{"meta":{"query_hash":"dc5e85179625","filters":{"venue":"International Journal of Applied Decision Sciences"},"cohort_total":7,"direct_labels_cover":0,"predictions_cover":7,"exported":7,"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/dc5e85179625","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Applied+Decision+Sciences"},"results":[{"id":"W2010895306","doi":"10.1504/ijads.2014.058038","title":"An empirical examination of corporate websites as a voluntary disclosure medium","year":2013,"lang":"en","type":"article","venue":"International Journal of Applied Decision Sciences","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","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":"Brock University","funders":"","keywords":"Voluntary disclosure; Business; Accounting; Earnings; Revenue; Capital market; Relevance (law); Investor relations; Turnover; Marketing; Strategic management; Economics; Finance","score_opus":0.026197305319392208,"score_gpt":0.2926694982445252,"score_spread":0.266472192925133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010895306","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":[0.9258518,0.00002109306,0.060389012,0.0010876493,0.000755006,0.00012279066,0.0000015760684,0.000016233975,0.0117548],"genre_scores_gemma":[0.99329585,0.000016289465,0.004548906,0.0011332952,0.0009086607,0.000004610156,0.0000043825776,0.000010299263,0.00007769078],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9965786,0.000012306275,0.0007290114,0.00025515942,0.0022580158,0.0001669473],"domain_scores_gemma":[0.9842351,0.00022283125,0.014257981,0.00012986781,0.0011244521,0.000029783097],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017218612,0.0001416118,0.00022641373,0.0006538233,0.00011314647,0.00054649246,0.0014068871,0.000047420264,0.0013481765],"category_scores_gemma":[0.0021630866,0.00010587179,0.00008405751,0.0006030023,0.00025575995,0.0027688767,0.00024512442,0.0001616075,0.00038347192],"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":[0.00030074225,0.000534502,0.18431333,0.000028399261,0.00013338383,0.00006237489,0.000510788,0.005332319,0.0141995745,0.029513957,0.025050981,0.7400196],"study_design_scores_gemma":[0.000998179,0.0001372558,0.8534634,0.00017181049,0.000039340994,0.000023754235,0.0015189014,0.0077263894,0.0011808808,0.10988094,0.02457901,0.00028014046],"about_ca_topic_score_codex":0.00007537686,"about_ca_topic_score_gemma":0.000015503403,"teacher_disagreement_score":0.7397395,"about_ca_system_score_codex":0.00004372252,"about_ca_system_score_gemma":0.000068902664,"threshold_uncertainty_score":0.9995647},"labels":[],"label_agreement":null},{"id":"W2561895128","doi":"10.1504/ijads.2016.081393","title":"A new heuristic memory-based simulated annealing approach applied to mine production scheduling problem","year":2016,"lang":"en","type":"article","venue":"International Journal of Applied Decision Sciences","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Simulated annealing; Mathematical optimization; Computer science; Scheduling (production processes); Heuristic; Job shop scheduling; Algorithm; Mathematics; Schedule","score_opus":0.02678745024188873,"score_gpt":0.2798924677841571,"score_spread":0.2531050175422684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2561895128","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":[0.3383279,0.000027051092,0.65774447,0.00034659976,0.00058513426,0.00014054618,0.0000021150115,0.00007073145,0.0027554508],"genre_scores_gemma":[0.62310064,0.000012019786,0.37652564,0.00005611899,0.00028147214,0.000002853961,5.412528e-7,0.000011166996,0.000009529457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998298,0.000004424068,0.0006496783,0.00024939518,0.00060580805,0.00019271659],"domain_scores_gemma":[0.9991391,0.00018356879,0.00021706116,0.00013216607,0.00017518959,0.00015295617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010541367,0.0001436113,0.00021622133,0.00053551263,0.00006356602,0.00014075961,0.0007266533,0.000059459406,0.00005277019],"category_scores_gemma":[0.00013006011,0.000099241675,0.000064238295,0.00030084664,0.000045129666,0.00018616188,0.000056377925,0.0001077303,0.000026835683],"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":[0.00014876259,0.000016605323,0.000023953124,0.00000425966,0.000021555941,0.0000021589,0.00011267746,0.86379236,0.023722477,0.0007732272,0.0017602,0.109621786],"study_design_scores_gemma":[0.005324698,0.0005137742,0.0006166284,0.0015826597,0.00007325478,0.00021333934,0.0007490145,0.57548606,0.32235983,0.07298414,0.01848548,0.0016111291],"about_ca_topic_score_codex":0.000003067621,"about_ca_topic_score_gemma":0.0000013141968,"teacher_disagreement_score":0.29863733,"about_ca_system_score_codex":0.00013760853,"about_ca_system_score_gemma":0.00010548873,"threshold_uncertainty_score":0.4046958},"labels":[],"label_agreement":null},{"id":"W4255696462","doi":"10.1504/ijads.2017.10002219","title":"A new heuristic memory-based simulated annealing approach applied to mine production scheduling problem","year":2016,"lang":"en","type":"article","venue":"International Journal of Applied Decision Sciences","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Simulated annealing; Mathematical optimization; Computer science; Scheduling (production processes); Heuristic; Job shop scheduling; Algorithm; Mathematics; Schedule","score_opus":0.02678745024188873,"score_gpt":0.2798924677841571,"score_spread":0.2531050175422684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255696462","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":[0.3383279,0.000027051092,0.65774447,0.00034659976,0.00058513426,0.00014054618,0.0000021150115,0.00007073145,0.0027554508],"genre_scores_gemma":[0.62310064,0.000012019786,0.37652564,0.00005611899,0.00028147214,0.000002853961,5.412528e-7,0.000011166996,0.000009529457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998298,0.000004424068,0.0006496783,0.00024939518,0.00060580805,0.00019271659],"domain_scores_gemma":[0.9991391,0.00018356879,0.00021706116,0.00013216607,0.00017518959,0.00015295617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010541367,0.0001436113,0.00021622133,0.00053551263,0.00006356602,0.00014075961,0.0007266533,0.000059459406,0.00005277019],"category_scores_gemma":[0.00013006011,0.000099241675,0.000064238295,0.00030084664,0.000045129666,0.00018616188,0.000056377925,0.0001077303,0.000026835683],"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":[0.00014876259,0.000016605323,0.000023953124,0.00000425966,0.000021555941,0.0000021589,0.00011267746,0.86379236,0.023722477,0.0007732272,0.0017602,0.109621786],"study_design_scores_gemma":[0.005324698,0.0005137742,0.0006166284,0.0015826597,0.00007325478,0.00021333934,0.0007490145,0.57548606,0.32235983,0.07298414,0.01848548,0.0016111291],"about_ca_topic_score_codex":0.000003067621,"about_ca_topic_score_gemma":0.0000013141968,"teacher_disagreement_score":0.29863733,"about_ca_system_score_codex":0.00013760853,"about_ca_system_score_gemma":0.00010548873,"threshold_uncertainty_score":0.4046958},"labels":[],"label_agreement":null},{"id":"W4323967610","doi":"10.1504/ijads.2023.129477","title":"Evaluation of cloud computing risks using an integrated fuzzy-ANP and FMEA approaches","year":2023,"lang":"en","type":"article","venue":"International Journal of Applied Decision Sciences","topic":"Cloud Data Security Solutions","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":"Toronto Metropolitan University","funders":"","keywords":"Risk analysis (engineering); Outsourcing; Cloud computing; Process management; Computer science; Risk management; Audit; Scope (computer science); Business; Accounting; Finance","score_opus":0.36031702245024766,"score_gpt":0.4343285576832485,"score_spread":0.07401153523300086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323967610","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":[0.6706453,0.00006634147,0.32773253,0.00017433666,0.00088379387,0.000084910025,0.000009113849,0.000020577192,0.00038309448],"genre_scores_gemma":[0.88062,0.000019374038,0.119164005,0.000035840796,0.0001523062,7.989133e-7,0.0000031781494,0.0000039178444,5.89064e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99520665,0.00012254537,0.000752219,0.00033336846,0.0034065736,0.00017864644],"domain_scores_gemma":[0.99695367,0.00060222583,0.00071775494,0.00023124543,0.0013880827,0.0001070262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010917851,0.00011740774,0.00021784795,0.0008681587,0.00020336792,0.00039783827,0.0019753515,0.000056048277,0.000008897381],"category_scores_gemma":[0.0007199908,0.000094430085,0.0000651126,0.0013522854,0.0002864156,0.0009291472,0.0005350624,0.00017452838,0.000006199335],"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":[0.00005247777,0.0002024063,0.0006452799,0.0000031851368,0.00009076542,0.000011072565,0.0032205884,0.18161787,0.0032135346,0.0797768,0.0003887267,0.7307773],"study_design_scores_gemma":[0.0005553787,0.00007809861,0.0038034858,0.00006743446,0.000025754287,0.00009858851,0.0012542664,0.90920836,0.0010154652,0.08359331,0.00019444338,0.00010541408],"about_ca_topic_score_codex":0.00003650653,"about_ca_topic_score_gemma":0.000016625232,"teacher_disagreement_score":0.7306719,"about_ca_system_score_codex":0.000112376416,"about_ca_system_score_gemma":0.0004590707,"threshold_uncertainty_score":0.3850747},"labels":[],"label_agreement":null},{"id":"W4392400691","doi":"10.1504/ijads.2024.137003","title":"Applying customer intelligence in marketing: a holistic approach","year":2024,"lang":"en","type":"article","venue":"International Journal of Applied Decision Sciences","topic":"Competitive and Knowledge Intelligence","field":"Business, Management and Accounting","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":"Université du Québec à Trois-Rivières","funders":"","keywords":"Marketing; Business; Relationship marketing; Marketing strategy; Customer engagement; Marketing management; Process management; Knowledge management; Industrial organization; Computer science","score_opus":0.06428480714197742,"score_gpt":0.3471221330413613,"score_spread":0.2828373258993839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392400691","genre_codex":"other","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":[0.099795885,0.0013604074,0.32902542,0.00046660934,0.0062025785,0.00035854912,0.0000021115466,0.00006271842,0.5627257],"genre_scores_gemma":[0.992739,0.000079128775,0.0055165067,0.00036376092,0.0012245476,0.000015709693,7.433229e-7,0.000009156663,0.0000514465],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977186,0.000007834766,0.00066187006,0.0002978295,0.0011236012,0.00019027345],"domain_scores_gemma":[0.99852425,0.00073990837,0.00022633445,0.000082544866,0.00040869723,0.00001827789],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0030946636,0.00013404973,0.00018587227,0.0015930728,0.00007377024,0.0010559905,0.0012082738,0.000039928065,0.00035929173],"category_scores_gemma":[0.00053905393,0.00009984335,0.00010803265,0.0012863465,0.0001789845,0.0009334138,0.0002471208,0.00025597253,0.0004902967],"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":[0.00022866901,0.000108463275,0.00088426605,0.000031389365,0.000024781137,0.00012797814,0.00013909143,0.0034000012,0.0004112969,0.27312797,0.0013750041,0.72014105],"study_design_scores_gemma":[0.00042206096,0.00003343072,0.004427024,0.001835665,0.000044556957,0.00024052152,0.0049600624,0.19327451,0.00062062155,0.2919578,0.5015112,0.00067249045],"about_ca_topic_score_codex":0.000013895898,"about_ca_topic_score_gemma":0.000008795934,"teacher_disagreement_score":0.89294314,"about_ca_system_score_codex":0.00007224487,"about_ca_system_score_gemma":0.00009364245,"threshold_uncertainty_score":0.999981},"labels":[],"label_agreement":null},{"id":"W4405511808","doi":"10.1504/ijads.2025.10068509","title":"Minimising makespan and total tardiness in no-wait open-shop scheduling problems using metaheuristic algorithms: a narrative review","year":2024,"lang":"en","type":"review","venue":"International Journal of Applied Decision Sciences","topic":"Scheduling and Optimization Algorithms","field":"Engineering","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":"Dalhousie University","funders":"","keywords":"Tardiness; Job shop scheduling; Metaheuristic; Computer science; Scheduling (production processes); Mathematical optimization; Narrative; Operations research; Algorithm; Mathematics","score_opus":0.08560608207754947,"score_gpt":0.4020833054340306,"score_spread":0.3164772233564811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405511808","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009256418,0.96979994,0.025970448,0.000047274527,0.0027806058,0.0004906647,0.000018299954,0.000026924725,0.0007732985],"genre_scores_gemma":[0.000057120225,0.781899,0.2176048,0.00003980412,0.00032514895,0.0000140529455,0.0000055053843,0.00003770846,0.000016871767],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960415,0.00005614413,0.0019120655,0.00048106728,0.0012492075,0.00026006633],"domain_scores_gemma":[0.99800074,0.00052858033,0.0006794977,0.00015213591,0.000496257,0.00014279164],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002712607,0.00044290908,0.0015841171,0.0011697036,0.00009607535,0.0010379507,0.0013940838,0.00016356024,0.00005674558],"category_scores_gemma":[0.00044229976,0.00031818042,0.00026066473,0.0012594305,0.00014588772,0.00050575176,0.0003547884,0.00067792874,0.000028010749],"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":[0.000015149344,0.000039659066,5.766043e-7,0.0034062453,0.00034631306,0.00012864644,0.00043570626,0.10016421,0.0000038136006,0.00017585981,0.00025312766,0.8950307],"study_design_scores_gemma":[0.0010765468,0.00011121754,0.0000011130365,0.29038906,0.0012191874,0.002923932,0.0010122627,0.5516648,0.0000059288705,0.0022299115,0.14806283,0.0013031948],"about_ca_topic_score_codex":0.000003745821,"about_ca_topic_score_gemma":0.00000167049,"teacher_disagreement_score":0.8937275,"about_ca_system_score_codex":0.00024838914,"about_ca_system_score_gemma":0.00044977912,"threshold_uncertainty_score":0.99999905},"labels":[],"label_agreement":null},{"id":"W7115008491","doi":"10.1504/ijads.2026.150373","title":"Minimising makespan and total tardiness in no-wait open-shop scheduling problems using metaheuristic algorithms: a narrative review","year":2025,"lang":"en","type":"article","venue":"International Journal of Applied Decision Sciences","topic":"Scheduling and Optimization Algorithms","field":"Engineering","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":"Dalhousie University","funders":"","keywords":"Tardiness; Metaheuristic; Job shop scheduling; Simulated annealing; Particle swarm optimization; Scheduling (production processes); Integer programming; Novelty; Heuristics","score_opus":0.0350360713101105,"score_gpt":0.3502053889591038,"score_spread":0.3151693176489933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115008491","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":[0.29457617,0.014861643,0.677297,0.0009017919,0.004106352,0.0006021541,0.0000088129,0.000046398392,0.0075996886],"genre_scores_gemma":[0.32714218,0.0023290764,0.6699792,0.00034204716,0.00015250301,0.0000074561653,0.0000016599524,0.000013964396,0.000031871226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980481,0.00002472695,0.0008450864,0.00023473271,0.0006833466,0.00016401037],"domain_scores_gemma":[0.99873906,0.00034732732,0.00023971344,0.00008837191,0.00051501766,0.000070535185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017241788,0.00015795468,0.0003853117,0.0005959937,0.00010327967,0.0004372461,0.00073743454,0.000052890504,0.000040464114],"category_scores_gemma":[0.00046044026,0.00012950673,0.00005770908,0.00080516346,0.000109207074,0.0004731063,0.00016941942,0.00022273595,0.0000036597953],"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":[0.000067637025,0.000052032352,0.00013570939,0.00007564892,0.00010600928,0.000027485301,0.00065033644,0.9384173,0.0008055999,0.00071174704,0.00018336103,0.05876712],"study_design_scores_gemma":[0.0012132123,0.00003653161,0.00019548544,0.0049481075,0.00003954203,0.0001454807,0.0013964442,0.9872175,0.00041899417,0.0036054722,0.00054829195,0.0002349287],"about_ca_topic_score_codex":0.0000057698944,"about_ca_topic_score_gemma":0.0000027798374,"teacher_disagreement_score":0.058532193,"about_ca_system_score_codex":0.000105512714,"about_ca_system_score_gemma":0.00016625265,"threshold_uncertainty_score":0.5281131},"labels":[],"label_agreement":null}]}