{"meta":{"query_hash":"5d65661dfa04","filters":{"venue":"Open Journal of Statistics"},"cohort_total":56,"direct_labels_cover":0,"predictions_cover":56,"exported":56,"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/5d65661dfa04","api":"https://metacan.xera.ac/api/v1/cohort?venue=Open+Journal+of+Statistics"},"results":[{"id":"W1497365461","doi":"10.4236/ojs.2017.72022","title":"Testing the Adding up Condition in Demand Systems","year":2017,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Economics of Agriculture and Food Markets","field":"Economics, Econometrics and Finance","cited_by":0,"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":"Almost ideal demand system; Commodity; Affine transformation; Sample (material); Econometrics; Computer science; On demand; Quadratic equation; Confidence interval; Statistics; Mathematics; Economics; Microeconomics; Production (economics); Finance","score_opus":0.08185088547037728,"score_gpt":0.27553035911295565,"score_spread":0.19367947364257837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1497365461","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":[0.48879796,0.0069522173,0.02664946,0.002934944,0.009051789,0.0014099607,0.0022637108,0.000011161885,0.46192878],"genre_scores_gemma":[0.99449986,0.00016843736,0.0046839486,0.00005283015,0.00017702254,0.0000034917084,0.000004727472,0.0000085882175,0.0004011043],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9990094,0.00001785851,0.00069971575,0.00010645945,0.000026536512,0.0001400768],"domain_scores_gemma":[0.9976966,0.0002414045,0.0017171126,0.00020592181,0.00008888425,0.000050107352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015387258,0.00007987286,0.00031576972,0.000062876396,0.0002902859,0.0009356717,0.00078089826,0.000042000967,0.00004444469],"category_scores_gemma":[0.00081772,0.000061991195,0.000028697785,0.00003434493,0.00004688958,0.00061452924,0.00012217654,0.0001897344,0.000035643727],"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":[0.000067066925,0.00007541782,0.13813739,0.00005860395,0.0001527492,0.00009908775,0.0006108637,0.0006797842,0.00006730901,0.81215656,0.043153163,0.0047419984],"study_design_scores_gemma":[0.0022050107,0.00028987188,0.8190205,0.00034189667,0.00002950452,0.00021651934,0.00070792664,0.0033559345,0.000020231922,0.12582035,0.04762327,0.00036899024],"about_ca_topic_score_codex":0.00018208766,"about_ca_topic_score_gemma":0.000052753643,"teacher_disagreement_score":0.6863362,"about_ca_system_score_codex":0.000058774014,"about_ca_system_score_gemma":0.000027125621,"threshold_uncertainty_score":0.9022704},"labels":[],"label_agreement":null},{"id":"W1953696579","doi":"10.4236/ojs.2015.55044","title":"Confirmatory Factor Analysis of the Youth Experiences Survey for Sport (YES-S)","year":2015,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Youth Development and Social Support","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Confirmatory factor analysis; Exploratory factor analysis; Psychology; Athletes; Scale (ratio); Construct validity; Context (archaeology); Structural equation modeling; Psychometrics; Social psychology; Applied psychology; Developmental psychology; Mathematics; Statistics; Medicine","score_opus":0.2168443356022707,"score_gpt":0.40661240212999794,"score_spread":0.18976806652772724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1953696579","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":[0.9879622,0.00003771567,0.0053432058,0.000100155,0.00093042996,0.00026808406,0.0007869619,0.0000022822287,0.004568976],"genre_scores_gemma":[0.99606735,0.000027599906,0.0029776245,0.000049027694,0.00006256686,0.000002482769,0.000018549634,0.000004974941,0.00078980427],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984874,0.00016651364,0.0005007668,0.00007207831,0.0006105848,0.00016266631],"domain_scores_gemma":[0.9978164,0.00021467303,0.0007317056,0.0000846238,0.0009902451,0.00016237199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027784957,0.00007008448,0.00037218214,0.00007207185,0.00022901862,0.00010671362,0.0007761424,0.000049208134,0.00023785396],"category_scores_gemma":[0.0009437564,0.00004861742,0.00009869342,0.00045098175,0.0002509372,0.00020044403,0.000058489866,0.00007256076,0.0000012107943],"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":[0.00005666249,0.000035027253,0.75965226,0.0000023338534,0.00028409043,0.0000024721728,0.23162957,0.0000056321364,8.9092873e-7,0.003982924,0.0037193622,0.00062875176],"study_design_scores_gemma":[0.0006070595,0.00007901833,0.72400814,0.000018574556,0.0004856186,4.469225e-7,0.26476863,0.000024110208,0.000050454473,0.00091829034,0.008868872,0.000170755],"about_ca_topic_score_codex":0.0018865871,"about_ca_topic_score_gemma":0.002856634,"teacher_disagreement_score":0.035644118,"about_ca_system_score_codex":0.00007149453,"about_ca_system_score_gemma":0.0018230918,"threshold_uncertainty_score":0.32340863},"labels":[],"label_agreement":null},{"id":"W1968980970","doi":"10.4236/ojs.2013.32017","title":"The Statistical Analysis of Interval-Censored Failure Time Data with Applications","year":2013,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":38,"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 Guelph; University of Waterloo","funders":"","keywords":"Censoring (clinical trials); Nonparametric statistics; Parametric statistics; Statistics; Interval (graph theory); Computer science; Accelerated failure time model; Parametric model; Survival analysis; Mathematics; Data mining","score_opus":0.09487944757262302,"score_gpt":0.40118155098211516,"score_spread":0.30630210340949215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968980970","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004631875,0.000024267594,0.9948341,0.0003575498,0.000024087069,0.0003914298,0.0028380097,0.0000036158044,0.0010637371],"genre_scores_gemma":[0.014860393,0.00003366514,0.98464686,0.00003658447,0.000031091156,0.000017024036,0.00006861062,0.000018758074,0.00028703184],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99791354,0.00026126523,0.0009717174,0.00017993675,0.0004625525,0.00021101233],"domain_scores_gemma":[0.9896557,0.007408615,0.0009221839,0.0008073426,0.0010392524,0.000166961],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012030666,0.00014722567,0.0006481001,0.000097925324,0.00013419219,0.00026578704,0.0016519375,0.000041770607,0.0016558409],"category_scores_gemma":[0.0029014398,0.000081910344,0.000040647876,0.00046909178,0.00032688613,0.00020466471,0.00035327175,0.00025248106,0.00003000963],"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":[0.0000854279,0.00019900347,0.0005218287,0.000044895198,0.0017397956,0.000013073504,0.00011773547,0.000013167111,0.00012044298,0.8524446,0.075289056,0.069411],"study_design_scores_gemma":[0.0011954017,0.00096322334,0.013521281,0.00016782469,0.005842085,0.00007330407,0.0011037965,0.05085832,0.00008809564,0.90999925,0.015760614,0.00042680238],"about_ca_topic_score_codex":0.00006046086,"about_ca_topic_score_gemma":0.00006197568,"teacher_disagreement_score":0.0689842,"about_ca_system_score_codex":0.000029275663,"about_ca_system_score_gemma":0.00015731124,"threshold_uncertainty_score":0.9992568},"labels":[],"label_agreement":null},{"id":"W1971272938","doi":"10.4236/ojs.2014.46043","title":"Confirmatory Factor and Invariance Analyses of the Motivation to Control Prejudiced Reactions Scale","year":2014,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Social and Intergroup Psychology","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Confirmatory factor analysis; Measurement invariance; Equivalence (formal languages); Scale (ratio); Psychology; Econometrics; Factor analysis; Statistics; Scale invariance; Mathematics; Social psychology; Structural equation modeling; Pure mathematics; Geography; Cartography","score_opus":0.085714206602683,"score_gpt":0.40932205660058335,"score_spread":0.3236078499979004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971272938","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":[0.71404266,0.00006842096,0.24544679,0.008124931,0.0017827761,0.0006317791,0.00021947561,0.0000061586857,0.029677033],"genre_scores_gemma":[0.9965888,0.000029795448,0.0025488005,0.00046080753,0.000098721044,0.0000013345613,2.7735192e-7,0.0000029221944,0.00026852236],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99915564,0.0002824541,0.00026919995,0.000050864553,0.0001653747,0.00007645138],"domain_scores_gemma":[0.9987402,0.00043883309,0.00038892377,0.000062945415,0.00029889974,0.000070230126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048147363,0.000038540868,0.00015691378,0.000029336868,0.00018453691,0.000050920404,0.00026173427,0.00003334707,0.000116957504],"category_scores_gemma":[0.001352983,0.000027550557,0.000022323637,0.000110157926,0.00016832133,0.00014623339,0.000025634397,0.000086503605,0.000001932842],"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":[0.0007432919,0.0005604936,0.23530713,0.00008330679,0.00067675556,0.000005503623,0.07294462,0.00019051916,0.12176126,0.400402,0.09916821,0.0681569],"study_design_scores_gemma":[0.0013228148,0.00035184648,0.91517544,0.00014299997,0.000095919255,0.0000061420346,0.0045431186,0.000078996156,0.0010289369,0.018873189,0.05825181,0.00012878742],"about_ca_topic_score_codex":0.0007331602,"about_ca_topic_score_gemma":0.00042802593,"teacher_disagreement_score":0.6798683,"about_ca_system_score_codex":0.000023545503,"about_ca_system_score_gemma":0.00007899064,"threshold_uncertainty_score":0.16197447},"labels":[],"label_agreement":null},{"id":"W1976628155","doi":"10.4236/ojs.2013.34a001","title":"A Comparison of Statistical Methods for Analyzing Discrete Hierarchical Data: A Case Study of Family Data on Alcohol Abuse","year":2013,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Bayesian 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":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Multilevel model; Hierarchical database model; Alcohol abuse; Computer science; Random effects model; Data mining; Psychology; Econometrics; Statistics; Meta-analysis; Medicine; Mathematics; Psychiatry; Machine learning","score_opus":0.42060277544744706,"score_gpt":0.585039938302551,"score_spread":0.1644371628551039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976628155","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017588,0.000040259165,0.9729668,0.000036354588,0.00016818242,0.0012395524,0.007880275,0.0000041805433,0.000076405326],"genre_scores_gemma":[0.13976994,0.000016565149,0.859987,0.0000199032,0.000058647143,0.000014173266,0.00008701589,0.00003709791,0.000009658844],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99481815,0.0013433949,0.002464239,0.00046358374,0.0005664833,0.00034413885],"domain_scores_gemma":[0.97221416,0.023304265,0.0017435104,0.001614375,0.00082566147,0.0002980316],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004534066,0.00028370883,0.0016714089,0.00018142359,0.00012826758,0.00019333512,0.0024142317,0.00008516895,0.00016497422],"category_scores_gemma":[0.013199023,0.00021289177,0.000048740218,0.0002437702,0.0002603757,0.00045405762,0.0008880149,0.00054236996,0.0000017961996],"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":[0.0012301373,0.0060313176,0.0033632081,0.0009302321,0.0014667514,0.0010022764,0.0063929064,0.000023076353,0.00089656305,0.32935902,0.033457737,0.61584675],"study_design_scores_gemma":[0.0074271485,0.010910256,0.004039788,0.0007515329,0.002772663,0.0006955407,0.015832983,0.20288575,0.00023109857,0.75319815,0.0005023435,0.0007527363],"about_ca_topic_score_codex":0.00046168978,"about_ca_topic_score_gemma":0.000073974305,"teacher_disagreement_score":0.615094,"about_ca_system_score_codex":0.00003261494,"about_ca_system_score_gemma":0.0002286416,"threshold_uncertainty_score":0.9951132},"labels":[],"label_agreement":null},{"id":"W1978696365","doi":"10.4236/ojs.2011.13018","title":"Revisit the Two Sample t-Test with a Known Ratio of Variances","year":2011,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","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":"York University","funders":"","keywords":"Statistics; Mathematics; Likelihood-ratio test; Inference; Sample size determination; Sample (material); Levene's test; Computer science; Artificial intelligence","score_opus":0.2083267799212133,"score_gpt":0.43597566145570543,"score_spread":0.22764888153449214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978696365","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021742744,0.000064646505,0.9953352,0.00006583629,0.000070864065,0.0002281745,0.00038635358,0.000002450609,0.0036290488],"genre_scores_gemma":[0.0341962,0.00007821483,0.9653979,0.000054083514,0.000057447243,0.0000039034076,0.000001519447,0.000019499419,0.00019124748],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99862504,0.00015737776,0.000666141,0.00010280908,0.00028856567,0.00016008194],"domain_scores_gemma":[0.99322206,0.0049465094,0.0009189828,0.00024689524,0.0005722868,0.000093273804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011157937,0.00012700412,0.00044393173,0.000030146592,0.000097525095,0.000050811923,0.00053756457,0.000022610142,0.00043008573],"category_scores_gemma":[0.003842032,0.000068830625,0.000033217897,0.00011789731,0.00016886086,0.00020184639,0.00008593231,0.00020642082,0.0000019146312],"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":[0.00018645544,0.00017985364,0.00024400745,0.00007151157,0.00008457901,0.00004659603,0.0011565818,0.000023178576,0.00008627374,0.96826375,0.0019066783,0.027750514],"study_design_scores_gemma":[0.00083455443,0.00088233757,0.0003200198,0.00023327471,0.00020559484,0.000092117094,0.00032211829,0.0006733037,0.00051370123,0.99408984,0.0017110095,0.00012210109],"about_ca_topic_score_codex":0.000052882933,"about_ca_topic_score_gemma":0.00004318166,"teacher_disagreement_score":0.03397877,"about_ca_system_score_codex":0.000016809048,"about_ca_system_score_gemma":0.00015043268,"threshold_uncertainty_score":0.4709137},"labels":[],"label_agreement":null},{"id":"W1997376302","doi":"10.4236/ojs.2012.25067","title":"Effective Truncation of a Student’s <i>t</i>-Distribution by Truncation of the Chi Distribution in a Chi-Normal Mixture","year":2012,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":7,"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","keywords":"Kurtosis; Truncation (statistics); Mathematics; Student's t-distribution; Normal distribution; Distribution (mathematics); Variance-gamma distribution; Inverse-chi-squared distribution; Truncated normal distribution; Statistics; F-distribution; Asymptotic distribution; Mathematical analysis; Exponential distribution; Probability distribution; Distribution fitting; Econometrics; Estimator; Autoregressive conditional heteroskedasticity","score_opus":0.06311558512117567,"score_gpt":0.4151881734304895,"score_spread":0.35207258830931387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997376302","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":[0.12336087,0.00029183744,0.86855596,0.00014870903,0.00040815462,0.0010274224,0.006032546,0.000007271538,0.00016721105],"genre_scores_gemma":[0.60611916,0.000098971694,0.39228776,0.000024085448,0.00012282302,0.000041606803,0.0010197702,0.00003862017,0.00024719717],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9955664,0.00097201444,0.0017267527,0.0002663915,0.00097253255,0.000495889],"domain_scores_gemma":[0.99416023,0.001658932,0.0024423108,0.00052338233,0.0009882654,0.00022686501],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0029954617,0.00036549967,0.00092331745,0.00009245681,0.0001873904,0.00007414755,0.0006671817,0.00021821323,0.00012380301],"category_scores_gemma":[0.005340474,0.00027878152,0.00016481928,0.0004995124,0.00022859978,0.0006246159,0.00021750567,0.00060422166,0.0000059534295],"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":[0.0020134817,0.005158579,0.00939554,0.0011452447,0.000676834,0.0000045194806,0.008598945,0.0007036029,0.039459873,0.8292711,0.04243143,0.061140846],"study_design_scores_gemma":[0.014518478,0.0017227134,0.27370536,0.0031368176,0.0033089572,0.00038663796,0.0009847374,0.0014610926,0.01080848,0.59221894,0.09545344,0.0022943541],"about_ca_topic_score_codex":0.000030138975,"about_ca_topic_score_gemma":0.00018333948,"teacher_disagreement_score":0.48275828,"about_ca_system_score_codex":0.00048112666,"about_ca_system_score_gemma":0.00017154077,"threshold_uncertainty_score":0.99996644},"labels":[],"label_agreement":null},{"id":"W1999151257","doi":"10.4236/ojs.2013.32013","title":"Bayesian Estimation for GEV-B-Spline Model","year":2013,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":20,"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 Moncton; Institut National de la Recherche Scientifique","funders":"","keywords":"Markov chain Monte Carlo; Quantile; Bayesian probability; Covariate; Statistics; Bayesian inference; Spline (mechanical); Mathematics; Econometrics; Computer science","score_opus":0.015786458317988663,"score_gpt":0.28378462343087807,"score_spread":0.26799816511288943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999151257","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007420266,0.000006290756,0.98929846,0.00060231757,0.000036282745,0.00014131489,0.000027085047,0.0000014455625,0.0024665256],"genre_scores_gemma":[0.44290885,0.000007809154,0.55577666,0.00021117262,0.000016890348,0.000004951861,0.000007978177,0.000005113335,0.0010606061],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993723,0.000020086989,0.00028814538,0.00007415236,0.00012972095,0.000115619565],"domain_scores_gemma":[0.99948233,0.00008025409,0.00022839768,0.000085737825,0.000038160568,0.000085096115],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030956426,0.000060480892,0.00015003553,0.00002203514,0.00009438467,0.0000612911,0.0002866111,0.00003468122,0.0028345308],"category_scores_gemma":[0.000102239734,0.000048394122,0.000032086835,0.000061275525,0.00005756488,0.0004206651,0.000069306465,0.00007530412,0.0001458097],"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":[0.000052358606,0.000104296814,0.0025388256,0.0000071284244,0.00005576881,0.000008788838,0.00026916977,0.8134749,0.00064244034,0.002390164,0.12710495,0.053351205],"study_design_scores_gemma":[0.0003103463,0.000093251,0.00084429764,0.000003964113,0.00005476416,0.000011949752,0.000014368232,0.90935665,0.00007752953,0.088507846,0.00066766,0.000057354595],"about_ca_topic_score_codex":0.00005975956,"about_ca_topic_score_gemma":0.000037555998,"teacher_disagreement_score":0.43548858,"about_ca_system_score_codex":0.000037703285,"about_ca_system_score_gemma":0.000018535238,"threshold_uncertainty_score":0.99807703},"labels":[],"label_agreement":null},{"id":"W2002447070","doi":"10.4236/ojs.2015.51007","title":"Combining Likelihood Information from Independent Investigations","year":2015,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Distribution Estimation and Applications","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":"York University","funders":"","keywords":"Likelihood function; Mathematics; Fisher information; Likelihood principle; Statistics; Maximum likelihood; Marginal likelihood; Scoring algorithm; Score test; Restricted maximum likelihood; Estimation theory; Likelihood-ratio test; Maximum likelihood sequence estimation; Expectation–maximization algorithm; Applied mathematics; Quasi-maximum likelihood","score_opus":0.13766552041140273,"score_gpt":0.3801974429491913,"score_spread":0.24253192253778855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002447070","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037444618,0.000010332996,0.988804,0.00072905136,0.00020333703,0.00020484986,0.0015890393,0.000014278667,0.0047006467],"genre_scores_gemma":[0.30601344,0.000006326084,0.6931547,0.00034254778,0.00005910358,0.000011828962,0.0003533476,0.000010862659,0.000047871275],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99834603,0.00008165537,0.00086950883,0.00007034401,0.0004925137,0.00013993576],"domain_scores_gemma":[0.9969156,0.00064658426,0.00078814966,0.0001728847,0.0011014178,0.00037538796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006370405,0.00010785823,0.0002456281,0.00007838419,0.00010991828,0.00030357132,0.0004272067,0.000054066295,0.00028788825],"category_scores_gemma":[0.003985049,0.000098741904,0.000028475304,0.00019021542,0.000071331335,0.00079571543,0.00010648507,0.00022302287,0.00014208908],"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":[0.000021933827,0.00010590597,0.00028884408,0.000008316313,0.000038297767,0.0000051820766,0.0007975933,0.000034741985,0.000018749954,0.8768735,0.11505121,0.0067557325],"study_design_scores_gemma":[0.0014522226,0.00010366704,0.0025086452,0.000057465335,0.000086959706,0.000026432484,0.001088743,0.0035176852,0.00013058147,0.97826344,0.012632736,0.00013140212],"about_ca_topic_score_codex":0.000055753422,"about_ca_topic_score_gemma":0.000014309702,"teacher_disagreement_score":0.30226898,"about_ca_system_score_codex":0.00011448529,"about_ca_system_score_gemma":0.00045249684,"threshold_uncertainty_score":0.4770764},"labels":[],"label_agreement":null},{"id":"W2008984269","doi":"10.4236/ojs.2014.45033","title":"Distribution of the Sample Correlation Matrix and Applications","year":2014,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":38,"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 Moncton","funders":"","keywords":"Mathematics; Null (SQL); Correlation; Sample (material); Statistics; Null distribution; Multivariate normal distribution; Matrix (chemical analysis); Population; Multivariate statistics; Applied mathematics; Statistical physics; Statistical hypothesis testing; Computer science; Physics; Test statistic; Thermodynamics; Geometry; Materials science","score_opus":0.04152441248663195,"score_gpt":0.36769506787396145,"score_spread":0.3261706553873295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008984269","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":[0.0011469692,0.000015047801,0.9960213,0.0003270287,0.00004770922,0.00028454105,0.0017997961,0.000003730935,0.00035391757],"genre_scores_gemma":[0.79075295,0.000017804421,0.20886548,0.000029687173,0.000049501916,0.000017318016,0.00011718532,0.000009100274,0.00014095815],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999021,0.00008272977,0.00053591636,0.000070836606,0.00021237103,0.00007714836],"domain_scores_gemma":[0.99707985,0.0015562031,0.000678948,0.0001764755,0.00044036334,0.000068169844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004714646,0.00006933056,0.00017943498,0.000019337745,0.0001532535,0.000052500633,0.0002687161,0.000035040284,0.00008158842],"category_scores_gemma":[0.0023589523,0.00005023086,0.00002921553,0.00016862842,0.00012910902,0.000090133064,0.00007490222,0.00011458504,0.0000038688113],"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":[0.000008193571,0.000061808896,0.0005678548,0.00003249649,0.000010710532,6.314361e-8,0.000027146625,0.00005018548,0.000034514836,0.98375684,0.006089428,0.009360755],"study_design_scores_gemma":[0.0005164075,0.00005947134,0.03112689,0.000051920793,0.00011873279,0.000024576686,0.00006903142,0.009412323,0.00010119364,0.93889236,0.01954621,0.00008090246],"about_ca_topic_score_codex":0.000010500622,"about_ca_topic_score_gemma":0.0000043291966,"teacher_disagreement_score":0.789606,"about_ca_system_score_codex":0.000031112657,"about_ca_system_score_gemma":0.00005641971,"threshold_uncertainty_score":0.28240567},"labels":[],"label_agreement":null},{"id":"W2012014667","doi":"10.4236/ojs.2015.53021","title":"Trace of the Wishart Matrix and Applications","year":2015,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":14,"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 Moncton","funders":"","keywords":"Wishart distribution; TRACE (psycholinguistics); Matrix normal distribution; Matrix (chemical analysis); Inverse-Wishart distribution; Mathematics; Computer science; Statistics; Multivariate statistics; Philosophy","score_opus":0.19182284680408182,"score_gpt":0.48336319733960803,"score_spread":0.2915403505355262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012014667","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005314263,0.00020335904,0.9973401,0.00017120726,0.00006413122,0.00022666788,0.00025581926,0.0000013983929,0.0012059149],"genre_scores_gemma":[0.017268632,0.000038149665,0.981891,0.00002076589,0.000044086504,0.000004468075,5.0204375e-7,0.000011219609,0.0007211972],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99911433,0.000089828674,0.00042760064,0.00006333808,0.00021962656,0.000085266554],"domain_scores_gemma":[0.9980503,0.00080655597,0.00047643014,0.0001588509,0.00038745752,0.00012042842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073731603,0.00006689799,0.00024510044,0.000019002739,0.00004646266,0.000033374843,0.00032036137,0.000025444828,0.000020238145],"category_scores_gemma":[0.0013042021,0.000042481224,0.000021703194,0.000075345844,0.00010496392,0.00010633938,0.00011559684,0.00013771586,9.0994166e-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":[0.000033301447,0.000071027964,0.000078245714,0.00004715224,0.000023037232,0.0000043524674,0.0002692487,0.000039908777,0.00010290356,0.96115494,0.008303288,0.029872587],"study_design_scores_gemma":[0.0004832134,0.00010340615,0.00013435472,0.000045254168,0.00007584981,0.0000707246,0.00028407626,0.00036168072,0.00014406016,0.98392415,0.014317276,0.00005594729],"about_ca_topic_score_codex":0.0000035496898,"about_ca_topic_score_gemma":0.0000036539038,"teacher_disagreement_score":0.02981664,"about_ca_system_score_codex":0.000017357963,"about_ca_system_score_gemma":0.00011452681,"threshold_uncertainty_score":0.1732334},"labels":[],"label_agreement":null},{"id":"W2012324263","doi":"10.4236/ojs.2011.12011","title":"Distributions of Ratios: From Random Variables to Random Matrices","year":2011,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Advanced Combinatorial Mathematics","field":"Mathematics","cited_by":12,"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 Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Random variable; Statistics; Applied mathematics; Sum of normally distributed random variables; Distribution (mathematics); Probability density function; Multivariate random variable; Mathematical analysis","score_opus":0.06628427761356873,"score_gpt":0.335975448819036,"score_spread":0.26969117120546726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012324263","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009758308,0.00007844631,0.9859359,0.000041859603,0.0005810815,0.000500319,0.0010008502,0.000008393884,0.0020948104],"genre_scores_gemma":[0.13122989,0.00006169408,0.8684259,0.000016556702,0.00010476096,0.000009669157,0.00001121516,0.000028841678,0.00011149079],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978114,0.00015041229,0.0013068084,0.00012393847,0.0004120279,0.00019545176],"domain_scores_gemma":[0.99456453,0.0027253218,0.0013890277,0.0003395657,0.0008028049,0.00017875012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009321193,0.0001803691,0.0008359208,0.00009581198,0.00009412043,0.00007595455,0.0008625703,0.00006397939,0.00056861545],"category_scores_gemma":[0.0046897917,0.00014729775,0.00008319642,0.0002424769,0.00006287354,0.0003228368,0.000205244,0.00018588208,0.000012850834],"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":[0.0020067047,0.0008058228,0.0002229513,0.00014410971,0.00042745555,0.00007639959,0.003744202,0.000030144301,0.0010238951,0.96727973,0.022499217,0.0017393923],"study_design_scores_gemma":[0.005768411,0.0002929873,0.00006402453,0.00027595492,0.00035476417,0.000021583808,0.00044102286,0.000108976994,0.004726202,0.986786,0.0009911087,0.00016898538],"about_ca_topic_score_codex":0.000054783206,"about_ca_topic_score_gemma":0.000012998939,"teacher_disagreement_score":0.121471584,"about_ca_system_score_codex":0.000059100614,"about_ca_system_score_gemma":0.0001745892,"threshold_uncertainty_score":0.62259406},"labels":[],"label_agreement":null},{"id":"W2013280027","doi":"10.4236/ojs.2012.22016","title":"Asymptotic Inference for the Weak Stationary Double AR(1) Model","year":2012,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","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":"York University","funders":"","keywords":"Autoregressive model; Inference; Applied mathematics; Rate of convergence; Monte Carlo method; Mathematics; Sample size determination; Algorithm; Convergence (economics); Scalar (mathematics); Computer science; Statistics; Artificial intelligence","score_opus":0.13919116221160627,"score_gpt":0.32765965452167184,"score_spread":0.18846849231006557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013280027","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":[0.004232046,0.0014411523,0.9895482,0.0003127804,0.00045647734,0.00024315878,0.0005816552,0.0000020360744,0.0031825048],"genre_scores_gemma":[0.85979676,0.00036439576,0.13893034,0.00011702953,0.00015978466,0.000010670873,0.000010431706,0.00001531652,0.0005952583],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989208,0.0000076290135,0.000698701,0.00009081594,0.000054604378,0.0002274244],"domain_scores_gemma":[0.9985682,0.00039487815,0.0006097919,0.00015621466,0.00018524675,0.00008567255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012821446,0.00009092883,0.00027093742,0.00006354383,0.0001882866,0.00013036522,0.00044321225,0.000041642892,0.00007756411],"category_scores_gemma":[0.00034802742,0.00007615645,0.00005667267,0.00007731675,0.000038132534,0.0006196437,0.000075900265,0.00015813594,0.00002974367],"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":[0.00012208424,0.00008269398,0.009812695,0.000018433399,0.000040023686,3.7974453e-7,0.00071820285,0.025953125,0.0000024879062,0.95414954,0.0056154695,0.0034848314],"study_design_scores_gemma":[0.0010006815,0.00010302156,0.0057263644,0.000021616168,0.000026674956,0.0000056196977,0.00014781307,0.6056236,0.000009229954,0.3671195,0.020068847,0.00014701975],"about_ca_topic_score_codex":0.00004938704,"about_ca_topic_score_gemma":0.0000104898445,"teacher_disagreement_score":0.8555647,"about_ca_system_score_codex":0.00005677331,"about_ca_system_score_gemma":0.0001040851,"threshold_uncertainty_score":0.31055698},"labels":[],"label_agreement":null},{"id":"W2013698137","doi":"10.4236/ojs.2011.12013","title":"Empirical Analysis of Impact of Conversion ofConvertible Bonds on Corporate Performance of Different Industries in China","year":2011,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Corporate Finance and Governance","field":"Business, Management and Accounting","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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Convertible bond; Wilcoxon signed-rank test; China; Convertible; Business; Empirical research; Bond; Econometrics; Accounting; Financial economics; Economics; Mathematics; Statistics; Finance; Engineering; Geography","score_opus":0.07866145090431585,"score_gpt":0.2826275167291125,"score_spread":0.20396606582479665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013698137","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":[0.9985879,0.000014055986,0.0004681087,0.00002051564,0.000055351746,0.00008036286,0.00009971855,8.6163413e-7,0.0006730978],"genre_scores_gemma":[0.99948794,0.000090854286,0.0002863899,0.000027275415,0.000021612246,4.6621926e-7,0.000012879467,0.0000074682544,0.000065147324],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987636,0.000015255744,0.0007255417,0.00009150892,0.00028186696,0.00012220732],"domain_scores_gemma":[0.99646884,0.000041931726,0.0029571105,0.0001483014,0.00036907985,0.000014757775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003296325,0.00012328947,0.00063443935,0.00042003713,0.000022050346,0.000020871155,0.00035784845,0.000047799036,0.0003299099],"category_scores_gemma":[0.00008163787,0.00009098002,0.00008398592,0.0010080636,0.000088881774,0.00056403264,0.000097920776,0.00016178847,0.0000022621964],"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":[0.0010690095,0.00031709534,0.9935337,0.000113343995,0.00020854891,0.000016996639,0.00013034194,0.0007276325,0.00018552605,0.0006680024,0.0019373145,0.0010924881],"study_design_scores_gemma":[0.00092216826,0.00056460005,0.9906386,0.00021263976,0.00030922293,8.170328e-7,0.000048577847,0.0055922926,0.0011675707,0.00041296135,0.00004046491,0.00009008553],"about_ca_topic_score_codex":0.00096572767,"about_ca_topic_score_gemma":0.00005544205,"teacher_disagreement_score":0.00486466,"about_ca_system_score_codex":0.000037811504,"about_ca_system_score_gemma":0.00010711265,"threshold_uncertainty_score":0.37100577},"labels":[],"label_agreement":null},{"id":"W2025324018","doi":"10.4236/ojs.2014.43018","title":"Theoretical Properties of Composite Likelihoods","year":2014,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Mechanics and Entropy","field":"Physics and Astronomy","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":"York University","funders":"","keywords":"Quasi-maximum likelihood; Inference; Principle of maximum entropy; Composite number; Mathematics; Kullback–Leibler divergence; Entropy (arrow of time); Maximum likelihood; Applied mathematics; Projection (relational algebra); Statistical physics; Computer science; Algorithm; Statistics; Likelihood function; Artificial intelligence; Physics","score_opus":0.013617888085025466,"score_gpt":0.25934336385030055,"score_spread":0.2457254757652751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025324018","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":[0.016167963,0.000023172786,0.9745971,0.000119318174,0.00020501086,0.000079348625,0.00013750262,0.0000012485389,0.008669365],"genre_scores_gemma":[0.9138345,0.000003909896,0.08593911,0.000038169717,0.00012987433,9.880371e-7,0.000003923817,0.0000109370185,0.000038562004],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99908316,0.00007812916,0.0004346054,0.000069058384,0.00019958321,0.0001354658],"domain_scores_gemma":[0.99907124,0.00014216182,0.00029245357,0.00010910663,0.00027507643,0.00010994056],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00033319177,0.00008859373,0.0003011163,0.000028310606,0.000046595454,0.000076601915,0.00034562903,0.00001667516,0.0010705187],"category_scores_gemma":[0.00006925322,0.000063221654,0.0000430213,0.000048457787,0.000109773566,0.0000706685,0.00009842227,0.00013940559,0.000010825617],"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":[0.000070197675,0.00010047091,0.0005277184,0.000014578632,0.000054735854,0.0000019664446,0.0000739089,0.000015520976,0.0016705806,0.96527004,0.0011805593,0.031019721],"study_design_scores_gemma":[0.0013523321,0.00073292316,0.0006481583,0.0002147759,0.00013990281,0.000010290868,0.00021517802,0.025480252,0.011301525,0.9572912,0.0024245018,0.00018895934],"about_ca_topic_score_codex":0.000019148858,"about_ca_topic_score_gemma":1.1661542e-7,"teacher_disagreement_score":0.8976666,"about_ca_system_score_codex":0.0000067681444,"about_ca_system_score_gemma":0.00005034963,"threshold_uncertainty_score":0.99984264},"labels":[],"label_agreement":null},{"id":"W2025347578","doi":"10.4236/ojs.2012.23034","title":"Subsampling Method for Robust Estimation of Regression Models","year":2012,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":17,"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":"Outlier; Robust regression; Robustness (evolution); Regression analysis; Regression; Mathematics; Computer science; Local regression; Linear regression; Statistics; Polynomial regression","score_opus":0.05906675417687906,"score_gpt":0.3394301466337274,"score_spread":0.2803633924568483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025347578","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001129434,0.00025310248,0.99744505,0.00001055884,0.00055369287,0.00013963495,0.00005105296,0.000005969218,0.00041149964],"genre_scores_gemma":[0.38495895,0.000017340604,0.6149008,0.000004359127,0.0000667964,0.0000031299971,0.0000025648608,0.000011760269,0.000034294724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993252,0.000029188734,0.0003915744,0.000029526653,0.00012014958,0.00010432215],"domain_scores_gemma":[0.9993341,0.00018163834,0.00021663378,0.00006344555,0.00013786858,0.00006629587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006877217,0.00006290442,0.00021326375,0.000055294662,0.000031541505,0.000032838972,0.00012123614,0.000035277088,0.000014715627],"category_scores_gemma":[0.00009268904,0.00005110219,0.0000327081,0.00005007761,0.0000053959147,0.0003366329,0.000010778936,0.00007519339,9.595467e-7],"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":[0.0000417216,0.000013265746,0.000013192353,0.00008167973,0.00003762662,4.6467977e-7,0.0002387499,0.93161255,0.0015139034,0.0025418522,0.0017440439,0.062160928],"study_design_scores_gemma":[0.00051832735,0.000052466592,0.00004165048,0.00011281361,0.000037576512,0.000030961222,0.00013418999,0.9949726,0.0016522774,0.0017037999,0.00068608706,0.000057239016],"about_ca_topic_score_codex":0.000008862273,"about_ca_topic_score_gemma":0.0000018435612,"teacher_disagreement_score":0.3838295,"about_ca_system_score_codex":0.000032770706,"about_ca_system_score_gemma":0.000015388126,"threshold_uncertainty_score":0.20838869},"labels":[],"label_agreement":null},{"id":"W2044890887","doi":"10.4236/ojs.2013.34030","title":"Change-Point Detection for General Nonparametric Regression Models","year":2013,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","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":"University of Calgary","funders":"","keywords":"Mathematics; Nonparametric regression; Nonparametric statistics; Statistics; Regression analysis; Covariate; Regression; Bounded function; Applied mathematics; Mathematical analysis","score_opus":0.2577878223394403,"score_gpt":0.42408492770983763,"score_spread":0.16629710537039732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044890887","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053634024,0.00004814591,0.99255663,0.00014227755,0.0004024491,0.0006643388,0.00012232573,0.000005327341,0.0006951254],"genre_scores_gemma":[0.060623474,0.000066437584,0.9386219,0.00011637149,0.00022924099,0.00005918852,0.000002380238,0.000024939414,0.00025606083],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99860096,0.00012331238,0.0006502078,0.00012866926,0.00027368325,0.00022315321],"domain_scores_gemma":[0.9963323,0.0018163047,0.0006460802,0.00016681742,0.00086969754,0.00016878865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008661788,0.00014207466,0.00040471577,0.0001326606,0.00010783733,0.0002009858,0.000356973,0.000071309594,0.00031136224],"category_scores_gemma":[0.003114446,0.00010004407,0.000058617767,0.00016839392,0.000040985185,0.00044272034,0.000090185895,0.00019514939,0.000010520316],"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":[0.000096434975,0.00013444043,0.000021813836,0.0001020257,0.000045835826,0.000010149806,0.00024318376,0.000013869996,0.00077092263,0.3054383,0.018030077,0.67509294],"study_design_scores_gemma":[0.00066352036,0.00063586474,0.00045025558,0.000114201925,0.00006478354,0.000047276983,0.00007607444,0.070004314,0.0011057451,0.9263128,0.0003876917,0.00013743955],"about_ca_topic_score_codex":0.00006683782,"about_ca_topic_score_gemma":0.0000055067344,"teacher_disagreement_score":0.6749555,"about_ca_system_score_codex":0.00006052223,"about_ca_system_score_gemma":0.000052903993,"threshold_uncertainty_score":0.40796787},"labels":[],"label_agreement":null},{"id":"W2052813455","doi":"10.4236/ojs.2014.47050","title":"HAC-Robust Measurement of the Duration of a Trendless Subsample in a Global Climate Time Series","year":2014,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Climate variability and models","field":"Environmental 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 Guelph","funders":"","keywords":"Hiatus; Heteroscedasticity; Series (stratigraphy); Autocorrelation; Estimator; Econometrics; Consistency (knowledge bases); Statistics; Duration (music); Climatology; Mathematics; Variance (accounting); Time series; Environmental science; Geology; Economics","score_opus":0.036197155388286335,"score_gpt":0.2564996501198835,"score_spread":0.2203024947315972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052813455","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":[0.9161767,0.000022070224,0.07242182,0.0006229944,0.00021985448,0.00043691267,0.00066012307,0.0000023934424,0.0094371475],"genre_scores_gemma":[0.9729558,0.000019233936,0.026977496,0.00001783664,0.0000070648953,0.0000011005732,0.0000017749492,0.0000034737277,0.000016213246],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987336,0.00014265369,0.0005383785,0.00007279814,0.00040222125,0.00011037469],"domain_scores_gemma":[0.99921525,0.000055537195,0.0004919279,0.00014105036,0.00006024093,0.00003602582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015872519,0.000063275045,0.00020571945,0.000011745966,0.000040978128,0.000024820074,0.00034887943,0.000024423702,0.0004018878],"category_scores_gemma":[0.00023551947,0.00004424984,0.000030121995,0.00012837266,0.00012925532,0.0002158368,0.00018262364,0.00006189915,0.0000045272454],"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":[0.0018327718,0.0021386028,0.7083375,0.00050869473,0.00013134726,0.000016068963,0.004622102,0.14294155,0.051151853,0.06185544,0.0073010833,0.01916301],"study_design_scores_gemma":[0.0026343174,0.0008412256,0.91968024,0.0005200694,0.00014584167,0.00006833715,0.0004614082,0.035429817,0.0062376834,0.0324349,0.0012550069,0.00029116173],"about_ca_topic_score_codex":0.00047567792,"about_ca_topic_score_gemma":0.0017499912,"teacher_disagreement_score":0.21134275,"about_ca_system_score_codex":0.000115594616,"about_ca_system_score_gemma":0.00003587612,"threshold_uncertainty_score":0.44003895},"labels":[],"label_agreement":null},{"id":"W2077456109","doi":"10.4236/ojs.2012.25070","title":"Data Fusion Using Empirical Likelihood","year":2012,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","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":"York University","funders":"","keywords":"Empirical likelihood; Estimator; Inference; Statistics; Maximum likelihood; Estimating equations; Mathematics; Computer science; Parametric statistics; Statistical inference; Confidence interval; Econometrics; Artificial intelligence","score_opus":0.5438412272793877,"score_gpt":0.5668018232046595,"score_spread":0.022960595925271887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077456109","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021549126,0.00014335626,0.9952622,0.00008225704,0.00044506948,0.0001340098,0.0010889116,0.0000042133483,0.000685048],"genre_scores_gemma":[0.011120365,0.000056172936,0.9882563,0.00012610774,0.00033259435,5.1155996e-7,0.000016342696,0.000030402321,0.000061215724],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982205,0.00021123781,0.00070453325,0.0001315294,0.0003815596,0.00035065156],"domain_scores_gemma":[0.99689794,0.0014911488,0.00056095415,0.00044142953,0.0002670185,0.00034149675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002125455,0.00013896618,0.0004298322,0.000050251972,0.000116437615,0.00008351245,0.00086179876,0.00005782292,0.0002546639],"category_scores_gemma":[0.003339228,0.00010651841,0.000028432121,0.00009950739,0.000057160094,0.00075086596,0.00069066754,0.00029617333,0.0000074775967],"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":[0.00036294738,0.00146813,0.0026471545,0.00021664954,0.00023954494,0.00024093466,0.0013314307,0.000036525515,0.0013566061,0.51335996,0.1435813,0.3351588],"study_design_scores_gemma":[0.0008339041,0.00019291652,0.0002488984,0.0001439485,0.00027949424,0.00037285744,0.00026467623,0.011541566,0.0001233219,0.9601643,0.025584806,0.00024927652],"about_ca_topic_score_codex":0.0000037941209,"about_ca_topic_score_gemma":0.0000032509633,"teacher_disagreement_score":0.44680434,"about_ca_system_score_codex":0.000054321747,"about_ca_system_score_gemma":0.00014661427,"threshold_uncertainty_score":0.43436947},"labels":[],"label_agreement":null},{"id":"W2087268427","doi":"10.4236/ojs.2013.36a005","title":"Inference for the Normal Mean with Known Coefficient of Variation","year":2013,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","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":"York University","funders":"","keywords":"Estimator; Mathematics; Inference; Statistic; Statistics; Sample size determination; Point estimation; Statistical inference; Applied mathematics; Exponential family; Variation (astronomy); Computer science; Artificial intelligence","score_opus":0.0971537910778619,"score_gpt":0.3822120823949879,"score_spread":0.285058291317126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087268427","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029027225,0.000014718444,0.99547535,0.00015656355,0.00012173661,0.0004631324,0.00016020388,0.0000016981555,0.0007038451],"genre_scores_gemma":[0.21297714,0.000014025212,0.7868087,0.000037706046,0.00003726823,0.000012647759,0.0000012436161,0.000009735754,0.00010149659],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988347,0.00008636345,0.00056936697,0.00007357043,0.00029272062,0.00014322154],"domain_scores_gemma":[0.9914217,0.0063035022,0.0007363365,0.00016781509,0.0012975107,0.000073116425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096999714,0.00009565976,0.00028802134,0.000033391647,0.00008807107,0.00011861709,0.00044909643,0.00002862214,0.00048411347],"category_scores_gemma":[0.0032062295,0.000051136878,0.00002667477,0.00009811577,0.00010266103,0.00013141448,0.00006715336,0.00013394502,0.000003566456],"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":[0.00013133297,0.00017196841,0.00021229988,0.00009941506,0.00010494067,0.0000026825765,0.001203236,0.00023579034,0.00031634537,0.92961365,0.0051776674,0.06273067],"study_design_scores_gemma":[0.0020364525,0.0027760654,0.015625266,0.00033022361,0.00040585882,0.000047748705,0.00080248294,0.05880789,0.00094842416,0.91645557,0.0015060332,0.00025796442],"about_ca_topic_score_codex":0.000101006364,"about_ca_topic_score_gemma":0.000016618564,"teacher_disagreement_score":0.21007442,"about_ca_system_score_codex":0.000021551996,"about_ca_system_score_gemma":0.00018369021,"threshold_uncertainty_score":0.5300703},"labels":[],"label_agreement":null},{"id":"W2221432182","doi":"10.4236/ojs.2015.57068","title":"Statistical Classification Using the Maximum Function","year":2015,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Neural Networks and Applications","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":"Université de Moncton","funders":"","keywords":"Linear discriminant analysis; Cluster analysis; Discriminant function analysis; Mathematics; Extension (predicate logic); Function (biology); Pattern recognition (psychology); Statistical hypothesis testing; Computer science; Artificial intelligence; Statistics; Algorithm","score_opus":0.1598425714878807,"score_gpt":0.35332995444597515,"score_spread":0.19348738295809445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2221432182","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006397699,0.00003504372,0.9970391,0.0011575111,0.00038344026,0.00010624637,0.00001733853,0.000004407889,0.00061714597],"genre_scores_gemma":[0.331597,0.00001608687,0.6678172,0.0003126669,0.00016991688,0.0000024187618,0.0000039180723,0.000006305426,0.00007450532],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991403,0.00008376324,0.00030418704,0.00009196166,0.00027045936,0.00010927824],"domain_scores_gemma":[0.9988234,0.00016330465,0.0003148752,0.00022400156,0.0003559932,0.000118435244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005733101,0.000060323353,0.0001033846,0.000025549563,0.00013339786,0.00040471338,0.00087802246,0.000019721174,0.000013883383],"category_scores_gemma":[0.000073040675,0.000039025872,0.000014274547,0.00018714408,0.00005353512,0.00040109255,0.00014864274,0.00015419937,0.0000125926745],"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":[0.000026130203,0.00005397888,0.00017815262,0.0000022941913,0.00001716293,0.000015310026,0.00012552115,0.0015303583,0.00028950715,0.84363073,0.06443571,0.08969512],"study_design_scores_gemma":[0.00076558325,0.00040292315,0.0052504768,0.000026840138,0.00006999891,0.0003384789,0.00034879707,0.4084157,0.000068135065,0.49298233,0.09114822,0.00018253042],"about_ca_topic_score_codex":0.0000143457655,"about_ca_topic_score_gemma":0.0000036618187,"teacher_disagreement_score":0.40688533,"about_ca_system_score_codex":0.000043530723,"about_ca_system_score_gemma":0.0001920176,"threshold_uncertainty_score":0.39026606},"labels":[],"label_agreement":null},{"id":"W2475118633","doi":"10.4236/ojs.2016.63045","title":"Inverse Problem for a Time-Series Valued Computer Simulator via Scalarization","year":2016,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Acadia University","keywords":"Computer science; Inverse; Focus (optics); Set (abstract data type); Simulation; Series (stratigraphy); Inverse problem; Mathematical optimization; Algorithm; Scalar (mathematics); Applied mathematics; Mathematics; Mathematical analysis","score_opus":0.01456073951975799,"score_gpt":0.2751931832559599,"score_spread":0.26063244373620187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2475118633","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017431285,0.000006143502,0.99867374,0.00040001638,0.0003116386,0.00043377982,0.00007380971,0.000016543161,0.00006687708],"genre_scores_gemma":[0.0008013235,0.000012680993,0.99803215,0.00017167,0.000103487255,0.000006762987,0.000005009189,0.000019021829,0.0008478751],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988117,0.00007678424,0.00047761854,0.00019415953,0.0002529109,0.00018678681],"domain_scores_gemma":[0.9975939,0.00027871417,0.00061744166,0.00020450041,0.0011727982,0.00013267428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038538803,0.00013228568,0.00025504272,0.00010076573,0.00012620607,0.00022993225,0.0008035872,0.000043415253,0.000047770744],"category_scores_gemma":[0.00021279228,0.000094102594,0.00004123245,0.00018893165,0.00006558797,0.0018609118,0.00024974457,0.000066758475,0.000027418608],"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":[0.00046060135,0.00048426355,0.000372879,0.00007540029,0.00028301415,0.00010842154,0.0013419349,0.16506216,0.004601406,0.120005675,0.025181383,0.68202287],"study_design_scores_gemma":[0.002884243,0.0006870885,0.0001911755,0.000113056165,0.00002325139,0.00008055182,0.000007145042,0.9567069,0.0011152772,0.033972144,0.0039893077,0.00022986453],"about_ca_topic_score_codex":0.0000012076822,"about_ca_topic_score_gemma":0.0000014121008,"teacher_disagreement_score":0.79164475,"about_ca_system_score_codex":0.0001005563,"about_ca_system_score_gemma":0.00017684382,"threshold_uncertainty_score":0.38373923},"labels":[],"label_agreement":null},{"id":"W2510584737","doi":"10.4236/ojs.2016.64054","title":"Efficiency of Some Estimators for a Generalized Poisson Autoregressive Process of Order 1","year":2016,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","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":"Université Laval; Actua; Ontario Universities’ Application Centre; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Autoregressive model; Mathematics; Applied mathematics; Series (stratigraphy); Asymptotic analysis; Poisson distribution; Statistics; Generalized method of moments; Conditional variance; STAR model; Time series; Econometrics; Autoregressive integrated moving average; Autoregressive conditional heteroskedasticity","score_opus":0.04548329583630735,"score_gpt":0.30814359709228534,"score_spread":0.262660301255978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2510584737","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":[0.33051968,0.0005874745,0.6670053,0.000118494645,0.00023906838,0.00019862936,0.0011956462,0.0000013792825,0.00013431658],"genre_scores_gemma":[0.8595283,0.00018761412,0.14005385,0.000016651804,0.000050079838,0.000004485803,0.0000029451899,0.000016122038,0.00013991554],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998528,0.000010793906,0.0011118947,0.00012900852,0.00006440893,0.00015589151],"domain_scores_gemma":[0.9973039,0.00017844442,0.0017931481,0.00012948581,0.0005364287,0.000058566144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006962787,0.00009646765,0.00057931733,0.00013742539,0.000045748744,0.000022824503,0.00039003772,0.00005436495,0.00007126576],"category_scores_gemma":[0.0013262224,0.0000741237,0.00006907308,0.00010370961,0.00007572817,0.00029354525,0.00004199783,0.000057295456,0.0000026633923],"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":[0.00093448634,0.00053732726,0.035010293,0.0005981248,0.0001774486,0.000009572162,0.0029860162,0.0021082195,0.00078751217,0.93005705,0.0020569658,0.024736958],"study_design_scores_gemma":[0.008389122,0.0018303784,0.008620869,0.00091446785,0.00010745165,0.000016565153,0.0002499808,0.06932153,0.006880614,0.90035164,0.0027818792,0.00053549255],"about_ca_topic_score_codex":0.00005724082,"about_ca_topic_score_gemma":0.000004644514,"teacher_disagreement_score":0.5290086,"about_ca_system_score_codex":0.000036707726,"about_ca_system_score_gemma":0.0001729918,"threshold_uncertainty_score":0.30226767},"labels":[],"label_agreement":null},{"id":"W2545722099","doi":"10.4236/ojs.2016.65078","title":"Hypergeometric Functions: From One Scalar Variable to Several Matrix Arguments, in Statistics and Beyond","year":2016,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Random Matrices and Applications","field":"Mathematics","cited_by":10,"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 Moncton","funders":"Université de Moncton","keywords":"Hypergeometric function of a matrix argument; Hypergeometric distribution; Hypergeometric function; Mathematics; Generalized hypergeometric function; Confusion; Basic hypergeometric series; Scalar (mathematics); Hypergeometric identity; Statistics; Matrix (chemical analysis); Univariate; Algebra over a field; Pure mathematics; Psychology; Multivariate statistics","score_opus":0.032758036461552585,"score_gpt":0.323125225805209,"score_spread":0.29036718934365646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2545722099","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032082405,0.0001543544,0.96271205,0.00052780693,0.00016141485,0.0004000936,0.0033145195,0.0000052571186,0.00064210995],"genre_scores_gemma":[0.03767742,0.00042106962,0.9592778,0.00010698849,0.00010920302,0.00001706885,0.000016800348,0.000029153216,0.0023445068],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984169,0.00007044642,0.00077571947,0.00017549409,0.00033881355,0.00022261328],"domain_scores_gemma":[0.9971372,0.0017298618,0.0004237789,0.0002152023,0.00028454,0.00020941331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067196763,0.00014212185,0.00043746224,0.00028000763,0.00009686615,0.00017838682,0.00034471328,0.00005507895,0.00073506474],"category_scores_gemma":[0.0009922714,0.000105254476,0.000020908168,0.0005156187,0.000033927037,0.00027391218,0.00015724999,0.00014414759,0.000053047977],"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":[0.0009294101,0.0016765967,0.008966341,0.00017325359,0.0005453999,0.00012633497,0.0008140647,0.0002457411,0.0030222863,0.5570985,0.33433187,0.09207023],"study_design_scores_gemma":[0.0067588515,0.00044182182,0.0077466588,0.00027646526,0.0003415787,0.000045216104,0.00027723762,0.00048268292,0.00010518545,0.95136577,0.03176237,0.00039616218],"about_ca_topic_score_codex":0.00012104835,"about_ca_topic_score_gemma":0.000041802876,"teacher_disagreement_score":0.3942673,"about_ca_system_score_codex":0.000097808625,"about_ca_system_score_gemma":0.00011533241,"threshold_uncertainty_score":0.8048443},"labels":[],"label_agreement":null},{"id":"W2609350886","doi":"10.4236/ojs.2017.72019","title":"Estimation of Attributable Risk from Clustered Binary Data: The Case of Cross-Sectional and Cohort Studies","year":2017,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Robarts Clinical Trials; Western University","funders":"","keywords":"Statistics; Confidence interval; Mathematics; Monte Carlo method; Wald test; Inference; Interval estimation; Coverage probability; Cluster (spacecraft); Correlation; Variance (accounting); Aggregate (composite); Econometrics; Statistical hypothesis testing; Binary data; Statistical inference; Binary number; Computer science","score_opus":0.2740426777950981,"score_gpt":0.5118138189660502,"score_spread":0.23777114117095205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609350886","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20774385,0.00016638607,0.783033,0.000041924006,0.00022063465,0.00018062988,0.008517547,0.0000010861157,0.00009491853],"genre_scores_gemma":[0.3856681,0.00022077507,0.61403364,0.0000037059483,0.000037155864,0.0000011787953,0.000010953954,0.0000061858323,0.000018347997],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985133,0.0002087098,0.00081628375,0.00013517332,0.00022533767,0.00010120257],"domain_scores_gemma":[0.99152666,0.005095758,0.0020900436,0.00056851876,0.00066245446,0.0000565479],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002418316,0.00010359002,0.0005018168,0.000029244111,0.00040079345,0.00024097497,0.0007018917,0.000043472857,0.00006705433],"category_scores_gemma":[0.015514649,0.00006663685,0.000023100978,0.000033186476,0.0005612601,0.00044429602,0.0006391013,0.00019875677,5.982976e-7],"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":[0.00086975883,0.00047088822,0.44497606,0.0007238416,0.0027080912,0.0013515723,0.0013291046,0.00018430522,0.00024169275,0.37825784,0.010986715,0.15790014],"study_design_scores_gemma":[0.0007755901,0.00022545242,0.23013787,0.00015878036,0.00041936236,0.00036834087,0.00026523168,0.018876705,0.00016486889,0.7484995,0.00002303406,0.00008527516],"about_ca_topic_score_codex":0.00052680325,"about_ca_topic_score_gemma":0.00007704987,"teacher_disagreement_score":0.37024164,"about_ca_system_score_codex":0.00002016488,"about_ca_system_score_gemma":0.000100376026,"threshold_uncertainty_score":0.99277806},"labels":[],"label_agreement":null},{"id":"W2622376831","doi":"10.4236/ojs.2017.73029","title":"Confidence Intervals for the Mean of Non-Normal Distribution: Transform or Not to Transform","year":2017,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":30,"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","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Confidence interval; Mathematics; Normality; CDF-based nonparametric confidence interval; Confidence distribution; Sample size determination; Coverage probability; Robust confidence intervals; Transformation (genetics); Normal distribution; Parametric statistics; Confidence region; Data transformation; Power transform; Computer science; Data mining","score_opus":0.13757602959727303,"score_gpt":0.45764766421103736,"score_spread":0.3200716346137643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2622376831","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001616114,0.0000074494455,0.9921568,0.0019164257,0.0003194937,0.0007306536,0.0037515413,0.0000019632432,0.0009540803],"genre_scores_gemma":[0.19427997,0.00006519533,0.8051651,0.000095097406,0.00009534511,0.00001617963,0.000004104726,0.000015870592,0.0002631543],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982294,0.000051797273,0.00097294425,0.00012268095,0.00037369202,0.0002494966],"domain_scores_gemma":[0.9941974,0.003748602,0.0007617912,0.0003855327,0.00073212903,0.00017449485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020691354,0.00016132995,0.0005835486,0.000027361213,0.0003687998,0.00031979472,0.0015463468,0.000055458797,0.000345328],"category_scores_gemma":[0.0041334275,0.00009315663,0.00010905825,0.00005298736,0.0002263231,0.00029089465,0.000080133555,0.00020901681,0.0000026304824],"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":[0.0015817196,0.00009350941,0.00001728019,0.00023821933,0.00012454728,0.000028095199,0.0012469243,0.0000014561806,0.00017686136,0.67954123,0.0081901755,0.30876],"study_design_scores_gemma":[0.0022619003,0.0022127097,0.0033085642,0.0008080619,0.00040309812,0.0001310268,0.00088715437,0.00144807,0.011774677,0.97182363,0.0046546664,0.0002864439],"about_ca_topic_score_codex":0.00005672551,"about_ca_topic_score_gemma":0.00012522962,"teacher_disagreement_score":0.30847353,"about_ca_system_score_codex":0.000039699265,"about_ca_system_score_gemma":0.00023719754,"threshold_uncertainty_score":0.49483976},"labels":[],"label_agreement":null},{"id":"W2626987868","doi":"10.4236/ojs.2017.73033","title":"Maximum Entropy Empirical Likelihood Methods Based on Laplace Transforms for Nonnegative Continuous Distribution with Actuarial Applications","year":2017,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"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 Laval","funders":"","keywords":"Mathematics; Laplace distribution; Laplace transform; Applied mathematics; Principle of maximum entropy; Likelihood function; Exponential function; Mathematical optimization; Mellin transform; Maximum likelihood; Statistics; Mathematical analysis","score_opus":0.11115748503376287,"score_gpt":0.46209650128601604,"score_spread":0.35093901625225316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626987868","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028253772,0.0000034518682,0.99337065,0.0010710054,0.00016897375,0.001061989,0.0030045302,0.0000068758245,0.0010300104],"genre_scores_gemma":[0.022096844,0.000007940922,0.9773579,0.0001292536,0.00017649824,0.000094677125,0.00004537916,0.000029552766,0.000061926934],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99824697,0.00022919395,0.00065582804,0.00021589267,0.00034837853,0.00030372263],"domain_scores_gemma":[0.9919641,0.0055079916,0.001066789,0.00041084757,0.0008203774,0.00022987326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017629107,0.00021885235,0.0006331678,0.000039911025,0.00051282503,0.000441961,0.0007695279,0.00009489545,0.0001233734],"category_scores_gemma":[0.005248789,0.00014684975,0.00008433178,0.00006012353,0.00021069327,0.00018452514,0.000046207642,0.00033220765,0.0000033547826],"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":[0.0037248463,0.00083399506,0.00032233127,0.00014587394,0.0002099307,0.000033282085,0.00035342725,0.00002075512,0.00022421073,0.6356377,0.009669928,0.34882373],"study_design_scores_gemma":[0.005438852,0.0035657012,0.0013810524,0.00020888959,0.00042495152,0.000024836216,0.00022730841,0.008516447,0.0024233737,0.96506906,0.012397847,0.0003217026],"about_ca_topic_score_codex":0.000011059789,"about_ca_topic_score_gemma":0.000009749083,"teacher_disagreement_score":0.34850204,"about_ca_system_score_codex":0.000099553574,"about_ca_system_score_gemma":0.00034052352,"threshold_uncertainty_score":0.628367},"labels":[],"label_agreement":null},{"id":"W2750166930","doi":"10.4236/ojs.2017.74047","title":"An Analysis of Fights in the National Hockey League","year":2017,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":3,"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":"League; TRIPS architecture; Football; Advertising; Test (biology); Ice hockey; Demographic economics; Geography; Aeronautics; Transport engineering; Engineering; Business; Economics; Archaeology; Medicine; Geology; Physical medicine and rehabilitation","score_opus":0.09707962080463815,"score_gpt":0.33646715820316847,"score_spread":0.2393875373985303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2750166930","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":[0.925848,0.00035001026,0.031854443,0.0012059448,0.00046344355,0.00019527183,0.0028003815,0.0000010836628,0.037281398],"genre_scores_gemma":[0.99640876,0.00020192159,0.0030471585,0.000114978706,0.00005732223,6.1477846e-7,0.000014650406,0.0000038872586,0.0001506857],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991027,0.000009216427,0.0006400565,0.00008055885,0.000085897685,0.00008157107],"domain_scores_gemma":[0.99812555,0.000056469216,0.0014038377,0.00021961058,0.00016098363,0.000033572378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015342424,0.00005432432,0.0003378163,0.00025194796,0.00012127429,0.00026182726,0.0009727092,0.000025736059,0.0004573813],"category_scores_gemma":[0.00015141122,0.00004200616,0.000059583504,0.0001314744,0.000051838077,0.00038471012,0.0000336359,0.00010847942,0.000006850814],"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":[0.000030539464,0.00017386775,0.55543,0.000007775722,0.00031614394,0.000023716431,0.00090502296,0.008847876,0.0000024654644,0.43021598,0.0031669515,0.00087967847],"study_design_scores_gemma":[0.00031729252,0.000094420044,0.90893185,0.0000105212075,0.000058044676,0.0000038593525,0.000093704686,0.057908975,0.000005040605,0.024438445,0.0080659855,0.00007187864],"about_ca_topic_score_codex":0.00048313136,"about_ca_topic_score_gemma":0.0005213226,"teacher_disagreement_score":0.40577754,"about_ca_system_score_codex":0.000026971202,"about_ca_system_score_gemma":0.000042461375,"threshold_uncertainty_score":0.50080043},"labels":[],"label_agreement":null},{"id":"W2753173657","doi":"10.4236/ojs.2017.74052","title":"Simulated Minimum Hellinger Distance Estimation for Some Continuous Financial and Actuarial Models","year":2017,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","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":"Université Laval; Ontario Universities’ Application Centre","funders":"","keywords":"Hellinger distance; Estimator; Consistency (knowledge bases); Mathematics; Probability density function; Closed-form expression; Applied mathematics; Expression (computer science); Maximization; Density estimation; Function (biology); Mathematical optimization; Statistics; Computer science; Mathematical analysis","score_opus":0.06807495672095115,"score_gpt":0.2950093762669844,"score_spread":0.22693441954603327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753173657","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":[0.117441386,0.0005972902,0.87956464,0.00019499168,0.0007053107,0.00028632098,0.0009564424,0.0000026143293,0.0002509894],"genre_scores_gemma":[0.9104596,0.0002704875,0.08875293,0.00005144007,0.00022665456,0.0000026608775,0.000012604386,0.000017660263,0.00020598949],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987421,0.0000082725865,0.00081790984,0.00018903206,0.000048533046,0.0001941663],"domain_scores_gemma":[0.99801195,0.00012643139,0.0013788203,0.00021994273,0.00017846716,0.00008439922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008049127,0.00012491619,0.00052169664,0.00006524809,0.0004603743,0.000546412,0.00042903752,0.00007890165,0.000013891296],"category_scores_gemma":[0.0013577665,0.00013533454,0.000056501314,0.000021747126,0.000078207304,0.0011081579,0.00008307168,0.0001454998,0.0000044818316],"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":[0.0009016225,0.000117239244,0.0027097166,0.000099403114,0.000066022505,0.000024782528,0.0010250937,0.033933103,0.000016045453,0.9222591,0.0024343512,0.036413547],"study_design_scores_gemma":[0.0011702575,0.00014117698,0.0012381362,0.000039377243,0.00001373621,0.0000026193527,0.0000074611717,0.5135876,0.000011745487,0.48204955,0.0016261565,0.000112222675],"about_ca_topic_score_codex":0.00010952238,"about_ca_topic_score_gemma":0.000022670607,"teacher_disagreement_score":0.79301816,"about_ca_system_score_codex":0.000041066396,"about_ca_system_score_gemma":0.000079002464,"threshold_uncertainty_score":0.5518783},"labels":[],"label_agreement":null},{"id":"W2765249720","doi":"10.4236/ojs.2017.75058","title":"Simulated Minimum Cramér-Von Mises Distance Estimation for Some Actuarial and Financial Models","year":2017,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":3,"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 Laval; Actua","funders":"","keywords":"Estimator; Mathematics; Hellinger distance; Density estimation; Probability density function; Applied mathematics; Poisson distribution; Mathematical optimization; Statistics","score_opus":0.10255801474009753,"score_gpt":0.31718561232266596,"score_spread":0.21462759758256844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765249720","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":[0.13278002,0.00047710366,0.8637992,0.00025591243,0.00069215655,0.0003037914,0.0014006258,0.0000033911988,0.00028777414],"genre_scores_gemma":[0.8693791,0.0003456681,0.12968634,0.000059626633,0.00023003625,0.0000034193756,0.000025055178,0.000020644826,0.00025007737],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985704,0.000010989672,0.0009033016,0.00022537795,0.00006318174,0.00022677751],"domain_scores_gemma":[0.99784553,0.00019089541,0.001388043,0.0002821631,0.00018689763,0.00010644158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077196676,0.00015362387,0.0005478247,0.00008667174,0.00054204057,0.0006534182,0.00050956116,0.0000950405,0.000023739038],"category_scores_gemma":[0.0020768086,0.00016635549,0.00006862647,0.000030842173,0.00009425049,0.0014087001,0.00010940362,0.00016669689,0.00000671819],"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":[0.0017589955,0.00025905116,0.003832477,0.00023999742,0.00009168035,0.000020523641,0.0016317874,0.03547178,0.000043526106,0.9030732,0.006303164,0.047273807],"study_design_scores_gemma":[0.001572612,0.0001612753,0.0027685785,0.0000695064,0.00002274727,0.0000041295743,0.000010784592,0.41731334,0.000014006189,0.57148045,0.0064048553,0.00017770809],"about_ca_topic_score_codex":0.00016251217,"about_ca_topic_score_gemma":0.00006662237,"teacher_disagreement_score":0.7365991,"about_ca_system_score_codex":0.00006088824,"about_ca_system_score_gemma":0.00010844209,"threshold_uncertainty_score":0.678378},"labels":[],"label_agreement":null},{"id":"W2771866761","doi":"10.4236/ojs.2017.76068","title":"Atmospheric Observation under Sampling Problem: The Impact of Unresolved Micro-Scale Boundary Layer Eddies on Climate Trends","year":2017,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","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 Calgary","funders":"","keywords":"Environmental science; Climatology; Atmosphere (unit); Eddy; Scale (ratio); Atmospheric sciences; Climate change; Climate model; Amplitude; Boundary layer; Meteorology; Geology; Geography; Turbulence; Physics","score_opus":0.11403849463181438,"score_gpt":0.34867102371330955,"score_spread":0.23463252908149518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771866761","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":[0.9842332,0.00015909245,0.0070848754,0.00041389326,0.00019438415,0.00012899768,0.0005984639,0.0000026309622,0.0071844687],"genre_scores_gemma":[0.9260429,0.00011171715,0.0734086,0.00007982994,0.00007236111,2.7070624e-7,0.00004043079,0.0000033788076,0.00024048527],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990293,0.00008681229,0.0004090475,0.00008952808,0.00020348279,0.00018187899],"domain_scores_gemma":[0.9983918,0.0004267731,0.0007438834,0.00020876243,0.00015076932,0.000078034296],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00063655083,0.00010218085,0.00024217495,0.000014378525,0.00065307744,0.00037928877,0.0005840333,0.000034661225,0.0014751729],"category_scores_gemma":[0.000100750214,0.000054052744,0.00007018806,0.000070169386,0.00014817438,0.00031072478,0.000036100515,0.00016485053,0.000010815462],"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":[0.0005125259,0.0000793975,0.711396,0.000016239206,0.00013932983,0.0000086137525,0.0005143496,0.22306116,0.0005011706,0.0009491178,0.0019346384,0.06088746],"study_design_scores_gemma":[0.000379039,0.0006508929,0.9760737,0.000029632773,0.00003486285,0.0000046921737,0.00010539748,0.0061286767,0.000011053824,0.016007384,0.00050098775,0.00007369388],"about_ca_topic_score_codex":0.00057346304,"about_ca_topic_score_gemma":0.00038588973,"teacher_disagreement_score":0.2646777,"about_ca_system_score_codex":0.0000098452665,"about_ca_system_score_gemma":0.00007925561,"threshold_uncertainty_score":0.99943763},"labels":[],"label_agreement":null},{"id":"W2773922713","doi":"10.4236/ojs.2017.76064","title":"A Neighborhood Analysis of Underage Tobacco Sales within the Serving Area of a Canadian Public Health Unit","year":2017,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Smoking Behavior and Cessation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Centers for Disease Control and Prevention; Imperial College London; Cancer Care Ontario","keywords":"Windsor; Unit (ring theory); Environmental health; Geography; Tobacco control; Enforcement; Business; Public health; Cluster (spacecraft); Relative risk; Statistics; Medicine; Environmental science; Mathematics; Computer science; Political science","score_opus":0.1855071922532952,"score_gpt":0.37935642229307154,"score_spread":0.19384923003977633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773922713","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":[0.9799554,0.00006776293,0.008452549,0.0090517625,0.00016583255,0.00021765813,0.00046157948,0.000001549515,0.001625907],"genre_scores_gemma":[0.9973951,0.00007208026,0.0021547046,0.00023263569,0.000021743946,7.3483574e-7,0.000041950123,0.0000087653625,0.0000722885],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99882686,0.0000800669,0.0005516128,0.00006693662,0.0003361405,0.00013840137],"domain_scores_gemma":[0.9972106,0.00010635809,0.0014902195,0.00037966788,0.0005897395,0.00022340013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001274164,0.00007471657,0.0004435724,0.0004980506,0.00024846377,0.00014787867,0.00048392094,0.000035507233,0.0001425489],"category_scores_gemma":[0.00052767334,0.000051051575,0.00006352231,0.00041545922,0.000081876315,0.00017170161,0.0000521486,0.00019332461,1.6873683e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003664502,0.000075715914,0.9815251,0.000034273657,0.00073584064,0.000043837987,0.0016845153,0.0000623069,0.00004198443,0.008254597,0.0008522103,0.006652932],"study_design_scores_gemma":[0.0006276877,0.00023578118,0.9947902,0.00015195402,0.0009920856,0.000041955886,0.0020324364,0.0003824469,0.000029045174,0.0004313406,0.00023553372,0.000049536204],"about_ca_topic_score_codex":0.04811683,"about_ca_topic_score_gemma":0.15512058,"teacher_disagreement_score":0.107003756,"about_ca_system_score_codex":0.00009677243,"about_ca_system_score_gemma":0.001705128,"threshold_uncertainty_score":0.95822185},"labels":[],"label_agreement":null},{"id":"W2791604340","doi":"10.4236/ojs.2018.81010","title":"A Chi-Square Approximation for the <i>F</i> Distribution","year":2018,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","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":"York University","funders":"","keywords":"Mathematics; Statistics; Distribution (mathematics); F-distribution; Square (algebra); Chi-square test; Statistic; Normal distribution; Cumulative distribution function; Combinatorics; Mathematical analysis; Probability distribution; Probability density function","score_opus":0.15015193871443944,"score_gpt":0.41541621275825474,"score_spread":0.2652642740438153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791604340","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013024694,0.0000716792,0.9832597,0.0018205678,0.0003993721,0.0013130083,0.010490728,0.000039818562,0.0013026571],"genre_scores_gemma":[0.12015199,0.00007974964,0.8719432,0.00029755384,0.0007123812,0.00021038164,0.003972903,0.00007583174,0.0025560237],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99679315,0.00018411393,0.0014694767,0.00031851328,0.00076890306,0.0004658624],"domain_scores_gemma":[0.9926643,0.0025396622,0.0014303514,0.0006832159,0.0023859781,0.0002965166],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016103226,0.0003415082,0.00055526354,0.00008525717,0.0009627752,0.0005627695,0.00093696616,0.00015490949,0.0019552],"category_scores_gemma":[0.0076915193,0.00025932756,0.0001653546,0.00047495659,0.00041453313,0.0003679966,0.00015879508,0.00034194445,0.00042664303],"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":[0.00014321879,0.00029499418,0.000028822256,0.00009151804,0.00011208689,7.4428783e-7,0.00037566994,0.000028060853,0.00033364346,0.66564375,0.3218212,0.0111262845],"study_design_scores_gemma":[0.0017890312,0.00020332029,0.0025349357,0.00022296105,0.0006188133,0.0001267712,0.000105737105,0.004431376,0.00011677782,0.3385841,0.6508206,0.0004455437],"about_ca_topic_score_codex":0.000009658859,"about_ca_topic_score_gemma":0.00018158407,"teacher_disagreement_score":0.32899943,"about_ca_system_score_codex":0.0002420436,"about_ca_system_score_gemma":0.00024951607,"threshold_uncertainty_score":0.9999859},"labels":[],"label_agreement":null},{"id":"W2792040161","doi":"10.4236/ojs.2018.81012","title":"Simulated Minimum Hellinger Distance Inference Methods for Count Data","year":2018,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","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":"Université Laval; Ontario Universities’ Application Centre","funders":"","keywords":"Hellinger distance; Mathematics; Count data; Goodness of fit; Parametric statistics; Statistics; Robustness (evolution); Homogeneity (statistics); Inference; Applied mathematics; Algorithm; Computer science; Poisson distribution; Artificial intelligence","score_opus":0.38061958097510695,"score_gpt":0.5567857153716702,"score_spread":0.17616613439656326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792040161","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027336602,0.00009142034,0.9957317,0.00011904048,0.0006739853,0.00036819372,0.0018388503,0.0000075023663,0.0008959593],"genre_scores_gemma":[0.009692611,0.00007422911,0.9893654,0.00016356673,0.0002864698,0.000002982852,0.000026274325,0.000035283676,0.00035317263],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99769706,0.00032178892,0.0010812475,0.00027819222,0.00029886424,0.00032286526],"domain_scores_gemma":[0.9836275,0.012770119,0.0009316195,0.0007050081,0.0017618946,0.00020387144],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0039026095,0.00020514411,0.0006452961,0.000058693247,0.00016912706,0.00027603828,0.0017796715,0.00008324116,0.0005223734],"category_scores_gemma":[0.0248456,0.00016068557,0.000038433634,0.00017491884,0.00026065717,0.00029393804,0.00039637514,0.00024693078,0.000011318826],"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":[0.0006367648,0.0002953072,0.00013409204,0.00022617096,0.00022394347,0.00003047072,0.00057405565,0.0000148018735,0.00073347794,0.67290515,0.05804627,0.2661795],"study_design_scores_gemma":[0.0009315947,0.0008113734,0.000078107,0.0002259457,0.00019799786,0.000019294419,0.000091912225,0.07735981,0.0006558933,0.8545236,0.0648552,0.00024927274],"about_ca_topic_score_codex":0.000012413921,"about_ca_topic_score_gemma":0.000013666486,"teacher_disagreement_score":0.2659302,"about_ca_system_score_codex":0.000047167,"about_ca_system_score_gemma":0.0003027469,"threshold_uncertainty_score":0.9833685},"labels":[],"label_agreement":null},{"id":"W2807069049","doi":"10.4236/ojs.2018.83028","title":"Minimum Quadratic Distance Methods Using Grouped Data for Parametric Families of Copulas","year":2018,"lang":"en","type":"article","venue":"Open Journal of Statistics","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":"Cegep de Sainte Foy; Université Laval","funders":"","keywords":"Contingency table; Mathematics; Quantile; Quadratic equation; Chi-square test; Parametric statistics; Statistics; Simple (philosophy); Applied mathematics","score_opus":0.4724223378420746,"score_gpt":0.5495598052020894,"score_spread":0.07713746736001476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807069049","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036447858,0.00010531497,0.99298984,0.000029544193,0.00049844023,0.0003714314,0.0020225726,0.0000033509857,0.0003347471],"genre_scores_gemma":[0.03008692,0.000047970065,0.96957064,0.0000367195,0.00014333775,0.0000028791553,0.000014094762,0.000030582105,0.00006684222],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99735934,0.00044027198,0.0013578604,0.0002241687,0.00035425115,0.00026409084],"domain_scores_gemma":[0.98625165,0.010427939,0.0014159246,0.0006581652,0.0011066903,0.00013963656],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003602209,0.00018562215,0.0008847565,0.00014420075,0.00012622993,0.00012587543,0.0013632353,0.00006811095,0.00017820117],"category_scores_gemma":[0.02913862,0.00014736356,0.00005185163,0.00038954697,0.0003498412,0.00027099522,0.00033863637,0.0001865132,0.0000019402614],"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":[0.00069478754,0.0005741275,0.00049432844,0.0009031671,0.00035067563,0.00002629682,0.0007279925,0.0000077786535,0.0021275827,0.8301853,0.018944284,0.14496367],"study_design_scores_gemma":[0.0014873329,0.0012703817,0.00035140011,0.00037972166,0.00052353996,0.00005028985,0.00072409684,0.09161384,0.0013642479,0.8991055,0.0028522578,0.00027738974],"about_ca_topic_score_codex":0.000042868487,"about_ca_topic_score_gemma":0.000020383699,"teacher_disagreement_score":0.14468627,"about_ca_system_score_codex":0.000045304867,"about_ca_system_score_gemma":0.00027483128,"threshold_uncertainty_score":0.9790394},"labels":[],"label_agreement":null},{"id":"W2893237262","doi":"10.4236/ojs.2018.85055","title":"An Examination of Male and Female Monthly Employment Rates over Time in Canada and the United States Using Hidden Markov Probability Models","year":2018,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Insurance, Mortality, Demography, Risk Management","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":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Hidden Markov model; Multivariate statistics; Markov chain; Markov model; Demography; Econometrics; Statistics; Multivariate analysis; Demographic economics; Geography; Economics; Mathematics; Computer science; Sociology; Artificial intelligence","score_opus":0.034342364853974176,"score_gpt":0.3106313745297916,"score_spread":0.27628900967581743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893237262","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":[0.9978919,0.00005483267,0.0007674413,0.00008568368,0.00005601552,0.00043173716,0.00013099867,0.0000010001133,0.0005804141],"genre_scores_gemma":[0.9876473,0.00017041486,0.012043355,0.000051546307,0.000024179139,0.0000022888578,0.000007002924,0.000005980357,0.00004791115],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99833834,0.0005966663,0.0004084023,0.00010868446,0.0003958153,0.0001521084],"domain_scores_gemma":[0.99879646,0.00025836416,0.00038765534,0.00010860835,0.00037704682,0.00007183604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002711208,0.0000797888,0.00022031562,0.00008528087,0.00018584124,0.00013888655,0.00025301144,0.000021167418,0.000059893304],"category_scores_gemma":[0.000107944055,0.000060643193,0.0000106391035,0.00022514626,0.00051795563,0.0003932848,0.00007580917,0.00007102458,7.563523e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007038231,0.00031464075,0.89196116,0.0001351716,0.0002229385,0.00006753548,0.04313916,0.005020146,0.000051159528,0.023971736,0.0013340737,0.033078436],"study_design_scores_gemma":[0.0015755363,0.00020078185,0.8665545,0.00010025026,0.00008587282,0.0000029806215,0.0075361235,0.097382866,0.000028653152,0.026178712,0.00018704943,0.00016669437],"about_ca_topic_score_codex":0.78220785,"about_ca_topic_score_gemma":0.8359174,"teacher_disagreement_score":0.09236272,"about_ca_system_score_codex":0.00017281157,"about_ca_system_score_gemma":0.00034097378,"threshold_uncertainty_score":0.24729577},"labels":[],"label_agreement":null},{"id":"W2898865804","doi":"10.4236/ojs.2018.85056","title":"Asymptotic Normality Distribution of Simulated Minimum Hellinger Distance Estimators for Continuous Models","year":2018,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","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":"Université Laval; Ontario Universities’ Application Centre","funders":"","keywords":"Estimator; Hellinger distance; Mathematics; Asymptotic distribution; Applied mathematics; Parametric statistics; Fisher information; Delta method; Statistics; Quantile","score_opus":0.05800429756137201,"score_gpt":0.2868693424299021,"score_spread":0.22886504486853007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898865804","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":[0.1894666,0.000276549,0.8065296,0.000039331746,0.00035536557,0.00021338042,0.0028366875,0.0000026813848,0.0002798376],"genre_scores_gemma":[0.9467229,0.00008338089,0.052904148,0.000022375052,0.000099540186,0.0000015416125,0.000054460495,0.00001602748,0.00009563112],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981773,0.00001678794,0.0013421766,0.00017488812,0.000066104716,0.00022277005],"domain_scores_gemma":[0.9974967,0.00019971075,0.0013485998,0.0001859711,0.0006815674,0.00008741591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011214493,0.00012644625,0.00061527226,0.000060841285,0.00012153305,0.000082943145,0.00036661568,0.000079028774,0.000037412705],"category_scores_gemma":[0.00062078144,0.0001349509,0.00009037709,0.00013648979,0.0001241327,0.00040350482,0.00005417573,0.000118794516,0.000008168055],"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":[0.0017448427,0.0006336291,0.03967586,0.0003223157,0.00025505104,0.000012639953,0.0017338443,0.061995875,0.00005172007,0.8796819,0.0056214877,0.008270817],"study_design_scores_gemma":[0.0011224103,0.00051350525,0.0028092095,0.00009270723,0.000032955806,0.000003712318,0.0000403269,0.6934566,0.00016612555,0.29819277,0.003388402,0.00018128073],"about_ca_topic_score_codex":0.000115240255,"about_ca_topic_score_gemma":0.000018348695,"teacher_disagreement_score":0.7572563,"about_ca_system_score_codex":0.00007817256,"about_ca_system_score_gemma":0.000078051235,"threshold_uncertainty_score":0.55031383},"labels":[],"label_agreement":null},{"id":"W2911568234","doi":"10.4236/ojs.2019.91006","title":"Analysis of Hospital Mortality Data: The Role of DRG’s","year":2019,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Insurance, Mortality, Demography, Risk Management","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":"Western University","funders":"","keywords":"Gee; Generalized estimating equation; Medicine; Logistic regression; Generalized linear model; Statistics; Cluster analysis; Statistical model; Linear regression; Regression analysis; Nuisance parameter; Econometrics; Mathematics; Internal medicine","score_opus":0.03274084228201806,"score_gpt":0.3591191087906706,"score_spread":0.32637826650865254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911568234","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":[0.9573937,0.00038902584,0.004704646,0.00029023516,0.00092206843,0.0006282076,0.0020138072,0.0000035836697,0.03365472],"genre_scores_gemma":[0.9947418,0.00043748826,0.0045516007,0.000028946335,0.00005965004,8.876425e-7,0.000025230009,0.000006389467,0.00014798099],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977072,0.0002815798,0.00071892195,0.00014385661,0.00096947316,0.0001789517],"domain_scores_gemma":[0.9971777,0.00023556022,0.0012982496,0.0006499351,0.00057034614,0.00006820118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036010947,0.00008829946,0.00048437584,0.00014782451,0.00012170445,0.00011087161,0.0021610367,0.00003704826,0.0003900371],"category_scores_gemma":[0.00026384756,0.000064660395,0.00012525405,0.0007955775,0.00035238214,0.00044048723,0.00035192716,0.0001473928,0.000004937192],"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":[0.000021791337,0.00021168584,0.9148744,0.000017971766,0.002235084,0.000006626335,0.0033642878,0.0002797733,0.000014354024,0.07150412,0.0018724147,0.0055975206],"study_design_scores_gemma":[0.0003026829,0.00017749352,0.95649457,0.00002801217,0.0020778084,2.629703e-7,0.011953694,0.00067460345,0.00002248716,0.008556748,0.019584084,0.00012754236],"about_ca_topic_score_codex":0.00468084,"about_ca_topic_score_gemma":0.0028059483,"teacher_disagreement_score":0.06294738,"about_ca_system_score_codex":0.000029242208,"about_ca_system_score_gemma":0.00019253179,"threshold_uncertainty_score":0.7076061},"labels":[],"label_agreement":null},{"id":"W2968687647","doi":"10.4236/ojs.2019.94032","title":"Demographic Expansion and Contraction in a Neotropical Fish during the Late Pleistocene-Holocene","year":2019,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Fish biology, ecology, and behavior","field":"Environmental 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":"Queen's University","funders":"Ministério do Meio Ambiente; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Holocene; Pleistocene; Population; Demographic history; Last Glacial Maximum; Ecology; Phylogeography; Biology; Geology; Paleontology; Demography; Genetic variation; Phylogenetics","score_opus":0.012481772610823427,"score_gpt":0.25266436762487376,"score_spread":0.24018259501405034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968687647","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":[0.9989175,0.000015671872,0.000107256645,0.00043459085,0.00017982343,0.0001814429,0.00001723604,0.0000014055646,0.00014503156],"genre_scores_gemma":[0.99776715,0.00015666743,0.0016758312,0.00018256457,0.000017652246,0.0000020932598,0.0000013481714,0.0000052155124,0.00019146535],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99931276,0.0000831072,0.0002637921,0.00009341786,0.00010205375,0.00014484857],"domain_scores_gemma":[0.9995637,0.00010385718,0.0001862745,0.00007977563,0.000013114838,0.00005330041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030481775,0.00006795004,0.00015924251,0.000027702139,0.0000756262,0.00004089985,0.00021011774,0.000058154277,0.00053954433],"category_scores_gemma":[0.00004732043,0.000045669938,0.000016836477,0.00007604404,0.00014283623,0.00017004315,0.00012113065,0.0002412623,0.000017411818],"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":[0.00015676873,0.00006173704,0.9877257,0.000004438969,0.0000055235305,0.000055119926,0.000068683825,0.00010670054,0.0078828,0.00003374896,0.0006023963,0.0032964111],"study_design_scores_gemma":[0.00084563205,0.00028093375,0.9975719,0.00001481393,0.000014077563,0.00013715263,0.000104258215,0.00015610381,0.00010095235,0.00027412013,0.00043944732,0.000060595536],"about_ca_topic_score_codex":0.00011489442,"about_ca_topic_score_gemma":0.0006880003,"teacher_disagreement_score":0.009846241,"about_ca_system_score_codex":0.000058225014,"about_ca_system_score_gemma":0.000011736304,"threshold_uncertainty_score":0.59076315},"labels":[],"label_agreement":null},{"id":"W2968975871","doi":"10.4236/ojs.2019.94031","title":"Using Excel to Explore the Effects of Assumption Violations on One-Way Analysis of Variance (ANOVA) Statistical Procedures","year":2019,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Time Series Analysis and Forecasting","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":"University of Saskatchewan","funders":"","keywords":"Computer science; Variance (accounting); Heteroscedasticity; Microsoft excel; Statistical analysis; Statistical model; Statistics; Data mining; Machine learning; Econometrics; Mathematics","score_opus":0.06967906361789952,"score_gpt":0.3327678796400093,"score_spread":0.26308881602210976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968975871","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":[0.09393605,0.000024952982,0.9054241,0.00007917275,0.00012615095,0.00018478345,0.000050611885,0.0000018872382,0.00017229868],"genre_scores_gemma":[0.6678011,0.000012950558,0.33208063,0.00004074391,0.000018900337,0.0000011819893,0.0000035794642,0.000005430565,0.00003548745],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839896,0.00012043745,0.0006672289,0.00016315917,0.0005001498,0.00015008889],"domain_scores_gemma":[0.9972948,0.0008644502,0.00090008846,0.00032894278,0.00053201517,0.0000797112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006184493,0.000106842694,0.0004983347,0.0002612705,0.00009309535,0.00014735298,0.0008298048,0.000029725796,0.00007652298],"category_scores_gemma":[0.0006346217,0.00007610368,0.00008634702,0.0010310909,0.00004055561,0.00031387535,0.00018925618,0.00013622043,0.0000040724067],"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":[0.00041477082,0.00092798297,0.020685868,0.00062762236,0.0049211667,0.000056227324,0.00913111,0.3036166,0.03952633,0.5427169,0.0017686499,0.07560673],"study_design_scores_gemma":[0.0008583477,0.0019483693,0.21381204,0.0008439784,0.0021899827,0.000018437802,0.0005247827,0.76903516,0.0029274067,0.0073296507,0.0001785636,0.0003332747],"about_ca_topic_score_codex":0.00005565705,"about_ca_topic_score_gemma":0.000024607818,"teacher_disagreement_score":0.57386506,"about_ca_system_score_codex":0.000040191877,"about_ca_system_score_gemma":0.00013607717,"threshold_uncertainty_score":0.3103418},"labels":[],"label_agreement":null},{"id":"W2969575993","doi":"10.4236/ojs.2019.94035","title":"On the Index of Repeatability: Estimation and Sample Size Requirements","year":2019,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","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":"Western University","funders":"","keywords":"Repeatability; Statistics; Sample size determination; Variance (accounting); Estimator; Mathematics; Sample (material); Index (typography); Computer science","score_opus":0.49683111304261746,"score_gpt":0.56086270556895,"score_spread":0.06403159252633256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969575993","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11682185,0.000009439055,0.8789332,0.0005750791,0.0005150411,0.00085675303,0.0006022412,0.0000039534593,0.0016824685],"genre_scores_gemma":[0.25142416,0.000016332377,0.74833864,0.000109988476,0.000025403535,0.000002013067,3.5843826e-7,0.000012434193,0.000070698334],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973169,0.0006363127,0.0012567989,0.00013832386,0.0005241361,0.00012752575],"domain_scores_gemma":[0.8141844,0.18384254,0.0011885762,0.00036899777,0.00033862283,0.00007683611],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0066094156,0.00011216438,0.0005715847,0.000031370793,0.000047463996,0.00007433669,0.0004416027,0.00005818702,0.0011010962],"category_scores_gemma":[0.34339163,0.00007005629,0.000043623168,0.000099356475,0.00014773187,0.000109863766,0.00017515215,0.0002649238,0.0000064605915],"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":[0.00079438504,0.0003199572,0.0059968005,0.0002665153,0.00018503105,0.000010258113,0.00028392737,0.0000416016,0.00016963668,0.9298846,0.004946855,0.057100393],"study_design_scores_gemma":[0.001010978,0.0006334858,0.0037221538,0.00023144929,0.0000819679,0.0000075694907,0.000104714745,0.0015276353,0.0002672003,0.9922714,0.00006721112,0.00007419734],"about_ca_topic_score_codex":0.000014399218,"about_ca_topic_score_gemma":0.0000018226708,"teacher_disagreement_score":0.33678222,"about_ca_system_score_codex":0.00003559451,"about_ca_system_score_gemma":0.00008987527,"threshold_uncertainty_score":0.999812},"labels":[],"label_agreement":null},{"id":"W2991299027","doi":"10.4236/ojs.2019.96040","title":"Likelihood Methods for Basic Stratified Sampling, with Application to Von Bertalanffy Growth Model Estimation","year":2019,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Bayesian 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":"Memorial University of Newfoundland","funders":"Canada First Research Excellence Fund; Ocean Frontier Institute; Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland","keywords":"Statistics; Estimator; Stratified sampling; Sampling (signal processing); Maximum likelihood; Mathematics; Computer science; Marginal likelihood","score_opus":0.06901775248005243,"score_gpt":0.4414852496311544,"score_spread":0.37246749715110195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991299027","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00080149394,0.0000060309285,0.99672157,0.00019373359,0.00009331873,0.001157739,0.0002542959,0.000008362309,0.0007634303],"genre_scores_gemma":[0.0137107,0.0000046838136,0.9859112,0.00012338087,0.000042216052,0.000049848844,0.000019460533,0.000039706287,0.000098806835],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99839747,0.00012741193,0.000720234,0.00022787221,0.00027730997,0.00024971724],"domain_scores_gemma":[0.9953484,0.0027117417,0.0006576606,0.00023311823,0.0008434223,0.00020566538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016267736,0.0001905067,0.000520927,0.0000978899,0.00008399812,0.00020590845,0.0004373736,0.000070706956,0.000065764885],"category_scores_gemma":[0.0018864027,0.00014546562,0.00004219077,0.00017015947,0.000028925924,0.00022774277,0.000060210336,0.00019345517,0.000010682913],"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":[0.00030044653,0.00010744645,0.00010124591,0.00019395494,0.00005688693,0.0000013212042,0.00028683446,0.00038017984,0.0014959859,0.74406064,0.001028595,0.25198647],"study_design_scores_gemma":[0.0007401316,0.00075302896,0.0001974583,0.00013538258,0.0001309699,0.000019239733,0.0000801742,0.21783596,0.000983286,0.77884364,0.00010134467,0.00017937463],"about_ca_topic_score_codex":0.00001476221,"about_ca_topic_score_gemma":0.000012358813,"teacher_disagreement_score":0.2518071,"about_ca_system_score_codex":0.000064045584,"about_ca_system_score_gemma":0.00024195258,"threshold_uncertainty_score":0.59319156},"labels":[],"label_agreement":null},{"id":"W3008678529","doi":"10.4236/ojs.2020.101008","title":"Mean Absolute Deviations about the Mean, the Cut Norm and Taxicab Correspondence Analysis","year":2020,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":8,"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 Moncton","funders":"","keywords":"Mathematics; Standard deviation; Maximization; Least absolute deviations; Statistics; Absolute deviation; Minification; Large deviations theory; Norm (philosophy); Combinatorics; Mathematical optimization; Estimator","score_opus":0.09323538382250615,"score_gpt":0.32679371224766535,"score_spread":0.2335583284251592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008678529","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":[0.44835487,0.0015177625,0.48903063,0.05415659,0.0003701065,0.0007536094,0.002143762,0.000018948556,0.0036537193],"genre_scores_gemma":[0.9737449,0.00039451863,0.02259759,0.0025346167,0.00023186716,0.0000026377506,0.000016552951,0.0000013996546,0.00047588296],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9984299,0.0004446426,0.0004780775,0.00014977039,0.00034092847,0.00015669188],"domain_scores_gemma":[0.99642783,0.0026113966,0.00042482058,0.00006890319,0.00028970145,0.00017732548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011161293,0.00010891245,0.0003284581,0.000010701943,0.0007815473,0.00069479505,0.0007304777,0.000031956963,0.0007670387],"category_scores_gemma":[0.0010017442,0.00002971206,0.00010643997,0.00077093486,0.00016540002,0.00011922728,0.00013586364,0.00024399467,0.000010654661],"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":[0.0007464752,0.00024583994,0.016483143,0.000024411936,0.0036239743,0.0002540355,0.007081886,0.0016545864,0.010298044,0.15967889,0.048282683,0.751626],"study_design_scores_gemma":[0.0005578554,0.0013029398,0.77101034,0.000044066735,0.0050105895,0.00009800846,0.009250896,0.051303905,0.00027412272,0.020529367,0.14003955,0.0005783907],"about_ca_topic_score_codex":0.00013768104,"about_ca_topic_score_gemma":0.0013810367,"teacher_disagreement_score":0.75452715,"about_ca_system_score_codex":0.000010051633,"about_ca_system_score_gemma":0.000018993669,"threshold_uncertainty_score":0.83985364},"labels":[],"label_agreement":null},{"id":"W3121383538","doi":"10.4236/ojs.2021.111010","title":"Uncovering and Displaying the Coherent Groups of Rank Data by Exploratory Riffle Shuffling","year":2021,"lang":"en","type":"preprint","venue":"Open Journal of Statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","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":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Shuffling; Riffle; Rank (graph theory); Set (abstract data type); Contingency table; Combinatorics; Mathematics; Computer science; Statistics","score_opus":0.127473884003913,"score_gpt":0.34213104145804285,"score_spread":0.21465715745412986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121383538","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":[0.85513794,0.0055010286,0.13035823,0.00063385995,0.00074360723,0.00037790806,0.006979963,0.00000455021,0.0002628777],"genre_scores_gemma":[0.9050414,0.0029376016,0.09105623,0.00013954287,0.0002524889,0.000003527822,0.00051723054,0.0000038059236,0.00004815895],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99781084,0.00054444914,0.00080102345,0.00029525848,0.00038657463,0.00016186145],"domain_scores_gemma":[0.99631625,0.0020981946,0.00095258607,0.00020180922,0.00031111634,0.000120053635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019584945,0.00018213973,0.0006585535,0.000010319693,0.00018248566,0.00051297655,0.0012920398,0.000092253,0.00030169738],"category_scores_gemma":[0.001028089,0.00006499848,0.000066584194,0.00011941109,0.00012960847,0.00020487062,0.0018542423,0.0005565206,3.7940575e-7],"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":[0.00053674175,0.00089725526,0.00936364,0.0006323445,0.0019768274,0.00043382958,0.0019895206,0.0014905367,0.09470349,0.0040330626,0.030539036,0.85340375],"study_design_scores_gemma":[0.008884246,0.00789171,0.24718152,0.016995465,0.015832892,0.0010569616,0.13975763,0.14679587,0.021531234,0.25433898,0.13026968,0.009463828],"about_ca_topic_score_codex":0.00019411379,"about_ca_topic_score_gemma":0.00017844643,"teacher_disagreement_score":0.8439399,"about_ca_system_score_codex":0.000019724044,"about_ca_system_score_gemma":0.000046931298,"threshold_uncertainty_score":0.4946645},"labels":[],"label_agreement":null},{"id":"W3121948562","doi":"10.4236/ojs.2021.111005","title":"Analysis of Length of Stay (LOS) Data from the Medical Records of Tertiary Care Hospital in Saudi Arabia for Five Diagnosis Related Groups: Application of Cox Prediction Model","year":2021,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Medicine; Medical record; Proportional hazards model; Emergency medicine; Hazard; Regression analysis; Medical emergency; Pediatrics; Internal medicine; Statistics","score_opus":0.03144689886815968,"score_gpt":0.33925003630060746,"score_spread":0.3078031374324478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121948562","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":[0.7543631,0.0015882847,0.2144108,0.00081854477,0.00013834382,0.0010050306,0.027405938,0.0000031647962,0.00026675352],"genre_scores_gemma":[0.98488927,0.001633542,0.010426305,0.000023266866,0.000027089432,0.000015224635,0.0029576449,0.000011928656,0.00001574409],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99782425,0.000093574505,0.0011967386,0.00018242643,0.0006087037,0.00009430018],"domain_scores_gemma":[0.9967869,0.0007099372,0.0011782472,0.00052349456,0.0007341104,0.000067343564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006265041,0.000098384706,0.0006327433,0.00013280634,0.00001899174,0.000010737534,0.0005072269,0.000069158006,0.0001626309],"category_scores_gemma":[0.0011031199,0.00007722513,0.00009759051,0.00041194327,0.00014171311,0.00018313486,0.00023494767,0.00016176069,1.0531631e-7],"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":[0.0016166557,0.003421932,0.8579507,0.002329642,0.014166343,0.00013495516,0.0076458342,0.023906086,0.00035067007,0.00440052,0.02668724,0.057389412],"study_design_scores_gemma":[0.0042363564,0.0005245436,0.49864125,0.0011804941,0.009393607,0.0000028535133,0.010918676,0.47163314,0.00032887715,0.0029505065,0.00008342172,0.00010627599],"about_ca_topic_score_codex":0.000501683,"about_ca_topic_score_gemma":0.00059668795,"teacher_disagreement_score":0.44772705,"about_ca_system_score_codex":0.00007991945,"about_ca_system_score_gemma":0.000551241,"threshold_uncertainty_score":0.31491494},"labels":[],"label_agreement":null},{"id":"W3135631171","doi":"10.4236/ojs.2021.112014","title":"Predictors of the Aggregate of COVID-19 Cases and Its Case-Fatality: A Global Investigation Involving 120 Countries","year":2021,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"COVID-19 Clinical Research Studies","field":"Medicine","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":"Western University","funders":"","keywords":"Case fatality rate; Pandemic; Medicine; Negative binomial distribution; Coronavirus disease 2019 (COVID-19); Demography; Population; Regression analysis; Disease; Environmental health; Statistics; Infectious disease (medical specialty); Internal medicine; Mathematics","score_opus":0.11280996965418358,"score_gpt":0.4420111225663732,"score_spread":0.32920115291218965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135631171","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":[0.98189896,0.003637146,0.0014027744,0.009431914,0.00024978188,0.0004908222,0.0027977761,0.0000037673638,0.000087044435],"genre_scores_gemma":[0.99276054,0.0033735717,0.002714122,0.00097101193,0.00006188235,0.0000018894293,0.0000079710835,0.000007545214,0.000101443096],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99797124,0.00027115774,0.0008115846,0.00013159332,0.0006668448,0.00014755833],"domain_scores_gemma":[0.99041533,0.0066878716,0.0008326992,0.00019858236,0.0015199073,0.0003456131],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0012946738,0.00010456376,0.0005183598,0.000039546674,0.00013101847,0.000045828623,0.00018862162,0.000048368056,0.00005467144],"category_scores_gemma":[0.14180094,0.000070858645,0.000057875055,0.0002899896,0.0005503822,0.00014809707,0.000484532,0.0002217092,4.04289e-7],"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":[0.0011206815,0.0002558312,0.93793124,0.0062259766,0.001261394,0.01831757,0.003142045,0.00015932154,0.00108871,0.017600033,0.0105110025,0.0023861742],"study_design_scores_gemma":[0.031097244,0.009823211,0.7331167,0.014878678,0.007316509,0.03188417,0.02220094,0.0049452754,0.031314738,0.061168976,0.051044706,0.001208869],"about_ca_topic_score_codex":0.00049249746,"about_ca_topic_score_gemma":0.000449989,"teacher_disagreement_score":0.20481457,"about_ca_system_score_codex":0.00017992491,"about_ca_system_score_gemma":0.0034442996,"threshold_uncertainty_score":0.86542803},"labels":[],"label_agreement":null},{"id":"W3175869339","doi":"10.4236/ojs.2021.113026","title":"Inference Procedures on the Generalized Poisson Distribution from Multiple Samples: Comparisons with Nonparametric Models for Analysis of Covariance (ANCOVA) of Count Data","year":2021,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Count data; Mathematics; Negative binomial distribution; Poisson distribution; Statistics; Nonparametric statistics; Analysis of covariance; Zero-inflated model; Quasi-likelihood; Covariate; Goodness of fit; Poisson regression; Population","score_opus":0.28560320261456945,"score_gpt":0.4368913635636237,"score_spread":0.15128816094905423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175869339","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053914636,0.00006801057,0.9498015,0.00012807758,0.00004430963,0.0003183909,0.044218693,0.0000020927353,0.000027435384],"genre_scores_gemma":[0.31510094,0.00009071332,0.68429023,0.000031808217,0.000015846063,0.0000071896566,0.0004487554,0.000009636309,0.000004877668],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977841,0.0002718218,0.0009991233,0.00024268028,0.0005270792,0.00017524512],"domain_scores_gemma":[0.9804051,0.015748568,0.0015553457,0.00058558374,0.0016227929,0.00008259608],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0010915986,0.00016835985,0.0010025852,0.000089531146,0.00010244382,0.00011387782,0.0009194038,0.000056541714,0.00008821773],"category_scores_gemma":[0.016804174,0.000106955886,0.00007048166,0.0008887584,0.00014434336,0.00016771145,0.00016562987,0.00019172863,2.3524476e-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":[0.00090208417,0.0006208232,0.0030773464,0.00019366325,0.0018670014,0.000011940033,0.00026898744,0.0039965245,0.00035884947,0.97805995,0.0062240968,0.004418709],"study_design_scores_gemma":[0.0015254169,0.00043707908,0.007227444,0.00048064356,0.0028366952,0.000005540185,0.00042375037,0.44726923,0.0016748512,0.53774834,0.00017420515,0.00019679563],"about_ca_topic_score_codex":0.0002982563,"about_ca_topic_score_gemma":0.0004146556,"teacher_disagreement_score":0.4432727,"about_ca_system_score_codex":0.000047474423,"about_ca_system_score_gemma":0.00054882315,"threshold_uncertainty_score":0.9914777},"labels":[],"label_agreement":null},{"id":"W4210672984","doi":"10.4236/ojs.2022.121001","title":"Quasi-Binomial Regression Model for the Analysis of Data with Extra-Binomial Variation","year":2022,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Binomial regression; Negative binomial distribution; Mathematics; Statistics; Count data; Quasi-likelihood; Binomial distribution; Beta-binomial distribution; Continuity correction; Binomial test; Binary data; Binomial proportion confidence interval; Regression analysis; Econometrics; Binary number; Poisson distribution","score_opus":0.20479152681040733,"score_gpt":0.43820705365536794,"score_spread":0.2334155268449606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210672984","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005134092,0.000023752504,0.9931626,0.00019255506,0.00012400122,0.00032561808,0.0055802474,0.0000020087446,0.0000758558],"genre_scores_gemma":[0.06284811,0.000018049428,0.936857,0.000035253404,0.00005597873,0.000013655026,0.00006257739,0.000016716478,0.00009265521],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812496,0.00025246624,0.0007752177,0.00017802554,0.00051592453,0.00015341231],"domain_scores_gemma":[0.9930404,0.0047012195,0.0012939093,0.00052451674,0.00037197646,0.00006798647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026105747,0.00012394003,0.0005571451,0.00012838365,0.0002880344,0.00009710563,0.0013487742,0.000029197676,0.00033644258],"category_scores_gemma":[0.0014836363,0.00007409837,0.00006344846,0.00034881794,0.000075956545,0.00021579934,0.00039458036,0.00025202316,1.6624519e-7],"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":[0.0023946846,0.00071136805,0.00023651868,0.000088262204,0.0018796286,0.00002700753,0.001732831,0.005988219,0.00022528929,0.8655709,0.018086944,0.10305832],"study_design_scores_gemma":[0.00090264075,0.00054084527,0.0005872634,0.000026676038,0.0027983421,0.000016730224,0.00029152737,0.7343869,0.000012091675,0.2600478,0.000286067,0.000103104016],"about_ca_topic_score_codex":0.0000399442,"about_ca_topic_score_gemma":0.000037221704,"teacher_disagreement_score":0.72839874,"about_ca_system_score_codex":0.000060260085,"about_ca_system_score_gemma":0.00037505373,"threshold_uncertainty_score":0.36838102},"labels":[],"label_agreement":null},{"id":"W4225609744","doi":"10.4236/ojs.2022.122016","title":"Quasi-Negative Binomial: Properties, Parametric Estimation, Regression Model and Application to RNA-SEQ Data","year":2022,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","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":"Western University","funders":"","keywords":"Overdispersion; Count data; Negative binomial distribution; Quasi-likelihood; Akaike information criterion; Beta-binomial distribution; Mathematics; Statistics; Binomial distribution; Multinomial distribution; Poisson distribution; Negative multinomial distribution; Goodness of fit","score_opus":0.07842162581472943,"score_gpt":0.3468887030059208,"score_spread":0.26846707719119134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225609744","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043643342,0.00014107753,0.9974813,0.0011826245,0.0001255531,0.00035196537,0.00014834238,0.000006508234,0.00012622823],"genre_scores_gemma":[0.099251196,0.000041580268,0.90019816,0.00031211335,0.000022287848,0.000015660757,0.00000966576,0.000009572726,0.00013974668],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985319,0.00022469366,0.00043101178,0.0002790024,0.0003956078,0.00013783202],"domain_scores_gemma":[0.9984091,0.00017019833,0.0004667255,0.0005749492,0.00022280597,0.00015621608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016138217,0.00010794906,0.00024045173,0.0001730305,0.00029601555,0.00031437282,0.0020784163,0.000022190121,0.0000062512854],"category_scores_gemma":[0.0004415415,0.000084051564,0.000012289171,0.00047792765,0.000029376377,0.0007700556,0.0018389755,0.00023993313,0.0000016781112],"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":[0.000092903894,0.0001865795,0.000016043718,0.000021260623,0.000025629404,0.000019730687,0.0014236978,0.032928247,0.00047009246,0.085057005,0.02364101,0.8561178],"study_design_scores_gemma":[0.00032341294,0.00028758694,0.00004925589,0.000025383293,0.000017094935,0.00007059149,0.000035048535,0.9025765,0.00013760042,0.0953628,0.0010039794,0.00011075504],"about_ca_topic_score_codex":0.00005207328,"about_ca_topic_score_gemma":0.0000041451244,"teacher_disagreement_score":0.8696482,"about_ca_system_score_codex":0.000079187,"about_ca_system_score_gemma":0.00033313723,"threshold_uncertainty_score":0.38622493},"labels":[],"label_agreement":null},{"id":"W4231925469","doi":"10.4236/ojs.2014.4811","title":"Interval Estimation for the Stress-Strength Reliability with Bivariate Normal Variables","year":2014,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Distribution Estimation and Applications","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":"York University; Simon Fraser University","funders":"","keywords":"Bivariate analysis; Reliability (semiconductor); Mathematics; Statistics; Confidence interval; Covariance matrix; Multivariate normal distribution; Coverage probability; Multivariate statistics; Power (physics)","score_opus":0.047853960321312776,"score_gpt":0.3611498025271931,"score_spread":0.3132958422058803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231925469","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005674923,0.0000039739775,0.99548453,0.0009438843,0.000096603944,0.0004263877,0.0017990365,0.000009605255,0.0006684991],"genre_scores_gemma":[0.2722006,0.000004802912,0.72741437,0.00007025191,0.00006088765,0.000033460747,0.00007685581,0.000013806126,0.00012494659],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99872124,0.00009649733,0.0006319518,0.00011834002,0.00027385348,0.00015811423],"domain_scores_gemma":[0.9935842,0.0046430915,0.0006465515,0.000256859,0.00076773774,0.00010155641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011662189,0.0001239598,0.00025924438,0.0000300228,0.0002559517,0.00022916876,0.00048357385,0.00003820595,0.0002434118],"category_scores_gemma":[0.004551958,0.00007520758,0.000038874536,0.0001273011,0.00012461998,0.00020849708,0.000060720344,0.00016554841,0.000006871808],"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":[0.00013921382,0.00017890541,0.00008237658,0.00006926252,0.000051586376,8.073197e-7,0.00008376516,0.001570637,0.000010874724,0.97050846,0.012908581,0.01439551],"study_design_scores_gemma":[0.0017709214,0.0004906492,0.0052312147,0.00014386642,0.0003475559,0.00003809583,0.00017812416,0.35245946,0.00023273606,0.6318663,0.0070612007,0.00017989289],"about_ca_topic_score_codex":0.000014776127,"about_ca_topic_score_gemma":0.000008860765,"teacher_disagreement_score":0.35088882,"about_ca_system_score_codex":0.000046880326,"about_ca_system_score_gemma":0.00012222177,"threshold_uncertainty_score":0.54494476},"labels":[],"label_agreement":null},{"id":"W4312577525","doi":"10.4236/ojs.2022.125043","title":"Statistical Analysis of Small Holder Farmer Financial Exclusion: Case Study of Migori County, Kenya","year":2022,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Microfinance and Financial Inclusion","field":"Economics, Econometrics and Finance","cited_by":1,"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":"International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Developing country; Scale (ratio); Business; Agricultural economics; Agricultural science; Economics; Finance; Economic growth; Geography","score_opus":0.06106595504378557,"score_gpt":0.28854415275640377,"score_spread":0.2274781977126182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312577525","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":[0.8744513,0.0006462903,0.114418596,0.000044487388,0.0007846643,0.00038868707,0.00857008,0.0000020069444,0.0006939093],"genre_scores_gemma":[0.98559046,0.00019021523,0.013642696,0.000089583904,0.000060393806,0.0000090247095,0.000052581923,0.000022702287,0.00034233977],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99706334,0.000094992836,0.0021092,0.00028640794,0.00019838713,0.00024770253],"domain_scores_gemma":[0.9965248,0.00021683756,0.0024762205,0.0003426955,0.0003422236,0.00009720691],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017712988,0.00018328559,0.0013059605,0.00062054006,0.00033791442,0.000048491987,0.00071858114,0.0000617279,0.0015681455],"category_scores_gemma":[0.00027273357,0.00020148202,0.00015356156,0.0010714745,0.000094554445,0.00015484345,0.0008321842,0.00038227308,0.0000041232715],"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":[0.001840576,0.012539681,0.5114913,0.00024194285,0.0031008886,0.018997205,0.022498228,0.013892407,0.000334564,0.35443497,0.035910923,0.024717351],"study_design_scores_gemma":[0.021801388,0.022626216,0.6311581,0.00015902877,0.0057818484,0.0038028527,0.030601839,0.01583978,0.00019682586,0.07078354,0.19437258,0.0028760144],"about_ca_topic_score_codex":0.0025913755,"about_ca_topic_score_gemma":0.0006472019,"teacher_disagreement_score":0.2836514,"about_ca_system_score_codex":0.00011602988,"about_ca_system_score_gemma":0.00020189401,"threshold_uncertainty_score":0.9993445},"labels":[],"label_agreement":null},{"id":"W4312720532","doi":"10.4236/ojs.2022.125041","title":"Extreme Values Approach in Food Risk Modeling","year":2022,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":0,"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":"Health Canada","keywords":"Statistics; Random variable; Cumulative distribution function; Mixture distribution; Probabilistic logic; Mathematics; Gaussian; Econometrics; Computer science; Applied mathematics; Probability density function","score_opus":0.043671054422176746,"score_gpt":0.26200132912541174,"score_spread":0.218330274703235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312720532","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":[0.5134539,0.00013806521,0.47385067,0.00009569473,0.00009522666,0.00010695713,0.00009473511,0.0000021977578,0.012162533],"genre_scores_gemma":[0.9082233,0.00004796575,0.091447674,0.000071366274,0.000014564438,0.0000031505037,0.0000046301034,0.0000059745375,0.00018138852],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988895,0.00023495492,0.0003356048,0.00011037678,0.0002920774,0.00013748818],"domain_scores_gemma":[0.999542,0.0000570601,0.00023314984,0.00010297904,0.000008835998,0.000055976467],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012460115,0.000063556574,0.00018200053,0.0000518426,0.00020819712,0.00002998688,0.0005201427,0.00001838537,0.0033688988],"category_scores_gemma":[0.000057283778,0.000057695685,0.00003377679,0.00021029342,0.000043977376,0.00018162666,0.00039988116,0.00034973136,0.000014329845],"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":[0.000055051423,0.00014971566,0.045698922,0.0000014151517,0.000035058936,0.000049566665,0.00084072753,0.9467601,0.000024625306,0.00036905185,0.0023853271,0.003630414],"study_design_scores_gemma":[0.00071799994,0.00037370998,0.0038922965,0.0000032877194,0.00007351198,0.00008270955,0.0009166745,0.95099694,0.000007828111,0.04155112,0.0012494632,0.00013444519],"about_ca_topic_score_codex":0.00024733724,"about_ca_topic_score_gemma":0.00010764064,"teacher_disagreement_score":0.39476937,"about_ca_system_score_codex":0.0001074545,"about_ca_system_score_gemma":0.000021954946,"threshold_uncertainty_score":0.99754214},"labels":[],"label_agreement":null},{"id":"W4380980112","doi":"10.4236/ojs.2023.133015","title":"Empirical Bayesian Approach to Testing Homogeneity of Several Means of Inflated Poisson Distributions (IPD)","year":2023,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Methods and Bayesian 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":"Western University","funders":"","keywords":"Mathematics; Prior probability; Conjugate prior; Poisson distribution; Statistics; Homogeneity (statistics); Bayesian linear regression; Applied mathematics; Bayesian probability; Gamma distribution; Likelihood function; Bayesian inference; Estimation theory","score_opus":0.18183485904472557,"score_gpt":0.4296123084514148,"score_spread":0.24777744940668922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380980112","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009181676,0.000005123461,0.9859334,0.00015988198,0.000096055104,0.0002645053,0.0021219815,0.000011443886,0.002225907],"genre_scores_gemma":[0.13893567,0.000005639673,0.8609045,0.000018969962,0.000039978713,0.000003834827,0.000021864696,0.000019982392,0.000049559065],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99744505,0.00031872152,0.0012689809,0.0001687902,0.000500912,0.00029753303],"domain_scores_gemma":[0.99456996,0.0030577558,0.00082778063,0.0002645292,0.0010178748,0.00026210587],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001742907,0.00017384146,0.000733881,0.00016490578,0.00010072379,0.000058803013,0.0006133216,0.00008502863,0.00007967911],"category_scores_gemma":[0.0124514075,0.00014545911,0.000073953706,0.0010373066,0.00012777124,0.00012237896,0.00024495137,0.00028910703,0.0000046006735],"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":[0.00035631054,0.0012510943,0.019497683,0.0008937487,0.0004302127,0.0001765941,0.0024049722,0.0005608662,0.002788751,0.86685306,0.044904783,0.059881937],"study_design_scores_gemma":[0.0010469403,0.00092614803,0.054382183,0.0004915869,0.00030442534,0.00012905542,0.00048639573,0.022963298,0.0015263812,0.9171391,0.00025658763,0.00034786892],"about_ca_topic_score_codex":0.000049339604,"about_ca_topic_score_gemma":0.000007284555,"teacher_disagreement_score":0.12975399,"about_ca_system_score_codex":0.000056202163,"about_ca_system_score_gemma":0.00028299334,"threshold_uncertainty_score":0.99586713},"labels":[],"label_agreement":null},{"id":"W4383739011","doi":"10.4236/ojs.2023.134021","title":"Modeling Cyber Loss Severity Using a Spliced Regression Distribution with Mixture Components","year":2023,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Bayesian Methods and Mixture Models","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":"Burnaby Hospital","funders":"","keywords":"Expectation–maximization algorithm; Covariate; Mixture model; Range (aeronautics); Computer science; Aggregate (composite); Generalized linear model; Statistics; Econometrics; Heavy-tailed distribution; Data mining; Mathematics; Probability distribution; Maximum likelihood; Engineering","score_opus":0.057860217162524014,"score_gpt":0.3354950161118946,"score_spread":0.2776347989493706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383739011","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04141407,0.000026503776,0.9577833,0.00026486706,0.00023673866,0.00011788875,0.000093572344,0.000013654242,0.00004945113],"genre_scores_gemma":[0.20511517,0.00004296656,0.79466164,0.000054382082,0.00005423363,7.2162675e-7,0.000020249154,0.000010099264,0.000040539617],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986343,0.00016548214,0.00036687485,0.00018788311,0.00041252092,0.00023295647],"domain_scores_gemma":[0.998819,0.00007687651,0.00031599644,0.00025358223,0.00038304957,0.0001514804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008924672,0.00013948177,0.00029208377,0.00006399505,0.00019648098,0.00032041763,0.0008317069,0.00005967212,0.0000053339318],"category_scores_gemma":[0.00005582441,0.0000967463,0.000035448884,0.00039863004,0.000030423185,0.000640348,0.0003249912,0.0002840748,0.0000036090992],"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":[0.0015823422,0.0008135552,0.0025636333,0.00040713558,0.00065036694,0.008357974,0.0059736827,0.12399682,0.01917905,0.36938438,0.030041097,0.43704998],"study_design_scores_gemma":[0.0006760954,0.00009055698,0.0005600647,0.00032922122,0.00003133796,0.00040763034,0.00001885748,0.96401167,0.0002334537,0.033192746,0.00028365353,0.00016471102],"about_ca_topic_score_codex":0.00004217363,"about_ca_topic_score_gemma":0.0000048569386,"teacher_disagreement_score":0.8400149,"about_ca_system_score_codex":0.00007093685,"about_ca_system_score_gemma":0.00015256245,"threshold_uncertainty_score":0.39451995},"labels":[],"label_agreement":null},{"id":"W4385899342","doi":"10.4236/ojs.2023.134024","title":"Non-Regular Example of Confidence-Interval Construction","year":2023,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Advanced Statistical Methods and Models","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":"Brock University","funders":"","keywords":"Estimator; Confidence interval; Mathematics; Interval (graph theory); Sampling (signal processing); Statistics; Binomial proportion confidence interval; Coverage probability; Simple (philosophy); CDF-based nonparametric confidence interval; Credible interval; Mathematical optimization; Construct (python library); Computer science; Poisson distribution; Combinatorics","score_opus":0.1976968003256333,"score_gpt":0.45550604155516805,"score_spread":0.25780924122953475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385899342","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007358986,0.000011455054,0.9908818,0.00006141547,0.0003716918,0.00016621231,0.0002605705,0.000006586782,0.0008812872],"genre_scores_gemma":[0.040115148,0.00007057485,0.9592838,0.000018327168,0.00006295227,0.0000024297372,0.0000054069465,0.000021221043,0.00042010014],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99843055,0.000093958544,0.0008289564,0.00011910783,0.00034271795,0.00018468511],"domain_scores_gemma":[0.9969345,0.0014547027,0.00075549306,0.00019047363,0.0005453766,0.00011950266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011823146,0.00011460089,0.0005203662,0.00009916624,0.000059492824,0.000045652167,0.00036632357,0.00004710311,0.00021697361],"category_scores_gemma":[0.0015118003,0.00009619571,0.000057163717,0.00018286717,0.00016860907,0.00018940859,0.00012729396,0.00017905375,0.000009040532],"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":[0.00011587645,0.000054358625,0.000085844724,0.00014022578,0.000085315514,0.000115222436,0.00044088607,0.00009854781,0.0015475987,0.9420593,0.011611507,0.043645356],"study_design_scores_gemma":[0.0007157834,0.00034284283,0.0002849846,0.0002124669,0.000086824366,0.000118983466,0.0006268902,0.002605955,0.0021178767,0.99130464,0.0014704597,0.000112287074],"about_ca_topic_score_codex":0.000037278372,"about_ca_topic_score_gemma":0.000006463725,"teacher_disagreement_score":0.049245387,"about_ca_system_score_codex":0.000026443257,"about_ca_system_score_gemma":0.00012141385,"threshold_uncertainty_score":0.39227474},"labels":[],"label_agreement":null},{"id":"W4399133254","doi":"10.4236/ojs.2024.143010","title":"Using Pearson’s System of Curves to Approximate the Distributions of the Difference between Two Correlated Estimates of Signal-to-Noise Ratios: The Cases of Bivariate Normal and Bivariate Lognormal Distributions","year":2024,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Advanced Statistical Methods and Models","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":"Western University","funders":"","keywords":"Bivariate analysis; Mathematics; Statistics; Multivariate normal distribution; Random noise; SIGNAL (programming language); Multivariate statistics; Computer science","score_opus":0.1771917334711863,"score_gpt":0.43407341791135656,"score_spread":0.25688168444017023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399133254","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":[0.059399184,0.00025450758,0.9227668,0.00021156763,0.00013601774,0.00068397145,0.01651544,0.0000057174707,0.000026797254],"genre_scores_gemma":[0.6006844,0.0000392642,0.39918888,0.000005536233,0.000020748454,0.000006718054,0.000023433366,0.000015737725,0.00001528227],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99710476,0.000491396,0.001503524,0.00017104774,0.00046692102,0.00026236396],"domain_scores_gemma":[0.99078757,0.006762215,0.0011397034,0.0003577994,0.00081526116,0.00013742669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016869948,0.0002290875,0.0007983088,0.0000806508,0.0002609039,0.000083959814,0.00064525823,0.000052891653,0.000024456103],"category_scores_gemma":[0.0037002703,0.000121278674,0.00010833111,0.0005771642,0.00042708137,0.000154879,0.0004449967,0.00034198456,5.331255e-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":[0.00038540276,0.00035959293,0.0027894778,0.00442007,0.0010251248,0.0000318334,0.00301942,0.008401876,0.024393441,0.94823414,0.00081486604,0.006124758],"study_design_scores_gemma":[0.0033673535,0.0016464699,0.026038596,0.031670704,0.011138478,0.0012906594,0.0030685277,0.18783599,0.033427272,0.69887644,0.00026416834,0.0013753644],"about_ca_topic_score_codex":0.00027891598,"about_ca_topic_score_gemma":0.000039367318,"teacher_disagreement_score":0.5412852,"about_ca_system_score_codex":0.000056244757,"about_ca_system_score_gemma":0.00026615578,"threshold_uncertainty_score":0.4945601},"labels":[],"label_agreement":null}]}