279941,0.00022992145,0.016259385,0.00517019,0.055500217,0.65108556,0.26769027],"study_design_scores_gemma":[0.00051159394,0.0001974304,0.003355549,0.00042632117,0.00023960839,0.0010462152,0.000056315115,0.18638654,0.014506294,0.1516405,0.6413303,0.0003033325],"about_ca_topic_score_codex":0.0030286612,"about_ca_topic_score_gemma":0.0036488303,"teacher_disagreement_score":0.1729673,"about_ca_system_score_codex":0.0005454118,"about_ca_system_score_gemma":0.0019937514,"threshold_uncertainty_score":0.57863325},"labels":[],"label_agreement":null},{"id":"W1947760575","doi":"10.18637/jss.v047.i05","title":"High-Dimensional Bayesian Clustering with Variable Selection: The<i>R</i>Package<b>bclust</b>","year":2012,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Neuchâtel; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"R package; Cluster analysis; Computer science; Bayesian probability; Hierarchical clustering; Bayes' theorem; Bayes factor; Parametric statistics; Variable (mathematics); Data mining; Mathematics; Artificial intelligence; Statistics","score_opus":0.01069729842721405,"score_gpt":0.2481134082660615,"score_spread":0.23741610983884748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1947760575","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00084969826,0.0002311524,0.96535647,0.00015858216,0.00006442881,0.0000856044,0.003685411,0.028769065,0.00079956505],"genre_scores_gemma":[0.010427566,0.00020163505,0.9617302,0.00020120763,0.00004980205,0.0007842419,0.0065150536,0.018499305,0.0015909959],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99474275,0.0028429355,0.0002832207,0.00088705687,0.0010464102,0.0001976792],"domain_scores_gemma":[0.9889016,0.006576676,0.0007632603,0.001960627,0.0015260365,0.00027171135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008881398,0.0026229636,0.0030907055,0.0034903318,0.0017928964,0.0035017359,0.0044959933,0.0021421532,0.04276253],"category_scores_gemma":[0.038377367,0.0021829668,0.0032503586,0.004908924,0.0011240885,0.0025244723,0.0040892893,0.0046343654,0.02986632],"study_design_candidate":"not_applicable","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":[0.0005625136,0.00020902835,0.005252825,0.002418891,0.0019099033,0.00027934456,0.0008569406,0.048996575,0.009427152,0.0656139,0.42354718,0.4409258],"study_design_scores_gemma":[0.00040956883,0.00011553091,0.0076880995,0.00048487424,0.00046354142,0.0009663184,0.0001599053,0.508558,0.019172702,0.21851183,0.24297555,0.00049402396],"about_ca_topic_score_codex":0.0056225737,"about_ca_topic_score_gemma":0.008676023,"teacher_disagreement_score":0.04276253,"about_ca_system_score_codex":0.0010164861,"about_ca_system_score_gemma":0.003184469,"threshold_uncertainty_score":0.1430549},"labels":[],"label_agreement":null},{"id":"W1948959238","doi":"10.18637/jss.v023.i05","title":"Algorithms for Linear Time Series Analysis: With<i>R</i>Package","year":2007,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":51,"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","keywords":"R package; Computer science; Series (stratigraphy); Software package; Time series; Algorithm; Base (topology); Software; Linear regression; Mathematics; Computational science; Machine learning","score_opus":0.025272372543365618,"score_gpt":0.2633249778170461,"score_spread":0.2380526052736805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1948959238","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026788385,0.00023175788,0.9575372,0.00017546568,0.00011397567,0.00007646537,0.0034271332,0.036579493,0.0015905747],"genre_scores_gemma":[0.006570501,0.00029199984,0.9619075,0.00023550027,0.00013119493,0.0010908395,0.0075473366,0.017465731,0.0047594034],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99753535,0.0008790666,0.00031330952,0.0004887853,0.0006433422,0.00014012629],"domain_scores_gemma":[0.9927368,0.003979676,0.0005491235,0.0013121217,0.001261027,0.00016121636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035031205,0.0029679295,0.0018357787,0.003063447,0.00076810445,0.002502231,0.0035168685,0.0014268291,0.10001703],"category_scores_gemma":[0.020724002,0.001514469,0.0024170994,0.0035489881,0.0007811777,0.0025623476,0.0029502835,0.0035215423,0.09527986],"study_design_candidate":"not_applicable","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":[0.00024467258,0.00014372781,0.0014969235,0.0013464785,0.00053534773,0.0003165574,0.00025705103,0.035400726,0.0041838926,0.057305247,0.38433176,0.51443756],"study_design_scores_gemma":[0.00031744965,0.000111697576,0.0021268097,0.00027740045,0.00016429227,0.00073083676,0.00008146859,0.4081544,0.00872826,0.22980896,0.34928352,0.00021493848],"about_ca_topic_score_codex":0.0036310754,"about_ca_topic_score_gemma":0.003766981,"teacher_disagreement_score":0.10001703,"about_ca_system_score_codex":0.0008019411,"about_ca_system_score_gemma":0.001884293,"threshold_uncertainty_score":0.33459032},"labels":[],"label_agreement":null},{"id":"W1951724000","doi":"10.18637/jss.v067.i01","title":"Fitting Linear Mixed-Effects Models Using <b>lme4</b>","year":2015,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Data Analysis with R","field":"Computer Science","cited_by":85618,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Banff International Research Station for Mathematical Innovation and Discovery","keywords":"Restricted maximum likelihood; Deviance (statistics); Mixed model; Smoothing; Applied mathematics; Likelihood function; Mathematics; Generalized linear model; Linear model; Maximum likelihood; Generalized linear mixed model; Covariate; Algorithm; Statistics; Mathematical optimization; Computer science","score_opus":0.057098437824987656,"score_gpt":0.30913463259089435,"score_spread":0.2520361947659067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1951724000","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008799061,0.00024107646,0.9752074,0.00032887154,0.00019364517,0.0006676998,0.0067310315,0.0140061965,0.0017441835],"genre_scores_gemma":[0.005517451,0.00028393863,0.9712832,0.00032298756,0.00005947013,0.0054309033,0.005262344,0.009718803,0.0021208592],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9750354,0.016281458,0.0018333566,0.0036027948,0.0025816504,0.00066532573],"domain_scores_gemma":[0.9609304,0.028311864,0.002079688,0.005703904,0.002660715,0.00031348114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037593838,0.0061596786,0.00493646,0.0043876846,0.0016492585,0.0048929015,0.008161397,0.003644082,0.10356729],"category_scores_gemma":[0.09536354,0.004190404,0.00969612,0.005529113,0.0018601223,0.003672303,0.004358776,0.008650399,0.048386246],"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":[0.0011846372,0.00079730374,0.007121037,0.008443345,0.0073408317,0.0012036505,0.0015196332,0.09763058,0.0125965625,0.13331036,0.32262483,0.4062271],"study_design_scores_gemma":[0.00091756653,0.00066990027,0.006530485,0.0017603537,0.0020699087,0.0010901366,0.00034567967,0.35057473,0.018408068,0.22278923,0.393927,0.0009169288],"about_ca_topic_score_codex":0.007569758,"about_ca_topic_score_gemma":0.011541399,"teacher_disagreement_score":0.10356729,"about_ca_system_score_codex":0.00240295,"about_ca_system_score_gemma":0.0060119303,"threshold_uncertainty_score":0.34646708},"labels":[],"label_agreement":null},{"id":"W2135892144","doi":"10.18637/jss.v033.i06","title":"Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with<b>tgp</b>Version 2, an<i>R</i>Package for Treed Gaussian Process Models","year":2010,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Booth University College","funders":"Engineering and Physical Sciences Research Council","keywords":"Categorical variable; Computer science; Gaussian process; Covariate; Markov chain Monte Carlo; Sensitivity (control systems); Bayesian probability; Algorithm; Gaussian; Artificial intelligence; Machine learning; Engineering","score_opus":0.00885045248584295,"score_gpt":0.251482053128426,"score_spread":0.24263160064258302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135892144","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012001285,0.00013751938,0.99563307,0.0001312079,0.000041420484,0.000056278208,0.00042902245,0.0010306255,0.0013406338],"genre_scores_gemma":[0.05141958,0.0003775281,0.9397017,0.00027835774,0.00010161429,0.00075628574,0.0013478502,0.002418007,0.0035990023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99249494,0.0050007277,0.00033714023,0.00059770123,0.0013782941,0.00019116794],"domain_scores_gemma":[0.9823481,0.013238163,0.00084350276,0.0021478182,0.0012387589,0.00018370991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010908943,0.0015012609,0.0016269238,0.0012982832,0.00066092005,0.002133305,0.0020076223,0.0015715117,0.019021533],"category_scores_gemma":[0.059224583,0.0011864608,0.0027089615,0.0019743717,0.001082857,0.0025749942,0.0028293435,0.0046095527,0.0042340797],"study_design_candidate":"not_applicable","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":[0.00026541305,0.00015785726,0.003923295,0.0009630301,0.00043681829,0.00026101127,0.00041531303,0.35696864,0.0032995436,0.34687847,0.046448674,0.239982],"study_design_scores_gemma":[0.000038591876,0.00005348007,0.001067234,0.00010490199,0.000085173044,0.00010546826,0.000041832962,0.7410543,0.0031723238,0.23327598,0.020928908,0.000071905066],"about_ca_topic_score_codex":0.0050852066,"about_ca_topic_score_gemma":0.005533356,"teacher_disagreement_score":0.019021533,"about_ca_system_score_codex":0.001156999,"about_ca_system_score_gemma":0.0019215982,"threshold_uncertainty_score":0.06363338},"labels":[],"label_agreement":null},{"id":"W2512875827","doi":"10.18637/jss.v072.i05","title":"<b>RSKC</b>: An<i>R</i>Package for a Robust and Sparse K-Means Clustering Algorithm","year":2016,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Outlier; Cluster analysis; Computer science; Data mining; R package; Identification (biology); Algorithm; Monte Carlo method; Artificial intelligence; Mathematics; Statistics","score_opus":0.033844006603445616,"score_gpt":0.3084657004614797,"score_spread":0.2746216938580341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2512875827","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019352706,0.00039641163,0.7657343,0.0005254048,0.00039996774,0.00030770662,0.03339776,0.1938272,0.0034759757],"genre_scores_gemma":[0.011701428,0.0003384122,0.86581796,0.0005724712,0.00011267564,0.0019829024,0.027909156,0.0869103,0.004654733],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974026,0.0008536855,0.00026243992,0.0005392933,0.00075646414,0.00018556468],"domain_scores_gemma":[0.9885315,0.0062944326,0.001022668,0.0018289811,0.002079226,0.00024328224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044858395,0.004450302,0.002675449,0.0036181735,0.0013874857,0.0027290052,0.0051713213,0.00230444,0.10193569],"category_scores_gemma":[0.029946018,0.0025826269,0.0029655811,0.0039844266,0.0012330262,0.003420288,0.0036006114,0.004842449,0.09441852],"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":[0.0006202791,0.00019717778,0.0025959658,0.0028712342,0.00085495313,0.0003623073,0.0006157177,0.02128648,0.009308229,0.021134142,0.7948718,0.14528172],"study_design_scores_gemma":[0.00069133064,0.0002138728,0.0055825664,0.00070645195,0.00038924866,0.0011108036,0.00019053342,0.22754563,0.029824585,0.066330105,0.6668005,0.0006144092],"about_ca_topic_score_codex":0.006139835,"about_ca_topic_score_gemma":0.007782201,"teacher_disagreement_score":0.10193569,"about_ca_system_score_codex":0.0010575907,"about_ca_system_score_gemma":0.0033869445,"threshold_uncertainty_score":0.34100884},"labels":[],"label_agreement":null},{"id":"W2515705452","doi":"10.18637/jss.v073.i07","title":"My Early Interactions with Jan and Some of His Lost Papers","year":2016,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Period (music); Scaling; Multidimensional scaling; Computer science; History; Mathematics; Art; Machine learning","score_opus":0.012775728765790148,"score_gpt":0.2838603270710328,"score_spread":0.2710845983052427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515705452","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013908357,0.03767016,0.015297783,0.7570931,0.14356121,0.00010837948,0.0006232682,0.0006968112,0.03104098],"genre_scores_gemma":[0.2297581,0.030430248,0.025682118,0.21791609,0.17133355,0.00051359774,0.00087186764,0.004381706,0.3191128],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.97608733,0.010319716,0.0009216042,0.0023239877,0.009088988,0.0012583982],"domain_scores_gemma":[0.88747436,0.031112704,0.006480469,0.00621169,0.0342914,0.03442943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02159546,0.0014505072,0.0016898907,0.003239064,0.010360385,0.015621328,0.002647136,0.00262353,0.017063998],"category_scores_gemma":[0.11842648,0.0010646909,0.0011197419,0.0033294049,0.007926658,0.009696931,0.00676886,0.01742462,0.014555188],"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":[0.00013404101,0.00012714417,0.0015426459,0.00023909823,0.00006604361,0.0010381023,0.017000841,0.00024273912,0.0008778372,0.029160118,0.90236336,0.04720797],"study_design_scores_gemma":[0.000013558423,0.000048876773,0.0009138462,0.00038699975,0.000017481065,0.0014205306,0.010366067,0.00026291475,0.00055640686,0.014261067,0.97166663,0.00008555746],"about_ca_topic_score_codex":0.0012396052,"about_ca_topic_score_gemma":0.0018491133,"teacher_disagreement_score":0.02159546,"about_ca_system_score_codex":0.0047917524,"about_ca_system_score_gemma":0.004040276,"threshold_uncertainty_score":0.114209116},"labels":[],"label_agreement":null},{"id":"W2577537660","doi":"10.18637/jss.v076.i01","title":"<i>Stan</i> : A Probabilistic Programming Language","year":2017,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7378,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Center for Research Resources; Institute of Education Sciences; U.S. Department of Energy; National Science Foundation; National Institutes of Health; Harvard University","keywords":"Python (programming language); Computer science; Markov chain Monte Carlo; Algorithm; Hybrid Monte Carlo; Monte Carlo method; Probabilistic logic; Bayesian inference; Importance sampling; Statistical inference; Inference; Monte Carlo integration; Applied mathematics; Bayesian probability; Mathematical optimization; Mathematics; Programming language; Artificial intelligence; Statistics","score_opus":0.05011962462799613,"score_gpt":0.3917699605443133,"score_spread":0.34165033591631716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577537660","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032531668,0.00018853624,0.8889701,0.0005256013,0.00014165303,0.00015732208,0.012222602,0.090229586,0.0072393594],"genre_scores_gemma":[0.010079351,0.00059414265,0.8950227,0.0019069046,0.00021019927,0.0016692957,0.017653944,0.06180873,0.011054799],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99709356,0.0009587079,0.00041685635,0.00056134776,0.0007694348,0.00020018037],"domain_scores_gemma":[0.9906446,0.00620515,0.0007649272,0.000964483,0.0011999671,0.00022088639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005296637,0.0029431176,0.0017734363,0.0020509015,0.0008684856,0.005120043,0.0049127894,0.0018008326,0.11569443],"category_scores_gemma":[0.021377606,0.002622225,0.0030360695,0.0027662367,0.0015440336,0.005046561,0.0034340085,0.0052781985,0.07530208],"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":[0.00019145742,0.00008589782,0.0013267365,0.0013443795,0.00020513526,0.00043281761,0.00041799428,0.01653264,0.0026667381,0.21370077,0.64043677,0.12265865],"study_design_scores_gemma":[0.00015482483,0.00003715806,0.00049557694,0.0004044862,0.00005710252,0.0005946974,0.000048391987,0.09834083,0.0068153366,0.20920832,0.68370813,0.00013510347],"about_ca_topic_score_codex":0.0036919285,"about_ca_topic_score_gemma":0.00568725,"teacher_disagreement_score":0.11569443,"about_ca_system_score_codex":0.0010780735,"about_ca_system_score_gemma":0.003688184,"threshold_uncertainty_score":0.38703644},"labels":[],"label_agreement":null},{"id":"W2801007199","doi":"10.18637/jss.v084.c01","title":"<b>stampr</b>: Spatial-Temporal Analysis of Moving Polygons in <i>R</i>","year":2018,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Data Analysis with R","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Polygon (computer graphics); Computer science; R package; Core (optical fiber); Computer graphics (images); Algorithm; Computational science","score_opus":0.011747742803746464,"score_gpt":0.27645266921670414,"score_spread":0.2647049264129577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801007199","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003523309,0.00041535732,0.71358347,0.00063235505,0.00067839917,0.0002680838,0.09225928,0.1832113,0.005428483],"genre_scores_gemma":[0.04936689,0.0007951885,0.7033441,0.00081993104,0.00036495965,0.004054158,0.07674163,0.15651014,0.008002975],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99625576,0.0014293687,0.00039971335,0.0008324531,0.00087708584,0.00020557552],"domain_scores_gemma":[0.9750743,0.015660882,0.0022928726,0.004544391,0.0019374186,0.0004901089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062460904,0.002757444,0.0022799412,0.003679453,0.0010724784,0.0037593693,0.00272913,0.0011588582,0.107938185],"category_scores_gemma":[0.048740406,0.0017452734,0.0033266654,0.0049492912,0.0011958418,0.0030057773,0.003300861,0.003147692,0.063263096],"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":[0.00044085065,0.000074094365,0.010445782,0.0031877346,0.0009861421,0.00044952895,0.0010303513,0.013581914,0.0071933027,0.027717406,0.80944705,0.12544583],"study_design_scores_gemma":[0.00043265958,0.00024003611,0.019924046,0.00071238464,0.00058528985,0.0014579787,0.00030660062,0.07874462,0.020852203,0.060684558,0.815453,0.0006066855],"about_ca_topic_score_codex":0.005119526,"about_ca_topic_score_gemma":0.0057815537,"teacher_disagreement_score":0.107938185,"about_ca_system_score_codex":0.0006591267,"about_ca_system_score_gemma":0.0024597861,"threshold_uncertainty_score":0.36108923},"labels":[],"label_agreement":null},{"id":"W3089708264","doi":"10.18637/jss.v095.i04","title":"Zigzag Expanded Navigation Plots in <i>R</i>: The <i>R</i> Package <b>zenplots</b>","year":2020,"lang":"ja","type":"article","venue":"Journal of Statistical Software","topic":"Data Analysis with R","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Zigzag; R package; Computer science; Mathematics; Statistics; Geometry","score_opus":0.027513842080723605,"score_gpt":0.2768311213842509,"score_spread":0.24931727930352732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089708264","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019353887,0.0016015209,0.37210318,0.0010701859,0.001437944,0.00028599793,0.15641257,0.45729467,0.007858485],"genre_scores_gemma":[0.020243447,0.001643659,0.50873536,0.0024561048,0.000583715,0.0039101453,0.11015004,0.3405888,0.011688716],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99468464,0.001757587,0.00052617124,0.0012856623,0.001359881,0.00038624014],"domain_scores_gemma":[0.9810203,0.011188285,0.0015888632,0.0027448772,0.0028626989,0.00059506757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007955892,0.0045178877,0.004156185,0.0054770093,0.0012195876,0.006272253,0.0054979837,0.0024084786,0.29510978],"category_scores_gemma":[0.041205417,0.0031603454,0.0040336098,0.006251568,0.0013672658,0.005145996,0.0052447296,0.0054184725,0.1821439],"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":[0.00039984635,0.000052371804,0.0024538308,0.0031048642,0.00050902646,0.0003116579,0.0005559014,0.0028449846,0.004359914,0.013153106,0.9231572,0.049097225],"study_design_scores_gemma":[0.0006177113,0.0001062491,0.0046879034,0.0009808098,0.0003669058,0.00088455714,0.00019327037,0.018643929,0.011507204,0.05069186,0.91082895,0.00049061235],"about_ca_topic_score_codex":0.0037348382,"about_ca_topic_score_gemma":0.0054906933,"teacher_disagreement_score":0.29510978,"about_ca_system_score_codex":0.001023408,"about_ca_system_score_gemma":0.003412017,"threshold_uncertainty_score":0.98724055},"labels":[],"label_agreement":null},{"id":"W3094281686","doi":"10.18637/jss.v114.i04","title":"Exploring Data Subsets with <b>vtree</b>","year":2025,"lang":"en","type":"preprint","venue":"Journal of Statistical Software","topic":"Data Analysis with R","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agricultural Research Institute of Ontario","funders":"","keywords":"Venn diagram; Contingency table; Variable (mathematics); Computer science; Missing data; Data mining; Diagram; Tree (set theory); Algorithm; Theoretical computer science; Mathematics; Machine learning; Combinatorics; Database","score_opus":0.16605877105624625,"score_gpt":0.3275761359707859,"score_spread":0.16151736491453964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094281686","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006985484,0.0008340012,0.8990484,0.0012300251,0.000468542,0.0005190176,0.0335445,0.053258937,0.004111094],"genre_scores_gemma":[0.032130294,0.00046009742,0.93346775,0.00043898966,0.0001397212,0.0025936011,0.0148768,0.014558222,0.001334498],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98888016,0.0062805656,0.0014001419,0.0016289002,0.001477893,0.00033236167],"domain_scores_gemma":[0.9015681,0.08408156,0.0037333171,0.0067489073,0.0031688407,0.00069930666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020110391,0.0022724418,0.0021460443,0.008261758,0.0014671513,0.0064237555,0.0029567585,0.0014961119,0.035929427],"category_scores_gemma":[0.10231955,0.0017035228,0.003636073,0.007855064,0.0015206734,0.0055209305,0.005403624,0.004251647,0.009262721],"study_design_candidate":"not_applicable","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":[0.0011091387,0.00026376406,0.01954437,0.007066017,0.0019158936,0.0012326979,0.007862434,0.011723355,0.008104412,0.1124224,0.32829684,0.5004588],"study_design_scores_gemma":[0.00050565536,0.00035583935,0.008892591,0.002208633,0.00080376875,0.0017877136,0.0014760015,0.09568246,0.013262489,0.35127744,0.5232012,0.0005461055],"about_ca_topic_score_codex":0.0028005326,"about_ca_topic_score_gemma":0.004022658,"teacher_disagreement_score":0.035929427,"about_ca_system_score_codex":0.0007682633,"about_ca_system_score_gemma":0.0034865872,"threshold_uncertainty_score":0.120195866},"labels":[],"label_agreement":null},{"id":"W3123059542","doi":"10.18637/jss.v091.i04","title":"Markov-Switching GARCH Models in <i>R</i>: The <b>MSGARCH</b> Package","year":2019,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":107,"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":"Institut de Valorisation des Données; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Autoregressive conditional heteroskedasticity; Heteroscedasticity; Econometrics; Markov chain; Markov chain Monte Carlo; Conditional variance; Computer science; Autoregressive model; Volatility (finance); Bayesian probability; Mathematics; Machine learning; Artificial intelligence","score_opus":0.03152622886809661,"score_gpt":0.24885392766239722,"score_spread":0.2173276987943006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123059542","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020301528,0.00037696844,0.8738119,0.0003951622,0.00020794939,0.00011164909,0.027782043,0.089872636,0.005411529],"genre_scores_gemma":[0.053860225,0.0011350695,0.8323595,0.00074936385,0.00035927363,0.0016072287,0.042965274,0.05506806,0.01189604],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986021,0.00046878017,0.00011064737,0.00023623892,0.00044677602,0.00013559035],"domain_scores_gemma":[0.9962423,0.0020702183,0.00047080667,0.0006044828,0.0005284804,0.000083732615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002610057,0.0018524138,0.0015503406,0.0020351831,0.00041770065,0.0019529632,0.0022214814,0.0014130925,0.0718322],"category_scores_gemma":[0.011988901,0.001509783,0.0025129772,0.0026195864,0.00035752324,0.0022020433,0.0013967009,0.0025847158,0.03446539],"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":[0.00018564286,0.00016287116,0.0048853667,0.0010588113,0.0007663825,0.00041571044,0.0003225672,0.13270757,0.0033547091,0.112325646,0.55669236,0.18712234],"study_design_scores_gemma":[0.00023397741,0.00007838884,0.0031736114,0.00028408985,0.0002133481,0.00038779245,0.000055771485,0.49941733,0.006257522,0.14737497,0.3422526,0.00027051332],"about_ca_topic_score_codex":0.0068898792,"about_ca_topic_score_gemma":0.0049250335,"teacher_disagreement_score":0.0718322,"about_ca_system_score_codex":0.00055575295,"about_ca_system_score_gemma":0.0016365506,"threshold_uncertainty_score":0.24030268},"labels":[],"label_agreement":null},{"id":"W3138858220","doi":"10.18637/jss.v097.i07","title":"<b>FamEvent</b>: An <i>R</i> Package for Generating and Modeling Time-to-Event Data in Family Designs","year":2021,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","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":"Alberta Health Services; University of Calgary; Lunenfeld-Tanenbaum Research Institute; Western University","funders":"National Cancer Institute","keywords":"Penetrance; Pedigree chart; Missing data; Computer science; Event (particle physics); Population; Statistics; Covariate; Confidence interval; R package; Data mining; Mathematics; Genetics; Medicine; Biology; Gene","score_opus":0.059603865856036185,"score_gpt":0.34019560006606203,"score_spread":0.28059173421002587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138858220","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008166296,0.00050285715,0.7219524,0.00049988646,0.0002701617,0.0006817758,0.12007448,0.14281516,0.0050370623],"genre_scores_gemma":[0.051833905,0.0007461943,0.76229066,0.0009542759,0.00018215558,0.0064303,0.07879498,0.087651685,0.011115824],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973654,0.0015913307,0.0002099375,0.0003509909,0.00032598033,0.00015632909],"domain_scores_gemma":[0.97871244,0.017221631,0.0012464154,0.0016353226,0.0009152194,0.0002690305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008806358,0.002226052,0.0021241596,0.0016767867,0.0006145296,0.0017170658,0.0036854637,0.0014951996,0.11044487],"category_scores_gemma":[0.030411934,0.0017163107,0.0029454103,0.0016111126,0.000650125,0.0015347967,0.0019078436,0.0023448227,0.03686372],"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":[0.0016118963,0.00034201433,0.018670198,0.004728423,0.0027182975,0.0010572592,0.000879627,0.05469326,0.0075232466,0.037662286,0.6520982,0.21801525],"study_design_scores_gemma":[0.0017762864,0.00066356873,0.012564302,0.0010051947,0.0009860873,0.0016915168,0.00013392145,0.3332543,0.0137149915,0.07291114,0.56075054,0.00054818974],"about_ca_topic_score_codex":0.0045764246,"about_ca_topic_score_gemma":0.005405818,"teacher_disagreement_score":0.11044487,"about_ca_system_score_codex":0.000700606,"about_ca_system_score_gemma":0.0018191212,"threshold_uncertainty_score":0.3694749},"labels":[],"label_agreement":null},{"id":"W3194665160","doi":"10.18637/jss.v099.i02","title":"The <i>R</i> Package <b>sentometrics</b> to Compute, Aggregate, and Predict with Textual Sentiment","year":2021,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Computational and Text Analysis Methods","field":"Social Sciences","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":"Institut de Valorisation des Données; Innoviris; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"R package; Computer science; Aggregate (composite); Indexation; Sentiment analysis; Workflow; Index (typography); Volatility (finance); Series (stratigraphy); Information retrieval; Econometrics; Natural language processing; Mathematics; World Wide Web; Database; Programming language; Economics","score_opus":0.017611648276824543,"score_gpt":0.32921699603574767,"score_spread":0.3116053477589231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194665160","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033619462,0.0008165541,0.3883452,0.0010697793,0.000885379,0.0004533298,0.11498995,0.4799657,0.010112184],"genre_scores_gemma":[0.0347128,0.001234905,0.5230596,0.0019063848,0.0005725621,0.0043660435,0.10732455,0.30952352,0.017299643],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967024,0.0010757579,0.0003167398,0.00068570965,0.0009864455,0.0002329718],"domain_scores_gemma":[0.9871176,0.0069939825,0.0012999745,0.0024512876,0.0017658203,0.00037138665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056522302,0.003960526,0.0023499674,0.0045859185,0.0010789293,0.0045581535,0.0023038096,0.0010473436,0.116957754],"category_scores_gemma":[0.0400102,0.0022168695,0.0033132557,0.0036040174,0.001142177,0.0026734662,0.003600307,0.0035390614,0.13140017],"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":[0.00043054455,0.000093046096,0.007651246,0.0027253858,0.001238498,0.0004298061,0.0004682694,0.005777791,0.0076007303,0.018045897,0.83556634,0.11997246],"study_design_scores_gemma":[0.00043812318,0.00018910918,0.010476276,0.0006309347,0.00048170146,0.0008715601,0.00018718856,0.047505904,0.022734657,0.06493273,0.85108244,0.00046945614],"about_ca_topic_score_codex":0.003885551,"about_ca_topic_score_gemma":0.005639477,"teacher_disagreement_score":0.116957754,"about_ca_system_score_codex":0.0009809092,"about_ca_system_score_gemma":0.0032870362,"threshold_uncertainty_score":0.39126265},"labels":[],"label_agreement":null},{"id":"W4226403402","doi":"10.18637/jss.v102.i02","title":"Multivariate Normal Variance Mixtures in <i>R</i>: The <i>R</i> Package <b>nvmix</b>","year":2022,"lang":"ja","type":"article","venue":"Journal of Statistical Software","topic":"Data Analysis with R","field":"Computer Science","cited_by":3,"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":"Univariate; Statistics; Multivariate statistics; Multivariate normal distribution; Random variate; Quantile; Variance (accounting); Mathematics; Normal distribution; Random variable; Econometrics","score_opus":0.014305318521646985,"score_gpt":0.2654126965182635,"score_spread":0.25110737799661653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226403402","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010881092,0.00047819724,0.923721,0.00035412508,0.00029995933,0.00015400817,0.015693545,0.05479789,0.0034131552],"genre_scores_gemma":[0.02219319,0.00088973844,0.85297316,0.0009398694,0.000285996,0.0023225727,0.027514618,0.08537148,0.0075093666],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9923706,0.0032157328,0.00059198146,0.0014670971,0.002010647,0.00034398862],"domain_scores_gemma":[0.9819857,0.010586335,0.0015261618,0.003158679,0.0024408263,0.00030227326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010513122,0.003762812,0.0027220272,0.003071047,0.00089061196,0.004993456,0.003915616,0.0018709082,0.111773975],"category_scores_gemma":[0.058009345,0.0024743772,0.0037351078,0.0035640262,0.0014157554,0.0050326334,0.0045033046,0.004953513,0.0717148],"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":[0.00051534706,0.00014754765,0.007845203,0.0033479233,0.0012850638,0.00072554365,0.00092749693,0.029912194,0.008858247,0.14428462,0.57201034,0.23014055],"study_design_scores_gemma":[0.0002564828,0.00012288353,0.004770161,0.00080577575,0.00039015934,0.0013818018,0.00016246186,0.10228109,0.013448235,0.19982494,0.6761842,0.00037179663],"about_ca_topic_score_codex":0.0034771005,"about_ca_topic_score_gemma":0.0038639891,"teacher_disagreement_score":0.111773975,"about_ca_system_score_codex":0.0009074792,"about_ca_system_score_gemma":0.0027599104,"threshold_uncertainty_score":0.37392116},"labels":[],"label_agreement":null},{"id":"W4294557430","doi":"10.18637/jss.v103.i07","title":"Hierarchical Clustering with Contiguity Constraint in <i>R</i>","year":2022,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Data Analysis with R","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Contiguity; Cluster analysis; Computer science; Hierarchical clustering; Theoretical computer science; Function (biology); Algorithm; Artificial intelligence","score_opus":0.01225028693388872,"score_gpt":0.2517438408299075,"score_spread":0.23949355389601876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294557430","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009822333,0.00033038805,0.94249004,0.00031201646,0.00024135195,0.000098069344,0.005866027,0.0473778,0.0023021852],"genre_scores_gemma":[0.017007243,0.00027953228,0.93409795,0.00063991675,0.00014243861,0.0014997729,0.011961977,0.031203523,0.003167594],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98912907,0.0045472966,0.00095561537,0.002593085,0.0022542698,0.00052069593],"domain_scores_gemma":[0.98526347,0.008174986,0.0011630931,0.0030323858,0.0020866988,0.0002793679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011301514,0.0036435227,0.0029844262,0.0036699611,0.0017457171,0.0045480994,0.0053112633,0.0023523567,0.0435128],"category_scores_gemma":[0.053829327,0.002597226,0.0046600066,0.0052377186,0.0016006221,0.0030771685,0.00450451,0.0046972125,0.03803607],"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":[0.0007080929,0.0001288009,0.005617324,0.003013858,0.0018053373,0.0009775717,0.0018355659,0.08054757,0.01352903,0.13494729,0.51031244,0.24657711],"study_design_scores_gemma":[0.00032244966,0.00012800378,0.004404438,0.00058608764,0.00042984704,0.0011464965,0.00019434627,0.2863388,0.024509002,0.1772109,0.5041659,0.00056378794],"about_ca_topic_score_codex":0.009737865,"about_ca_topic_score_gemma":0.009413737,"teacher_disagreement_score":0.0435128,"about_ca_system_score_codex":0.0016380567,"about_ca_system_score_gemma":0.0048053404,"threshold_uncertainty_score":0.1455648},"labels":[],"label_agreement":null},{"id":"W4402023001","doi":"10.18637/jss.v110.i06","title":"<b>sparsegl</b>: An <i>R</i> Package for Estimating Sparse Group Lasso","year":2024,"lang":"ru","type":"article","venue":"Journal of Statistical Software","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; Carnegie Mellon University; National Institutes of Health; National Science Foundation","keywords":"R package; Lasso (programming language); Group (periodic table); Computer science; Statistics; Mathematics; Chemistry; Programming language","score_opus":0.07643798190945737,"score_gpt":0.38374974547277274,"score_spread":0.30731176356331535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402023001","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00093295274,0.0004689922,0.83378035,0.0004964195,0.00028964426,0.0001833352,0.031613443,0.12860781,0.003627028],"genre_scores_gemma":[0.010436886,0.0005887537,0.82302046,0.0011422473,0.00021622592,0.0020605905,0.04471958,0.108736776,0.009078409],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99801934,0.00084490865,0.00017982084,0.00029163653,0.00050374033,0.00016066151],"domain_scores_gemma":[0.9906024,0.0057834624,0.0008333227,0.0014804173,0.0010924182,0.00020794003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042491825,0.0029118613,0.002083172,0.0018996999,0.00070057326,0.0019788558,0.0029286516,0.0016956691,0.11558866],"category_scores_gemma":[0.023826499,0.0015522668,0.0022988785,0.0022347483,0.00084302505,0.002121813,0.002955263,0.0050478396,0.09415567],"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":[0.00028301758,0.000117748736,0.0018381608,0.0015789918,0.00042204076,0.00025967587,0.0002454675,0.0122668985,0.0064722295,0.026347887,0.8154072,0.13476075],"study_design_scores_gemma":[0.000373852,0.00016554471,0.0033370564,0.00053363905,0.0001996123,0.0007103654,0.00008088698,0.14645046,0.018230835,0.08316566,0.746432,0.00032004635],"about_ca_topic_score_codex":0.0028126957,"about_ca_topic_score_gemma":0.0048097116,"teacher_disagreement_score":0.11558866,"about_ca_system_score_codex":0.00044886465,"about_ca_system_score_gemma":0.0022378955,"threshold_uncertainty_score":0.38668263},"labels":[],"label_agreement":null},{"id":"W4404880964","doi":"10.18637/jss.v111.i09","title":"How to Interpret Statistical Models Using <b>marginaleffects</b> for <i>R</i> and <i>Python</i>","year":2024,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":371,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Python (programming language); Computer science; Programming language","score_opus":0.021788826802131425,"score_gpt":0.2979243716317229,"score_spread":0.2761355448295915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404880964","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009005559,0.00023078249,0.9722569,0.0023599628,0.00047551506,0.00012180871,0.0033355132,0.016695056,0.0036239366],"genre_scores_gemma":[0.03333946,0.00090608833,0.9304814,0.0035884264,0.0005169175,0.001415543,0.0035518308,0.022512585,0.0036877326],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9862,0.00858934,0.0009984935,0.0016693226,0.0022012207,0.0003415831],"domain_scores_gemma":[0.90501076,0.073960714,0.00490422,0.011115516,0.0043283016,0.00068051316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02853688,0.0030615998,0.0021484524,0.0039487625,0.0012042628,0.007859647,0.00478331,0.0026536868,0.04071879],"category_scores_gemma":[0.15926859,0.0019288035,0.0049711457,0.0034019076,0.004380631,0.010132583,0.0047334055,0.008965528,0.024817694],"study_design_candidate":"not_applicable","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":[0.00030371244,0.00019374615,0.007961187,0.0030746679,0.0011345535,0.00063068385,0.002486776,0.021992298,0.0057271854,0.4755096,0.26589292,0.21509267],"study_design_scores_gemma":[0.00009829815,0.00009056297,0.0028930875,0.00092004624,0.0002338573,0.00046203658,0.0004784074,0.054786623,0.005452622,0.7516074,0.1827172,0.00025974397],"about_ca_topic_score_codex":0.0054644626,"about_ca_topic_score_gemma":0.0048434166,"teacher_disagreement_score":0.04071879,"about_ca_system_score_codex":0.0013389923,"about_ca_system_score_gemma":0.0042201844,"threshold_uncertainty_score":0.15091926},"labels":[],"label_agreement":null},{"id":"W4409376239","doi":"10.18637/jss.v112.i01","title":"Parsimoniously Fitting Large Multivariate Random Effects in <b>glmmTMB</b>","year":2025,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":302,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"University of New South Wales; Analytical Center for the Government of the Russian Federation; McMaster University","keywords":"Multivariate statistics; Computer science; Statistics; Mathematics; Econometrics","score_opus":0.027375578816853038,"score_gpt":0.37556120705876894,"score_spread":0.3481856282419159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409376239","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014583989,0.00014887589,0.9860595,0.00042497274,0.00016067183,0.00011517366,0.0011087086,0.009998663,0.00052516354],"genre_scores_gemma":[0.015171967,0.0001903717,0.97358006,0.0006373848,0.00007072408,0.0014014591,0.0018366758,0.0060224463,0.0010890384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9851953,0.011370173,0.0007361904,0.0014079663,0.0009332965,0.0003570625],"domain_scores_gemma":[0.9389098,0.04814992,0.0018898905,0.008100853,0.002464333,0.00048527235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031179972,0.004397108,0.0030623495,0.0020193502,0.0016241991,0.0050049406,0.0050430424,0.0037124925,0.028764417],"category_scores_gemma":[0.11352036,0.0035718172,0.005469687,0.0037251632,0.0019880002,0.0060941014,0.004136767,0.009145276,0.017485604],"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":[0.0009488367,0.00056275557,0.012264398,0.0030379125,0.0038350183,0.0006611225,0.0021320444,0.06503744,0.014650758,0.124256015,0.15964971,0.61296403],"study_design_scores_gemma":[0.00060949894,0.00040588243,0.0066085984,0.0009950969,0.00068326783,0.00066405715,0.00076447206,0.37830004,0.01394047,0.43041626,0.16585685,0.00075562333],"about_ca_topic_score_codex":0.009488997,"about_ca_topic_score_gemma":0.026959429,"teacher_disagreement_score":0.031179972,"about_ca_system_score_codex":0.0013793658,"about_ca_system_score_gemma":0.0039017978,"threshold_uncertainty_score":0.16489744},"labels":[],"label_agreement":null},{"id":"W7125397753","doi":"10.18637/jss.v115.i08","title":"<b>SMLE</b> : An <i>R</i> Package for Joint Feature Screening in Ultrahigh-Dimensional GLMs","year":2025,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Statistical Methods and Inference","field":"Mathematics","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 Ottawa","funders":"","keywords":"R package; Categorical variable; Feature selection; Feature (linguistics); Flexibility (engineering); Joint (building); Pattern recognition (psychology)","score_opus":0.06437155034120659,"score_gpt":0.3788384425209867,"score_spread":0.3144668921797801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125397753","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015937734,0.00044142158,0.90451056,0.00050133996,0.0002021646,0.00013640776,0.018031552,0.07224785,0.0023349975],"genre_scores_gemma":[0.018007115,0.0006378411,0.9027854,0.00087409536,0.00017315046,0.0014751718,0.02208463,0.049165055,0.0047974195],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757487,0.0012572166,0.00016939241,0.0003667918,0.0004922378,0.0001395621],"domain_scores_gemma":[0.9878628,0.008093091,0.0009831178,0.0015987022,0.001237374,0.00022503803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052097933,0.003183579,0.0023169916,0.002114061,0.0007234987,0.0020920387,0.0030994979,0.0014866684,0.095235266],"category_scores_gemma":[0.032643247,0.0013925213,0.0027574375,0.0022033688,0.000954835,0.0023092567,0.002820575,0.003963166,0.052277237],"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":[0.0003712095,0.00015095128,0.0040982426,0.0028386235,0.0010632718,0.00058630784,0.00042747834,0.025974285,0.010067714,0.05126372,0.64891165,0.2542465],"study_design_scores_gemma":[0.0003867926,0.00026359363,0.0070442925,0.0007105533,0.00037491694,0.0011474198,0.00015074402,0.3015268,0.014646986,0.1469921,0.52628917,0.00046658877],"about_ca_topic_score_codex":0.0033178078,"about_ca_topic_score_gemma":0.0058628838,"teacher_disagreement_score":0.095235266,"about_ca_system_score_codex":0.00047812518,"about_ca_system_score_gemma":0.0025403826,"threshold_uncertainty_score":0.31859368},"labels":[],"label_agreement":null},{"id":"W7126223455","doi":"10.18637/jss.v115.i02","title":"<b>sdmTMB</b> : An <i>R</i> Package for Fast, Flexible, and User-Friendly Generalized Linear Mixed Effects Models with Spatial and Spatiotemporal Random Fields","year":2025,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":10,"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":"Fisheries and Oceans Canada; Bird Studies Canada","keywords":"Random field; Inference; Gaussian; Flexibility (engineering); Bayesian probability; Markov random field; Random effects model; Generalized linear mixed model; Spatial analysis; Spatial correlation","score_opus":0.008286097233459026,"score_gpt":0.24851377118353352,"score_spread":0.2402276739500745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126223455","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014789365,0.0004771116,0.73493266,0.000712283,0.0003813316,0.00037818786,0.061368518,0.19716337,0.0031076],"genre_scores_gemma":[0.012045759,0.00050887326,0.8057046,0.0010624785,0.00015521534,0.0043280246,0.036359474,0.13317364,0.0066619916],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967158,0.0015568611,0.00035974066,0.00060210796,0.00057070755,0.00019484283],"domain_scores_gemma":[0.97757214,0.016129786,0.0016636847,0.0022418231,0.0020508752,0.00034165534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008257161,0.0045825327,0.0031972711,0.0029565026,0.0009923489,0.0031823353,0.0053321547,0.0022417146,0.17073797],"category_scores_gemma":[0.047500473,0.0035186103,0.0052234028,0.0029769668,0.0011669468,0.003130611,0.0041246363,0.005408123,0.08741796],"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":[0.00046291313,0.00015844467,0.0046583074,0.004213283,0.0019983244,0.0006379568,0.00062952074,0.014287409,0.004858282,0.03088936,0.7992937,0.13791242],"study_design_scores_gemma":[0.0009104188,0.00021897236,0.006851474,0.0014033898,0.00080970116,0.0012915441,0.00015605746,0.11299036,0.0111456085,0.09181263,0.7718388,0.0005711102],"about_ca_topic_score_codex":0.00927716,"about_ca_topic_score_gemma":0.0129947355,"teacher_disagreement_score":0.17073797,"about_ca_system_score_codex":0.0010837875,"about_ca_system_score_gemma":0.004443228,"threshold_uncertainty_score":0.5711754},"labels":[],"label_agreement":null}]}