{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":6,"total_is_capped":false,"direct_labels_cover":1,"predictions_cover":6,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"93d3b287c181","filters":{"venue":"Journal of Management Analytics"}},"results":[{"id":"W2792543142","doi":"10.1080/23270012.2018.1443405","title":"Editorial to the special issue “Transdisciplinary analytics in supply chain management”","year":2018,"lang":"en","type":"article","venue":"Journal of Management Analytics","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":45,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of Toronto","keywords":"Analytics; Supply chain; Supply chain management; Data science; Scale (ratio); Computer science; Management science; Engineering management; Systems engineering; Engineering ethics; Risk analysis (engineering); Engineering; Business; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.03174084415734832,"gpt":0.2917784412518352,"spread":0.2600375970944868,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001796594,0.0003334259,0.000434846,0.001417826,0.0002558847,0.0005533259,0.001662597,0.00009261857,0.00122897],"category_scores_gemma":[0.00006887696,0.0002451751,0.0002088107,0.002653044,0.0001549386,0.001091069,0.0007441094,0.0003745244,0.0009384119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001142941,"about_ca_system_score_gemma":0.00002057182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003287118,"about_ca_topic_score_gemma":0.0003196126,"domain_scores_codex":[0.9968097,0.00002305504,0.001034193,0.0003528685,0.001219813,0.0005603631],"domain_scores_gemma":[0.9982653,0.00004389337,0.0005539993,0.0005763168,0.0005140789,0.00004635685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003650822,0.0002322584,0.002079413,0.0001819982,0.0002375703,0.0001620371,0.0002966106,0.001258391,0.000004273083,0.0079084,0.971795,0.01547903],"study_design_scores_gemma":[0.0007262857,0.00006903758,0.006325524,0.0002166156,0.0004338808,0.000005317289,0.001751696,0.002527832,0.00001210009,0.002680716,0.9849363,0.000314698],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"editorial","genre_scores_codex":[0.05013651,0.0002990806,0.03565996,0.08300958,0.3703945,0.003447694,0.00004617032,0.000184386,0.4568221],"genre_scores_gemma":[0.2146805,0.0003926598,0.001867001,0.004915719,0.7711038,0.00001137952,0.00002615446,0.00007541697,0.006927417],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.4498947,"threshold_uncertainty_score":0.9998395,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4401522263","doi":"10.1080/23270012.2024.2377168","title":"Handling highly imbalanced data for classifying fatality of auto collisions using machine learning techniques","year":2024,"lang":"en","type":"article","venue":"Journal of Management Analytics","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"","keywords":"Machine learning; Computer science; Artificial intelligence; Risk analysis (engineering); Data mining; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.09543007381265481,"gpt":0.3582703427381831,"spread":0.2628402689255284,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002169744,0.0001573977,0.0003554191,0.0006048384,0.0001320606,0.0003189941,0.001929004,0.00006421229,0.000003877311],"category_scores_gemma":[0.0001915901,0.0001372998,0.000133442,0.0007774124,0.00005170323,0.001249123,0.0008318648,0.0002849544,8.143509e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001552407,"about_ca_system_score_gemma":0.00009646601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007109853,"about_ca_topic_score_gemma":0.000001630757,"domain_scores_codex":[0.9980083,0.00006593639,0.0008859651,0.0003326211,0.0004887424,0.0002184293],"domain_scores_gemma":[0.9978564,0.0002159399,0.0006748139,0.0009105497,0.0002792806,0.00006299149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002450291,0.0009656135,0.005899534,0.006103319,0.003852649,0.0005119792,0.001206925,0.008872193,0.1239242,0.5355315,0.02463197,0.2882551],"study_design_scores_gemma":[0.0001857219,0.0001028862,0.0001458519,0.0005608661,0.000189979,0.00001772298,0.00005143878,0.9192517,0.01474377,0.00210905,0.06248531,0.0001556552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007105991,0.0005934963,0.9969203,0.0008100796,0.0002510105,0.0002455799,0.00006966694,0.000191697,0.0002075738],"genre_scores_gemma":[0.2012928,0.0005766393,0.7976859,0.00007698117,0.0001072137,0.000003706122,0.00005032765,0.00001980731,0.0001866329],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9103795,"threshold_uncertainty_score":0.5598925,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3137447171","doi":"10.1080/23270012.2021.1884619","title":"Comparison of Ontario’s roundwood and recycled fibre pulp and paper mills’ performance using data Envelopment analysis","year":2021,"lang":"en","type":"article","venue":"Journal of Management Analytics","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Laurentian University","funders":"","keywords":"Data envelopment analysis; Pulp (tooth); Pulp and paper industry; Engineering; Environmental science; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.1860991478510491,"gpt":0.4049466400189752,"spread":0.2188474921679261,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003856246,0.0001865031,0.0009887876,0.001093189,0.0001812209,0.0003543458,0.0008484607,0.0000622169,0.0002028716],"category_scores_gemma":[0.0003039851,0.0001464843,0.0001973926,0.002446139,0.000122804,0.0007443856,0.000854906,0.00020312,0.000001779924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001778647,"about_ca_system_score_gemma":0.0002170206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002529544,"about_ca_topic_score_gemma":0.003439888,"domain_scores_codex":[0.995425,0.000160209,0.001780596,0.0004675216,0.001937479,0.0002291996],"domain_scores_gemma":[0.9965,0.000313536,0.001443001,0.000991385,0.0006194835,0.0001326313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007631745,0.0003840505,0.9036689,0.00005473894,0.004728124,0.0001099784,0.002274204,0.03227251,0.0002223477,0.0002161588,0.001434964,0.05455773],"study_design_scores_gemma":[0.0009587585,0.0001464527,0.3761694,0.000156764,0.009302406,0.00005007818,0.004273976,0.5683662,0.0003961399,0.0006953991,0.03910249,0.0003819962],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9738527,0.001482078,0.0228481,0.0005766451,0.0001258539,0.00004972705,0.000005870675,0.000003380314,0.001055672],"genre_scores_gemma":[0.9740496,0.0006372945,0.02341366,0.0001252869,0.00003097732,1.429855e-7,0.000008669579,0.00000762004,0.001726764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5360937,"threshold_uncertainty_score":0.5973457,"prediction_status":"machine_predicted_unvalidated"},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":true,"confidence":"medium"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":true,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4362659071","doi":"10.1080/23270012.2023.2187716","title":"Classification of territory risk by generalized linear and generalized linear mixed models","year":2023,"lang":"en","type":"article","venue":"Journal of Management Analytics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"","keywords":"Generalized linear model; Cluster analysis; Generalized linear mixed model; Econometrics; Statistics; Cluster (spacecraft); Mathematics; Computer science; Actuarial science; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.06626251894335605,"gpt":0.2528737028977341,"spread":0.186611183954378,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001057614,0.0001362221,0.0005336872,0.0006841274,0.00006683035,0.00003786345,0.0002664695,0.00007447958,0.00007338762],"category_scores_gemma":[0.00004518683,0.0001341319,0.0002200023,0.0005078507,0.00004749579,0.000236953,0.00008516146,0.0001319259,0.00003526063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004038397,"about_ca_system_score_gemma":0.000007646682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001937724,"about_ca_topic_score_gemma":0.00002049205,"domain_scores_codex":[0.9983596,0.00003974026,0.001099664,0.0002047697,0.0001245018,0.0001717178],"domain_scores_gemma":[0.9982204,0.00004096585,0.001296873,0.0002725756,0.00008157846,0.00008761292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006957815,0.001079617,0.2968372,0.0008432583,0.01150499,0.0001272385,0.001617724,0.2201155,0.001076044,0.1554535,0.2912474,0.01940173],"study_design_scores_gemma":[0.001462369,0.00008786433,0.01442286,0.00002326155,0.0003912465,0.000001265638,0.0002001444,0.9243305,0.00007814231,0.01184428,0.04694402,0.0002140575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9248692,0.002176141,0.0687855,0.001266201,0.0004830362,0.0001798358,0.0005139599,0.00002758933,0.00169849],"genre_scores_gemma":[0.9618019,0.02479917,0.009514693,0.0001432802,0.0003397492,0.000004127998,0.0002010303,0.00003398558,0.003162007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.704215,"threshold_uncertainty_score":0.546974,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4401134137","doi":"10.1080/23270012.2024.2372632","title":"A review of big data analytics models for assessing non-pharmaceutical interventions for COVID-19 pandemic management","year":2024,"lang":"en","type":"review","venue":"Journal of Management Analytics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Pandemic; Big data; Coronavirus disease 2019 (COVID-19); Analytics; Psychological intervention; Data science; Computer science; Data analysis; 2019-20 coronavirus outbreak; Risk analysis (engineering); Business; Management science; Medicine; Economics; Data mining; Infectious disease (medical specialty); Disease; Virology","retraction":null,"screen_n_in":null,"score":{"opus":0.885379257249426,"gpt":0.6439655592079637,"spread":0.2414136980414623,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.01322222,0.0008379774,0.005607428,0.001183247,0.0001706178,0.0001431507,0.002538643,0.0002445023,0.00002474348],"category_scores_gemma":[0.005736438,0.0005841753,0.004456758,0.001158215,0.000165191,0.0002482851,0.002394011,0.0007556974,0.000005175323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001113857,"about_ca_system_score_gemma":0.0003153529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002040603,"about_ca_topic_score_gemma":0.000006622404,"domain_scores_codex":[0.991683,0.0003000521,0.005718104,0.0008480669,0.0008122605,0.0006384945],"domain_scores_gemma":[0.9868743,0.006214802,0.004587381,0.001485978,0.0004885059,0.0003490434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001555103,0.000238833,0.000002625378,0.5983185,0.008496949,0.00004698428,0.00001022404,0.00008540603,3.842092e-9,0.008759801,0.1408177,0.2432074],"study_design_scores_gemma":[0.0005113938,0.00009659184,2.227872e-7,0.1959437,0.08039516,0.00002331819,0.00007171428,0.01019115,8.897195e-9,0.04945264,0.6629634,0.0003506949],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[2.430442e-8,0.5420418,0.4539448,0.0007155472,0.00040996,0.002285439,0.0002841237,0.00002662906,0.0002917573],"genre_scores_gemma":[0.000002509257,0.9279217,0.06871111,0.001541182,0.0005006277,0.0001739722,0.0001738964,0.0001072715,0.000867728],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.5221457,"threshold_uncertainty_score":0.999661,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407103082","doi":"10.1080/23270012.2025.2455550","title":"The adoption of human resources analytics in construction projects in Jordan: antecedents and consequences","year":2025,"lang":"en","type":"article","venue":"Journal of Management Analytics","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec","funders":"","keywords":"Analytics; Knowledge management; Business; Data science; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.0218353666992485,"gpt":0.2642648503919301,"spread":0.2424294836926816,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008443008,0.0001100634,0.0002562346,0.001332895,0.0001078248,0.0001636138,0.0003004204,0.00005779814,0.00000323544],"category_scores_gemma":[0.0001131533,0.00008068424,0.00005562484,0.001048358,0.0003164619,0.0003720066,0.0001785923,0.0001902569,7.687289e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005205015,"about_ca_system_score_gemma":0.00001385309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007331963,"about_ca_topic_score_gemma":0.0004101274,"domain_scores_codex":[0.9988025,0.00001501802,0.0006614517,0.0001135402,0.0002486047,0.000158927],"domain_scores_gemma":[0.9990181,0.00005102981,0.0006530582,0.0001355233,0.000137518,0.000004786205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000162543,0.0001586436,0.7164106,0.0006800536,0.0002592194,0.00009903873,0.0001479715,0.0009189027,0.0001885954,0.2598672,0.001484015,0.01962321],"study_design_scores_gemma":[0.004971627,0.0002204158,0.7029229,0.003044427,0.0009745582,0.0000193608,0.06004722,0.01602501,0.0004412041,0.1849853,0.02575947,0.0005884551],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901476,0.0004028012,0.0001234091,0.002175672,0.0001406813,0.0001741958,3.04924e-7,0.00001167916,0.006823686],"genre_scores_gemma":[0.9983477,0.000990344,0.0003004624,0.00009734496,0.00004901591,0.00000167163,7.578257e-7,0.000004548518,0.0002081442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07488184,"threshold_uncertainty_score":0.3290208,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}