{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":87,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":87,"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":"fba9324e71f5","filters":{"venue":"Omega"}},"results":[{"id":"W2074531309","doi":"10.1016/j.omega.2013.09.004","title":"Data envelopment analysis: Prior to choosing a model","year":2013,"lang":"en","type":"article","venue":"Omega","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":880,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Data envelopment analysis; Benchmarking; Computer science; Selection (genetic algorithm); Efficient frontier; Frontier; Production (economics); Raw data; Operations research; Production–possibility frontier; Economics; Marketing; Business; Artificial intelligence; Statistics; Mathematics; Microeconomics; Political science","retraction":null,"screen_n_in":null,"score":{"opus":0.1825064853590864,"gpt":0.4111379470208777,"spread":0.2286314616617913,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003085065,0.0001627337,0.0004237833,0.001186843,0.0002475363,0.0008474812,0.002837762,0.00005537481,0.0006392885],"category_scores_gemma":[0.002095052,0.0001187549,0.0001491068,0.005463985,0.00004961566,0.0007126042,0.0009615686,0.0001110655,0.004841405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006816378,"about_ca_system_score_gemma":0.0001293924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001472408,"about_ca_topic_score_gemma":0.0001817118,"domain_scores_codex":[0.9958346,0.0001128907,0.000746679,0.001047705,0.001864926,0.0003932344],"domain_scores_gemma":[0.9959326,0.0003530847,0.0001889639,0.002940592,0.0003240615,0.0002607368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001928909,0.0003980285,0.01826047,0.000004373991,0.0008620286,0.00001173374,0.003598246,0.4889399,0.006120085,0.0008921404,0.1570387,0.323855],"study_design_scores_gemma":[0.00009488821,0.00001027306,0.01483491,0.000006275419,0.0001885702,8.084413e-7,0.0002301592,0.9602622,0.0001398363,0.002131403,0.02188479,0.0002158565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6100187,0.0001061059,0.3758025,0.005096044,0.0001426264,0.0003310551,0.00004213209,0.00008488911,0.00837591],"genre_scores_gemma":[0.9496012,0.000003155493,0.042742,0.001230821,0.00003800295,0.00001821631,0.00001963231,0.00001002671,0.006337004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4713223,"threshold_uncertainty_score":0.9959334,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2008581871","doi":"10.1016/j.omega.2011.11.005","title":"Implementing coordination contracts in a manufacturer Stackelberg dual-channel supply chain","year":2011,"lang":"en","type":"article","venue":"Omega","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":523,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Lakehead University; University of Winnipeg","funders":"","keywords":"Stackelberg competition; Supply chain; Dual (grammatical number); Channel coordination; Business; Channel (broadcasting); Profit (economics); Industrial organization; Tariff; Supply chain management; Microeconomics; Computer science; Economics; Marketing; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.02849757032960306,"gpt":0.2236703098947279,"spread":0.1951727395651248,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008553296,0.0002414973,0.0002297048,0.0005608965,0.0001563847,0.0001773231,0.0002000417,0.00007338759,0.001654118],"category_scores_gemma":[0.00006277036,0.0002381486,0.00008099761,0.0003934572,0.0000343796,0.001166177,0.0002661574,0.0001516471,0.0003926248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007495471,"about_ca_system_score_gemma":0.00001018315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001718213,"about_ca_topic_score_gemma":0.001234787,"domain_scores_codex":[0.9982301,0.00001894089,0.0004026257,0.0003747264,0.0002867081,0.0006869179],"domain_scores_gemma":[0.9993979,0.00002274227,0.0002201071,0.0002627653,0.00007444799,0.00002198805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006270825,0.002479765,0.2266447,0.001181556,0.0004294696,0.0009244998,0.006986978,0.0002284343,0.002390295,0.4824414,0.2141202,0.06154567],"study_design_scores_gemma":[0.003934307,0.00004455265,0.1641135,0.0001262749,0.00007669121,0.000003701656,0.003424904,0.005501951,0.001080975,0.01795487,0.802799,0.0009391794],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7572329,0.00009821869,0.0004793613,0.001820412,0.001072365,0.001011616,0.0000069944,0.000238043,0.2380401],"genre_scores_gemma":[0.9940944,0.000006872712,0.00008884179,0.002618524,0.0005241082,0.00008616914,0.00008360512,0.00003956883,0.00245795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5886789,"threshold_uncertainty_score":0.9992585,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2157513099","doi":"10.1016/j.omega.2005.01.003","title":"Empirical research opportunities in reverse supply chains","year":2006,"lang":"en","type":"article","venue":"Omega","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":425,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Business; Supply chain; Reverse logistics; Product (mathematics); Empirical research; Supply chain management; Service management; Service (business); Marketing; Process (computing); Process management; Industrial organization; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.1374397191353977,"gpt":0.3377662084762025,"spread":0.2003264893408048,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001799604,0.0001754585,0.0001999518,0.001434186,0.0002079171,0.0003562837,0.0003910656,0.00008174162,0.0006120147],"category_scores_gemma":[0.0001770273,0.0001778581,0.00006022487,0.00138693,0.0001464779,0.0008377507,0.0004682844,0.0002960154,0.0004865495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002270516,"about_ca_system_score_gemma":0.00005434897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00608256,"about_ca_topic_score_gemma":0.001118731,"domain_scores_codex":[0.9978425,0.00005122791,0.0003162739,0.0003753964,0.0006304322,0.0007842071],"domain_scores_gemma":[0.9991951,0.0001034848,0.00006839333,0.0003940789,0.0002226691,0.00001624919],"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":[0.00005144584,0.0002657827,0.1499875,0.0003003156,0.00001047028,0.000947358,0.0001077869,0.0002734249,0.00003398816,0.1733346,0.6717449,0.002942476],"study_design_scores_gemma":[0.0006201754,0.000009454802,0.04959003,0.00003826302,0.000007542924,0.000001106435,0.005756466,0.001370697,0.000007778253,0.01536824,0.9269943,0.0002359823],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5572166,0.000141894,0.00006791304,0.02777742,0.0003222654,0.0007231805,0.000002979676,0.0002161076,0.4135316],"genre_scores_gemma":[0.9520442,0.00001628082,0.00009422916,0.002449267,0.001305754,0.0001036335,0.00006401959,0.00004064979,0.04388197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3948276,"threshold_uncertainty_score":0.9195052,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2085444045","doi":"10.1016/j.omega.2011.07.008","title":"Stock index forecasting based on a hybrid model","year":2011,"lang":"en","type":"article","venue":"Omega","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":377,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Lanzhou University","keywords":"Autoregressive integrated moving average; Exponential smoothing; Index (typography); Autoregressive model; Artificial neural network; Computer science; Stock market index; Moving average; Time series; Econometrics; Stock market; Statistics; Mathematics; Artificial intelligence; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.3318230884209961,"gpt":0.388666807430418,"spread":0.05684371900942187,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005703243,0.0002333103,0.000338848,0.0005628301,0.0002121768,0.0001242757,0.001001029,0.00007870749,0.0006251857],"category_scores_gemma":[0.01195242,0.0001792255,0.0001823949,0.0008029329,0.00009539187,0.0002192017,0.0001735652,0.0002803099,0.0001813378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006364092,"about_ca_system_score_gemma":0.000144404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002653838,"about_ca_topic_score_gemma":0.00001001719,"domain_scores_codex":[0.9963889,0.000356801,0.0005981966,0.0007057293,0.001468027,0.0004824086],"domain_scores_gemma":[0.9955524,0.002615012,0.0003068922,0.001057251,0.0002583188,0.0002101077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006220411,0.0002190681,0.05462619,0.000008541487,0.00001655166,0.00007637643,0.0007355348,0.01494771,0.0001088899,0.0005835555,0.01856288,0.9094927],"study_design_scores_gemma":[0.0004305015,0.0001446612,0.005614806,0.00002752071,0.000006837601,0.00001496544,0.00003472966,0.9494491,0.0007568322,0.04181946,0.001482763,0.0002178507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3106948,0.000007887091,0.4659243,0.0000782596,0.000568128,0.0002270829,0.00001322449,0.0001192295,0.2223671],"genre_scores_gemma":[0.870268,1.360534e-7,0.1249392,0.0004808702,0.00007168357,0.0000311197,0.000001063424,0.00003170076,0.004176203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9345013,"threshold_uncertainty_score":0.9963703,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2869560040","doi":"10.1016/j.omega.2018.06.012","title":"The impact of government subsidy on supply Chains’ sustainability innovation","year":2018,"lang":"en","type":"article","venue":"Omega","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":328,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University; University of Waterloo","funders":"","keywords":"Subsidy; Business; Production (economics); Industrial organization; Government (linguistics); Unit (ring theory); Supply chain; Product (mathematics); Economics; Commerce; Marketing; Microeconomics; Market economy","retraction":null,"screen_n_in":null,"score":{"opus":0.01039133207132479,"gpt":0.2571711457558376,"spread":0.2467798136845128,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001291335,0.0002074444,0.0001779944,0.0002025442,0.0003593229,0.0002277473,0.0004128521,0.00005056272,0.000192523],"category_scores_gemma":[0.0009035719,0.0001403604,0.0001044916,0.001572398,0.0002194188,0.0004590252,0.0002905914,0.0001122346,0.0000804534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008008607,"about_ca_system_score_gemma":0.0000577098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001134872,"about_ca_topic_score_gemma":0.0000791196,"domain_scores_codex":[0.9982097,0.00002275622,0.0003852627,0.0002949267,0.0006163676,0.0004709587],"domain_scores_gemma":[0.9981402,0.0001053355,0.0003354166,0.0006485192,0.0007607545,0.00000982579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0007472244,0.0004875443,0.1999547,0.00033597,0.0001880893,0.0000153585,0.0002548684,0.0005686603,0.0003082474,0.6675048,0.08283442,0.04680001],"study_design_scores_gemma":[0.001758937,0.0004572919,0.6732514,0.0000513842,0.00005671774,7.428916e-7,0.007562987,0.008385247,0.0007224533,0.05426662,0.2529008,0.000585462],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9548319,0.0000146467,0.0002589575,0.003033968,0.0002780602,0.0008112519,0.000005084018,0.00007133924,0.04069482],"genre_scores_gemma":[0.9950055,0.00000311593,0.00001456856,0.0004861546,0.0008748337,0.00005651001,0.00001361595,0.0000273483,0.003518288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6132382,"threshold_uncertainty_score":0.5723729,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2008954415","doi":"10.1016/j.omega.2013.02.002","title":"An integrated supply chain model with errors in quality inspection and learning in production","year":2013,"lang":"en","type":"article","venue":"Omega","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":193,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University; Toronto Metropolitan University","funders":"","keywords":"Supply chain; Vendor; Quality (philosophy); Production (economics); Product (mathematics); Computer science; Sizing; Supply chain risk management; Process (computing); Supply chain management; Risk analysis (engineering); Service management; Process management; Business; Operations management; Marketing; Engineering; Economics; Microeconomics","retraction":null,"screen_n_in":null,"score":{"opus":0.02075188101686629,"gpt":0.2362695874669107,"spread":0.2155177064500444,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006058425,0.0001295436,0.0001458792,0.0004590936,0.00008799345,0.0001639598,0.00007620572,0.0000427517,0.00005230844],"category_scores_gemma":[0.00006172048,0.0001116606,0.00001189008,0.0005792055,0.00004368102,0.001741323,0.00003857768,0.000223606,0.00002766189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000749849,"about_ca_system_score_gemma":0.000009694844,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008084008,"about_ca_topic_score_gemma":0.004732024,"domain_scores_codex":[0.9990911,0.00003950224,0.0002079023,0.000315703,0.0001446737,0.0002011548],"domain_scores_gemma":[0.9996957,0.000006225843,0.00009139259,0.0001385687,0.00005676984,0.00001132219],"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":[0.0001560728,0.00041382,0.8820421,0.0002651432,0.00001472833,0.000006291106,0.001547274,0.07917522,0.002114069,0.008376746,0.001494477,0.02439411],"study_design_scores_gemma":[0.0009469208,0.00004315274,0.3981388,0.00009758261,0.000007791558,7.754995e-7,0.007821938,0.5872569,0.00004839025,0.001307266,0.004012182,0.0003183437],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942055,0.00001289212,0.0003225617,0.001298207,0.00009038971,0.0004099837,1.628755e-7,0.0001441457,0.003516133],"genre_scores_gemma":[0.9983011,0.000006938544,0.0002540412,0.0003287255,0.0001224124,0.00008845812,0.00003210858,0.00001787752,0.0008483732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5080817,"threshold_uncertainty_score":0.9985213,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2058693307","doi":"10.1016/j.omega.2004.07.007","title":"Matrix games with fuzzy goals and fuzzy payoffs","year":2004,"lang":"en","type":"article","venue":"Omega","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":150,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"","keywords":"Defuzzification; Fuzzy logic; Fuzzy associative matrix; Fuzzy number; Mathematics; Mathematical optimization; Fuzzy set operations; Fuzzy classification; Matrix (chemical analysis); Fuzzy mathematics; Dual (grammatical number); Computer science; Fuzzy set; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.07725751484461374,"gpt":0.3920464369452687,"spread":0.314788922100655,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001663621,0.0002378212,0.0004253482,0.000408768,0.0001954311,0.0008268965,0.0006860136,0.0001021836,0.000276973],"category_scores_gemma":[0.001324537,0.0001496856,0.00007758577,0.0008925805,0.0001910843,0.0005450801,0.0002615557,0.0001654303,0.0008017487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004817358,"about_ca_system_score_gemma":0.00009138987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000481302,"about_ca_topic_score_gemma":0.0000760686,"domain_scores_codex":[0.9964756,0.0001138839,0.0006163956,0.0007602162,0.001627551,0.0004063965],"domain_scores_gemma":[0.9975401,0.0008327597,0.0002348433,0.0009057721,0.0002492464,0.0002373091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001257625,0.0006606068,0.1405084,0.00006741867,0.0001791348,0.001381125,0.008514835,0.00332726,0.03184831,0.07715429,0.04381738,0.6912836],"study_design_scores_gemma":[0.007298762,0.0005864692,0.1946813,0.0002983259,0.00005557916,0.0005449395,0.002883243,0.0003662422,0.002413175,0.4157561,0.3737357,0.001380098],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9753516,0.0007194921,0.003664746,0.001794043,0.0003741459,0.0002636405,0.00002251151,0.0001015924,0.01770817],"genre_scores_gemma":[0.9734218,0.00002720765,0.02157487,0.0004194652,0.0001303094,0.00001281696,0.000001526174,0.00002650681,0.004385517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6899035,"threshold_uncertainty_score":0.9999762,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1751793533","doi":"10.1016/j.omega.2015.10.007","title":"Performance of hospital services in Ontario: DEA with truncated regression approach","year":2015,"lang":"en","type":"article","venue":"Omega","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":147,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Centennial College","funders":"","keywords":"Data envelopment analysis; Statistics; Econometrics; Index (typography); Regression; Regression analysis; Occupancy; Unit (ring theory); Operations management; Actuarial science; Computer science; Mathematics; Economics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.05285991637258351,"gpt":0.3021262513518181,"spread":0.2492663349792346,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00180698,0.0001328717,0.0003328855,0.0003763315,0.00004957235,0.0000718442,0.0007059058,0.00007001143,0.00003100887],"category_scores_gemma":[0.0001324062,0.00007673338,0.00004696782,0.001707546,0.0001084885,0.0004115946,0.00009933366,0.0001623223,0.00005441228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001031972,"about_ca_system_score_gemma":0.0002443471,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01131931,"about_ca_topic_score_gemma":0.02744327,"domain_scores_codex":[0.9973701,0.00009005381,0.0004743759,0.0004088518,0.001439959,0.0002166275],"domain_scores_gemma":[0.9985412,0.0001000096,0.0003090911,0.00058303,0.0003731302,0.00009350774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001084928,0.0002385422,0.9870039,0.000007559954,0.000008589825,0.000004885489,0.004301383,0.006702139,0.00008623277,0.0000367997,0.0001747177,0.00132682],"study_design_scores_gemma":[0.001401259,0.0008637366,0.8794522,0.0001653016,0.00003163067,0.000006435335,0.003343018,0.1103586,0.0009977985,0.0003749478,0.002666332,0.0003387542],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847122,0.00005392891,0.00009613628,0.00007623475,0.0001012703,0.00009583229,0.000001034163,0.00001679716,0.01484659],"genre_scores_gemma":[0.9960229,0.000001114785,0.002398654,0.00002585871,0.00001053174,0.000005097329,0.000006473641,0.000007419219,0.001521923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1075516,"threshold_uncertainty_score":0.9952644,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3129737171","doi":"10.1016/j.omega.2021.102429","title":"Robust facility location under demand uncertainty and facility disruptions","year":2021,"lang":"en","type":"article","venue":"Omega","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":115,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; HEC Montréal; Group for Research in Decision Analysis","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Facility location problem; Skilled Nursing Facility; Environmental science; Computer science; Operations research; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.04835012753881051,"gpt":0.2438579353963864,"spread":0.1955078078575759,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000388536,0.0001600391,0.0001581215,0.00008964601,0.0003050458,0.0002259887,0.0001044187,0.00006024522,0.0008858353],"category_scores_gemma":[0.0001788397,0.0001603493,0.00005445139,0.0005809343,0.00008846638,0.0006646925,0.0001801644,0.0001020791,0.0007029634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005546057,"about_ca_system_score_gemma":0.00003128061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009458912,"about_ca_topic_score_gemma":0.00254824,"domain_scores_codex":[0.9987883,0.00002515272,0.0002808177,0.0004382393,0.0002312667,0.0002362179],"domain_scores_gemma":[0.9992186,0.00001824267,0.00004337052,0.0003848248,0.0003089601,0.00002594408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001843629,0.001933086,0.1499075,0.004319574,0.0004436462,0.00003377192,0.0008537435,0.4650574,0.002008132,0.2237281,0.04758092,0.1039497],"study_design_scores_gemma":[0.001247267,0.00001100434,0.4764656,0.00004208183,0.000158841,0.000003478432,0.003220838,0.06440079,0.0000586656,0.008682869,0.4449077,0.0008009479],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9143994,0.0004498701,0.05857552,0.009495597,0.0006884413,0.0004339654,0.00006683255,0.000276988,0.01561336],"genre_scores_gemma":[0.9960377,0.00004660779,0.00008073859,0.001229744,0.00009678892,0.00003010743,0.0004011675,0.000004736427,0.00207242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4006566,"threshold_uncertainty_score":0.9699275,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2103111596","doi":"10.1016/j.omega.2004.01.001","title":"Models for performance benchmarking: measuring the effect of e-business activities on banking performance","year":2004,"lang":"en","type":"article","venue":"Omega","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":115,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Benchmarking; Benchmark (surveying); Data envelopment analysis; Productivity; Computer science; Set (abstract data type); Service (business); Unit (ring theory); Point (geometry); Strengths and weaknesses; Variable (mathematics); Performance indicator; Operations research; Industrial engineering; Econometrics; Business; Economics; Engineering; Marketing; Mathematical optimization; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.06339025431261022,"gpt":0.3113756130864061,"spread":0.2479853587737959,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004922278,0.0002092183,0.0004164056,0.0003775563,0.0005802329,0.0001700399,0.0009637784,0.00006720544,0.00001574967],"category_scores_gemma":[0.000718693,0.0001185402,0.0001896674,0.001641176,0.0001965855,0.0006672161,0.0001011277,0.0001640038,0.00001541236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009528097,"about_ca_system_score_gemma":0.00008658108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000178128,"about_ca_topic_score_gemma":0.00000972843,"domain_scores_codex":[0.9970454,0.0001200892,0.0005139266,0.0004387943,0.001521133,0.0003606607],"domain_scores_gemma":[0.996603,0.001992378,0.0003544797,0.000754922,0.0002574657,0.00003776671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001724976,0.00003169191,0.004837939,0.00005941779,0.0000227392,5.009275e-7,0.0006695181,0.939966,0.0008770833,0.0006878495,0.00003721971,0.05263752],"study_design_scores_gemma":[0.002161782,0.001513014,0.03370624,0.000798952,0.0001390577,0.00001538157,0.0002493811,0.7864252,0.168116,0.00481045,0.001440603,0.0006239584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9775047,0.00009100961,0.01711848,0.0002089424,0.0003540158,0.000287999,0.000003373505,0.00002920606,0.004402247],"genre_scores_gemma":[0.9991708,0.00001944495,0.0003884614,0.00005423024,0.0001233859,0.00004032916,0.000001437287,0.00001666839,0.000185232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1672389,"threshold_uncertainty_score":0.4833927,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2068586653","doi":"10.1016/j.omega.2014.10.009","title":"Evaluation of bank branch growth potential using data envelopment analysis","year":2014,"lang":"en","type":"article","venue":"Omega","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":98,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Data envelopment analysis; Profitability index; Envelopment; Intermediation; Computer science; Focus (optics); Operations research; Industrial organization; Econometrics; Business; Economics; Engineering; Mathematics; Finance; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.2049934617388478,"gpt":0.4246943409870935,"spread":0.2197008792482457,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02939744,0.0001250522,0.0004351614,0.001098297,0.0001646986,0.0001635965,0.001530444,0.00006391337,0.0005803495],"category_scores_gemma":[0.006833723,0.00009869329,0.0001858359,0.004454665,0.0001072017,0.0004074701,0.0003754403,0.00007480734,0.00008821814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007371224,"about_ca_system_score_gemma":0.00020188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002076824,"about_ca_topic_score_gemma":0.0001215403,"domain_scores_codex":[0.9916614,0.00103567,0.0008434168,0.0007353435,0.005504853,0.0002193743],"domain_scores_gemma":[0.9955971,0.0004359983,0.000503834,0.001862142,0.001528264,0.00007260964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004052056,0.0005312703,0.06888305,0.0000123366,0.002122083,0.000002585299,0.001066534,0.5055756,0.02988026,0.001537973,0.002642039,0.3877058],"study_design_scores_gemma":[0.0002591012,0.00001275757,0.03684535,0.000005261208,0.002139222,0.000001130882,0.00005059485,0.9531195,0.001170433,0.005698674,0.0005743481,0.0001236092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.716987,0.00008725704,0.2802003,0.0001724228,0.000196585,0.00008223875,0.00001633857,0.00001266801,0.002245204],"genre_scores_gemma":[0.9951978,0.000003351175,0.004530058,0.00005318362,0.00007011335,0.000001345003,0.00004074584,0.000006708011,0.00009673173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4475439,"threshold_uncertainty_score":0.9994396,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2884960304","doi":"10.1016/j.omega.2018.07.006","title":"Solving a large multi-product production-routing problem with delivery time windows","year":2018,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Supply chain; Production (economics); Sizing; Vendor; Routing (electronic design automation); Product (mathematics); Integer programming; Mathematical optimization; Set (abstract data type); Vehicle routing problem; Lead time; Operations research; Linear programming; Operations management; Engineering; Mathematics; Algorithm; Economics; Business","retraction":null,"screen_n_in":null,"score":{"opus":0.01476060833286832,"gpt":0.241340783280429,"spread":0.2265801749475607,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006935456,0.0002185535,0.0002090455,0.0001256361,0.0002523358,0.00007447279,0.0001633292,0.00006388128,0.0001010072],"category_scores_gemma":[0.0001660305,0.0002058571,0.00003664146,0.0005390398,0.00006221889,0.0003197072,0.00005785217,0.0002118505,0.0002737773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008970789,"about_ca_system_score_gemma":0.00003729147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008360392,"about_ca_topic_score_gemma":0.000015468,"domain_scores_codex":[0.9985801,0.00006686198,0.000263043,0.0003991388,0.0001980658,0.0004927883],"domain_scores_gemma":[0.9991997,0.00003660029,0.0000670272,0.0004098531,0.0002047175,0.000082084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001269677,0.0003955072,0.03184726,0.0005679199,0.000564562,0.00003803367,0.01079943,0.694056,0.2048627,0.0003409622,0.009459732,0.04694088],"study_design_scores_gemma":[0.00113525,0.0001015675,0.00256983,0.0002394681,0.00005807468,0.00007293356,0.0001476736,0.9481252,0.03919373,0.00001750485,0.00761298,0.0007257381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5362799,0.0003286129,0.4428637,0.0003273559,0.001098769,0.001273524,0.0000154416,0.003517572,0.01429508],"genre_scores_gemma":[0.6225517,0.000006126997,0.3750322,0.00003251666,0.0005900831,0.00002360899,0.000007841531,0.00008542148,0.001670464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2540692,"threshold_uncertainty_score":0.839461,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4212771885","doi":"10.1016/j.omega.2022.102617","title":"Bi-objective optimization for a multi-period COVID-19 vaccination planning problem","year":2022,"lang":"en","type":"article","venue":"Omega","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":89,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Vaccination; Heuristic; Computer science; Integer programming; Operations research; Total cost; Service (business); Mathematical optimization; Constraint (computer-aided design); Plan (archaeology); Linear programming; Mathematics; Business; Artificial intelligence; Medicine; Algorithm; Geography; Marketing","retraction":null,"screen_n_in":null,"score":{"opus":0.2892527771431228,"gpt":0.453510380342583,"spread":0.1642576031994602,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001109649,0.0001569671,0.0003045216,0.0001149738,0.0008294669,0.00002354943,0.0001710163,0.0000566999,0.0002664473],"category_scores_gemma":[0.00836163,0.0001414217,0.0001004514,0.0003200439,0.00001993186,0.00008429617,0.0002418699,0.0001596762,0.000002980392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007489152,"about_ca_system_score_gemma":0.00009242298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004611009,"about_ca_topic_score_gemma":0.00001541523,"domain_scores_codex":[0.9986169,0.0002123128,0.000336453,0.0003703277,0.0001911346,0.0002728227],"domain_scores_gemma":[0.9975213,0.001903301,0.0002416254,0.0001688961,0.00008347835,0.00008141378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005291074,0.0008881263,0.01574531,0.0009794291,0.0002844337,0.00002490757,0.01860487,0.8634537,0.0001651154,0.05024586,0.04519492,0.003884243],"study_design_scores_gemma":[0.006303683,0.001097246,0.003926018,0.00003807607,0.0001721072,0.00002495253,0.007053458,0.7431637,0.0000815337,0.149865,0.08731329,0.0009608966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007387408,0.0002043389,0.9851252,0.004187812,0.0001537279,0.001817738,0.00008923921,0.0003554654,0.0006790208],"genre_scores_gemma":[0.5457852,0.00001525609,0.44518,0.003345899,0.000136681,0.004091315,0.0001034934,0.00006212217,0.001279926],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5399452,"threshold_uncertainty_score":0.9999914,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2002455648","doi":"10.1016/s0305-0483(01)00048-2","title":"Heuristic algorithms for the two-machine flowshop with limited machine availability","year":2001,"lang":"en","type":"article","venue":"Omega","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Job shop scheduling; Computer science; Heuristic; Mathematical optimization; Constructive; Algorithm; Scheduling (production processes); Mathematics; Artificial intelligence; Schedule; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.01634579333903639,"gpt":0.2402695771085099,"spread":0.2239237837694735,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002640909,0.0002020023,0.0001848816,0.000061699,0.0001669744,0.00006576355,0.0002045822,0.00005404569,0.0001558994],"category_scores_gemma":[0.0001111767,0.0001326361,0.00006326018,0.0003845957,0.00005342649,0.0000756862,0.00001938499,0.0001958036,0.00004455157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004742101,"about_ca_system_score_gemma":0.00001557768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000426055,"about_ca_topic_score_gemma":0.00007129846,"domain_scores_codex":[0.9990706,0.00002062607,0.0002137949,0.0002302947,0.0001713412,0.0002932796],"domain_scores_gemma":[0.9990483,0.0003071459,0.00002948013,0.0004293423,0.00009167846,0.0000940584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001041485,0.00008777485,0.002171343,0.00005053724,0.0001444793,0.0000123557,0.0002035262,0.9504374,0.00006142741,0.0001272374,0.0007638393,0.04583596],"study_design_scores_gemma":[0.001092002,0.00005904867,0.0006451265,0.00001219533,0.0000483443,0.00002806585,0.00002724091,0.9838952,0.0001150455,0.00005314553,0.01382355,0.0002010472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01611933,0.001819318,0.9764962,0.000675951,0.0006817782,0.0005776016,0.00008559234,0.0007975404,0.002746683],"genre_scores_gemma":[0.8387725,0.0002764334,0.1577042,0.0003040398,0.0004642374,0.0001843585,0.0001515263,0.000124111,0.002018553],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8226532,"threshold_uncertainty_score":0.5408741,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2797350939","doi":"10.1016/j.omega.2018.04.004","title":"DEA-based benchmarking for performance evaluation in pay-for-performance incentive plans","year":2018,"lang":"en","type":"article","venue":"Omega","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":72,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Benchmarking; Incentive; Data envelopment analysis; Payment; Context (archaeology); Set (abstract data type); Performance measurement; Incentive program; Business; Computer science; Pay for performance; Operations research; Environmental economics; Economics; Finance; Microeconomics; Marketing; Engineering; Mathematics; Mathematical optimization","retraction":null,"screen_n_in":null,"score":{"opus":0.1036606648969862,"gpt":0.3956076374766312,"spread":0.2919469725796451,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008593011,0.000162732,0.0002827039,0.0006034911,0.0004143064,0.0001606503,0.0006275457,0.00009297046,0.0001153422],"category_scores_gemma":[0.001850336,0.0001178275,0.0001150803,0.001528728,0.0001499865,0.0004798913,0.00004792745,0.0001082777,0.0001071848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001922297,"about_ca_system_score_gemma":0.0002780534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001647556,"about_ca_topic_score_gemma":0.0003093577,"domain_scores_codex":[0.9968247,0.0001363755,0.0006737777,0.0006065801,0.001324485,0.0004341293],"domain_scores_gemma":[0.9968625,0.001246718,0.0003181518,0.0005239276,0.0009906247,0.00005803349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004847432,0.0001816826,0.4350232,0.00003737217,0.00002499026,5.896713e-7,0.001495593,0.05188926,0.0008480001,0.0003218088,0.002593388,0.5070993],"study_design_scores_gemma":[0.0009307924,0.0003093668,0.09862439,0.00006874587,0.00002623751,3.869885e-7,0.00008202274,0.8845129,0.004187807,0.0006513701,0.01042879,0.0001772235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9703171,0.00002842609,0.02639119,0.0002717126,0.0006079282,0.0006450582,0.00002360305,0.00002338577,0.00169154],"genre_scores_gemma":[0.9949062,0.000003944014,0.00413448,0.0002637005,0.0002616573,0.0001511938,0.00003206804,0.00001271512,0.0002339775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8326236,"threshold_uncertainty_score":0.4804867,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2012545458","doi":"10.1016/j.omega.2006.11.005","title":"The dynamic plant layout problem: Incorporating rolling horizons and forecast uncertainty☆","year":2007,"lang":"en","type":"article","venue":"Omega","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Heuristics; Horizon; Time horizon; Mathematical optimization; Computer science; Flow (mathematics); New horizons; Certainty; Operations research; Mathematics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.007238085130259491,"gpt":0.2090055322715077,"spread":0.2017674471412482,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002121786,0.00009592486,0.00007499749,0.00003728937,0.0002054718,0.00004705768,0.00006023899,0.00004341999,6.580318e-7],"category_scores_gemma":[0.00002930043,0.00007251865,0.00001329805,0.00007014813,0.00003761097,0.00005723464,0.00002017136,0.0001317768,0.00000213775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000475284,"about_ca_system_score_gemma":0.000005386704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008971573,"about_ca_topic_score_gemma":0.0002551017,"domain_scores_codex":[0.999473,0.000005180308,0.0001469612,0.00009785967,0.00006996052,0.0002070109],"domain_scores_gemma":[0.9996682,0.0001373483,0.00003462051,0.00009954314,0.00001618544,0.00004409087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007995913,0.000002408963,0.00008050557,0.00001650852,0.000009654719,0.000004329946,0.0001294118,0.9761465,0.0001299044,0.00217521,0.00003284755,0.02126474],"study_design_scores_gemma":[0.0002780432,0.0000425214,0.0002662849,0.00003854802,0.00001261986,0.00001704236,0.0003085577,0.9850184,0.0007050815,0.009494898,0.003598417,0.0002195945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05643386,0.000403584,0.9364814,0.00005244888,0.0002593465,0.000181837,0.0000122539,0.0003085829,0.005866678],"genre_scores_gemma":[0.9824391,0.00008641565,0.01724877,0.000007246972,0.00004774191,0.000006646756,0.0000243056,0.00002082645,0.0001189691],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9260052,"threshold_uncertainty_score":0.2957225,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4353050218","doi":"10.1016/j.omega.2023.102874","title":"Returns operations in omnichannel retailing with buy-online-and-return-to-store","year":2023,"lang":"en","type":"article","venue":"Omega","topic":"Consumer Retail Behavior Studies","field":"Business, Management and Accounting","cited_by":54,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"National University's Basic Research Foundation of China; Science and Technology Commission of Shanghai Municipality; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Omnichannel; Business; Marketing; Advertising","retraction":null,"screen_n_in":null,"score":{"opus":0.03629398189731583,"gpt":0.2624303204039248,"spread":0.2261363385066089,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002221688,0.0001822305,0.0002261758,0.0005038157,0.0002055138,0.0001963292,0.0001599591,0.00005038337,0.00003891872],"category_scores_gemma":[0.0001256171,0.0001554879,0.0000329198,0.001408175,0.00004862306,0.0004584845,0.000289015,0.000179601,0.0002378728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003066149,"about_ca_system_score_gemma":0.00001845888,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005501058,"about_ca_topic_score_gemma":0.01965548,"domain_scores_codex":[0.9988921,0.000007497476,0.0002182505,0.0003473418,0.0002061937,0.0003286259],"domain_scores_gemma":[0.9995647,0.00002992172,0.00004035704,0.0002375135,0.0001079208,0.00001955677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004122361,0.00006324882,0.9843386,0.0001019655,0.00003770446,0.0001099624,0.0008148752,0.0001643169,0.001145101,0.001364856,0.001206401,0.01061168],"study_design_scores_gemma":[0.001776273,0.00004489456,0.8977146,0.0003907164,0.0001990235,0.00001066384,0.0064107,0.01157201,0.00005049991,0.0001661211,0.0806535,0.001011009],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937613,0.0001531927,0.00001402903,0.00388558,0.0001766131,0.0003168602,0.000008592467,0.0002381559,0.001445658],"genre_scores_gemma":[0.9974775,0.00002986462,0.0002020472,0.000322553,0.0001646261,0.00007289798,0.0000581819,0.00003776935,0.001634552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08662409,"threshold_uncertainty_score":0.9982333,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2893792581","doi":"10.1016/j.omega.2018.09.011","title":"Innovation uncertainty, new product press timing and strategic consumers","year":2018,"lang":"en","type":"article","venue":"Omega","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":52,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Product innovation; Profit (economics); Microeconomics; Subgame perfect equilibrium; New product development; Industrial organization; Product (mathematics); Economics; Business; Marketing; Nash equilibrium","retraction":null,"screen_n_in":null,"score":{"opus":0.07686594335901123,"gpt":0.2648752133273767,"spread":0.1880092699683655,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002237275,0.0001193583,0.00009933922,0.0001917161,0.0001367723,0.00029445,0.0001171735,0.00002566862,0.0003008313],"category_scores_gemma":[0.00004135141,0.0001107274,0.00001459476,0.00043667,0.00008990508,0.0005297904,0.0001202955,0.00005740534,0.0001451113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000182727,"about_ca_system_score_gemma":0.00001623312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009118203,"about_ca_topic_score_gemma":0.00007539386,"domain_scores_codex":[0.9992198,0.000004957792,0.0001714827,0.0002554392,0.0001490284,0.0001992537],"domain_scores_gemma":[0.9995787,0.00000790278,0.0001077549,0.0001743231,0.0001212955,0.00001009168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009904551,0.00009863114,0.01293943,0.000434407,0.0001105223,0.000009980367,0.0002743022,0.00003405824,0.001521824,0.6617992,0.2494018,0.07327681],"study_design_scores_gemma":[0.000803854,0.00002399803,0.002571439,0.00004952638,0.00004685264,0.000001237694,0.0003925175,0.004997063,0.000194573,0.01545305,0.9751585,0.0003073681],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5864279,0.000277423,0.0001580299,0.003479018,0.001178481,0.0005462951,0.000001138545,0.0002223708,0.4077094],"genre_scores_gemma":[0.9912856,0.00001589613,0.0001079433,0.00167801,0.001583954,0.000009393452,0.00002467247,0.00001615357,0.005278397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7257568,"threshold_uncertainty_score":0.4515332,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2019132869","doi":"10.1016/j.omega.2012.10.003","title":"Profitability of loyalty reward programs: An analytical investigation","year":2012,"lang":"en","type":"article","venue":"Omega","topic":"Customer Service Quality and Loyalty","field":"Business, Management and Accounting","cited_by":51,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Profitability index; Loyalty; Valuation (finance); Loyalty program; Profit (economics); Marketing; Business; Loyalty business model; Microeconomics; Service (business); Service quality; Economics; Finance","retraction":null,"screen_n_in":null,"score":{"opus":0.07200016452749859,"gpt":0.29453856307194,"spread":0.2225383985444415,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001143828,0.0001227424,0.0001967029,0.0001079974,0.00007669287,0.00007441515,0.0001783165,0.00008294793,0.0001270092],"category_scores_gemma":[0.0001091206,0.0001096171,0.00006759931,0.0005659645,0.0001188829,0.001744969,0.00008838753,0.0001160644,0.0001700622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000240182,"about_ca_system_score_gemma":0.00001978611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008737761,"about_ca_topic_score_gemma":0.0001443917,"domain_scores_codex":[0.9988658,0.00003190962,0.0003170697,0.0001940835,0.0002805959,0.000310497],"domain_scores_gemma":[0.9992502,0.00002147051,0.0001659461,0.000351829,0.0001669184,0.00004360391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003349427,0.000356426,0.9521219,0.0005071489,0.00001494684,3.390854e-7,0.0003176461,0.000002813016,0.0001073532,0.03879462,0.0005367677,0.007206563],"study_design_scores_gemma":[0.0004581258,0.00003923114,0.9439732,0.00004541659,0.0001121416,0.000001009166,0.0009139559,0.00215792,0.0002071954,0.005220003,0.04653356,0.0003382612],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910551,0.00003519407,0.00004032121,0.0007943132,0.0001932031,0.0002501949,0.000001476148,0.0001173828,0.007512827],"genre_scores_gemma":[0.997845,5.02006e-7,0.0002959897,0.0008520953,0.0007785207,0.00001945701,0.00007374596,0.00001387155,0.0001208365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04599679,"threshold_uncertainty_score":0.4470056,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W563031061","doi":"10.1016/j.omega.2015.05.006","title":"Supply vessel planning under cost, environment and robustness considerations","year":2015,"lang":"en","type":"article","venue":"Omega","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Greenhouse gas; Minification; Idle; Environmental science; Fuel efficiency; Fossil fuel; Computer science; Engineering; Reliability engineering; Automotive engineering; Waste management","retraction":null,"screen_n_in":null,"score":{"opus":0.03450954162763896,"gpt":0.2440372872063557,"spread":0.2095277455787167,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000153676,0.00009274278,0.0000873149,0.00001868515,0.0001574959,0.00003310962,0.00005688385,0.00004319893,0.003012982],"category_scores_gemma":[0.00001025043,0.00008226209,0.0000147128,0.00005556774,0.0001545514,0.0001272018,0.00005739425,0.00007730768,0.0001235167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005770491,"about_ca_system_score_gemma":0.00001369134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008106013,"about_ca_topic_score_gemma":0.000006074949,"domain_scores_codex":[0.9992662,0.00001837493,0.0001210347,0.0002156231,0.0001880108,0.0001907774],"domain_scores_gemma":[0.9995695,0.0000335224,0.00002391708,0.0001483143,0.00000257429,0.0002221589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0000197627,0.0002498231,0.2149798,0.000009271432,0.00001200489,0.00008066597,0.001330124,0.7343411,0.002036663,0.001510403,0.04197291,0.003457475],"study_design_scores_gemma":[0.00305454,0.0002618186,0.5327114,0.00007165958,0.00008391282,0.000241793,0.002370108,0.06705356,0.001054512,0.005760191,0.386084,0.001252483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9563434,0.0001679094,0.00841316,0.001288997,0.0000739135,0.0002115211,0.00001045332,0.00004277045,0.03344794],"genre_scores_gemma":[0.9955006,0.00001437998,0.002637557,0.0001762317,0.00001578747,0.00001665223,0.00001039298,0.000008501012,0.001619888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6672876,"threshold_uncertainty_score":0.9978984,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2037327357","doi":"10.1016/j.omega.2006.04.002","title":"Evaluating manufacturer's buyback policies in a single-period two-echelon framework under price-dependent stochastic demand","year":2006,"lang":"en","type":"article","venue":"Omega","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":49,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Newsvendor model; Profitability index; Incentive; Profit (economics); Microeconomics; Business; Economics; Profit maximization; Finance; Marketing; Supply chain","retraction":null,"screen_n_in":null,"score":{"opus":0.03899440469269302,"gpt":0.2869535539557287,"spread":0.2479591492630357,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006720236,0.0003512369,0.0003244579,0.0005329119,0.0002165871,0.0006007839,0.0003578388,0.0001282516,0.0005288468],"category_scores_gemma":[0.0001263416,0.0003357686,0.0001045645,0.0005933036,0.00007080197,0.000652215,0.0002909747,0.0002983547,0.0003778448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002437638,"about_ca_system_score_gemma":0.00002105094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001533017,"about_ca_topic_score_gemma":0.0006346581,"domain_scores_codex":[0.9976772,0.00003423909,0.0004980778,0.0005072973,0.0006204169,0.0006627667],"domain_scores_gemma":[0.9991595,0.00008471763,0.0002554309,0.0004039029,0.00007389235,0.00002252366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004415494,0.002795848,0.04051103,0.001161046,0.0003037573,0.0002089873,0.001792877,0.5543488,0.009026316,0.3513379,0.0202262,0.01784568],"study_design_scores_gemma":[0.01574784,0.0003685121,0.2428556,0.002275235,0.0007136373,0.00003868119,0.009501503,0.1398913,0.001796796,0.5013609,0.07960589,0.005844098],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9423184,0.0003329336,0.01400571,0.002471835,0.0008852718,0.0008178992,0.000002421679,0.0002667212,0.03889883],"genre_scores_gemma":[0.9932081,0.000001716359,0.0007062329,0.002839773,0.001565383,0.0000730817,0.00003168379,0.00006130146,0.00151271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4144575,"threshold_uncertainty_score":0.9999095,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2046000609","doi":"10.1016/j.omega.2010.08.003","title":"The exact fill rate in a periodic review base stock system under normally distributed demand","year":2010,"lang":"en","type":"review","venue":"Omega","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":49,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stock (firearms); Computer science; Base (topology); Econometrics; Operations research; Mathematical economics; Mathematics; Engineering; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.03609174154955906,"gpt":0.2658710360956316,"spread":0.2297792945460725,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002358183,0.0006557869,0.00150636,0.0003069183,0.0004038113,0.0007300219,0.0009987209,0.0002360922,0.0002992283],"category_scores_gemma":[0.0002030258,0.0004175546,0.0005368405,0.001067352,0.0000928086,0.000490122,0.0004806859,0.000724246,0.001015399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002393856,"about_ca_system_score_gemma":0.0001095755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001102103,"about_ca_topic_score_gemma":0.0002548101,"domain_scores_codex":[0.9971198,0.0001676598,0.001085806,0.0005997971,0.0003901592,0.0006367709],"domain_scores_gemma":[0.9978474,0.0001801062,0.0008375748,0.0009892064,0.000109053,0.00003665946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001418971,0.0001216175,0.00006155031,0.2916463,0.0002310683,0.0001705735,0.000009232698,0.000008598645,1.895318e-7,0.0129974,0.1171572,0.577582],"study_design_scores_gemma":[0.0002636173,0.000005802686,0.00004353231,0.03917908,0.0006260517,0.000005726238,0.0000467922,0.0002085256,1.823521e-8,0.00002311263,0.9591256,0.0004721075],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000008463794,0.9859824,0.00007494024,0.0005421291,0.001138011,0.002435587,0.00003639002,0.0001344204,0.009647725],"genre_scores_gemma":[0.0001153958,0.9944008,0.000005679909,0.001398314,0.0008541567,0.0006801444,0.0006744982,0.00008783307,0.001783152],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8419684,"threshold_uncertainty_score":0.9998276,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2953220880","doi":"10.1016/j.omega.2019.06.004","title":"An optimization approach to planning rail hazmat shipments in the presence of random disruptions","year":2019,"lang":"en","type":"article","venue":"Omega","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":48,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University; Ontario Tech University; École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Contingency plan; Hazardous waste; Constraint (computer-aided design); Contingency; Stochastic programming; Risk analysis (engineering); Plan (archaeology); Transport engineering; Operations research; Computer science; Business; Engineering; Waste management; Computer security; Mathematical optimization; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.07651204499200619,"gpt":0.3780066192102539,"spread":0.3014945742182477,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002381749,0.00006440827,0.0001932644,0.0002321765,0.00007426308,0.0001190846,0.0007248996,0.0000347561,0.00007105991],"category_scores_gemma":[0.0006186045,0.0000373291,0.00006219987,0.001197961,0.00002793974,0.0003483626,0.00004498927,0.00006917003,0.00005864711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009892696,"about_ca_system_score_gemma":0.00001746694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004145971,"about_ca_topic_score_gemma":0.00001003833,"domain_scores_codex":[0.9981782,0.0003196406,0.0003524409,0.0002531603,0.000768918,0.0001276398],"domain_scores_gemma":[0.9986444,0.0005684598,0.000103942,0.0005465091,0.00009185549,0.00004485195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006440411,0.00008328644,0.0483713,0.000001589873,0.000005150247,4.187408e-7,0.005499599,0.9431728,0.00008341529,0.0003405558,0.0002974391,0.002080067],"study_design_scores_gemma":[0.001142589,0.00006127316,0.07401449,0.00002241784,0.00001658881,0.000002042219,0.01149179,0.9098719,0.00004641315,0.002037445,0.001155663,0.0001374318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6934403,0.00005203299,0.2846074,0.0007743809,0.00008760917,0.0003653028,0.000007960795,0.00000984775,0.02065518],"genre_scores_gemma":[0.991017,0.00001018681,0.008182749,0.0001101694,0.00001454791,0.00001672586,0.000009251853,0.000003355624,0.0006359872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2975767,"threshold_uncertainty_score":0.1522236,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2003839915","doi":"10.1016/j.omega.2010.04.003","title":"Optimal ordering and pricing decisions for a target oriented newsvendor","year":2010,"lang":"en","type":"article","venue":"Omega","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":48,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada; Project 211; Southwestern University of Finance and Economics","keywords":"Newsvendor model; Profit margin; Revenue; Profit (economics); Microeconomics; Economics; Economic order quantity; Order (exchange); Margin (machine learning); Computer science; Business; Supply chain; Marketing; Finance","retraction":null,"screen_n_in":null,"score":{"opus":0.01746695686166047,"gpt":0.2344742324395724,"spread":0.2170072755779119,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002563888,0.0001339732,0.0001381122,0.0002075463,0.0002490992,0.0002162636,0.0001403691,0.00004324544,0.0002194706],"category_scores_gemma":[0.0002963553,0.0001224921,0.00005318597,0.0002463765,0.00003614439,0.0005239717,0.0002014723,0.0001087869,0.00004952039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009436442,"about_ca_system_score_gemma":0.000007932194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006993756,"about_ca_topic_score_gemma":0.00006045179,"domain_scores_codex":[0.9991503,0.000002211532,0.0001755739,0.0002603245,0.0001339376,0.0002776787],"domain_scores_gemma":[0.999572,0.00006144457,0.00007955045,0.0001934906,0.00007290799,0.00002058404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005358192,0.0007513869,0.06645696,0.000842518,0.0002980649,0.00006217008,0.0007850267,0.001757035,0.03062867,0.5931416,0.1805081,0.1242326],"study_design_scores_gemma":[0.0008903649,0.00001283292,0.002022967,0.00002241091,0.00002593348,0.000001343859,0.0002513011,0.0323567,0.0001356937,0.001200667,0.9628924,0.0001874347],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9546502,0.00004436647,0.02402687,0.001171337,0.001497623,0.0006326839,0.000003502566,0.0001742268,0.01779915],"genre_scores_gemma":[0.9753715,0.000005437404,0.020235,0.001356301,0.001240115,0.00009948222,0.00003358765,0.00003867826,0.001619894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7823842,"threshold_uncertainty_score":0.4995083,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2919819692","doi":"10.1016/j.omega.2019.03.001","title":"Reformulation, linearization, and decomposition techniques for balanced distributed operating room scheduling","year":2019,"lang":"en","type":"article","venue":"Omega","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":48,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto General Hospital; University Health Network; University of Toronto","funders":"","keywords":"Linearization; Macro; Mathematical optimization; Scheduling (production processes); Integer programming; Computer science; Suite; Cutting-plane method; Penalty method; Linear programming; Integer (computer science); Operations research; Mathematics; Nonlinear system","retraction":null,"screen_n_in":null,"score":{"opus":0.03017583421218678,"gpt":0.4026499025833016,"spread":0.3724740683711148,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006018851,0.0001146738,0.0001883141,0.00008378092,0.0009935797,0.00004743918,0.00005759392,0.0002013463,0.00005641957],"category_scores_gemma":[0.0002776066,0.0001044538,0.00002436221,0.0002219932,0.00000989434,0.0003164059,0.00003252997,0.0002090943,0.00002403085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001237835,"about_ca_system_score_gemma":0.0001711103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004876965,"about_ca_topic_score_gemma":0.00002575402,"domain_scores_codex":[0.9987546,0.0001261144,0.000495506,0.000257446,0.0001045807,0.0002617506],"domain_scores_gemma":[0.9987988,0.0001959972,0.0001483808,0.0001832084,0.0005923804,0.00008126673],"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":[0.000344328,0.0002538763,0.6826016,0.00247697,0.0001114924,0.000001449589,0.007586383,0.1056201,0.07903117,0.0919842,0.001903788,0.02808459],"study_design_scores_gemma":[0.00152145,0.0001974151,0.01660833,0.0005032053,0.00001902207,0.000002041419,0.0007976079,0.9668454,0.001769036,0.0006297494,0.01076791,0.0003388551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.441123,0.00004389831,0.55393,0.00206733,0.000243376,0.001721226,0.00006778746,0.0001907279,0.0006126598],"genre_scores_gemma":[0.8207847,0.00003357461,0.1763023,0.0005670065,0.0002225288,0.0002326871,0.001396833,0.00002498273,0.0004352944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8612252,"threshold_uncertainty_score":0.7641912,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4309400251","doi":"10.1016/j.omega.2022.102805","title":"A novel and efficient exact technique for integrated staffing, assignment, routing, and scheduling of home care services under uncertainty","year":2022,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Western University; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Computer science; Staffing; Benders' decomposition; Scheduling (production processes); Robustness (evolution); Schedule; Integer programming; Job shop scheduling; Operations research; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01336753236872482,"gpt":0.257707383525797,"spread":0.2443398511570722,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004313362,0.0001366215,0.0001913878,0.0001235797,0.0001486128,0.00003227513,0.0000983671,0.00005234478,0.00001155545],"category_scores_gemma":[0.00002528475,0.0001425499,0.0000282591,0.0002501329,0.00002528688,0.00003772482,0.0001066539,0.0001597444,6.531062e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001429636,"about_ca_system_score_gemma":0.00002597847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005741397,"about_ca_topic_score_gemma":0.000009057338,"domain_scores_codex":[0.9992232,0.00004367451,0.0002153471,0.0001990388,0.0001310808,0.0001876197],"domain_scores_gemma":[0.9995582,0.0001185989,0.00007832763,0.0001337959,0.00006356173,0.00004756704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001196149,0.00001745325,0.000788091,0.0002133449,0.00002499834,2.762396e-7,0.001272899,0.9474834,0.04900742,0.0002406503,0.000002583414,0.0009368708],"study_design_scores_gemma":[0.0007599858,0.00008301994,0.0004020986,0.00007721576,0.0000266405,0.000007911747,0.005316787,0.9854966,0.007325044,0.00002904134,0.0002771333,0.0001985283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4669287,0.0002731143,0.5321804,0.0000125461,0.00006156618,0.0003085168,0.00005794172,0.0001118002,0.00006532654],"genre_scores_gemma":[0.8910291,0.000008573916,0.1087517,0.00001819154,0.00001105025,0.0001023007,0.00002838579,0.00003548489,0.00001527701],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4241003,"threshold_uncertainty_score":0.5813015,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3120440647","doi":"10.1016/j.omega.2021.102413","title":"Optimal pricing of customized bus services and ride-sharing based on a competitive game model","year":2021,"lang":"en","type":"article","venue":"Omega","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Fonds de Recherche du Québec-Société et Culture; National Natural Science Foundation of China","keywords":"Mode (computer interface); Public transport; Value of time; Computer science; Service (business); Measure (data warehouse); Value (mathematics); Operations research; Simulation; Transport engineering; Travel time; Business; Marketing; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01068363782439253,"gpt":0.2202083692290696,"spread":0.209524731404677,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004983191,0.00006644827,0.0001195018,0.00006484526,0.00001918957,0.00001299706,0.0000358418,0.00002873521,0.00002355926],"category_scores_gemma":[0.000005817243,0.00007279375,0.00002335297,0.0001708695,0.00001317455,0.00005738622,0.000005519801,0.00007043243,0.000001932822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001582604,"about_ca_system_score_gemma":0.00001782645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008907133,"about_ca_topic_score_gemma":0.00003885597,"domain_scores_codex":[0.9995848,0.00000415101,0.00015058,0.0001048718,0.00007511272,0.00008051185],"domain_scores_gemma":[0.9997402,0.00003321156,0.00001968515,0.0001097811,0.00007161593,0.00002550154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001627508,0.00002540459,0.0007984671,0.0001533074,0.00001673456,0.000002951454,0.0005765648,0.9814267,0.01251001,0.004290025,0.000003301911,0.0001802959],"study_design_scores_gemma":[0.0008169168,0.00000767901,0.005923941,0.00008499284,0.00001418718,6.253666e-7,0.0002196811,0.9814814,0.01115397,0.00004380869,0.0001760036,0.00007674276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9670948,0.00003064474,0.0278948,0.00006088017,0.00003875918,0.00007087489,0.00003483854,0.00008747491,0.004686866],"genre_scores_gemma":[0.9923769,0.000007428138,0.007395606,0.00009430312,0.000007202774,0.000009762422,0.00004045494,0.00001064017,0.00005774085],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.025282,"threshold_uncertainty_score":0.2968443,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4294983533","doi":"10.1016/j.omega.2022.102750","title":"A resilient, robust transformation of healthcare systems to cope with COVID-19 through alternative resources","year":2022,"lang":"en","type":"article","venue":"Omega","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":40,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University; Toronto Metropolitan University; University of Victoria","funders":"","keywords":"Backup; Coronavirus disease 2019 (COVID-19); Healthcare system; Health care; Workload; Medical emergency; Field (mathematics); Medicine; Computer science; Disease; Mathematics; Infectious disease (medical specialty)","retraction":null,"screen_n_in":null,"score":{"opus":0.05418526913273906,"gpt":0.2727117843536772,"spread":0.2185265152209381,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004379252,0.0001157891,0.0001666058,0.0002258821,0.0003501381,0.00006671814,0.0002562589,0.0000153143,0.0003061028],"category_scores_gemma":[0.00003933218,0.0001055841,0.00003579467,0.0008513628,0.00002746822,0.0004724637,0.000112512,0.00008492928,0.00005570714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001431535,"about_ca_system_score_gemma":0.0000330045,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02555174,"about_ca_topic_score_gemma":0.001999872,"domain_scores_codex":[0.9987092,0.00003954576,0.0003205975,0.0002340999,0.0005091826,0.0001873551],"domain_scores_gemma":[0.9994931,0.00001355009,0.0001138914,0.0002333364,0.0001209413,0.00002518196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004649647,0.0001517388,0.001943539,0.001859052,0.00005852697,0.000005350304,0.009171458,0.8486168,0.0000237375,0.1154819,0.02155812,0.0006648032],"study_design_scores_gemma":[0.0006766037,0.00008677317,0.0006354147,0.00003280364,0.00002344522,0.000001019407,0.01680624,0.01144668,0.000008920445,0.000206321,0.9698992,0.0001765248],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7825645,0.0006674947,0.05289039,0.07766548,0.001762218,0.00448056,0.0001844617,0.0004352381,0.07934964],"genre_scores_gemma":[0.9941714,0.00001084066,0.00008424102,0.004319165,0.00009883297,0.000213095,0.00008320482,0.00001263411,0.001006539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9483411,"threshold_uncertainty_score":0.9809372,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386575177","doi":"10.1016/j.omega.2023.102959","title":"Distributionally robust facility location with uncertain facility capacity and customer demand","year":2023,"lang":"en","type":"article","venue":"Omega","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":36,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"Fundamental Research Funds for the Central Universities; Civil Aviation University of China; National Office for Philosophy and Social Sciences; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Robust optimization; Facility location problem; Computer science; Mathematical optimization; Ambiguity; Robustness (evolution); Stochastic programming; Linear programming; Supply chain; Integer programming; Operations research; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.04194389163119159,"gpt":0.2246359380254053,"spread":0.1826920463942137,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007029979,0.0001903237,0.0001697518,0.0001654183,0.000317799,0.0001579447,0.0001433164,0.00005851939,0.0002817132],"category_scores_gemma":[0.00014809,0.0001646859,0.00003600372,0.001142676,0.0001503822,0.0007113616,0.0001270551,0.000110425,0.00174404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006551844,"about_ca_system_score_gemma":0.0000237972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001508942,"about_ca_topic_score_gemma":0.0009278882,"domain_scores_codex":[0.9986163,0.00001997577,0.0002559387,0.000430674,0.0003690917,0.000308067],"domain_scores_gemma":[0.9993202,0.00002080144,0.00005636747,0.0003021341,0.0002713087,0.00002918686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005858552,0.0007868315,0.574518,0.004944991,0.0004035541,0.00003188668,0.0007128143,0.1771524,0.0006033409,0.08391809,0.1143146,0.04202767],"study_design_scores_gemma":[0.0009386286,0.00001655801,0.6656905,0.00003318849,0.00007033155,0.000001392904,0.0005684064,0.03443976,0.00002566153,0.001156769,0.2965162,0.000542546],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9800733,0.00003164476,0.01072013,0.002961075,0.0002098846,0.000471578,0.0001400048,0.000407776,0.004984567],"genre_scores_gemma":[0.9976054,0.00001512285,0.00004925228,0.0003448718,0.0000770232,0.00006043883,0.0008157842,0.000005581217,0.001026517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1822016,"threshold_uncertainty_score":0.9990332,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2366073799","doi":"10.1016/j.omega.2016.05.001","title":"Design-balanced capacitated multicommodity network design with heterogeneous assets","year":2016,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"Program for New Century Excellent Talents in University; National Natural Science Foundation of China","keywords":"Tabu search; Network planning and design; Computer science; Mathematical optimization; Quality of service; Range (aeronautics); Metaheuristic; Mathematics; Computer network; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03269431108211469,"gpt":0.2429360692517961,"spread":0.2102417581696814,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004694549,0.0002062148,0.0002221285,0.00004911212,0.00008241627,0.00003727266,0.0001663791,0.0001004336,0.0000439471],"category_scores_gemma":[0.00007751743,0.0001453272,0.00002995321,0.0002536471,0.00004586339,0.0001138033,0.00001666456,0.0001042985,0.00006069279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001058792,"about_ca_system_score_gemma":0.00002137729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002545117,"about_ca_topic_score_gemma":0.000002151166,"domain_scores_codex":[0.9987233,0.0002700605,0.0002010141,0.0002130335,0.0001596739,0.0004328968],"domain_scores_gemma":[0.9989306,0.0005071573,0.00004755417,0.0003335275,0.00006482188,0.0001163428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003785567,0.00001068355,0.0008303483,0.000008504914,0.00005015196,0.00001532805,0.00005285565,0.9831124,0.01042321,0.00000905358,0.0006850273,0.004764576],"study_design_scores_gemma":[0.001561194,0.0001394085,0.001233247,0.0001241224,0.00003251174,0.00004512115,0.000003307112,0.942565,0.05298966,0.0001787269,0.00059677,0.0005309304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04148877,0.00006269848,0.9570182,0.00004678667,0.0001967042,0.0002648023,0.000004530818,0.0007436558,0.0001738528],"genre_scores_gemma":[0.5706304,0.00001316314,0.429134,0.00002298431,0.00005666741,0.00002618433,0.000001827042,0.00005039856,0.00006432122],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5291417,"threshold_uncertainty_score":0.592627,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2052526166","doi":"10.1016/j.omega.2012.09.002","title":"Location and reliability problems on a line: Impact of objectives and correlated failures on optimal location patterns","year":2012,"lang":"en","type":"article","venue":"Omega","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":33,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Reliability engineering; Line (geometry); Computer science; Statistics; Mathematics; Engineering; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01978284793078772,"gpt":0.2576395162294171,"spread":0.2378566682986294,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003644506,0.0001386113,0.0001314579,0.0001744802,0.00007991429,0.00005201675,0.00005788189,0.00005257497,0.00005617125],"category_scores_gemma":[0.0001547563,0.0001145355,0.00003036135,0.0003201111,0.0000469457,0.0006123005,0.00005212947,0.00008259141,0.00004447328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004384505,"about_ca_system_score_gemma":0.000009355155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002531819,"about_ca_topic_score_gemma":0.00008782413,"domain_scores_codex":[0.9992178,0.00001724073,0.0002237066,0.0002121005,0.0001603504,0.0001688049],"domain_scores_gemma":[0.999502,0.00002383668,0.00009185859,0.0001970265,0.0001656793,0.00001960924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005108768,0.001566646,0.7628492,0.002936592,0.0001441155,5.093173e-7,0.001792252,0.1896887,0.0003768766,0.01771055,0.001730693,0.02069299],"study_design_scores_gemma":[0.0004559637,0.0001263249,0.9555146,0.0001325967,0.00003575708,3.267136e-7,0.0002676524,0.04221128,0.00005980408,0.0001236549,0.0009042135,0.0001678369],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961908,0.00008473307,0.00178705,0.0004230275,0.0001458357,0.0003953309,0.000002613004,0.00004955662,0.0009210169],"genre_scores_gemma":[0.9995051,0.0000313234,0.00003612824,0.0001119622,0.0001102623,0.00002601328,0.00003691845,0.00001013637,0.0001321619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1926654,"threshold_uncertainty_score":0.4670623,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2031613270","doi":"10.1016/j.omega.2011.06.005","title":"Information systems outsourcing projects as a double moral hazard problem","year":2011,"lang":"en","type":"article","venue":"Omega","topic":"Outsourcing and Supply Chain Management","field":"Business, Management and Accounting","cited_by":33,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Outsourcing; Moral hazard; Vendor; Business; Incentive; Payment; Knowledge process outsourcing; Information system; Industrial organization; Information sharing; Computer science; Microeconomics; Risk analysis (engineering); Economics; Finance; Marketing; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03316650308624972,"gpt":0.2085329993335773,"spread":0.1753664962473276,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003971579,0.000199465,0.0001950515,0.000390941,0.0001809079,0.0006110091,0.0002440414,0.00006865829,0.00006460509],"category_scores_gemma":[0.00001338573,0.0001786899,0.00006584854,0.0004021197,0.00002579077,0.002212567,0.0001515575,0.0001224915,0.001920166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004615529,"about_ca_system_score_gemma":0.00001680243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002111526,"about_ca_topic_score_gemma":0.00002914802,"domain_scores_codex":[0.9988331,0.000006846678,0.0003250621,0.0001529062,0.0003085918,0.0003734481],"domain_scores_gemma":[0.9994256,0.000006093271,0.0001696984,0.0002699578,0.0001095869,0.000019099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005884416,0.0004548369,0.1781871,0.00732477,0.0004188093,0.00009740191,0.0192313,0.01735039,0.0001180527,0.603348,0.145783,0.02709787],"study_design_scores_gemma":[0.003742436,0.00007764975,0.008144257,0.0004192647,0.0001872343,0.00001512711,0.01273896,0.04659457,0.0001202365,0.003828974,0.9228567,0.001274538],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.09578554,0.00005259872,0.002688045,0.0003659599,0.0009696031,0.001064997,0.000001360833,0.0007797044,0.8982922],"genre_scores_gemma":[0.9966462,0.000003094382,0.000313299,0.0006603765,0.0004645159,0.0001233245,0.00002808892,0.00002624259,0.001734881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9008606,"threshold_uncertainty_score":0.998857,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2145092964","doi":"10.1016/j.omega.2005.01.001","title":"An experimental study of brand signal quality of products in an asymmetric information environment","year":2005,"lang":"en","type":"article","venue":"Omega","topic":"Innovation Diffusion and Forecasting","field":"Decision Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Signalling; Business; Brand management; Brand awareness; Marketing; Profit (economics); Brand equity; Context (archaeology); Incentive; Quality (philosophy); Perspective (graphical); Advertising; Information asymmetry; Information economics; Corporate branding; Function (biology); Microeconomics; Economics; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.135006747122769,"gpt":0.3937910068501275,"spread":0.2587842597273585,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002749664,0.00006754512,0.000184471,0.0006164298,0.00003981908,0.00004081139,0.0002421558,0.00003023004,0.0002292694],"category_scores_gemma":[0.0002954666,0.00005357058,0.00001724937,0.001103446,0.00003730197,0.001178458,0.00004865709,0.0000568169,0.00002249104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003410533,"about_ca_system_score_gemma":0.00002039447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004011843,"about_ca_topic_score_gemma":0.00002039307,"domain_scores_codex":[0.9975398,0.0001872075,0.0009995182,0.000165954,0.001011743,0.00009582679],"domain_scores_gemma":[0.9989983,0.00008828485,0.0004356957,0.0003086867,0.0001337829,0.00003526587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004098952,0.01122556,0.3289971,0.00001428411,0.000008696891,7.183172e-7,0.05629332,0.004530638,0.1289066,0.001509014,0.0001718366,0.4679323],"study_design_scores_gemma":[0.003835969,0.002107748,0.8106512,0.00000781304,0.000003024099,0.000001260996,0.04115177,0.009991255,0.1302835,0.0001573643,0.00158028,0.0002287725],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981028,0.00001046382,0.0002328386,0.0000328418,0.00003961679,0.000244364,0.000004418917,0.000005748429,0.001326943],"genre_scores_gemma":[0.9990598,2.915017e-7,0.0008201078,0.00004283997,0.00002409267,0.000006084758,0.000006361898,0.000002427799,0.00003797697],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4816541,"threshold_uncertainty_score":0.2510339,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2013229035","doi":"10.1016/j.omega.2005.05.005","title":"The impact of training on the formulation of ill-structured problems","year":2005,"lang":"en","type":"article","venue":"Omega","topic":"Complex Systems and Decision Making","field":"Decision Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Training (meteorology); Psychology; Computer science; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.2002704976656938,"gpt":0.4135512970822455,"spread":0.2132807994165517,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002624233,0.00008695164,0.0002167815,0.0001138293,0.0001978922,0.0001097921,0.0005645778,0.00004844968,0.0001716221],"category_scores_gemma":[0.002040337,0.00003208943,0.0002114903,0.0005284224,0.00005747689,0.0001186784,0.00005818287,0.0001027364,0.00002211441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002649196,"about_ca_system_score_gemma":0.0000489316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004092027,"about_ca_topic_score_gemma":0.00007109263,"domain_scores_codex":[0.9978624,0.0001233657,0.0006938094,0.0001660999,0.001000486,0.0001538303],"domain_scores_gemma":[0.9956449,0.002878784,0.0005118486,0.0006338821,0.0002978905,0.00003270302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001714793,0.00004763661,0.004707697,0.000004280314,0.00006710525,5.46422e-7,0.010718,0.06706159,0.01607253,0.1893293,0.02639659,0.6854233],"study_design_scores_gemma":[0.0009978454,0.000453153,0.3964389,0.0001276906,0.0000112073,0.00002205542,0.003626316,0.1459467,0.001628302,0.3261442,0.1243299,0.0002737346],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827129,0.0001702288,0.001399204,0.0005634259,0.0001903199,0.0002700605,0.00001042118,0.00000928392,0.01467415],"genre_scores_gemma":[0.9987855,0.000001214479,0.0001656451,0.00003049871,0.00008181499,0.000004027778,2.434882e-7,0.0000054775,0.0009255603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6851496,"threshold_uncertainty_score":0.2442621,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2034417744","doi":"10.1016/j.omega.2014.03.002","title":"Responsive contingency planning in supply risk management by considering congestion effects","year":2014,"lang":"en","type":"article","venue":"Omega","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":30,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Mitacs","keywords":"Contingency plan; Contingency; Business; Risk management; Congestion management; Risk analysis (engineering); Operations management; Computer science; Economics; Computer security; Finance","retraction":null,"screen_n_in":null,"score":{"opus":0.005774272749993698,"gpt":0.2167963114752058,"spread":0.2110220387252121,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008688367,0.000251598,0.0002810308,0.0005191787,0.0002156978,0.0002482822,0.0002621994,0.00007606346,0.00005254726],"category_scores_gemma":[0.0003214708,0.0002405269,0.00006398161,0.0004977951,0.00006408901,0.0005986537,0.0002340919,0.0002050023,0.000280308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005271177,"about_ca_system_score_gemma":0.000005106227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004163513,"about_ca_topic_score_gemma":0.00007256529,"domain_scores_codex":[0.9983084,0.0000603197,0.0003381146,0.0004781374,0.0003152206,0.0004998701],"domain_scores_gemma":[0.9991274,0.0002528717,0.0002363896,0.0003120234,0.00004956362,0.00002168904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002175061,0.0001857899,0.8390085,0.0007720321,0.0001007946,0.0002357816,0.0003380374,0.003187535,0.0005068681,0.03694078,0.03687471,0.08163165],"study_design_scores_gemma":[0.003632896,0.00006020265,0.388721,0.0006704835,0.0001609514,0.000002551162,0.001499292,0.009677753,0.0003525587,0.008899658,0.5854644,0.0008582415],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9309389,0.0005796303,0.002019045,0.0005935284,0.0007298395,0.0008760522,0.000002160308,0.0002218306,0.06403901],"genre_scores_gemma":[0.9969628,0.0001057678,0.000420162,0.00123516,0.0002909575,0.0001092152,0.00002808503,0.00003291099,0.0008148783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5485897,"threshold_uncertainty_score":0.9808404,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318035562","doi":"10.1016/j.omega.2023.102843","title":"Lenient vs. stringent returns policies in the presence of fraudulent returns: The role of customers’ fairness perceptions","year":2023,"lang":"en","type":"article","venue":"Omega","topic":"Consumer Retail Behavior Studies","field":"Business, Management and Accounting","cited_by":29,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Cape Breton University; Dalhousie University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Business; Perception; Marketing; Product (mathematics); Customer satisfaction; Service (business); Offset (computer science)","retraction":null,"screen_n_in":null,"score":{"opus":0.02618852634475129,"gpt":0.2630527914551271,"spread":0.2368642651103758,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005333281,0.000142102,0.0002142541,0.0002816271,0.0001595405,0.00006593962,0.0005658373,0.00003985733,0.00005386416],"category_scores_gemma":[0.0001299505,0.00008647305,0.0001116213,0.00126768,0.0002187932,0.0002235643,0.000427069,0.0001709739,0.00006208805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002512139,"about_ca_system_score_gemma":0.00001571785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002325078,"about_ca_topic_score_gemma":0.002232461,"domain_scores_codex":[0.9987124,0.00003453843,0.0003321717,0.0001866032,0.0004602019,0.0002740517],"domain_scores_gemma":[0.999094,0.0001209682,0.00019743,0.000456429,0.0001249187,0.000006264534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002214075,0.0001177714,0.9725557,0.0001231973,0.00003696068,0.000003657342,0.008566997,0.00006398556,0.001826204,0.007289811,0.00113549,0.008258071],"study_design_scores_gemma":[0.000197032,0.00000998031,0.9325197,0.0000804609,0.00007917143,7.347064e-7,0.03946967,0.0002943815,0.0001147361,0.0002753072,0.02684206,0.0001167689],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922619,0.0003739593,0.000001287635,0.002646655,0.0001592609,0.0003901952,0.00001050353,0.00004577891,0.004110401],"genre_scores_gemma":[0.9993798,0.0001815844,0.000003928542,0.00006134011,0.00007023932,0.00008827741,0.000007938966,0.00001357771,0.0001933628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04003602,"threshold_uncertainty_score":0.3526269,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2293558007","doi":"10.1016/j.omega.2016.03.002","title":"Location choice and risk attitude of a decision maker","year":2016,"lang":"en","type":"article","venue":"Omega","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":29,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Linearization; Mathematical optimization; Facility location problem; Vertex (graph theory); Heuristic; Variance (accounting); Mathematics; Decision maker; Quadratic programming; Linear programming; Set (abstract data type); Computer science; Operations research; Combinatorics; Graph; Nonlinear system","retraction":null,"screen_n_in":null,"score":{"opus":0.0168784571004403,"gpt":0.2353289277307635,"spread":0.2184504706303232,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002409349,0.00006887208,0.00008001151,0.0001171413,0.00005561017,0.00002863472,0.00008357794,0.00002447669,0.0002445829],"category_scores_gemma":[0.0002518201,0.00004763031,0.00002150697,0.0002358029,0.00003379212,0.0004314532,0.00008681329,0.00002155314,0.000355151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001125796,"about_ca_system_score_gemma":0.000004057356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008820537,"about_ca_topic_score_gemma":0.0007713541,"domain_scores_codex":[0.9994188,0.000004829711,0.0001731601,0.0001573418,0.0001500553,0.00009579968],"domain_scores_gemma":[0.9995966,0.00003009446,0.00006206302,0.0001876983,0.0001162538,0.000007270939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005115252,0.0001446404,0.498554,0.0003717189,0.00005507157,7.293545e-7,0.00004590184,0.000181885,0.0008472113,0.02527151,0.01291059,0.4615656],"study_design_scores_gemma":[0.000658234,0.000007136632,0.7106577,0.00008984348,0.0000392277,1.433932e-7,0.0000307711,0.001371174,0.00004847776,0.001957922,0.285016,0.0001234037],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797477,0.0001381926,0.01339147,0.0009553942,0.0002478362,0.0001489836,0.000001558954,0.00004542901,0.005323489],"genre_scores_gemma":[0.9983018,0.0001282197,0.0002088299,0.0001949467,0.0001198856,0.000008566995,0.000002310628,0.000006029763,0.001029413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4614422,"threshold_uncertainty_score":0.4564866,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2068251675","doi":"10.1016/j.omega.2014.07.007","title":"Joint production and subcontracting planning of unreliable multi-facility multi-product production systems","year":2014,"lang":"en","type":"article","venue":"Omega","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":28,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Production (economics); Joint (building); Product (mathematics); Business; Integrated production; Production planning; Computer science; Operations management; Engineering; Economics; Architectural engineering; Mathematics; Microeconomics","retraction":null,"screen_n_in":null,"score":{"opus":0.0568036555519055,"gpt":0.243070518972111,"spread":0.1862668634202055,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00162162,0.000200323,0.0002936795,0.0002610156,0.000229264,0.0001594075,0.0001074898,0.00004829359,0.00001619701],"category_scores_gemma":[0.00124244,0.0001884055,0.00004937948,0.0003051437,0.00007392855,0.001079067,0.0001155887,0.0001439492,0.00004276494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004417933,"about_ca_system_score_gemma":0.00000782414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007326875,"about_ca_topic_score_gemma":0.00003027542,"domain_scores_codex":[0.9984233,0.00003589236,0.0004302601,0.0005692545,0.0002515351,0.000289757],"domain_scores_gemma":[0.9989799,0.00001701423,0.0003840438,0.0003952963,0.0002063743,0.00001739564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004502428,0.002328912,0.6852697,0.01714683,0.000465549,0.00001464865,0.002396611,0.05166867,0.1444433,0.009030108,0.04110029,0.04568509],"study_design_scores_gemma":[0.004517475,0.0001347536,0.2580272,0.002233636,0.0005253308,0.00003130881,0.01019114,0.271588,0.01945941,0.0003774275,0.4305674,0.002346894],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873376,0.0005154001,0.002816194,0.0007831657,0.004856923,0.001015929,0.00000195083,0.0002387869,0.002434037],"genre_scores_gemma":[0.9960716,0.00001050388,0.0004956697,0.00007573358,0.001354811,0.00004355915,0.00002110334,0.00002017783,0.00190678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4272425,"threshold_uncertainty_score":0.7682955,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2196400453","doi":"10.1016/j.omega.2015.10.011","title":"A framework for sustainable forest resource allocation: A Canadian case study","year":2015,"lang":"en","type":"article","venue":"Omega","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Analytic hierarchy process; Sustainability; Environmental economics; Resource allocation; Stewardship (theology); Business; Environmental resource management; Operations research; Economics; Engineering; Management; Political science","retraction":null,"screen_n_in":null,"score":{"opus":0.02590761435455614,"gpt":0.2764813488913137,"spread":0.2505737345367576,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003395548,0.00008651899,0.00007258306,0.00006505176,0.0001987463,0.00007596985,0.0001782829,0.00004174017,0.000303459],"category_scores_gemma":[0.0001414076,0.00008300625,0.00002355214,0.0003068258,0.00004751767,0.0001579186,0.00009665098,0.00005994504,0.0005337336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003198535,"about_ca_system_score_gemma":0.00005170226,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4248972,"about_ca_topic_score_gemma":0.3957041,"domain_scores_codex":[0.9991689,0.00002329838,0.00009500295,0.0001864435,0.000138225,0.0003881193],"domain_scores_gemma":[0.9993246,0.00003399914,0.00002774913,0.0002835212,0.00001307528,0.0003171215],"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":[0.00004013631,0.0001959994,0.2269978,0.00002061226,0.00002819055,0.002653086,0.01055774,0.003638786,2.744722e-7,0.05586932,0.6976531,0.002344979],"study_design_scores_gemma":[0.0004210992,0.0002341895,0.002266754,0.000002471224,0.00001741455,0.00004784215,0.008629128,0.000908954,5.868788e-7,0.006169384,0.9811596,0.0001426087],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8579487,0.00001627402,0.0002912898,0.001734395,0.00005401419,0.001160598,0.000002575685,0.00004063476,0.1387515],"genre_scores_gemma":[0.9472875,2.140618e-7,0.000905181,0.0005480025,0.00009892639,0.0001399891,0.000005863293,0.00001370799,0.05100059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2835065,"threshold_uncertainty_score":0.6860244,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4399359381","doi":"10.1016/j.omega.2024.103129","title":"Short video creation and traffic investment decision in social e-commerce platforms","year":2024,"lang":"en","type":"article","venue":"Omega","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Business; Investment (military); Computer science; Industrial organization; Political science","retraction":null,"screen_n_in":null,"score":{"opus":0.09160436639871647,"gpt":0.3958822349668092,"spread":0.3042778685680927,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00101577,0.0001039604,0.0001651203,0.0005939315,0.0001175952,0.00021742,0.000225277,0.0001745893,0.00009498312],"category_scores_gemma":[0.0002110851,0.0000752307,0.00005135855,0.0008004748,0.00009834592,0.0003441714,0.00008617941,0.0002157577,0.0002406695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000653246,"about_ca_system_score_gemma":0.000029885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004831446,"about_ca_topic_score_gemma":0.00006929519,"domain_scores_codex":[0.9985839,0.00002288905,0.0003720642,0.0003765752,0.0004887836,0.000155864],"domain_scores_gemma":[0.9994103,0.0002859049,0.00002709401,0.0001903474,0.00003214533,0.00005417783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00003876082,0.00008147104,0.05964774,0.000003986063,0.000006897843,0.00004704941,0.001409501,0.00004130055,0.0002303007,0.02941234,0.02326669,0.885814],"study_design_scores_gemma":[0.0003770388,0.00006187092,0.885801,0.00003905768,0.00001192154,0.00002824607,0.001009425,0.002747603,0.00009309319,0.05509957,0.05453958,0.0001915761],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956946,0.0002776542,0.0005126059,0.001384291,0.0002625855,0.0001226994,0.00000649071,0.0001411017,0.001597973],"genre_scores_gemma":[0.9984814,0.00002631,0.0003801987,0.000274127,0.00003400022,0.00001700466,0.000005821921,0.000006736085,0.0007743842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8856224,"threshold_uncertainty_score":0.30934,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2002372855","doi":"10.1016/j.omega.2011.02.003","title":"An inventory model with random discount offerings","year":2011,"lang":"en","type":"article","venue":"Omega","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":25,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University; University of New Brunswick; University of Toronto; Dalhousie University","funders":"","keywords":"Poisson distribution; Stochastic discount factor; Discounting; Random variable; Poisson process; Inventory control; Econometrics; Economics; Process (computing); Mathematical optimization; Computer science; Mathematics; Finance; Statistics; Operations management; Capital asset pricing model","retraction":null,"screen_n_in":null,"score":{"opus":0.0358210079911643,"gpt":0.2075135404670809,"spread":0.1716925324759166,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002510982,0.0001969536,0.0001800053,0.0002019379,0.0001298698,0.0001582997,0.0003331706,0.00003873082,0.0005259712],"category_scores_gemma":[0.00001310989,0.0001534472,0.00005622417,0.0002064467,0.00007821817,0.001574378,0.0001042989,0.00008850879,0.0002521166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003175117,"about_ca_system_score_gemma":0.00001308497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003970615,"about_ca_topic_score_gemma":0.0001138557,"domain_scores_codex":[0.9989344,0.000006742399,0.0001769706,0.0003093357,0.00026798,0.0003045944],"domain_scores_gemma":[0.9993751,0.000003504015,0.0001047893,0.0003970818,0.00009298597,0.00002656219],"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":[0.003850436,0.003108181,0.1697711,0.001385903,0.0005538267,0.0001854992,0.004285982,0.006469857,0.001813102,0.6496766,0.1472954,0.01160411],"study_design_scores_gemma":[0.01088873,0.0002169516,0.01541433,0.0002102157,0.0003755259,0.00000293394,0.002528223,0.5251734,0.0003937741,0.02436565,0.4183787,0.002051531],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6431682,0.00004374631,0.005562396,0.0001712902,0.0004705192,0.0004688466,0.000001554084,0.0003429303,0.3497705],"genre_scores_gemma":[0.994033,0.000004604433,0.0004534267,0.002471503,0.0005576177,0.000064314,0.00002147067,0.00004421685,0.002349785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6253109,"threshold_uncertainty_score":0.6257395,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2950435173","doi":"10.1016/j.omega.2019.06.005","title":"Minimum cost network design in strategic alliances","year":2019,"lang":"en","type":"article","venue":"Omega","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":24,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Transaction cost; Alliance; Business; Network planning and design; Service (business); Strategic alliance; Industrial organization; Heuristic; Fixed cost; Economies of scale; Variable cost; Scale (ratio); Marketing; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.05106569848888062,"gpt":0.2365874486431118,"spread":0.1855217501542311,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004372379,0.0001418277,0.0001649648,0.0001268137,0.000047721,0.0002199604,0.0002552353,0.00004540966,0.001433414],"category_scores_gemma":[0.000008761213,0.0001326292,0.0000434897,0.0004668598,0.00002196877,0.0005555029,0.00009213782,0.0000909036,0.002953208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003283894,"about_ca_system_score_gemma":0.00001144026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001060469,"about_ca_topic_score_gemma":0.00007404171,"domain_scores_codex":[0.9989846,0.00001477907,0.0001971933,0.0002575582,0.0001785016,0.0003673346],"domain_scores_gemma":[0.9996254,0.00003327219,0.00008994451,0.0002168855,0.00002467289,0.000009864326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003099594,0.0004354898,0.2375832,0.0006963484,0.0001071996,0.0001279177,0.0001296076,0.09445596,0.0003517169,0.3419607,0.3127933,0.01104855],"study_design_scores_gemma":[0.001634949,0.00003337518,0.01132145,0.0001586826,0.00002277531,4.832748e-7,0.000807308,0.06571041,0.00001602677,0.02233544,0.8974273,0.0005318066],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3177123,0.0004768665,0.0006991565,0.001495338,0.00283459,0.001689216,8.057214e-7,0.0002047432,0.6748869],"genre_scores_gemma":[0.9904625,0.00002217735,0.000247005,0.002890642,0.0009499656,0.00004926259,0.00001491505,0.00002147876,0.005342097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6727501,"threshold_uncertainty_score":0.9994794,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2770488731","doi":"10.1016/j.omega.2017.11.004","title":"Planned lead times optimization for multi-level assembly systems under uncertainties","year":2017,"lang":"en","type":"article","venue":"Omega","topic":"Quality and Supply Management","field":"Business, Management and Accounting","cited_by":23,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Lead time; Probabilistic logic; Lead (geology); Mathematical optimization; Computer science; Order (exchange); Supply chain; Object (grammar); Inventory control; Operations research; Holding cost; Mathematics; Operations management; Engineering; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.1254714838982406,"gpt":0.3081500879138482,"spread":0.1826786040156076,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004344004,0.0001590087,0.000203415,0.0001322804,0.0007587142,0.001468694,0.0003974017,0.0000727465,0.00005940115],"category_scores_gemma":[0.0001474744,0.0001458423,0.00007446365,0.0000574776,0.00005606236,0.001259334,0.0001637806,0.00005427345,0.0001590735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002998226,"about_ca_system_score_gemma":0.00001186012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006866991,"about_ca_topic_score_gemma":0.0001773449,"domain_scores_codex":[0.9990463,0.000009467364,0.0002260821,0.0002543344,0.0001905178,0.0002733025],"domain_scores_gemma":[0.9991385,0.0000360893,0.0002636089,0.0004130318,0.0001387522,0.000009959797],"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":[0.0003343195,0.0003917213,0.004791473,0.002348633,0.0003568594,0.00001172724,0.0001626181,0.2603727,0.00009971063,0.5775644,0.1455571,0.00800875],"study_design_scores_gemma":[0.001478666,0.00001875298,0.004285869,0.00009596265,0.00007722536,3.659803e-7,0.001058018,0.8607011,0.00001671995,0.00101637,0.1308956,0.0003552855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02341977,0.0002382818,0.9004396,0.01013947,0.003682909,0.002121329,0.00006349308,0.0004382361,0.05945691],"genre_scores_gemma":[0.9692131,0.00001044486,0.003534876,0.0008524318,0.0008888792,0.0001224163,0.0001686723,0.00003517634,0.02517403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9457933,"threshold_uncertainty_score":0.9995679,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2047726268","doi":"10.1016/j.omega.2015.01.021","title":"Portfolio optimization in hedge funds by OGARCH and Markov Switching Model","year":2015,"lang":"en","type":"article","venue":"Omega","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":22,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Portfolio; EWMA chart; Sharpe ratio; Hedge fund; Econometrics; Portfolio optimization; Markov chain; Asset (computer security); Post-modern portfolio theory; Mathematics; Sensitivity (control systems); Economics; Replicating portfolio; Actuarial science; Computer science; Statistics; Financial economics; Finance; Process (computing); Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03445848350230071,"gpt":0.2328977707838322,"spread":0.1984392872815315,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008127762,0.0000903437,0.0001996416,0.0001535749,0.00003281612,0.00005905941,0.00008767305,0.00007649204,0.00005272639],"category_scores_gemma":[0.00007165931,0.0001097151,0.00002324369,0.0001619596,0.00001477377,0.0001984378,0.00006379323,0.0001095412,0.000005436403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008251282,"about_ca_system_score_gemma":0.00001887216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002378689,"about_ca_topic_score_gemma":0.00004088405,"domain_scores_codex":[0.9991943,0.00001007409,0.0003155823,0.0002705142,0.00002804903,0.0001815413],"domain_scores_gemma":[0.9995891,0.00001821703,0.00009935659,0.0001778598,0.00001754027,0.00009786557],"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":[0.0000686806,0.0001362146,0.946219,0.00003943634,0.00001684472,0.000003667527,0.0005763493,0.01905423,0.000009211811,0.0263748,0.003623595,0.003877945],"study_design_scores_gemma":[0.0004483246,0.00001544105,0.004499202,0.000003871525,9.742575e-7,0.000001039804,0.00001949996,0.9745498,6.458261e-7,0.01811386,0.002223369,0.0001239907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7598834,0.0009725274,0.1185242,0.0003171352,0.0001357033,0.000146364,0.0000795261,0.00002221759,0.1199189],"genre_scores_gemma":[0.9939922,0.0001409001,0.003826778,0.00009416832,0.00001726935,0.000008663271,0.00003048593,0.00001408019,0.001875421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9554955,"threshold_uncertainty_score":0.447405,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3083873786","doi":"10.1016/j.omega.2020.102335","title":"The value of aggregate service levels in stochastic lot sizing problems","year":2020,"lang":"en","type":"article","venue":"Omega","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":22,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Service (business); Aggregate (composite); Service level; Sizing; Computer science; Constraint (computer-aided design); Mathematical optimization; Operations research; Level of service; Aggregate planning; Production (economics); Reliability engineering; Engineering; Mathematics; Production planning; Economics; Statistics; Microeconomics","retraction":null,"screen_n_in":null,"score":{"opus":0.0444924200274433,"gpt":0.2242043200908682,"spread":0.1797119000634249,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003466587,0.0001280229,0.000156313,0.00008539928,0.0001018611,0.0001313686,0.0003731528,0.0000284494,0.0000682915],"category_scores_gemma":[0.0001120159,0.00009883013,0.00004573662,0.0007219049,0.00003389576,0.0003735101,0.0002651133,0.0001060521,0.0001857072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002044356,"about_ca_system_score_gemma":0.0000121709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003854615,"about_ca_topic_score_gemma":0.0001777514,"domain_scores_codex":[0.9990006,0.00001286202,0.0002880646,0.0002089656,0.0002355807,0.0002538971],"domain_scores_gemma":[0.9995035,0.00005014076,0.000173618,0.0001952427,0.00006415063,0.00001333606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004738077,0.0007288802,0.02523562,0.007505535,0.0004877078,0.00009131034,0.008183656,0.2501697,0.01306983,0.5959042,0.0344579,0.06369191],"study_design_scores_gemma":[0.002905502,0.00005692595,0.01878997,0.0005753213,0.00009991867,8.730621e-7,0.003397328,0.4373206,0.0002883559,0.01111283,0.5247139,0.0007384216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8713643,0.00122168,0.002876326,0.05938629,0.001777769,0.002213872,0.000007412692,0.0003257261,0.06082663],"genre_scores_gemma":[0.9935574,0.000005664018,0.00002491773,0.005730741,0.0003461393,0.00002677856,0.000004534915,0.00002135643,0.0002824551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5847913,"threshold_uncertainty_score":0.4030176,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2198954614","doi":"10.1016/j.omega.2015.12.006","title":"Optimal coordination of resource allocation, due date assignment and scheduling decisions","year":2016,"lang":"en","type":"article","venue":"Omega","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Scheduling (production processes); Computer science; Due date; Resource allocation; Assignment problem; Resource consumption; Single-machine scheduling; Time complexity; Polynomial-time approximation scheme; Regular polygon; Job shop scheduling; Deadline-monotonic scheduling; Operations research; Dynamic priority scheduling; Mathematics; Algorithm; Rate-monotonic scheduling; Schedule","retraction":null,"screen_n_in":null,"score":{"opus":0.01297310040881851,"gpt":0.2270800008963192,"spread":0.2141069004875007,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000170331,0.00007270624,0.00009336261,0.00008436463,0.00004170254,0.00001712387,0.00006672634,0.00004762239,0.00002725209],"category_scores_gemma":[0.0001452413,0.00005725405,0.00001647242,0.0001279763,0.00002820262,0.0001017839,0.00002162044,0.00003808305,0.00001209321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002071865,"about_ca_system_score_gemma":0.000009332141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001175675,"about_ca_topic_score_gemma":5.061121e-7,"domain_scores_codex":[0.999477,0.00001454521,0.0001746854,0.0001106457,0.0001219922,0.000101187],"domain_scores_gemma":[0.9995865,0.000129068,0.0000323093,0.0001391066,0.00005718469,0.00005580336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001815218,0.00003558936,0.0005543192,0.00002074174,0.00005429368,0.000002204407,0.0003407963,0.9000322,0.01747594,0.002540703,0.0005206615,0.07840437],"study_design_scores_gemma":[0.001524336,0.0000592718,0.002607176,0.0002651891,0.0000333565,0.00001421851,0.0002770718,0.9271857,0.05850782,0.0001616731,0.009025857,0.0003383642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2695366,0.0001537917,0.7287284,0.0004226052,0.00009361182,0.00006356199,0.000006700026,0.0001020605,0.0008926971],"genre_scores_gemma":[0.8828716,0.00006331668,0.1167025,0.00001379503,0.00003612922,0.000007562011,0.000007265594,0.00001545904,0.0002823675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.613335,"threshold_uncertainty_score":0.2334752,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2979687838","doi":"10.1016/j.omega.2019.102135","title":"Integrating natural wood drying and seasonal trucks’ workload restrictions into forestry transportation planning","year":2019,"lang":"en","type":"article","venue":"Omega","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Truck; Payload (computing); Yard; Procurement; Workload; Environmental science; Haulage; Operations management; Transport engineering; Operations research; Agricultural engineering; Business; Engineering; Computer science; Automotive engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.00576240024665743,"gpt":0.2131020710101722,"spread":0.2073396707635148,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004634906,0.0001086135,0.00009023683,0.0001080434,0.00007177144,0.00005744661,0.00005673308,0.00005165793,0.00002421381],"category_scores_gemma":[0.000008455276,0.0001079551,0.00002857595,0.0002324011,0.00001419292,0.000198806,0.000007905953,0.0001356912,0.00001377796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004092425,"about_ca_system_score_gemma":0.000006727191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001469421,"about_ca_topic_score_gemma":0.00008273348,"domain_scores_codex":[0.9994507,0.000006908013,0.0001436163,0.0001361411,0.0001136946,0.0001489635],"domain_scores_gemma":[0.9998001,0.00002361257,0.00002357015,0.00008872797,0.00001615895,0.0000478227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007587753,0.00005751137,0.755105,0.0009926446,0.000297758,0.00004387249,0.008651525,0.06481235,0.01586586,0.04145472,0.003763524,0.1088793],"study_design_scores_gemma":[0.001955894,0.00009123445,0.696716,0.0007994037,0.00007369908,0.00001028663,0.004774333,0.2637252,0.001633224,0.001404816,0.02798058,0.0008353151],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891747,0.00066614,0.004131742,0.00004924443,0.0004711486,0.0001409876,0.000002641593,0.0002518979,0.005111518],"genre_scores_gemma":[0.9975244,0.00003510198,0.001939366,0.00003268435,0.00004168719,0.00001394619,0.00004413844,0.00001875378,0.000349914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1989129,"threshold_uncertainty_score":0.4402282,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3023445885","doi":"10.1016/j.omega.2020.102275","title":"A physician planning framework for polyclinics under uncertainty","year":2020,"lang":"en","type":"article","venue":"Omega","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":20,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Business; Computer science; Process management","retraction":null,"screen_n_in":null,"score":{"opus":0.2228238612818233,"gpt":0.5010964044376114,"spread":0.2782725431557881,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002596563,0.00009545246,0.0001834956,0.00002650437,0.0007956715,0.00001540669,0.00009464601,0.0002097396,0.0000498855],"category_scores_gemma":[0.0006071299,0.00008716092,0.00005233488,0.0002276649,0.00001608507,0.00007340948,0.00002778454,0.0004624587,0.000109881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005835379,"about_ca_system_score_gemma":0.0003247296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002597186,"about_ca_topic_score_gemma":0.00001071286,"domain_scores_codex":[0.9988694,0.0001403802,0.0003484104,0.0002180335,0.000103476,0.0003202576],"domain_scores_gemma":[0.9987217,0.0006921673,0.0001128189,0.0001529312,0.0001483248,0.0001720124],"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":[0.000505537,0.0001393555,0.0159232,0.0006647834,0.0001032076,0.000004160172,0.03889049,0.2740116,0.0002664734,0.6212747,0.02727155,0.02094496],"study_design_scores_gemma":[0.002878113,0.000627048,0.002749282,0.0009110471,0.00006541987,3.664038e-7,0.03064773,0.6946539,0.00003128139,0.04270513,0.2239752,0.0007554815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05774809,0.000156881,0.8853632,0.05209805,0.0008621757,0.001035763,0.00004682601,0.0001950105,0.00249405],"genre_scores_gemma":[0.8516756,0.00001641161,0.08829533,0.05772683,0.001551041,0.000179536,0.0001134065,0.00003734142,0.0004044597],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7970678,"threshold_uncertainty_score":0.6119743,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386629894","doi":"10.1016/j.omega.2023.102965","title":"Compensation guarantees in crowdsourced delivery: Impact on platform and driver welfare","year":2023,"lang":"en","type":"article","venue":"Omega","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wage; Flexibility (engineering); Welfare; Computer science; Profit (economics); Matching (statistics); Labour economics; Microeconomics; Work (physics); Scheduling (production processes); Business; Economics; Operations research; Operations management; Engineering; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01458762318414245,"gpt":0.2422788548093121,"spread":0.2276912316251697,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005210615,0.00006895718,0.00007360775,0.0001947876,0.000036015,0.00001541817,0.0000291728,0.00003736077,0.00005310459],"category_scores_gemma":[0.000004569636,0.00006498204,0.00001908145,0.0003647438,0.00001300507,0.00009039917,0.000002610402,0.00007310416,0.00004434824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003563755,"about_ca_system_score_gemma":0.000004131137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004142019,"about_ca_topic_score_gemma":0.0001803916,"domain_scores_codex":[0.9996384,0.000002715876,0.0001125743,0.00007422013,0.00006672138,0.0001054009],"domain_scores_gemma":[0.9998586,0.00002198251,0.000008603432,0.00007412459,0.00001542434,0.00002129288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001571242,0.0001300075,0.2441704,0.0002580036,0.0001753616,0.00006414688,0.01317564,0.6244486,0.02900462,0.04059857,0.004573742,0.04324373],"study_design_scores_gemma":[0.0003890104,0.0000197975,0.9803922,0.00001493685,0.00000274279,6.631681e-7,0.0002126464,0.01627042,0.0003098606,0.00009046136,0.002216734,0.000080573],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982796,0.000008392817,0.00005382914,0.0001610508,0.00006039632,0.00009674055,0.00002981661,0.0002436256,0.001066503],"genre_scores_gemma":[0.9997371,0.00001766912,0.00002862568,0.000029555,0.000009451574,0.00001004562,0.0001090179,0.00001044153,0.00004811486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7362217,"threshold_uncertainty_score":0.2649891,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2048060905","doi":"10.1016/j.omega.2015.01.019","title":"Time-staged outputs in DEA","year":2015,"lang":"en","type":"article","venue":"Omega","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Data envelopment analysis; Internship; Principal (computer security); Set (abstract data type); Institution; Quality (philosophy); Dual (grammatical number); Computer science; Meaning (existential); Operations research; Business; Economics; Mathematics; Sociology; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.1298726501139269,"gpt":0.3832691657159598,"spread":0.2533965156020329,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005298569,0.00009907826,0.0002560813,0.0005307241,0.00005152552,0.0001660455,0.0007271846,0.00006334511,0.0002443821],"category_scores_gemma":[0.005365476,0.00007400351,0.00007922144,0.002025653,0.0001006369,0.000232488,0.00012183,0.0001299351,0.008685702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000731397,"about_ca_system_score_gemma":0.0001201096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000622252,"about_ca_topic_score_gemma":0.000125989,"domain_scores_codex":[0.9972057,0.000262747,0.0004708653,0.0004023094,0.001394952,0.0002634352],"domain_scores_gemma":[0.9983278,0.0005366978,0.0001211783,0.0006492838,0.0002159464,0.0001490779],"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":[0.0002283959,0.001278548,0.2838965,0.000007712012,0.00006755731,0.0005792785,0.01711054,0.04759597,0.007007889,0.008931275,0.5101993,0.123097],"study_design_scores_gemma":[0.003279397,0.0002931843,0.05070926,0.0000544629,0.00004615929,0.00002612301,0.003166897,0.114346,0.003101775,0.06474815,0.7590485,0.001180038],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9404357,0.0001836095,0.001340884,0.001421524,0.0002625845,0.00007623949,0.000003368321,0.00005000441,0.05622608],"genre_scores_gemma":[0.9764972,7.157402e-7,0.0007620711,0.0003176026,0.00004634144,0.000002573036,0.000001755096,0.000007342551,0.02236436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2488492,"threshold_uncertainty_score":0.9920862,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}