{"meta":{"query_hash":"1a9f3bd2850a","filters":{"topic":"Optimization and Mathematical Programming"},"cohort_total":263,"direct_labels_cover":0,"predictions_cover":263,"exported":263,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/1a9f3bd2850a","api":"https://metacan.xera.ac/api/v1/cohort?topic=Optimization+and+Mathematical+Programming"},"results":[{"id":"W1003559035","doi":"10.5267/j.msl.2015.6.008","title":"A scenario based project portfolio selection","year":2015,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Portfolio; Computer science; Project portfolio management; Business; Finance; Project management; Artificial intelligence; Economics; Management","score_opus":0.022940278854839295,"score_gpt":0.2460824025597104,"score_spread":0.2231421237048711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1003559035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023158437,0.0000058516744,0.9069407,0.0011127384,0.00039602077,0.0005253524,2.2011149e-7,0.00087036693,0.06699034],"genre_scores_gemma":[0.88125443,0.000001299857,0.11641383,0.001948324,0.00003405198,0.000046289962,0.0000020856753,0.000016804044,0.00028286313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992135,0.0000054772786,0.0000971221,0.00014226482,0.00030765886,0.00023396367],"domain_scores_gemma":[0.999793,0.0000037784246,0.000013463984,0.00010827008,0.000011893022,0.00006961982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036098826,0.00007089329,0.000053689626,0.00024719184,0.000063554224,0.00012740368,0.00015485106,0.000010662164,0.000018800436],"category_scores_gemma":[0.000014595447,0.00006629868,0.000016800579,0.0008892086,0.00007428054,0.00023992056,0.000027107018,0.000046087094,0.00005046503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007397386,0.0000887189,0.0015217204,0.00023905004,0.000027648675,0.000028043187,0.0004972337,0.87622994,0.0029055064,0.007913993,0.08706977,0.023470996],"study_design_scores_gemma":[0.0002920477,0.000016462158,0.00021053592,0.00001818943,0.000011568592,0.0000016782751,0.00012467793,0.9744533,0.00065288536,0.00008342234,0.023972692,0.00016257835],"about_ca_topic_score_codex":0.0000040339187,"about_ca_topic_score_gemma":8.027664e-7,"teacher_disagreement_score":0.858096,"about_ca_system_score_codex":0.000116922085,"about_ca_system_score_gemma":0.0000074158606,"threshold_uncertainty_score":0.27035818},"labels":[],"label_agreement":null},{"id":"W1182730838","doi":"10.1007/s10479-015-1969-3","title":"Goal programming model for management accounting and auditing: a new typology","year":2015,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Typology; Audit; Compromise; Accounting; Computer science; Field (mathematics); Management accounting; Theory of computation; Guideline; Management science; Aggregate (composite); Business; Economics; Sociology; Political science; Mathematics; Algorithm","score_opus":0.3251146592553192,"score_gpt":0.4538097761543216,"score_spread":0.12869511689900243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1182730838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023938216,0.00039495953,0.96172094,0.0021700782,0.00005231986,0.0011264894,0.0000053621375,0.00014009509,0.010451558],"genre_scores_gemma":[0.70372754,0.00006432742,0.29449776,0.000038206097,0.000051871102,0.00012670862,0.00001134873,0.000020998556,0.0014612193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992808,0.000016727794,0.00017643595,0.0001025628,0.00016956183,0.00025388476],"domain_scores_gemma":[0.9994015,0.00005332992,0.000007236359,0.00010328682,0.00031878025,0.00011585407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007613829,0.000057314694,0.00009654744,0.00014681452,0.00009557869,0.00009279381,0.00008605721,0.000039806215,0.000007834354],"category_scores_gemma":[0.00026076802,0.00005583333,0.000019908854,0.00018674532,0.000051542727,0.00015266427,0.00005713939,0.00008608386,0.000005798211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015693877,0.00007136279,0.00002526471,0.0003485528,0.000069291724,0.0000012391281,0.001897109,0.7347684,0.00016385353,0.15064907,0.015231176,0.096759014],"study_design_scores_gemma":[0.00021729333,0.000042420997,0.0000038425287,0.000022066153,0.0000038197386,0.0000011305126,0.0006561408,0.9917578,0.00024545376,0.0027179227,0.0042741573,0.000057966605],"about_ca_topic_score_codex":0.000010467253,"about_ca_topic_score_gemma":0.000016980059,"teacher_disagreement_score":0.67978936,"about_ca_system_score_codex":0.0000099808985,"about_ca_system_score_gemma":0.000039840408,"threshold_uncertainty_score":0.22768171},"labels":[],"label_agreement":null},{"id":"W1276039536","doi":"10.5267/j.dsl.2015.6.003","title":"Fuzzy goal programming applied to multi-objective programming problem with FREs as constraints","year":2015,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Goal programming; Fuzzy logic; Mathematical optimization; Computer science; Artificial intelligence; Mathematics","score_opus":0.021802615391837244,"score_gpt":0.27728586561695073,"score_spread":0.2554832502251135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1276039536","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11908177,0.000013288256,0.8735177,0.00037557134,0.00021218478,0.0015115282,0.0000014782438,0.0006946048,0.004591872],"genre_scores_gemma":[0.48006746,3.439597e-7,0.5193522,0.00040106178,0.000023323,0.00012080402,0.0000012808295,0.000024566647,0.000008950007],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973957,0.000013641523,0.00034830146,0.00051740493,0.0010166749,0.0007082703],"domain_scores_gemma":[0.9987295,0.000106343,0.000057292327,0.00031923418,0.000177509,0.0006100826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094323786,0.00025456995,0.00025150448,0.00036069617,0.00020140107,0.000544781,0.00048385691,0.00005670629,0.000011388087],"category_scores_gemma":[0.0003287619,0.0002003585,0.000039912502,0.0016355482,0.0006009965,0.00043763907,0.00011767574,0.0002036394,0.00021606921],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056355926,0.00012202973,0.00023157672,0.000032714994,0.000023950313,0.000040375897,0.005992185,0.08640554,0.008639154,0.0025803084,0.0006595158,0.8952163],"study_design_scores_gemma":[0.04424667,0.006034959,0.0043965066,0.0054318574,0.0005799854,0.0019721496,0.11842453,0.37863412,0.11525415,0.019135213,0.2854226,0.020467248],"about_ca_topic_score_codex":0.0000068211084,"about_ca_topic_score_gemma":0.000008961662,"teacher_disagreement_score":0.87474906,"about_ca_system_score_codex":0.0001824813,"about_ca_system_score_gemma":0.00008755707,"threshold_uncertainty_score":0.81703824},"labels":[],"label_agreement":null},{"id":"W1475261338","doi":"","title":"Manager Preferences Modelling for Stochastic Aggregate Planning","year":2011,"lang":"en","type":"article","venue":"Silesian Digital Library (Silesian Library)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Aggregate (composite); Computer science; Aggregate planning; Production planning; Microeconomics; Economics","score_opus":0.03118836438388797,"score_gpt":0.19292546228336727,"score_spread":0.1617370978994793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1475261338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005764015,0.00055208337,0.7415079,0.000085359316,0.00025665824,0.00089467235,0.00026142108,0.003523439,0.24715447],"genre_scores_gemma":[0.8892809,0.000016782038,0.10609173,0.0001282125,0.00020667321,0.00013907284,0.00056496874,0.0002978891,0.003273735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817026,0.000016378213,0.00054154877,0.00042942952,0.00018195692,0.0006604553],"domain_scores_gemma":[0.99905705,0.00011351953,0.000091699556,0.00035960748,0.00000974891,0.00036835205],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00003201924,0.00044472062,0.0003931189,0.00027541636,0.00012429651,0.00074591977,0.0005241648,0.00017256443,0.0006021361],"category_scores_gemma":[0.000012176629,0.00042965962,0.00019699639,0.00038577084,0.00010019035,0.0070316046,0.0001194215,0.0002098052,0.00014352807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005260958,0.00081270933,0.0031622997,0.0039556716,0.00086171535,0.00026800926,0.00521777,0.46905252,0.000021278614,0.304552,0.07310614,0.13846381],"study_design_scores_gemma":[0.0009819312,0.00023111551,0.00016073333,0.0007999118,0.000076117016,0.000019284918,0.0006529857,0.84756845,0.0015745824,0.12739621,0.018872872,0.0016658104],"about_ca_topic_score_codex":3.0130008e-7,"about_ca_topic_score_gemma":2.9457885e-8,"teacher_disagreement_score":0.8835169,"about_ca_system_score_codex":0.00000853654,"about_ca_system_score_gemma":0.000035894653,"threshold_uncertainty_score":0.9998155},"labels":[],"label_agreement":null},{"id":"W1512988686","doi":"","title":"Kairos Management : Nouveaux outils pour le Management des Opportunités","year":2006,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Kairos; Political science; Philosophy; Linguistics","score_opus":0.021159530198354887,"score_gpt":0.228338705815791,"score_spread":0.2071791756174361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1512988686","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017193834,0.0008456708,0.5925012,0.00364228,0.0002727936,0.00084237766,0.000028930719,0.00051566854,0.39963165],"genre_scores_gemma":[0.081086725,0.0022740082,0.77448106,0.00008779968,0.000038729155,0.00028388915,0.0005989156,0.00017326648,0.1409756],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947135,0.0016094333,0.0011650145,0.0009938773,0.0006148312,0.00090337364],"domain_scores_gemma":[0.99528927,0.000529403,0.00040381853,0.002321229,0.0010257727,0.00043051547],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0038051181,0.0007386004,0.0006397578,0.0003544013,0.00061768055,0.0007396421,0.0014560284,0.0003954091,0.00056943455],"category_scores_gemma":[0.00019970584,0.00088570797,0.0004051366,0.0005878601,0.0003886167,0.0002510858,0.001766155,0.00070444704,0.00041908256],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005107019,0.0011604645,0.00013035574,0.002200298,0.00034531564,0.00003874381,0.0020716134,0.0111747,0.00010626248,0.7159067,0.004117395,0.26274306],"study_design_scores_gemma":[0.0035485057,0.0000016934102,0.0053235292,0.013201264,0.0008960959,0.000046357556,0.0023943654,0.52699804,0.011249659,0.107966565,0.32514164,0.0032322807],"about_ca_topic_score_codex":0.001037728,"about_ca_topic_score_gemma":0.00050686154,"teacher_disagreement_score":0.60794014,"about_ca_system_score_codex":0.0004364678,"about_ca_system_score_gemma":0.00013255498,"threshold_uncertainty_score":0.99935937},"labels":[],"label_agreement":null},{"id":"W1529531754","doi":"10.1023/a:1021874104605","title":"Selecting Sites for New Facilities Using Data Envelopment Analysis","year":2003,"lang":"en","type":"article","venue":"Journal of Productivity Analysis","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Set (abstract data type); Selection (genetic algorithm); Computer science; Aggregate (composite); Mathematical optimization; Operations research; Resource (disambiguation); Mathematics; Artificial intelligence","score_opus":0.09226992933860148,"score_gpt":0.30245573831588957,"score_spread":0.21018580897728809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1529531754","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07797471,0.00036201323,0.921287,0.000072000636,0.00007044446,0.000084378655,0.000009046351,0.00003188737,0.000108519744],"genre_scores_gemma":[0.68373996,0.000024409655,0.31583667,0.000004742297,0.00009868196,8.486741e-7,0.000013942879,0.00001157738,0.0002691481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987951,0.000054057768,0.0005099785,0.00019016418,0.00024615965,0.00020452826],"domain_scores_gemma":[0.9990185,0.00013232295,0.00020690679,0.00034302566,0.00019087274,0.00010838504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001315531,0.00013259696,0.0005441578,0.00072917336,0.00009793637,0.00010046108,0.00019558544,0.000042391075,0.000105531],"category_scores_gemma":[0.0010958753,0.00011980955,0.00032748084,0.0023677084,0.000018873336,0.000501952,0.000025446729,0.00013091651,8.600132e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000626637,0.00006727837,0.0025647264,0.000090266054,0.011460101,0.0000012235943,0.000684476,0.97926605,0.0014228619,0.00014919587,0.0002571724,0.0040303804],"study_design_scores_gemma":[0.00029181121,0.000032214397,0.00016473206,0.0000124506,0.015956292,0.0000140367865,0.000795983,0.9661991,0.00703896,0.0008982784,0.008277946,0.00031821383],"about_ca_topic_score_codex":0.000011479839,"about_ca_topic_score_gemma":0.00006254217,"teacher_disagreement_score":0.6057653,"about_ca_system_score_codex":0.0000853896,"about_ca_system_score_gemma":0.0000681276,"threshold_uncertainty_score":0.48856917},"labels":[],"label_agreement":null},{"id":"W1545660827","doi":"10.1002/9783527630844.app2","title":"Appendix B: Chance Constrained Programming","year":2010,"lang":"en","type":"other","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Library science; Citation; Operations research; Computer science; Engineering","score_opus":0.008141916425967996,"score_gpt":0.22379729797170353,"score_spread":0.21565538154573555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1545660827","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.562129e-7,0.0001507481,0.14689253,0.000015522695,0.00038057487,0.00032081743,0.0000045368984,0.0029851045,0.8492499],"genre_scores_gemma":[0.00019184034,0.000052422736,0.2668567,0.00003521996,0.00023848064,0.000058004825,0.00008085219,0.00065290096,0.7318336],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993835,0.0000033884385,0.00014002543,0.00013736574,0.00009977211,0.00023593947],"domain_scores_gemma":[0.9996664,0.00001225411,0.000029863071,0.00019496461,0.000008340458,0.00008820792],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000044729986,0.00020056368,0.00021310966,0.00010292619,0.000015146089,0.000047975587,0.00011581523,0.00030049356,0.018647242],"category_scores_gemma":[0.000015007589,0.00018076721,0.0000500659,0.00008899051,0.000053046064,0.000019649262,0.000016780019,0.00025690495,0.0013189802],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.43175e-7,0.000049214897,0.0000012710318,0.0010644649,0.000115440016,0.000010579572,0.00006228997,0.00008802579,0.00011300669,0.029699834,0.5908834,0.37791187],"study_design_scores_gemma":[0.00010486537,0.0000048416346,3.373488e-8,0.00008071061,0.000010756415,0.000004398409,0.000013498915,0.006397488,0.000056651683,0.00006430512,0.9930319,0.00023050893],"about_ca_topic_score_codex":0.0000053343497,"about_ca_topic_score_gemma":0.000050107843,"teacher_disagreement_score":0.40214857,"about_ca_system_score_codex":0.000007661876,"about_ca_system_score_gemma":0.0000064172827,"threshold_uncertainty_score":0.9994586},"labels":[],"label_agreement":null},{"id":"W1573636743","doi":"10.1023/a:1011392328632","title":"Generalized Assignment Type Goal Programming Problem: Application to Nurse Scheduling","year":2001,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IBM (Canada); Université du Québec à Trois-Rivières; Université de Montréal","funders":"","keywords":"Tabu search; Mathematical optimization; Nurse scheduling problem; Computer science; Scheduling (production processes); Job shop scheduling; Mathematics; Schedule; Flow shop scheduling","score_opus":0.012108327788104863,"score_gpt":0.26485392777579,"score_spread":0.2527455999876851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1573636743","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013555021,0.00016283336,0.98433644,0.00020663303,0.000268087,0.0002333059,4.667609e-7,0.00008180803,0.0011554047],"genre_scores_gemma":[0.39100164,0.00008818911,0.608481,0.00006252327,0.00023485045,0.000008295722,0.000002307266,0.00003128789,0.000089936184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990651,0.000014123318,0.00044279592,0.00006750154,0.00022353714,0.00018696582],"domain_scores_gemma":[0.99941176,0.0000303106,0.00010370784,0.000099826706,0.00018760409,0.00016677087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024075074,0.000103062026,0.00017854926,0.00009029621,0.000041182648,0.000061592014,0.000113240865,0.000049988143,0.000028533997],"category_scores_gemma":[0.00010059911,0.00009172705,0.0000476735,0.0002434755,0.000011591432,0.00007201373,0.000011209147,0.00015521514,0.00003098596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054493386,0.00019102382,0.00023297037,0.00013966557,0.00007183717,0.00003467438,0.0003666089,0.84531015,0.0037727174,0.004505263,0.0015986979,0.14372192],"study_design_scores_gemma":[0.0014168569,0.00056668755,0.000067939385,0.00027794778,0.00018309403,0.00030584107,0.00035659134,0.7099045,0.0013224443,0.004310832,0.28073248,0.0005547636],"about_ca_topic_score_codex":6.080503e-7,"about_ca_topic_score_gemma":5.4744834e-7,"teacher_disagreement_score":0.37744662,"about_ca_system_score_codex":0.0000808967,"about_ca_system_score_gemma":0.000020049145,"threshold_uncertainty_score":0.37405205},"labels":[],"label_agreement":null},{"id":"W1683347786","doi":"","title":"OPTIMIZING MULTI-OBJECTIVE DECISIONS ABOUT DISTRIBUTION CENTRES AND PLANT LOCATIONS IN SUPPLY CHAIN MANAGEMENT","year":2007,"lang":"en","type":"article","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Supply chain; Analytic hierarchy process; Operations research; Supply chain management; Distribution (mathematics); Computer science; Hierarchy; Process (computing); Focus (optics); Risk analysis (engineering); Operations management; Engineering; Business; Mathematics; Economics","score_opus":0.012317344724643218,"score_gpt":0.24345697936119004,"score_spread":0.23113963463654683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1683347786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061210184,0.00013943813,0.9895525,0.00004018446,0.000053570944,0.00027490032,0.000013740472,0.000161818,0.0036428063],"genre_scores_gemma":[0.85824907,0.00022302661,0.1412773,0.000018799807,0.000007946811,0.000016013815,0.00007348632,0.000010891061,0.000123493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940574,0.000006685469,0.00019840758,0.000109465946,0.00007504773,0.00020462397],"domain_scores_gemma":[0.9997153,0.00010428916,0.000012079441,0.000080533064,0.000015405896,0.00007236564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001766541,0.00008480547,0.00008549555,0.00009497089,0.000052992826,0.000035349294,0.0000417888,0.000038104703,0.000028611808],"category_scores_gemma":[0.000037169742,0.00007912992,0.000016042015,0.00017584919,0.000019564533,0.000079815974,0.000028449642,0.00006849272,0.000009049119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040397263,0.00066467293,0.0030469818,0.00029949032,0.00014787192,0.00007749384,0.0050754435,0.47660434,0.00022448965,0.2604495,0.0013503853,0.25201893],"study_design_scores_gemma":[0.0009115032,0.000012388571,0.015085809,0.00016630984,0.000017010496,0.0000057798197,0.002949857,0.9772321,0.0006702938,0.00042671355,0.0022773442,0.00024490317],"about_ca_topic_score_codex":0.000012221973,"about_ca_topic_score_gemma":0.00018618134,"teacher_disagreement_score":0.852128,"about_ca_system_score_codex":0.0000709727,"about_ca_system_score_gemma":0.0000021979279,"threshold_uncertainty_score":0.32268244},"labels":[],"label_agreement":null},{"id":"W1729641220","doi":"10.3233/jid-2003-7407","title":"AGGREGATE PRODUCTION PLANNING UTILIZING A FUZZY LINEAR PROGRAMMING","year":2003,"lang":"en","type":"article","venue":"Journal of Integrated Design and Process Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Aggregate planning; Aggregate (composite); Production (economics); Fuzzy logic; Linear programming; Production planning; Computer science; Mathematical optimization; Mathematics; Economics; Microeconomics; Artificial intelligence; Materials science","score_opus":0.033196541970355174,"score_gpt":0.2882482717904504,"score_spread":0.2550517298200953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1729641220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07138953,0.0018359977,0.9246448,0.000045936333,0.00038353755,0.00024153959,1.9543508e-7,0.00011199444,0.0013464172],"genre_scores_gemma":[0.79526407,0.000101536396,0.20454688,0.0000142133085,0.000026098198,0.000003988367,1.5373607e-7,0.000012166501,0.000030881576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900484,0.000032464784,0.0003300766,0.00012343362,0.00027378413,0.00023537691],"domain_scores_gemma":[0.9992145,0.000038991253,0.00012826099,0.00006138294,0.00041457606,0.00014232301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013907205,0.000116591626,0.00015975791,0.00023937931,0.00015323212,0.00015212389,0.00013415427,0.000040348332,0.0000061550627],"category_scores_gemma":[0.00089944707,0.000085031854,0.000025050045,0.0009353939,0.00015825072,0.00071000814,0.0000054913244,0.00025040383,0.0000013681579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012566822,0.00023182866,0.0003252006,0.0010298772,0.00007759972,0.00011882259,0.009412111,0.68097883,0.10897939,0.0037506437,0.00030943457,0.19466056],"study_design_scores_gemma":[0.00072539266,0.00047513845,0.000011217724,0.0018958591,0.000069611044,0.0019826274,0.007862635,0.5722124,0.40434495,0.005473879,0.0043670908,0.0005791819],"about_ca_topic_score_codex":1.7516378e-7,"about_ca_topic_score_gemma":5.9554925e-8,"teacher_disagreement_score":0.72387457,"about_ca_system_score_codex":0.000044669625,"about_ca_system_score_gemma":0.00013234245,"threshold_uncertainty_score":0.34674984},"labels":[],"label_agreement":null},{"id":"W178407265","doi":"10.1007/978-1-4419-1665-5","title":"Handbook of Metaheuristics","year":2010,"lang":"en","type":"book","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1600,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Metaheuristic; Philosophy; Computer science; Artificial intelligence","score_opus":0.047795274687229924,"score_gpt":0.3949777228414696,"score_spread":0.34718244815423965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W178407265","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010221227,0.00020436641,0.01037998,0.0029641006,0.0037420113,0.0037143924,0.00015714092,0.00020868935,0.9776072],"genre_scores_gemma":[0.082486,0.013715407,0.1521325,0.00010859775,0.0005080253,0.0025168732,0.0005913069,0.00020152847,0.7477398],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98614675,0.00020677022,0.001760218,0.0015583293,0.008738667,0.0015892814],"domain_scores_gemma":[0.99477273,0.00022900364,0.00007554522,0.0014114645,0.0031623554,0.0003489269],"candidate_categories":["metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.012710514,0.0005545681,0.00054612354,0.011404358,0.0014978079,0.002827009,0.007042945,0.00025105922,0.0019424472],"category_scores_gemma":[0.0015540365,0.00057837984,0.00013563521,0.005799676,0.0091511365,0.004929994,0.0035510473,0.0021774862,0.00028147662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005330098,0.00031687925,0.00003577051,0.0002578575,0.00011961993,0.00010540815,0.00066860317,0.2763981,0.0010745226,0.71335137,0.0036528858,0.003965688],"study_design_scores_gemma":[0.0016472157,0.00024464386,0.000624404,0.0021650179,0.000040765266,0.000041490734,0.004135163,0.5473665,0.0029894612,0.042009335,0.3972454,0.0014905918],"about_ca_topic_score_codex":0.00014509913,"about_ca_topic_score_gemma":0.002649293,"teacher_disagreement_score":0.6713421,"about_ca_system_score_codex":0.0033304014,"about_ca_system_score_gemma":0.000791559,"threshold_uncertainty_score":0.9998021},"labels":[],"label_agreement":null},{"id":"W1795817323","doi":"10.1016/j.ejor.2015.10.006","title":"The bipartite quadratic assignment problem and extensions","year":2015,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Northwestern Polytechnical University; National Natural Science Foundation of China","keywords":"Tabu search; Quadratic assignment problem; Quadratic equation; Bipartite graph; Mathematics; Simple (philosophy); Mathematical optimization; Exponential function; Local search (optimization); Time complexity; Binary number; Polynomial; Exponential family; Combinatorial optimization; Algorithm; Computer science; Combinatorics; Applied mathematics","score_opus":0.13391053166741307,"score_gpt":0.3505228172317594,"score_spread":0.2166122855643463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1795817323","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12132313,0.011323526,0.47187048,0.030728796,0.0012343375,0.0015914252,0.0000064348155,0.00019432536,0.36172754],"genre_scores_gemma":[0.9799109,0.000194856,0.018897042,0.00003589044,0.00014379671,0.0000027946323,7.6391746e-7,0.000019540363,0.0007944185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99870306,0.00032183525,0.00027062342,0.000045007695,0.0005166597,0.00014281408],"domain_scores_gemma":[0.99901897,0.00022562842,0.000022466864,0.00007046664,0.00047339613,0.00018907232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041605597,0.0000473758,0.00006352362,0.000061320345,0.00018692906,0.00024684012,0.00011602373,0.000007655454,0.000021000531],"category_scores_gemma":[0.0005879864,0.00002840755,0.000017694783,0.0001183763,0.00008314822,0.00013799133,0.000041731706,0.00024066304,0.000057230678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013120016,0.0003077569,0.00024138394,0.00013497988,0.00027113626,0.00037511496,0.006549938,0.42158175,0.00822299,0.20385839,0.23134564,0.12697974],"study_design_scores_gemma":[0.0024417173,0.0013009798,0.0010129391,0.00035661133,0.000029213968,0.0005549812,0.0035145697,0.44801188,0.0008377567,0.011833047,0.5297079,0.00039838356],"about_ca_topic_score_codex":2.0602693e-7,"about_ca_topic_score_gemma":5.554286e-7,"teacher_disagreement_score":0.85858774,"about_ca_system_score_codex":0.00003442239,"about_ca_system_score_gemma":0.000060522798,"threshold_uncertainty_score":0.23802851},"labels":[],"label_agreement":null},{"id":"W1832590774","doi":"","title":"La sélection de portefeuille à laide du Goal Programming imprécis : intégration des préférences du gestionnaire","year":2007,"lang":"fr","type":"article","venue":"ASAC","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Political science; Humanities; Philosophy","score_opus":0.01058738135514629,"score_gpt":0.24848656928709584,"score_spread":0.23789918793194956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1832590774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08787265,0.0016291556,0.8985127,0.0011824378,0.0008301501,0.0003767889,0.0000020243735,0.0005846968,0.00900938],"genre_scores_gemma":[0.87286526,0.0003864304,0.12521315,0.00003989722,0.00044985095,0.000035635763,0.000017920405,0.00004981455,0.0009420562],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984993,0.000072196075,0.00044309488,0.00022959667,0.00020358141,0.00055224216],"domain_scores_gemma":[0.9992064,0.00020991193,0.00009909182,0.00013966614,0.00013481935,0.00021011457],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011347544,0.00023268079,0.00019650717,0.00013402916,0.00028211443,0.00019724238,0.00010837634,0.00028884347,0.00025173894],"category_scores_gemma":[0.00049189513,0.00025200367,0.00010248732,0.00047577475,0.00019054268,0.0004961072,0.00003577832,0.00029620228,0.00008324311],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000890013,0.0001920227,0.006293283,0.00040099377,0.000059078502,0.00003667279,0.0033895483,0.0074047884,0.0003011515,0.013074651,0.00079702976,0.9680419],"study_design_scores_gemma":[0.002423055,0.0008686005,0.09297509,0.0019254918,0.0006511514,0.0012159973,0.008764296,0.5554323,0.00997842,0.023732565,0.29964545,0.0023875607],"about_ca_topic_score_codex":0.0003063648,"about_ca_topic_score_gemma":0.0006141542,"teacher_disagreement_score":0.9656543,"about_ca_system_score_codex":0.00031322945,"about_ca_system_score_gemma":0.000060879778,"threshold_uncertainty_score":0.9999932},"labels":[],"label_agreement":null},{"id":"W1882825307","doi":"10.1007/s10479-015-2007-1","title":"Supply chain management through the stochastic goal programming model","year":2015,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Goal programming; Stochastic programming; Supply chain; Theory of computation; Computer science; Supply chain management; Mathematical optimization; Function (biology); Operations research; Programming paradigm; Mathematics; Algorithm; Business; Marketing","score_opus":0.23925848199437783,"score_gpt":0.42602666382515814,"score_spread":0.1867681818307803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1882825307","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005439249,0.0004632303,0.9652766,0.0034211345,0.00006082211,0.0011502432,0.0000075049397,0.00014906151,0.024032142],"genre_scores_gemma":[0.94223386,0.00006424331,0.05592829,0.000062824525,0.00003589099,0.00025812179,0.000013550855,0.000024498067,0.0013787062],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882525,0.000055087665,0.00021857875,0.00010948318,0.00047558895,0.00031603582],"domain_scores_gemma":[0.99920815,0.000050606417,0.000006409994,0.00026445743,0.00038259753,0.000087767454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009370104,0.000079343954,0.00009943442,0.000095311254,0.0001617926,0.00012540654,0.00023690939,0.00003580472,0.000025716061],"category_scores_gemma":[0.00014707206,0.000058380567,0.000035306028,0.00042759892,0.00011780562,0.00019525157,0.000090380134,0.00018422412,0.00004962585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003224822,0.000051348514,4.554633e-7,0.000040562445,0.000025281883,8.568858e-7,0.0015184479,0.9055613,0.00002041559,0.08245277,0.0032457607,0.0070795673],"study_design_scores_gemma":[0.00013356423,0.000039882667,0.0000015159777,0.000027388694,0.0000040464424,0.0000012060938,0.0014927267,0.9911557,0.00024166143,0.00408156,0.0027535001,0.00006728286],"about_ca_topic_score_codex":0.000017949944,"about_ca_topic_score_gemma":0.000013056521,"teacher_disagreement_score":0.93679464,"about_ca_system_score_codex":0.000018442486,"about_ca_system_score_gemma":0.000034971035,"threshold_uncertainty_score":0.23806904},"labels":[],"label_agreement":null},{"id":"W1886666858","doi":"10.1139/cjce-2012-0212","title":"Rough approximation-based random model for quarry location and stone materials transportation problem","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Mathematical optimization; Computer science; Operator (biology); Nonlinear system; Genetic algorithm; Fuzzy logic; Scale (ratio); Nonlinear programming; Mathematics; Artificial intelligence","score_opus":0.00821702361592906,"score_gpt":0.18203145276124974,"score_spread":0.1738144291453207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1886666858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0137557415,0.0001768486,0.98538095,0.00012804817,0.000111347035,0.00035451478,0.000008906027,0.000038643335,0.000045024004],"genre_scores_gemma":[0.9279223,0.000005849909,0.07191175,0.000016975171,0.0000391346,0.00004767052,0.00001485458,0.000029801186,0.000011628454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993499,0.000004418191,0.00034960566,0.000055268323,0.00006925646,0.00017155605],"domain_scores_gemma":[0.9994434,0.000055239183,0.000058351365,0.00006791295,0.00015738279,0.0002177027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017314714,0.00010277254,0.00017497837,0.00016783451,0.000035495057,0.000084196436,0.00006966841,0.000056134617,0.000046491674],"category_scores_gemma":[0.000087884364,0.000104183826,0.000031610496,0.00009238242,0.000011563935,0.00029358,6.267956e-7,0.00006387655,0.0000011785432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025558566,0.0000027532767,0.0000055996934,0.00043603935,0.000015492948,4.81226e-7,0.00039040946,0.9961724,0.0014355517,0.00078299805,0.0001930061,0.0005626821],"study_design_scores_gemma":[0.0006958549,0.000015809412,0.000054996635,0.00013392359,0.000023178432,0.0000032796793,0.000028503984,0.99700856,0.00087345747,0.0009433585,0.00010496124,0.00011409832],"about_ca_topic_score_codex":0.0000644124,"about_ca_topic_score_gemma":0.0017198743,"teacher_disagreement_score":0.91416657,"about_ca_system_score_codex":0.000058149973,"about_ca_system_score_gemma":0.00009153836,"threshold_uncertainty_score":0.42484933},"labels":[],"label_agreement":null},{"id":"W1889768074","doi":"","title":"On the Pareto Optimality in Goal Programming","year":2007,"lang":"en","type":"article","venue":"ASAC","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Goal programming; Pareto principle; Mathematical optimization; Computer science; Mathematics; Mathematical economics","score_opus":0.013157132423274445,"score_gpt":0.24485732095524657,"score_spread":0.2317001885319721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1889768074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4024111,0.00016687677,0.46644413,0.00085810194,0.00042503473,0.0010228403,0.0000015904408,0.0010386753,0.12763162],"genre_scores_gemma":[0.9894474,0.0000024633755,0.010349468,0.000099411955,0.000020850588,0.000013921206,0.0000013212594,0.000012566372,0.000052570806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949664,0.000008487977,0.00013866348,0.00006752164,0.00008967131,0.00019899182],"domain_scores_gemma":[0.99967515,0.00015104073,0.000009624927,0.00011793933,0.000008853693,0.000037384772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041482245,0.00006513273,0.00006608237,0.000029322122,0.00002714757,0.000026890819,0.00007347629,0.000035842597,0.000061826526],"category_scores_gemma":[0.000086929904,0.00004640755,0.000023501949,0.00015493095,0.000019370835,0.0000364498,0.000011063178,0.00012245796,0.000056897436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003514991,0.0003563885,0.0021346114,0.00022568734,0.00005897238,0.000058334885,0.0023638417,0.082371876,0.00030301904,0.41621694,0.0027670548,0.49310812],"study_design_scores_gemma":[0.0025501784,0.00037285665,0.014901383,0.00056109915,0.000053555123,0.000024055813,0.0035257663,0.7687739,0.01087563,0.037954234,0.1583697,0.002037657],"about_ca_topic_score_codex":0.0000035231744,"about_ca_topic_score_gemma":0.00001696178,"teacher_disagreement_score":0.686402,"about_ca_system_score_codex":0.000028720953,"about_ca_system_score_gemma":0.000002577446,"threshold_uncertainty_score":0.1892445},"labels":[],"label_agreement":null},{"id":"W1905923734","doi":"10.1002/mcda.1466","title":"The Stochastic Goal Programming Model: Theory and Applications","year":2012,"lang":"en","type":"article","venue":"Journal of Multi-Criteria Decision Analysis","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Certainty; Computer science; Decision maker; Stochastic programming; Operations research; Goal programming; Stochastic modelling; Management science; Mathematical economics; Mathematical optimization; Mathematics; Economics","score_opus":0.018157295886323775,"score_gpt":0.30989797519897205,"score_spread":0.2917406793126483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1905923734","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005701807,0.0019375546,0.99203587,0.00002656554,0.00009242065,0.00012557638,0.0000018073134,0.000037845457,0.000040577575],"genre_scores_gemma":[0.82545596,0.00014963438,0.17419687,0.00002106891,0.00009406322,0.000015432724,0.0000013224056,0.000018873858,0.00004676017],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988483,0.00005789451,0.0005483886,0.000072963696,0.00024906074,0.00022337309],"domain_scores_gemma":[0.9987313,0.0005935611,0.00013515133,0.0001855785,0.00014672262,0.00020769521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015831422,0.000120508696,0.00026606306,0.00024640406,0.00016345238,0.00017224981,0.00017294048,0.000054281852,0.000030968287],"category_scores_gemma":[0.00031283704,0.00007693921,0.00019421952,0.0004901105,0.00005529032,0.00023796766,0.000040646923,0.000157775,0.0000067341157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005807787,0.00020515364,0.000111042216,0.00002762246,0.0009396512,0.0000012070489,0.00083019654,0.41517606,0.0003584079,0.0045194943,0.00016893423,0.5776041],"study_design_scores_gemma":[0.00031204917,0.000011449814,0.00011818618,0.000017284572,0.0006916773,0.000018319743,0.0003868905,0.9942735,0.00001841675,0.0014512656,0.0025918183,0.0001091396],"about_ca_topic_score_codex":2.288764e-7,"about_ca_topic_score_gemma":0.0000016091051,"teacher_disagreement_score":0.8197542,"about_ca_system_score_codex":0.00003209427,"about_ca_system_score_gemma":0.000007858049,"threshold_uncertainty_score":0.31374902},"labels":[],"label_agreement":null},{"id":"W1956780501","doi":"","title":"Aggregate production planning in an imprecise environment through the goal programming and the satisfaction functions","year":2008,"lang":"en","type":"article","venue":"ASAC","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Production (economics); Aggregate (composite); Aggregate planning; Computer science; Production planning; Goal programming; Process management; Management science; Operations research; Economics; Mathematics; Engineering; Microeconomics","score_opus":0.018148050940111755,"score_gpt":0.22893083663578015,"score_spread":0.21078278569566838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1956780501","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87417585,0.0011500634,0.1209619,0.000945362,0.00042786443,0.0011393529,0.0000010753473,0.00034272682,0.0008558289],"genre_scores_gemma":[0.9949249,0.0002023749,0.00456252,0.000022027762,0.000067634195,0.000114274975,0.0000037220593,0.000015298943,0.00008725897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994665,0.000035273206,0.0001480502,0.00011601729,0.00009668252,0.00013752759],"domain_scores_gemma":[0.9997494,0.00004615459,0.00002568894,0.000148564,0.0000068009185,0.000023393326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001507069,0.00008448879,0.00008240922,0.000021012524,0.00021247994,0.000035704103,0.00003983625,0.00003366157,0.0000138238365],"category_scores_gemma":[0.000024597013,0.00005203302,0.00002003589,0.00009562781,0.00011693328,0.00030133702,0.000015460831,0.0001476837,0.00001060933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008804524,0.000117844684,0.009405133,0.000093924595,0.0000768031,0.000009618892,0.028656168,0.6097974,0.0005507058,0.0024648833,0.00064388465,0.34809557],"study_design_scores_gemma":[0.0062856707,0.00034868048,0.07540814,0.0003338156,0.0002486162,0.0005341485,0.014730993,0.8154166,0.0017905685,0.0078111943,0.07558083,0.0015107947],"about_ca_topic_score_codex":0.000020826654,"about_ca_topic_score_gemma":0.000013240725,"teacher_disagreement_score":0.34658477,"about_ca_system_score_codex":0.000029212244,"about_ca_system_score_gemma":0.0000032844491,"threshold_uncertainty_score":0.2121845},"labels":[],"label_agreement":null},{"id":"W1963636653","doi":"10.1139/x10-179","title":"Using preference information in developing alternative forest plans","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multiple-criteria decision analysis; Preference; Preference elicitation; Context (archaeology); Computer science; Variety (cybernetics); Operations research; Group decision-making; Process (computing); Management science; Function (biology); Mathematics; Economics; Artificial intelligence; Statistics; Geography; Psychology","score_opus":0.12085289201064812,"score_gpt":0.33662517212653137,"score_spread":0.21577228011588323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963636653","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9487582,0.000020947615,0.04703,0.00012418929,0.0002745467,0.0001375265,0.0000043557743,0.000008161533,0.0036420622],"genre_scores_gemma":[0.98335624,0.000010315794,0.016541895,0.00001259752,0.00005652219,0.0000024827546,0.0000025092916,0.000008992986,0.000008422985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990801,0.000023141662,0.00029767773,0.000037761318,0.00022191538,0.00033938815],"domain_scores_gemma":[0.999201,0.00008256637,0.00003804129,0.0000764114,0.00029963546,0.00030232905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007137609,0.00006300207,0.000101773665,0.0007513694,0.00007741393,0.0001376627,0.00022123585,0.00006610419,0.00004411584],"category_scores_gemma":[0.00044222013,0.00005856067,0.000020987358,0.00034590994,0.00007042716,0.00057608256,0.000010657992,0.00065226475,0.000012947217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020148276,0.000017976508,0.17528534,0.000376838,0.000055408847,0.00018150754,0.0066197366,0.6778048,0.0002756719,0.11834318,0.0008240169,0.020195367],"study_design_scores_gemma":[0.0014327071,0.00014470184,0.07428889,0.00089878135,0.000009790401,0.00033041058,0.0013465997,0.8497223,0.0011285464,0.034099642,0.03606456,0.0005330762],"about_ca_topic_score_codex":0.001427976,"about_ca_topic_score_gemma":0.1652894,"teacher_disagreement_score":0.1719175,"about_ca_system_score_codex":0.0001929163,"about_ca_system_score_gemma":0.00062835985,"threshold_uncertainty_score":0.8499419},"labels":[],"label_agreement":null},{"id":"W1964676846","doi":"10.4236/ajor.2011.14024","title":"Solving Bilevel Linear Multiobjective Programming Problems","year":2011,"lang":"en","type":"article","venue":"American Journal of Operations Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Bilevel optimization; Mathematical optimization; Linear programming; Decision maker; Pareto principle; Set (abstract data type); Pareto optimal; Computer science; Linear-fractional programming; Multiobjective programming; Mathematics; Resolution (logic); Multi-objective optimization; Optimization problem; Operations research; Artificial intelligence","score_opus":0.10967015286265903,"score_gpt":0.3538734093731688,"score_spread":0.24420325651050978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964676846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14273241,0.00026897952,0.84768736,0.00015964988,0.00022903395,0.00076070224,0.0000033454417,0.00017417474,0.0079843635],"genre_scores_gemma":[0.6926812,0.000056833996,0.30707014,0.0000073070464,0.000066050525,0.000022885655,8.1419574e-7,0.000024067524,0.00007071249],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868405,0.00011075175,0.00041116547,0.00009372566,0.00037770692,0.00032259553],"domain_scores_gemma":[0.99874836,0.000099199264,0.000037267673,0.00013645872,0.0008126678,0.00016603418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009880494,0.00009509427,0.00019897173,0.0003745774,0.00018457287,0.000085931315,0.00021228516,0.000028693801,0.00012657481],"category_scores_gemma":[0.00034898028,0.00007865444,0.0000648874,0.0007724771,0.00025140028,0.0003390519,0.00003535572,0.0004885405,0.00003509285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004107261,0.00064005173,0.0005070441,0.00013454047,0.0003100927,0.00004481035,0.0298766,0.27863777,0.004604815,0.0035054614,0.00029904273,0.6813987],"study_design_scores_gemma":[0.000965432,0.002105522,0.0005549016,0.00034546066,0.00004272552,0.00022759794,0.015511943,0.9673388,0.0061862883,0.00053121365,0.0056652552,0.0005248505],"about_ca_topic_score_codex":0.000053860073,"about_ca_topic_score_gemma":0.000021927039,"teacher_disagreement_score":0.68870103,"about_ca_system_score_codex":0.00008382089,"about_ca_system_score_gemma":0.00008944294,"threshold_uncertainty_score":0.3207435},"labels":[],"label_agreement":null},{"id":"W1966664777","doi":"10.5539/mas.v6n3p2","title":"A General Approach for Solving Assignment Problems Involving with Fuzzy Cost Coefficients","year":2012,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Assignment problem; Generalized assignment problem; Weapon target assignment problem; Mathematical optimization; Fuzzy number; Fuzzy logic; Mathematics; Quadratic assignment problem; Linear bottleneck assignment problem; Computer science; Function (biology); Optimization problem; Fuzzy set; Artificial intelligence","score_opus":0.02617851153748724,"score_gpt":0.23368535288512907,"score_spread":0.20750684134764183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966664777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038123834,0.000063172665,0.9805144,0.0000054573793,0.00006811391,0.0009686374,0.0000016292983,0.00023793693,0.014328289],"genre_scores_gemma":[0.7663065,0.0000015684504,0.23320426,0.00003447183,0.00003553674,0.00033812108,0.000004582031,0.000024147386,0.000050788556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998601,0.000002720124,0.00016489426,0.00023116273,0.00036551963,0.00063467585],"domain_scores_gemma":[0.9995151,0.000022906632,0.00003306194,0.00018496758,0.000040303345,0.00020364205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054516125,0.00014547154,0.0001362913,0.00008023886,0.00025756558,0.0001478881,0.00023110794,0.00003661837,0.0000044885496],"category_scores_gemma":[0.000014628042,0.00011791282,0.00002038873,0.00032599719,0.00015114987,0.00024445757,0.000056017525,0.00008416541,0.0000060101506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005343574,0.00015294578,0.00007338815,0.00018445139,0.000009820629,7.5731336e-8,0.0022307057,0.8672975,0.10227156,0.015815044,0.00005597577,0.011903219],"study_design_scores_gemma":[0.0002811146,0.000011847064,0.000013435266,0.000013126093,0.000008472603,0.0000018188787,0.000113612514,0.99506843,0.0037507147,0.00032244893,0.00022848119,0.0001864796],"about_ca_topic_score_codex":6.7406796e-7,"about_ca_topic_score_gemma":3.6334677e-7,"teacher_disagreement_score":0.76249415,"about_ca_system_score_codex":0.00012436831,"about_ca_system_score_gemma":0.000025231022,"threshold_uncertainty_score":0.48083454},"labels":[],"label_agreement":null},{"id":"W1970535376","doi":"10.1007/s10479-008-0419-x","title":"An XML-based schema for stochastic programs","year":2008,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Schema (genetic algorithms); XML; Theory of computation; XML Schema (W3C); Programming language; XML Schema Editor; Document Structure Description; Theoretical computer science; Document type definition; Information retrieval; World Wide Web","score_opus":0.3896662908511025,"score_gpt":0.46968170142637294,"score_spread":0.08001541057527045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970535376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.102881715,0.00014310678,0.8939166,0.000716781,0.000035373334,0.0011364513,0.0000119882725,0.00020069028,0.0009573149],"genre_scores_gemma":[0.9426778,0.00001582262,0.056708653,0.000022763415,0.000034059518,0.00029498094,0.00005124522,0.000022740529,0.00017191053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916023,0.000031766383,0.0001892205,0.00010671091,0.00024940775,0.00026265156],"domain_scores_gemma":[0.9989449,0.00008934853,0.0000050262843,0.00021252898,0.00063763774,0.00011059793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000450055,0.00006529947,0.00010821335,0.00015400552,0.00020880086,0.00004899219,0.00013618135,0.00004826057,0.000052337662],"category_scores_gemma":[0.00024101349,0.00006141965,0.000042526157,0.00032644594,0.00009817407,0.00015882848,0.000009457076,0.00010627709,0.00001719855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000128999945,0.00037347098,0.000011348659,0.00015290976,0.000021883727,0.000001133832,0.00040791,0.9766981,0.0026451147,0.008456768,0.0013272584,0.00989123],"study_design_scores_gemma":[0.00018742894,0.00021655265,0.00000958826,0.000024624735,0.0000016678904,0.0000013254491,0.00007415874,0.98863804,0.009761803,0.00017102867,0.00083947234,0.000074297335],"about_ca_topic_score_codex":0.000010222441,"about_ca_topic_score_gemma":0.000016245269,"teacher_disagreement_score":0.8397961,"about_ca_system_score_codex":0.000009881555,"about_ca_system_score_gemma":0.000067726294,"threshold_uncertainty_score":0.25046206},"labels":[],"label_agreement":null},{"id":"W1973531844","doi":"10.3168/jds.s0022-0302(06)72233-0","title":"Modeling Small-Scale Dairy Farms in Central Mexico Using Multi-Criteria Programming","year":2006,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Forage; Agricultural science; Production (economics); Linear programming; Gross margin; Scale (ratio); Goal programming; Herd; Dairy cattle; Agricultural engineering; Business; Mathematics; Economics; Environmental science; Engineering; Operations research; Agronomy; Animal science; Biology; Geography; Mathematical optimization","score_opus":0.033195995780763515,"score_gpt":0.27804017874099135,"score_spread":0.24484418296022784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973531844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49138555,0.00011960594,0.507925,0.00002110714,0.00031238233,0.000075892356,4.4656267e-7,0.00003828696,0.00012175221],"genre_scores_gemma":[0.68096477,0.0000049208365,0.3189368,0.000010956878,0.00006361794,7.601526e-7,3.108547e-7,0.000011806996,0.0000060724283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844193,0.000016729016,0.0005992812,0.00013440165,0.00032875818,0.00047888624],"domain_scores_gemma":[0.99953073,0.000018094164,0.000086702225,0.00011203298,0.000105621526,0.00014680516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000715235,0.00012807769,0.00021097418,0.00029728783,0.00009905983,0.00019026626,0.00030914968,0.000048342023,0.000009583042],"category_scores_gemma":[0.00006572409,0.00011355402,0.00006987328,0.00065157155,0.00011043222,0.00069801876,0.000043286545,0.00023251302,0.0000014697364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004164674,0.00008996475,0.0010962584,0.000041809926,0.000002918213,0.000016780747,0.00048155955,0.97857535,0.014610147,0.000060307408,0.0000037019101,0.0050170617],"study_design_scores_gemma":[0.00041513477,0.000019809126,0.000338804,0.00013838502,0.000008232495,0.00006585478,0.00031466398,0.9966893,0.0016233608,0.00017439015,0.00007631757,0.00013570166],"about_ca_topic_score_codex":0.000023399565,"about_ca_topic_score_gemma":0.00002872205,"teacher_disagreement_score":0.18957922,"about_ca_system_score_codex":0.00016873174,"about_ca_system_score_gemma":0.000078596786,"threshold_uncertainty_score":0.46305984},"labels":[],"label_agreement":null},{"id":"W1974926966","doi":"10.1016/s0895-7177(03)00086-4","title":"Goal-optimal pareto solution of multiobjective linear programs and its computing with standard single objective LP software","year":2003,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Mathematical optimization; Pareto principle; Computer science; Linear programming; Software; Pareto optimal; Multi-objective optimization; Set (abstract data type); Mathematics","score_opus":0.017528573626382438,"score_gpt":0.20776368745023208,"score_spread":0.19023511382384964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974926966","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1657905,0.00015792676,0.83328754,0.00000460231,0.000024799927,0.00032992577,0.0000017877271,0.00019667136,0.00020625039],"genre_scores_gemma":[0.54795974,0.0000067107367,0.45197794,0.0000047477906,0.000015469184,0.000007039601,0.0000019373945,0.000020900792,0.0000054983034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895823,0.000031177977,0.00032134895,0.00023199354,0.00018647793,0.00027076076],"domain_scores_gemma":[0.9994601,0.0001415061,0.000058324298,0.000094765644,0.00012881088,0.000116495125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017837026,0.00021157431,0.00036116102,0.00005575801,0.00009899408,0.0000624002,0.00004874687,0.0000806939,0.000004896251],"category_scores_gemma":[0.000020721749,0.00017263816,0.00003948218,0.00013456495,0.000076312834,0.00013351692,0.000038924278,0.00014812461,0.0000020355726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035909597,0.00022305973,0.000056231038,0.0011516376,0.00011876433,0.0000050717463,0.005564947,0.9484278,0.00007417991,0.02612738,0.0000024350343,0.018212566],"study_design_scores_gemma":[0.0004903364,0.00025309302,0.000002068125,0.0003536856,0.000037365913,0.000029186638,0.00021742453,0.9939316,0.0010858628,0.0033643586,0.000020437034,0.00021461071],"about_ca_topic_score_codex":8.6145275e-7,"about_ca_topic_score_gemma":5.624325e-7,"teacher_disagreement_score":0.38216928,"about_ca_system_score_codex":0.000035583933,"about_ca_system_score_gemma":0.000010428384,"threshold_uncertainty_score":0.703998},"labels":[],"label_agreement":null},{"id":"W1978157509","doi":"10.1016/s0898-1221(01)00264-4","title":"A two-phase optimization procedure for integer programming problems","year":2001,"lang":"en","type":"article","venue":"Computers & Mathematics with Applications","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Mathematical optimization; Integer programming; Mathematics; Nonlinear programming; Linear programming; Optimization problem; Integer (computer science); Simple (philosophy); Gradient descent; Nonlinear system; Computer science; Artificial intelligence","score_opus":0.01522489898792866,"score_gpt":0.26510343156140387,"score_spread":0.2498785325734752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978157509","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002170174,0.000066382105,0.9935702,0.00012901226,0.00003636932,0.003276049,0.0000051738134,0.0009945902,0.0017052192],"genre_scores_gemma":[0.02218382,0.000024348496,0.9733078,0.00004895987,0.00008404015,0.004082859,0.00009434446,0.000090764086,0.000083049694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889225,0.00000556776,0.00039015085,0.00023953638,0.00015314523,0.00031935063],"domain_scores_gemma":[0.99917847,0.00011417981,0.00007118119,0.00032911624,0.0001680773,0.00013899173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012696393,0.000233487,0.0002448788,0.000113245485,0.00015600289,0.00015867915,0.0002152185,0.000063443484,0.000018299064],"category_scores_gemma":[0.000018150628,0.00020232907,0.0000617264,0.00049810193,0.00005692289,0.00015908584,0.000025951469,0.00010915929,0.000017312594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000072777198,0.00056064985,0.0000054114653,0.0010478591,0.00007541268,7.850922e-7,0.00084768527,0.9302746,0.000053089618,0.032564066,0.0003786152,0.03418454],"study_design_scores_gemma":[0.0011367709,0.0000759649,1.8897684e-7,0.000141897,0.000060442464,0.000040889907,0.0001758007,0.98084706,0.000059624606,0.0024191912,0.014778251,0.00026393987],"about_ca_topic_score_codex":3.780113e-7,"about_ca_topic_score_gemma":0.0000021583107,"teacher_disagreement_score":0.050572433,"about_ca_system_score_codex":0.00005128763,"about_ca_system_score_gemma":0.00002402871,"threshold_uncertainty_score":0.825074},"labels":[],"label_agreement":null},{"id":"W1978616476","doi":"10.1155/2013/201907","title":"Collaborative Decision-Making in Product Design: An Interactive Multiobjective Approach","year":2013,"lang":"en","type":"article","venue":"Journal of Industrial Engineering","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal; Université du Québec à Trois-Rivières","funders":"","keywords":"Mathematical optimization; Computer science; Compromise; Multi-objective optimization; Pareto principle; Convergence (economics); Linear programming; Product (mathematics); Multiobjective programming; Process (computing); Pareto optimal; Group decision-making; Goal programming; Operations research; Mathematics","score_opus":0.022233138726763337,"score_gpt":0.2584301544068281,"score_spread":0.23619701568006476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978616476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08111526,0.00012897828,0.9167366,0.000017706117,0.00076760456,0.0006661817,0.0000011860391,0.00009763058,0.00046886437],"genre_scores_gemma":[0.79578084,0.000007589932,0.20389104,0.0000045299967,0.00026185013,0.000020591393,5.0490905e-7,0.000030760428,0.0000022878642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988493,0.000045779994,0.0005687209,0.00011334882,0.00020321818,0.00021964351],"domain_scores_gemma":[0.999081,0.0003451792,0.00012369294,0.00010216868,0.00023885783,0.00010905837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044106392,0.00017268433,0.00032445684,0.00038253085,0.000022147042,0.00011332458,0.00015633448,0.00010811271,0.00004010974],"category_scores_gemma":[0.0011331968,0.00015228007,0.000050957544,0.00058134104,0.000013134896,0.0010165423,0.00001913347,0.0006047143,0.000005098739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033979704,0.00006938031,0.000022398743,0.000012338363,0.00004519766,0.0000074088102,0.0012002906,0.95522267,0.0008591568,0.00004644274,0.00013161966,0.042349126],"study_design_scores_gemma":[0.001015628,0.00012741091,0.00010958747,0.00039964984,0.000015070163,0.0000420697,0.0012508843,0.99470186,0.0018268328,0.00018507928,0.00010116895,0.0002247579],"about_ca_topic_score_codex":0.0000024944372,"about_ca_topic_score_gemma":4.436351e-7,"teacher_disagreement_score":0.7146656,"about_ca_system_score_codex":0.00021885999,"about_ca_system_score_gemma":0.000051836305,"threshold_uncertainty_score":0.62098014},"labels":[],"label_agreement":null},{"id":"W1979596386","doi":"10.5267/j.uscm.2013.09.003","title":"A fuzzy multi-objective multi-follower linear Bi-level programming problem to supply chain optimization","year":2013,"lang":"en","type":"article","venue":"Uncertain Supply Chain Management","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Linear programming; Supply chain; Mathematical optimization; Fuzzy logic; Computer science; Chain (unit); Mathematics; Business; Artificial intelligence","score_opus":0.021702572576845054,"score_gpt":0.2512654341608052,"score_spread":0.22956286158396016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979596386","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056521234,0.0000956383,0.987046,0.0008038923,0.00032482267,0.0061879475,0.000022036114,0.0012257323,0.0037287408],"genre_scores_gemma":[0.07882264,0.000063868676,0.91162384,0.0004370635,0.000095044204,0.002769104,0.00015850457,0.00017534259,0.005854572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697655,0.00008469655,0.0007343646,0.00068641163,0.00049935264,0.0010186371],"domain_scores_gemma":[0.99866116,0.00008134524,0.00010002244,0.00054802303,0.0002130121,0.0003964118],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00051558384,0.00058131764,0.00046320705,0.00054551306,0.00021785403,0.00030133687,0.00044511183,0.00017702456,0.00044202863],"category_scores_gemma":[0.0000939791,0.0005660641,0.00016721839,0.0010225312,0.00006291129,0.00037248162,0.0002481066,0.0002739193,0.00047251012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012474029,0.0003706118,0.00014709984,0.00048255976,0.00020405512,0.00001968182,0.0022425991,0.9367267,0.00007673463,0.0017016708,0.0022188704,0.05579693],"study_design_scores_gemma":[0.0017846351,0.00011179308,0.00016867333,0.00025054577,0.00006509903,0.000003093043,0.0022837026,0.9840828,0.00012672896,0.00021381317,0.010094929,0.00081417954],"about_ca_topic_score_codex":0.00014261305,"about_ca_topic_score_gemma":0.00005988935,"teacher_disagreement_score":0.07825743,"about_ca_system_score_codex":0.00034639964,"about_ca_system_score_gemma":0.000018662398,"threshold_uncertainty_score":0.9996791},"labels":[],"label_agreement":null},{"id":"W1980725479","doi":"10.1109/icmit.2008.4654515","title":"A decision framework for location-allocation problems: A case study in tea industry","year":2008,"lang":"en","type":"article","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University","keywords":"Computer science; Analytic hierarchy process; Operations research; Fuzzy logic; Process (computing); Selection (genetic algorithm); Decision support system; Product (mathematics); Hierarchy; Management science; Risk analysis (engineering); Engineering; Artificial intelligence; Business; Mathematics; Economics","score_opus":0.042914596485492965,"score_gpt":0.30192810399588327,"score_spread":0.2590135075103903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980725479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26322925,0.000022200607,0.7353831,0.000023890427,0.00005212775,0.0007954191,2.4251761e-7,0.00017023516,0.0003235565],"genre_scores_gemma":[0.8218731,0.0000034381258,0.17776641,0.000022105445,0.000018533265,0.0002314355,0.0000015601986,0.000016554652,0.00006685184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940866,0.000008609924,0.00025191216,0.00011426621,0.00008729048,0.00012923582],"domain_scores_gemma":[0.9995521,0.00018089658,0.000016470289,0.00014086383,0.00006028406,0.000049436516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014373472,0.00008334824,0.000103149934,0.000093633156,0.00005634412,0.000020422118,0.000051384905,0.00014232165,0.000039434133],"category_scores_gemma":[0.00024048825,0.00007676772,0.000016531989,0.00036385222,0.000011119558,0.00010830998,0.00001100452,0.0001774695,0.000011761078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015168278,0.0013191509,0.0075576333,0.0003386384,0.000043607826,0.00019000562,0.012391109,0.9077232,0.000015280444,0.011221004,0.00075793377,0.05842729],"study_design_scores_gemma":[0.0011786846,0.00013583244,0.00037958866,0.00015618862,0.000014997936,0.00033807836,0.0068442104,0.98226655,0.000063507,0.007934825,0.0003883383,0.00029918543],"about_ca_topic_score_codex":0.000027632987,"about_ca_topic_score_gemma":0.000110485526,"teacher_disagreement_score":0.5586439,"about_ca_system_score_codex":0.000045712855,"about_ca_system_score_gemma":0.000017153094,"threshold_uncertainty_score":0.31304967},"labels":[],"label_agreement":null},{"id":"W1981955289","doi":"10.1023/b:jogo.0000035028.40292.36","title":"An Exact Method for Fractional Goal Programming","year":2004,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal; Polytechnique Montréal","funders":"","keywords":"Mathematics; Quadratic programming; Fractional programming; Mathematical optimization; Goal programming; Sequential quadratic programming; Heuristic; Quadratic equation; Quadratically constrained quadratic program; Linear-fractional programming; Linear programming; Field (mathematics); Nonlinear programming; Nonlinear system","score_opus":0.010637421346521701,"score_gpt":0.3041844384937457,"score_spread":0.29354701714722403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981955289","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041292742,0.00006898844,0.99832433,0.00015189988,0.000348603,0.00019073494,0.000005036436,0.00009771252,0.0003997587],"genre_scores_gemma":[0.09187178,0.00001730447,0.9078322,0.00004854822,0.00019002592,0.0000066055272,0.00001164009,0.000018426197,0.000003436492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912393,0.00001788763,0.00040806946,0.000078136414,0.00020564189,0.00016634497],"domain_scores_gemma":[0.99933994,0.00003478513,0.00015000369,0.000073913914,0.0002673103,0.00013404904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031099902,0.000111108726,0.00017804913,0.00005071824,0.00006135723,0.00009759442,0.000106694504,0.000083616294,0.000031455038],"category_scores_gemma":[0.00010739458,0.00010237508,0.00010382627,0.00021677212,0.000013158852,0.00058761577,0.000005176495,0.00009358332,0.0000019018279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024537765,0.000093780116,0.000020628851,0.00002955577,0.00003528446,0.0000019281308,0.000035580564,0.97524637,0.00005214157,0.0044277096,0.00007457656,0.019957928],"study_design_scores_gemma":[0.0010876607,0.00024229758,0.00003253679,0.00004164402,0.00005646964,0.00011305952,0.000089466994,0.99306875,0.00015297435,0.0027881712,0.0021891082,0.0001378318],"about_ca_topic_score_codex":0.0000014979922,"about_ca_topic_score_gemma":0.0000013928968,"teacher_disagreement_score":0.09145886,"about_ca_system_score_codex":0.00024196762,"about_ca_system_score_gemma":0.000053474778,"threshold_uncertainty_score":0.41747347},"labels":[],"label_agreement":null},{"id":"W1994418144","doi":"10.5539/jmr.v2n4p135","title":"Solving the Multiobjective Two Stage Fuzzy Transportation Problem by Zero Suffix Method","year":2010,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Suffix; Fuzzy logic; Fuzzy number; Fuzzy transportation; Mathematical optimization; Zero (linguistics); Membership function; Fuzzy set; Computer science; Artificial intelligence","score_opus":0.03969795164962393,"score_gpt":0.38096736576963824,"score_spread":0.3412694141200143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994418144","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04091526,0.00014368595,0.9513976,0.000307245,0.00018843322,0.0005068176,0.00000848729,0.00006717106,0.0064653093],"genre_scores_gemma":[0.46942317,0.00007178605,0.5297852,0.000011586592,0.00008990311,0.000020793888,0.0000026462264,0.00005544486,0.0005394556],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806,0.00010387268,0.00061024877,0.000087838765,0.0008091044,0.00032892035],"domain_scores_gemma":[0.9978736,0.0011044408,0.00014924463,0.00020214303,0.0005351986,0.00013537101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044078366,0.00012553493,0.00024353033,0.00017947002,0.00016238226,0.00016104583,0.00034298393,0.00008271229,0.00013393663],"category_scores_gemma":[0.00045998208,0.00008275508,0.00010484084,0.00032783556,0.00008640913,0.00023806572,0.000016706203,0.0012836203,0.000018906147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007492733,0.0021741993,0.000137327,0.0042027375,0.00087937404,0.00008389798,0.06605116,0.12566169,0.48106068,0.2089955,0.026262665,0.08441583],"study_design_scores_gemma":[0.0029818167,0.00037997772,0.00006240466,0.0005932342,0.0001407778,0.00014742084,0.010028955,0.71268696,0.060822487,0.19550231,0.01598172,0.0006719119],"about_ca_topic_score_codex":0.000008004353,"about_ca_topic_score_gemma":0.000028584083,"teacher_disagreement_score":0.5870253,"about_ca_system_score_codex":0.00005055052,"about_ca_system_score_gemma":0.00005078041,"threshold_uncertainty_score":0.55767626},"labels":[],"label_agreement":null},{"id":"W1996400852","doi":"10.1002/mcda.447","title":"Decision‐maker's preferences modelling within the goal‐programming model: a new typology","year":2009,"lang":"en","type":"article","venue":"Journal of Multi-Criteria Decision Analysis","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Laurentian University","funders":"","keywords":"Typology; Decision maker; Goal programming; Preference; Computer science; Operations research; Artificial intelligence; Management science; Machine learning; Economics; Mathematics; Microeconomics; Sociology","score_opus":0.04376550800103314,"score_gpt":0.3173666911509759,"score_spread":0.27360118314994275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996400852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04155771,0.0007787549,0.9568507,0.00019311703,0.00029800754,0.00012027266,0.000001848502,0.00007975346,0.000119806886],"genre_scores_gemma":[0.51646376,0.00012963083,0.48318464,0.000097112104,0.000060922797,0.0000011535681,0.0000015965917,0.000015218858,0.000045972014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742657,0.00006969179,0.0013746005,0.0002167975,0.0005998003,0.00031256428],"domain_scores_gemma":[0.9982363,0.00041341988,0.0003569881,0.0003881113,0.00030710068,0.00029808286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010961763,0.00026972822,0.0007039164,0.00064683176,0.00014643809,0.00032578106,0.00057059,0.0001618961,0.00016107826],"category_scores_gemma":[0.00041275815,0.00017126123,0.0005454644,0.0012325613,0.000044985438,0.0003469387,0.00004353891,0.00040634884,0.000016138158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005433301,0.00007745746,0.00001574015,0.0000042726488,0.00029643433,0.000009264904,0.0005723006,0.8715151,0.00009582628,0.00025009026,0.00041835633,0.12669085],"study_design_scores_gemma":[0.0006519144,0.000072504816,0.00003839369,0.00007762941,0.0006655757,0.000030920855,0.00030643775,0.98691785,0.000059929873,0.010318492,0.00066988566,0.00019048135],"about_ca_topic_score_codex":0.000007135899,"about_ca_topic_score_gemma":0.000033218912,"teacher_disagreement_score":0.47490606,"about_ca_system_score_codex":0.00006149837,"about_ca_system_score_gemma":0.00005555276,"threshold_uncertainty_score":0.69838303},"labels":[],"label_agreement":null},{"id":"W1998488507","doi":"10.1007/s12597-009-0024-z","title":"A simplex algorithm for network flow problems with piecewise linear fractional objective function","year":2009,"lang":"en","type":"article","venue":"OPSEARCH","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of New Brunswick","funders":"","keywords":"Piecewise linear function; Simplex algorithm; Linear-fractional programming; Linear programming; Simplex; Mathematics; Algorithm; Flow network; Criss-cross algorithm; Mathematical optimization; Function (biology); Flow (mathematics); Revised simplex method; Computer science; Mathematical analysis; Combinatorics; Geometry","score_opus":0.016767459184671568,"score_gpt":0.25869578936088833,"score_spread":0.24192833017621676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998488507","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015192323,0.000053054926,0.9963821,0.00009608466,0.00008000808,0.00052486965,0.000006155462,0.00024401306,0.002461787],"genre_scores_gemma":[0.10427539,0.00002379717,0.89358056,0.00015462848,0.0007428364,0.00022568897,0.000112500195,0.000059072863,0.00082552194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992664,0.000008295411,0.00012352178,0.00013720944,0.00017556599,0.00028901905],"domain_scores_gemma":[0.99960995,0.00008151855,0.00001491335,0.00009341615,0.000117288546,0.00008293447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016362927,0.00010214517,0.00011347829,0.00004319893,0.0001108983,0.000045895496,0.0000492137,0.00005755251,0.000109451146],"category_scores_gemma":[0.000018563156,0.00008754286,0.000037188343,0.00023861599,0.000018077855,0.000130406,0.0000065675276,0.0001501437,0.000027115533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024617075,0.00005206909,0.000005291765,0.000036278398,0.000036569287,8.1241905e-7,0.000089711146,0.60671055,0.00003165621,0.0007773983,0.0014354535,0.39079958],"study_design_scores_gemma":[0.00042695642,0.0002583238,0.000078475605,0.000025379803,0.000013429545,0.000004079357,0.000031677715,0.9803774,0.000051728413,0.00335309,0.015262101,0.000117406635],"about_ca_topic_score_codex":0.0000013832826,"about_ca_topic_score_gemma":0.000002326932,"teacher_disagreement_score":0.39068216,"about_ca_system_score_codex":0.000044958106,"about_ca_system_score_gemma":0.000022520495,"threshold_uncertainty_score":0.35698944},"labels":[],"label_agreement":null},{"id":"W2000238862","doi":"10.1007/s10666-014-9441-3","title":"Development of an Improved Fuzzy Robust Chance-Constrained Programming Model for Air Quality Management","year":2015,"lang":"en","type":"article","venue":"Environmental Modeling & Assessment","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Fuzzy logic; Mathematical optimization; Computer science; Operations research; Quality (philosophy); Robust optimization; Engineering; Artificial intelligence; Mathematics","score_opus":0.05943022732978743,"score_gpt":0.2967075953785811,"score_spread":0.23727736804879368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000238862","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10379186,0.00002803749,0.8942364,0.0000140698985,0.00008828024,0.00093559216,0.000011771018,0.00019520227,0.0006988137],"genre_scores_gemma":[0.49589384,0.000004100908,0.5037247,0.0000115429875,0.000012312444,0.0002239813,0.000067387286,0.000030342728,0.000031802854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983115,0.00001853272,0.0006662811,0.00030626904,0.00033036174,0.00036705946],"domain_scores_gemma":[0.99939936,0.00001214081,0.00008878599,0.00026833496,0.000016285612,0.00021507988],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006078417,0.00024989567,0.00028756497,0.000068204354,0.00008829977,0.000029698042,0.00017204425,0.000076556425,0.0000066239],"category_scores_gemma":[0.0000045741804,0.00025918204,0.00007670897,0.00005637686,0.00003870377,0.00021785079,0.000081465754,0.0001042769,0.0000025268512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013606758,0.00029474072,0.0000059279278,0.00019680969,0.000065949134,3.941521e-7,0.0009472952,0.9221419,0.0011724923,0.00080527493,0.0000022149313,0.07435337],"study_design_scores_gemma":[0.0010267149,0.00005238439,0.000008979023,0.000035015288,0.000033646105,7.2201647e-7,0.0021568646,0.9951126,0.0005286005,0.0006544834,0.00008947774,0.00030050855],"about_ca_topic_score_codex":0.0000014216546,"about_ca_topic_score_gemma":0.0000037397979,"teacher_disagreement_score":0.39210197,"about_ca_system_score_codex":0.00036012367,"about_ca_system_score_gemma":0.000035460354,"threshold_uncertainty_score":0.99998605},"labels":[],"label_agreement":null},{"id":"W2001887693","doi":"10.1109/icmsao.2013.6552647","title":"Generating maximal efficient faces for the multiobjective multicommodity flow problem","year":2013,"lang":"en","type":"article","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Adjacency list; Mathematical optimization; Flow (mathematics); Computer science; Flow network; Space (punctuation); Mathematics; Multi-commodity flow problem; Minimum-cost flow problem; Algorithm","score_opus":0.014141374820394039,"score_gpt":0.22395485196296563,"score_spread":0.2098134771425716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001887693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01072684,0.00005880062,0.9856155,0.00014382732,0.000105939056,0.0011482547,0.0000021836163,0.00033357617,0.0018650683],"genre_scores_gemma":[0.5342323,0.0000015964232,0.4651772,0.000041328807,0.000035283236,0.0003615657,0.0000026157136,0.000015143649,0.0001329457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949974,0.0000080913605,0.00014574238,0.00009000262,0.00007333458,0.00018306414],"domain_scores_gemma":[0.9995821,0.00019778845,0.000014273155,0.00010081761,0.00006432934,0.00004065807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009945136,0.00009281626,0.00008711953,0.00001660095,0.00013946243,0.000106784246,0.000079240046,0.000033494838,0.00017106095],"category_scores_gemma":[0.000053938424,0.0000568046,0.000044490218,0.00005409946,0.000022441303,0.000061568855,0.000021333812,0.00006504576,0.00005586784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.6505587e-7,0.000022327802,0.0000032623402,0.000032294625,0.000016638185,2.9944395e-8,0.0004521959,0.9555029,0.0005782098,0.0006351861,0.0004431366,0.0423132],"study_design_scores_gemma":[0.00020498494,0.000009648224,0.000021842583,0.000005655067,0.0000075970306,4.353169e-7,0.00027321512,0.9974026,0.0013787311,0.00012391442,0.00048292417,0.000088426204],"about_ca_topic_score_codex":0.000015405623,"about_ca_topic_score_gemma":0.0000072467096,"teacher_disagreement_score":0.52350545,"about_ca_system_score_codex":0.000022607112,"about_ca_system_score_gemma":0.0000034537359,"threshold_uncertainty_score":0.23164244},"labels":[],"label_agreement":null},{"id":"W2003435156","doi":"10.1016/s0377-2217(00)00304-0","title":"PariTOP: A goal programming-based software for real estate assessment","year":2001,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Québec Metro High Tech Park (Canada); Modellium (Canada); Université Laval","funders":"","keywords":"Computer science; Software; Software engineering; Programming language; Operations research; Mathematics","score_opus":0.07453186034599547,"score_gpt":0.3809128863409613,"score_spread":0.30638102599496586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003435156","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015128581,0.00004374311,0.9662268,0.0008449567,0.00019095342,0.00048099898,0.0000069681364,0.000087270586,0.016989732],"genre_scores_gemma":[0.42900988,0.00021234943,0.5697113,0.00004523469,0.00046183905,0.000026628537,0.000032750097,0.00007383495,0.00042614824],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983046,0.00022824839,0.00043436317,0.00009800149,0.00063381996,0.00030099097],"domain_scores_gemma":[0.9983092,0.00033446713,0.000044700526,0.00010221519,0.0010248078,0.00018459598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030442188,0.000095637675,0.00013825482,0.00017717344,0.00017765503,0.0002628004,0.00021662438,0.000019870104,0.00011477782],"category_scores_gemma":[0.0005445057,0.00008083178,0.000074999494,0.00026476564,0.000060646034,0.00018744812,0.00002462061,0.00034699496,0.000022579146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019100544,0.0004923697,0.00086894975,0.00026527396,0.00013333454,0.00039295762,0.00048827878,0.40747255,0.0010752354,0.0037563115,0.013078673,0.5717851],"study_design_scores_gemma":[0.0033147198,0.0015145523,0.0020709895,0.00022814418,0.000027097261,0.00014171998,0.00034022648,0.48179492,0.00033894926,0.00028211108,0.5095745,0.00037206346],"about_ca_topic_score_codex":0.0000011093205,"about_ca_topic_score_gemma":0.0000019887445,"teacher_disagreement_score":0.571413,"about_ca_system_score_codex":0.00009896247,"about_ca_system_score_gemma":0.0001865042,"threshold_uncertainty_score":0.32962242},"labels":[],"label_agreement":null},{"id":"W2004862272","doi":"10.1016/j.amc.2008.06.034","title":"Post-optimality analysis of priority vectors derived from interval comparison matrices by lexicographic goal programming","year":2008,"lang":"en","type":"article","venue":"Applied Mathematics and Computation","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Lexicographical order; Interval (graph theory); Ranking (information retrieval); Mathematics; Goal programming; Matrix (chemical analysis); Reciprocal; Interval arithmetic; Preference; Order (exchange); Linear programming; Mathematical optimization; Computer science; Combinatorics; Algorithm; Statistics; Artificial intelligence","score_opus":0.014982284959775017,"score_gpt":0.2513182937700201,"score_spread":0.23633600881024505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004862272","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5732021,0.00014002527,0.42617422,0.0000051653205,0.000019025836,0.0001712136,0.000012520777,0.00012996569,0.00014578507],"genre_scores_gemma":[0.7748983,0.000040278876,0.22491127,0.000006328626,0.000007122022,0.00001607526,0.0001042376,0.00001541633,9.924591e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894196,0.000010098762,0.00051881414,0.00017103853,0.00020190215,0.00015619384],"domain_scores_gemma":[0.99941045,0.0001691617,0.00016225336,0.000115964634,0.00006765101,0.00007453287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012428111,0.00016822483,0.0004702835,0.00014491561,0.000082068145,0.0000467924,0.000083648425,0.000078778336,0.000013619979],"category_scores_gemma":[0.000014803144,0.00016420525,0.00009612271,0.0005355167,0.000084172985,0.000072124094,0.000034587607,0.00010051811,0.000002258112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014121231,0.0041833385,0.011149381,0.004689068,0.008965016,0.000009796371,0.054993186,0.47661862,0.07020553,0.020873977,0.00045149215,0.3477194],"study_design_scores_gemma":[0.00030185745,0.000038494753,0.0020705094,0.00002406684,0.0004991016,0.0000011314926,0.0007138912,0.99347764,0.0016243431,0.0010048762,0.000027898253,0.00021618558],"about_ca_topic_score_codex":0.000025264599,"about_ca_topic_score_gemma":0.000007434569,"teacher_disagreement_score":0.51685905,"about_ca_system_score_codex":0.000016798323,"about_ca_system_score_gemma":0.0000063978755,"threshold_uncertainty_score":0.66960955},"labels":[],"label_agreement":null},{"id":"W2008695031","doi":"10.3138/infor.50.3.106","title":"Portfolio Selection with Multiple Time Horizons: A Mean Variance—Stochastic Goal Programming Approach","year":2012,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Portfolio; Time horizon; Mathematical optimization; Generalization; Horizon; Selection (genetic algorithm); Context (archaeology); Variance (accounting); Portfolio optimization; Stochastic programming; Arrow; Reliability (semiconductor); Modern portfolio theory; Computer science; Risk aversion (psychology); Mathematics; Econometrics; Mathematical economics; Economics; Expected utility hypothesis; Finance","score_opus":0.026234109610019873,"score_gpt":0.2757100540266441,"score_spread":0.24947594441662424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008695031","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009559702,0.00020432215,0.96086407,0.000029959294,0.00016230682,0.0020554627,0.00001888546,0.00038655938,0.026718713],"genre_scores_gemma":[0.97957253,0.0000074337677,0.019161051,0.000017082377,0.00018945211,0.0005121255,0.00016819368,0.000018665478,0.00035348372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983519,0.00003844233,0.0004221219,0.00008263359,0.0006736169,0.0004312897],"domain_scores_gemma":[0.99907386,0.00012397314,0.00004509818,0.00010019767,0.0004495419,0.00020735241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012185628,0.00014035552,0.00015961607,0.00028505805,0.00032652158,0.0005887217,0.00007873476,0.000099715515,0.000036041423],"category_scores_gemma":[0.00014845522,0.000111745096,0.000021143152,0.0004770754,0.00006282104,0.0026886174,0.00002990138,0.00024385343,0.00015672861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013725301,0.00020620546,0.0013307862,0.0016557071,0.0002517296,6.7499275e-7,0.010242135,0.7639013,0.00021085628,0.17550094,0.0045700083,0.041992404],"study_design_scores_gemma":[0.00050548045,0.00008340917,0.00017204903,0.00006128111,0.00000713304,0.00007683798,0.0011799642,0.96476567,0.000023123057,0.000010199478,0.03292494,0.0001898915],"about_ca_topic_score_codex":0.000031806056,"about_ca_topic_score_gemma":0.0000016954682,"teacher_disagreement_score":0.9700128,"about_ca_system_score_codex":0.000105529536,"about_ca_system_score_gemma":0.0000682359,"threshold_uncertainty_score":0.5677057},"labels":[],"label_agreement":null},{"id":"W2011446372","doi":"10.1016/j.apm.2014.12.022","title":"An improved multi-choice goal programming approach for supplier selection problems","year":2014,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":123,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Goal programming; Purchasing; Mathematical optimization; Selection (genetic algorithm); Computer science; Operations research; Interval (graph theory); Minification; Linear programming; Control (management); Mathematics; Operations management; Artificial intelligence; Economics","score_opus":0.023833072171402844,"score_gpt":0.24282065346095896,"score_spread":0.21898758128955612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011446372","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000659286,0.000012704343,0.991511,0.000006269509,0.000037000762,0.0020852142,0.0000020529603,0.0012377987,0.004448682],"genre_scores_gemma":[0.34678245,0.0000017833631,0.65191376,0.000021211054,0.000106811174,0.0009879451,0.000043286487,0.00010543447,0.000037296686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810123,0.000017779817,0.000572021,0.00045503996,0.0002087289,0.00064520695],"domain_scores_gemma":[0.99907726,0.0002064682,0.000071997856,0.00031316298,0.00007839998,0.00025270446],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005507009,0.00035519473,0.00044339194,0.00009564203,0.00018934098,0.00020364254,0.00023267392,0.00023212128,0.000016683101],"category_scores_gemma":[0.000045757155,0.00032891316,0.00011030918,0.00021605959,0.000054941433,0.00019559906,0.000024837827,0.0002698303,0.00001810711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000813857,0.00041970046,9.872211e-7,0.00145986,0.000035851892,2.2089637e-8,0.00041071596,0.9110433,0.0027226568,0.0746286,0.00001135695,0.009258853],"study_design_scores_gemma":[0.0008182149,0.000056008506,1.5152122e-7,0.00002417488,0.00006299471,0.0000030565736,0.00008489952,0.98022,0.00087673264,0.016856901,0.00058640045,0.00041046308],"about_ca_topic_score_codex":0.0000018308641,"about_ca_topic_score_gemma":7.224625e-7,"teacher_disagreement_score":0.34612316,"about_ca_system_score_codex":0.00005553278,"about_ca_system_score_gemma":0.000009368564,"threshold_uncertainty_score":0.9999163},"labels":[],"label_agreement":null},{"id":"W2014193286","doi":"10.1023/b:fodm.0000036866.18909.f1","title":"Matrix Games with Fuzzy Goals and Fuzzy Linear Programming Duality","year":2004,"lang":"en","type":"article","venue":"Fuzzy Optimization and Decision Making","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Duality (order theory); Fuzzy logic; Dual (grammatical number); Mathematical optimization; Mathematics; Linear programming; Matrix (chemical analysis); Fuzzy number; Fuzzy associative matrix; Fuzzy set operations; Algebra over a field; Computer science; Fuzzy set; Artificial intelligence; Discrete mathematics; Pure mathematics","score_opus":0.013539111936895953,"score_gpt":0.2800384306364021,"score_spread":0.26649931869950616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014193286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020642122,0.0007754189,0.9740038,0.0000925056,0.000117404285,0.00049710565,0.000004986096,0.0006283431,0.0032383106],"genre_scores_gemma":[0.35872626,0.00022600433,0.6408071,0.00007776367,0.000048165173,0.000022533515,0.000011718859,0.00004986552,0.000030605832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984319,0.000024270454,0.00047750995,0.00037300683,0.00034115542,0.00035211563],"domain_scores_gemma":[0.9991873,0.00015942637,0.00009511944,0.0002414165,0.00012549963,0.00019125752],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032444304,0.0002915785,0.00033436943,0.00020646468,0.00023125678,0.00034008687,0.000099387784,0.000150384,0.000035825986],"category_scores_gemma":[0.000143616,0.00024692147,0.00004500455,0.00046327157,0.00009702286,0.0004087041,0.00007980787,0.00018312875,0.000012368499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000349879,0.00004123051,0.00023412557,0.0001298197,0.000024741015,0.000009994262,0.00030273138,0.9250632,0.00001104302,0.0073108324,0.000038565347,0.066798724],"study_design_scores_gemma":[0.0045770262,0.0002863348,0.00043013782,0.0013466899,0.00014606936,0.00021143418,0.0010714519,0.9621585,0.00010594414,0.025028914,0.0032641871,0.0013732974],"about_ca_topic_score_codex":0.000002532982,"about_ca_topic_score_gemma":0.0000067826045,"teacher_disagreement_score":0.33808416,"about_ca_system_score_codex":0.000048239417,"about_ca_system_score_gemma":0.000022767157,"threshold_uncertainty_score":0.99999833},"labels":[],"label_agreement":null},{"id":"W2014231587","doi":"10.5539/mas.v3n9p84","title":"Optimal Programming Models for Portfolio Selection with Uncertain Chance Constraint","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Portfolio; Mathematical optimization; Constraint (computer-aided design); Maximization; Computer science; Selection (genetic algorithm); Profit maximization; Profit (economics); Order (exchange); Mathematics; Economics; Finance; Artificial intelligence; Microeconomics","score_opus":0.017524524380848817,"score_gpt":0.2416400442603717,"score_spread":0.22411551987952288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014231587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003874805,0.00001613689,0.98748696,0.000040601186,0.00002466604,0.00063657865,9.776736e-7,0.00046050604,0.0074587604],"genre_scores_gemma":[0.6601087,0.0000015957264,0.33971107,0.000041299834,0.000017106935,0.000082337916,0.0000016614695,0.000010481637,0.00002577511],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988868,0.0000017480265,0.00015726537,0.00027368136,0.00025143492,0.0004290458],"domain_scores_gemma":[0.9996412,0.000014611561,0.00003188922,0.00012069861,0.00007377877,0.000117844844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026383242,0.00013730295,0.00013419213,0.00009174028,0.00019132324,0.00013453451,0.00016573314,0.00003935515,0.0000052839177],"category_scores_gemma":[0.00000863271,0.000119554585,0.000022105636,0.0004620621,0.00016362207,0.0002393935,0.000009742989,0.000086922024,0.0000021568262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010516628,0.00002813796,5.126948e-7,0.000022282393,0.00000323109,4.1056495e-7,0.0003083227,0.8449166,0.015671987,0.032067798,0.000011367428,0.10695882],"study_design_scores_gemma":[0.00023661785,0.00006418204,0.000002135726,0.000014647089,0.0000065156332,0.000008043561,0.00007165855,0.986974,0.0051405984,0.0071699214,0.00013911827,0.0001725344],"about_ca_topic_score_codex":5.557984e-7,"about_ca_topic_score_gemma":9.2842936e-7,"teacher_disagreement_score":0.65623385,"about_ca_system_score_codex":0.00007538712,"about_ca_system_score_gemma":0.000054911045,"threshold_uncertainty_score":0.48752943},"labels":[],"label_agreement":null},{"id":"W2017428041","doi":"10.3329/ganit.v33i0.17660","title":"An Improved Decomposition Approach and its Computational Technique for Analyzing Primal-Dual Relationship in LP &amp; LFP Problems","year":2014,"lang":"en","type":"article","venue":"GANIT Journal of Bangladesh Mathematical Society","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Dual polyhedron; Dual (grammatical number); Linear programming; Decomposition; Decomposition method (queueing theory); Mathematical optimization; Computation; Computer science; Mathematics; Algorithm; Applied mathematics; Discrete mathematics; Combinatorics; Chemistry","score_opus":0.021823145500673442,"score_gpt":0.27346529454738905,"score_spread":0.2516421490467156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017428041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025333412,0.00007533641,0.9736748,0.00008511183,0.000020157453,0.0005097105,0.0000033843437,0.000064966305,0.00023315141],"genre_scores_gemma":[0.5043118,0.00000485422,0.49554864,0.000018095514,0.000045065866,0.000029188317,0.0000138754785,0.000022083126,0.000006366481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875253,0.00005550626,0.0006650879,0.00012770492,0.00018283022,0.00021631732],"domain_scores_gemma":[0.9989779,0.00046880968,0.00015911474,0.00008469239,0.00017005872,0.00013940211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012593421,0.00015533423,0.00032334772,0.00008217721,0.00009197513,0.00009612353,0.00010068553,0.00015436788,0.000019042927],"category_scores_gemma":[0.00023573582,0.0001391635,0.00013274566,0.00021592125,0.000039909813,0.00033250218,0.00001609455,0.00030778325,0.0000019555453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088611865,0.0024494543,0.0010192429,0.0113742035,0.00047645392,0.0000020604882,0.015100208,0.67519265,0.06759692,0.22027847,0.000760321,0.0056613795],"study_design_scores_gemma":[0.0007361402,0.00010061257,0.00015272564,0.00015543106,0.000052437692,0.00008404463,0.00013501677,0.9269367,0.00033455767,0.07105773,0.000069599875,0.00018503392],"about_ca_topic_score_codex":1.4774315e-7,"about_ca_topic_score_gemma":4.018693e-7,"teacher_disagreement_score":0.4789784,"about_ca_system_score_codex":0.00007882992,"about_ca_system_score_gemma":0.000017515482,"threshold_uncertainty_score":0.5674923},"labels":[],"label_agreement":null},{"id":"W2022253420","doi":"10.1007/s10732-008-9093-z","title":"Special issue on mathematical contributions to metaheuristics editorial","year":2008,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Metaheuristic; Computer science; Mathematical optimization; Heuristics; Optimization problem; Scheduling (production processes); Combinatorial optimization; Parallel metaheuristic; Embedding; Mathematics; Algorithm; Artificial intelligence; Meta-optimization","score_opus":0.012329317977126508,"score_gpt":0.2686253448139557,"score_spread":0.2562960268368292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022253420","genre_codex":"methods","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001573736,0.000051608862,0.8914358,0.0007335934,0.07208193,0.0003229936,0.00007909635,0.00016404559,0.033557184],"genre_scores_gemma":[0.057570647,0.00021211291,0.2263952,0.00031872667,0.71432483,0.0000074370223,0.000007838897,0.000118109245,0.0010450851],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986511,0.000022344837,0.0005917899,0.00006238834,0.00046340752,0.00020899613],"domain_scores_gemma":[0.99882406,0.00028234336,0.00008867641,0.0001264003,0.00040958982,0.00026890036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024181517,0.00013384757,0.0003360465,0.00012276926,0.0000834839,0.00003291077,0.00014851792,0.00009345297,0.0002924599],"category_scores_gemma":[0.0022670468,0.00011347463,0.00010681855,0.00016016678,0.000042047333,0.000053415715,0.00001822634,0.0003360713,0.00033597532],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031058033,0.0001670535,0.0000031698512,0.00004167002,0.00005528527,0.00009217998,0.00022371099,0.018550383,0.000039749604,0.007199924,0.9725163,0.0010794947],"study_design_scores_gemma":[0.000551201,0.00027279076,0.000009477002,0.000057691042,0.00007683613,0.00017255274,0.000032295447,0.0055599324,0.00042663142,0.004853532,0.98781097,0.00017607701],"about_ca_topic_score_codex":9.6077414e-8,"about_ca_topic_score_gemma":9.717555e-8,"teacher_disagreement_score":0.6650406,"about_ca_system_score_codex":0.00009050369,"about_ca_system_score_gemma":0.00004180957,"threshold_uncertainty_score":0.4627361},"labels":[],"label_agreement":null},{"id":"W2022952894","doi":"10.3138/infor.50.3.134","title":"A Recourse Goal Programming Approach for the Portfolio Selection Problem","year":2012,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Portfolio; Selection (genetic algorithm); Mathematical optimization; Stochastic programming; Goal programming; Computer science; Index (typography); Dynamic programming; Portfolio optimization; Modern portfolio theory; Post-modern portfolio theory; Expected return; Actuarial science; Operations research; Econometrics; Replicating portfolio; Economics; Mathematics; Financial economics; Artificial intelligence","score_opus":0.05204912035603266,"score_gpt":0.32489149592884015,"score_spread":0.2728423755728075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022952894","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012007897,0.00053853047,0.9647829,0.00015421532,0.00027469263,0.0037049777,0.000018222609,0.00022523926,0.029100452],"genre_scores_gemma":[0.9429976,0.000070002956,0.052818473,0.000057238944,0.00040626826,0.0027284594,0.00018072118,0.00002092662,0.00072032656],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872077,0.00002609017,0.00039757864,0.000054999433,0.00043814315,0.00036241696],"domain_scores_gemma":[0.99910504,0.0001961353,0.00003730441,0.00008258333,0.00046363976,0.000115272276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002241045,0.00009614546,0.000101363454,0.00016228991,0.00047739272,0.000618932,0.00008608457,0.00008314812,0.00001726789],"category_scores_gemma":[0.000145218,0.00006621533,0.00003052249,0.00030294998,0.000049465045,0.0018771798,0.0000249436,0.00018496602,0.000025277672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049744038,0.00009060825,0.000860978,0.0018571056,0.00015869594,5.6553926e-8,0.0059363195,0.26188943,0.000049253653,0.5278502,0.025057815,0.17619981],"study_design_scores_gemma":[0.0002171661,0.000028463564,0.00007938237,0.000016265485,0.0000052372907,0.000018716344,0.0017041258,0.6716751,0.000022490689,0.000021715558,0.32612446,0.00008686665],"about_ca_topic_score_codex":0.000022547509,"about_ca_topic_score_gemma":0.0000011274302,"teacher_disagreement_score":0.9417968,"about_ca_system_score_codex":0.0000688073,"about_ca_system_score_gemma":0.00004801348,"threshold_uncertainty_score":0.5968376},"labels":[],"label_agreement":null},{"id":"W2030995871","doi":"10.1007/s13675-014-0020-9","title":"Branch-and-price-and-cut for large-scale multicommodity capacitated fixed-charge network design","year":2014,"lang":"en","type":"article","venue":"EURO Journal on Computational Optimization","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Column generation; Mathematics; Mathematical optimization; Lagrangian relaxation; Flow network; Fixed charge; Relaxation (psychology); Scale (ratio); Flow (mathematics)","score_opus":0.018767236074810463,"score_gpt":0.23482112105970493,"score_spread":0.21605388498489447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030995871","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012364436,0.00005646738,0.9972929,0.00035040668,0.0003106345,0.00029952184,0.0000073770434,0.00015648326,0.00028979487],"genre_scores_gemma":[0.23098494,0.000063066465,0.7680709,0.00046070878,0.00025036617,0.000015368618,0.00005533162,0.000052854288,0.000046471796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989108,0.00011560631,0.00036712684,0.00015612185,0.00019387293,0.0002564668],"domain_scores_gemma":[0.99874127,0.0007338625,0.00010926478,0.00006621382,0.0001819997,0.00016736571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066055846,0.00017187509,0.00020766155,0.000096599986,0.00031474026,0.00016877757,0.00006895055,0.00006976942,0.000045065804],"category_scores_gemma":[0.00022546707,0.00016565179,0.000050939154,0.00014523121,0.000023887125,0.00021554364,0.000016177688,0.00019689869,0.000009825107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026655056,0.000039469483,0.000020438101,0.00003572661,0.00002549868,5.026171e-7,0.00015137214,0.99158686,0.000005836347,0.004702344,0.0012657874,0.0021394962],"study_design_scores_gemma":[0.0010722211,0.00012533627,0.000090533045,0.000048248974,0.000023111155,0.000029583889,0.0000076777005,0.99238616,0.000017719784,0.004490466,0.0015321979,0.00017673921],"about_ca_topic_score_codex":1.13081505e-7,"about_ca_topic_score_gemma":2.251355e-7,"teacher_disagreement_score":0.22974849,"about_ca_system_score_codex":0.000034684148,"about_ca_system_score_gemma":0.0000121515,"threshold_uncertainty_score":0.6755084},"labels":[],"label_agreement":null},{"id":"W2033740771","doi":"10.1007/s10732-010-9132-4","title":"Special issue on recent advances in metaheuristics","year":2010,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Metaheuristic; Computer science; Artificial intelligence","score_opus":0.008997029815665738,"score_gpt":0.2597852151168496,"score_spread":0.25078818530118385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033740771","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0296855,0.0035821781,0.23662725,0.0018652149,0.06372537,0.00082484557,0.000036201644,0.0003605619,0.6632929],"genre_scores_gemma":[0.39485008,0.029148124,0.513834,0.000606561,0.060351767,0.000006982278,0.0000087731705,0.00023859419,0.00095509103],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992035,0.0000114796685,0.0004156988,0.000044738637,0.00019297768,0.00013161791],"domain_scores_gemma":[0.9995219,0.000121508754,0.000087432,0.00008515083,0.00009963707,0.00008434732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002170767,0.00008830032,0.0001941529,0.000114550174,0.000016886841,0.000027991064,0.000109070745,0.000055020053,0.0004024022],"category_scores_gemma":[0.0006617299,0.00007430365,0.000039191236,0.00012696366,0.000024194538,0.00008359686,0.00000844046,0.00047371097,0.000035380483],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007754731,0.0005875668,0.00031547583,0.00031542094,0.000056380275,0.000406613,0.0005195449,0.14121194,0.00033578137,0.021746852,0.076814204,0.7576127],"study_design_scores_gemma":[0.00027640822,0.00007106554,0.000049266713,0.000029094635,0.000015746908,0.000028415476,0.000027807355,0.0099314,0.00031427894,0.0024815274,0.98668844,0.000086566695],"about_ca_topic_score_codex":4.770553e-8,"about_ca_topic_score_gemma":0.0000034674597,"teacher_disagreement_score":0.9098742,"about_ca_system_score_codex":0.00002351111,"about_ca_system_score_gemma":0.000014038733,"threshold_uncertainty_score":0.44060218},"labels":[],"label_agreement":null},{"id":"W2035108481","doi":"10.1287/mnsc.1070.0848","title":"Interactive Coordination of Objective Decompositions in Multiobjective Programming","year":2008,"lang":"en","type":"article","venue":"Management Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Automotive Research Center; University of Michigan","keywords":"Mathematical optimization; Decision maker; Selection (genetic algorithm); Decomposition; Multiobjective programming; Computer science; Multi-objective optimization; Sensitivity (control systems); Portfolio; Linear programming; Function (biology); Mathematics; Operations research; Artificial intelligence; Economics; Engineering","score_opus":0.009861900291178453,"score_gpt":0.2510570572414315,"score_spread":0.24119515695025306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035108481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27994806,0.000023946299,0.63205934,0.000049535472,0.00023376134,0.0010796573,0.000001502156,0.0002856405,0.08631855],"genre_scores_gemma":[0.9449843,0.0000070383635,0.054891743,0.0000064467054,0.000003156955,0.000048000737,0.0000010497272,0.0000052044766,0.000053036387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993887,0.000008565163,0.00015097353,0.00013109234,0.00016832034,0.00015237232],"domain_scores_gemma":[0.9997721,0.000029966102,0.00002800714,0.00008633433,0.00005411333,0.000029485767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015962466,0.00006216616,0.00008119915,0.0003269793,0.0000818202,0.000016658909,0.0001185342,0.0000123435,0.0000101424275],"category_scores_gemma":[0.00003205361,0.00006391764,0.000019006733,0.00095201225,0.00017451777,0.00036819838,0.00004655138,0.000055577213,0.0000076347205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048266847,0.0016049857,0.008423832,0.0007093611,0.00015212597,0.00008352688,0.035012968,0.5559478,0.009925323,0.0987418,0.00022094639,0.28912908],"study_design_scores_gemma":[0.0010422328,0.00009735483,0.04469522,0.00020801721,0.000020180782,0.000011771581,0.0051220544,0.9305936,0.01564212,0.001830888,0.0003632878,0.0003732607],"about_ca_topic_score_codex":0.0000070246756,"about_ca_topic_score_gemma":0.0000053286976,"teacher_disagreement_score":0.66503626,"about_ca_system_score_codex":0.00013137361,"about_ca_system_score_gemma":0.000006346293,"threshold_uncertainty_score":0.26064858},"labels":[],"label_agreement":null},{"id":"W2035305502","doi":"10.1057/jors.2012.111","title":"An interval-coefficient fuzzy binary linear programming, the solution, and its application under uncertainties","year":2012,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Interval (graph theory); Mathematical optimization; Fuzzy logic; Binary number; Mathematics; Linear programming; Fuzzy number; Computer science; Fuzzy set; Applied mathematics; Artificial intelligence","score_opus":0.05726540508139351,"score_gpt":0.3608952675132997,"score_spread":0.3036298624319062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035305502","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5519722,0.0076044174,0.4128666,0.023567889,0.00095516775,0.0020910345,0.000010258593,0.00013574529,0.0007966787],"genre_scores_gemma":[0.9927939,0.00017299557,0.006390505,0.00012442977,0.00031093226,0.000023302993,0.0000025260292,0.000013691252,0.00016768131],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875623,0.00012969019,0.00024561098,0.00005990808,0.0005717917,0.00023677341],"domain_scores_gemma":[0.999096,0.00014254374,0.000048106736,0.00012472567,0.00047891805,0.000109710476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024340504,0.00007376318,0.00008483394,0.00002851435,0.0004556456,0.0001145988,0.00024221608,0.000053621465,0.000017574388],"category_scores_gemma":[0.00014926615,0.00003958491,0.00009106289,0.00024326854,0.00014091973,0.0003577417,0.0000732197,0.00040791984,0.0000055568325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003999835,0.00089376396,0.0006426527,0.00029806758,0.00034795122,4.3385387e-7,0.016109977,0.8254018,0.0270485,0.08648297,0.025871165,0.016862739],"study_design_scores_gemma":[0.00024928185,0.00009746215,0.00081319275,0.000052955296,0.000020873491,0.00002965802,0.0029757668,0.97135,0.0012919997,0.0008458397,0.022167142,0.00010581734],"about_ca_topic_score_codex":0.0000022318964,"about_ca_topic_score_gemma":0.0000011637208,"teacher_disagreement_score":0.4408217,"about_ca_system_score_codex":0.00011713342,"about_ca_system_score_gemma":0.000060086077,"threshold_uncertainty_score":0.35045037},"labels":[],"label_agreement":null},{"id":"W2037044553","doi":"10.1007/s00170-012-4469-5","title":"Model and algorithm for fuzzy joint replenishment and delivery scheduling without explicit membership function","year":2012,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Mathematical optimization; Fuzzy logic; Piecewise; Computer science; Fuzzy number; Scheduling (production processes); Ranking (information retrieval); Fuzzy set; Algorithm; Mathematics","score_opus":0.023828985167893717,"score_gpt":0.25053684613483745,"score_spread":0.22670786096694373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037044553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48069352,0.00063946703,0.51743627,0.0006158482,0.0003254655,0.00012044126,0.0000015864919,0.00008921053,0.00007819239],"genre_scores_gemma":[0.73200446,0.00026236076,0.26755986,0.00005421059,0.00007880586,0.000011028038,7.2170934e-7,0.000013760048,0.000014791804],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937814,0.0000046587775,0.00026090353,0.000070284084,0.00013156484,0.00015442484],"domain_scores_gemma":[0.9996197,0.000052855135,0.000117264615,0.00007963311,0.00008186341,0.000048645707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024756914,0.00009895315,0.00013952349,0.0001612599,0.00005108269,0.000029435632,0.0001318597,0.00006199614,0.00000402126],"category_scores_gemma":[0.000049417817,0.0000751462,0.000034925448,0.0000248491,0.000038966817,0.00026532233,0.000066317494,0.00017959665,5.500782e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097873315,0.000059164802,0.000057192563,0.000075710195,0.00035878774,0.0000024974015,0.0004826423,0.25629884,0.017277274,0.014659294,0.000061877414,0.71056885],"study_design_scores_gemma":[0.0018593993,0.00016487546,0.00009055349,0.00020043724,0.00012524003,0.00048819155,0.0013701195,0.67172545,0.20723341,0.11426096,0.0021609904,0.00032039674],"about_ca_topic_score_codex":3.365187e-7,"about_ca_topic_score_gemma":5.5920134e-7,"teacher_disagreement_score":0.7102485,"about_ca_system_score_codex":0.00005795167,"about_ca_system_score_gemma":0.000005801626,"threshold_uncertainty_score":0.3064373},"labels":[],"label_agreement":null},{"id":"W2039870850","doi":"10.2139/ssrn.1360248","title":"Polynomial Goal Programming and the Implicit Higher Moment Preferences of U.S. Institutional Investors in Hedge Funds","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Hedge fund; Institutional investor; Alternative beta; Moment (physics); Business; Hedge accounting; Economics; Mathematics; Global assets under management; Financial economics; Accounting; Finance; Open-end fund; Corporate governance; Physics","score_opus":0.007924493455207123,"score_gpt":0.22009507733110228,"score_spread":0.21217058387589516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039870850","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9645928,0.0057900995,0.023823705,0.0012079164,0.0002580784,0.00044445408,0.0000010820798,0.000092063325,0.003789779],"genre_scores_gemma":[0.99832654,0.00063232024,0.00085584517,0.00002693685,0.0000721062,0.000007085343,0.0000011027033,0.0000067596798,0.000071329225],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988354,0.00002707168,0.00027871598,0.00007485327,0.00013736404,0.00064662687],"domain_scores_gemma":[0.99979573,0.00002684616,0.0000496112,0.000058068217,0.00002007323,0.000049643517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061551755,0.00010056854,0.00016071576,0.00008455737,0.000070749826,0.00004157112,0.00010972935,0.000047697034,0.000009457889],"category_scores_gemma":[0.000014276043,0.00006851678,0.000042879554,0.00015097711,0.00009666986,0.00012522678,0.0000128155,0.0005712941,9.989456e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105431805,0.00012970137,0.0008772874,0.00004568893,0.000119860946,0.0000015337445,0.0010173325,0.008807171,0.00036322346,0.6402433,0.00007517431,0.34821427],"study_design_scores_gemma":[0.02871236,0.002922887,0.035381332,0.0006726835,0.00035141723,0.0010811896,0.0051741116,0.08001261,0.0012255466,0.8045473,0.037661415,0.0022571578],"about_ca_topic_score_codex":0.0000117916225,"about_ca_topic_score_gemma":0.000058541256,"teacher_disagreement_score":0.34595713,"about_ca_system_score_codex":0.00021960441,"about_ca_system_score_gemma":0.00017588623,"threshold_uncertainty_score":0.27940336},"labels":[],"label_agreement":null},{"id":"W2041943131","doi":"10.5267/j.msl.2011.05.005","title":"A BSC method for supplier selection strategy using TOPSIS and VIKOR: A case study of part maker industry","year":2011,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"TOPSIS; VIKOR method; Selection (genetic algorithm); Computer science; Operations research; Supplier evaluation; Operations management; Business; Process management; Supply chain management; Supply chain; Mathematics; Marketing; Economics; Artificial intelligence","score_opus":0.06245540750417248,"score_gpt":0.3049574885431869,"score_spread":0.2425020810390144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041943131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49905813,0.0000012621706,0.49977002,0.000016341182,0.00003954576,0.00039399587,3.6561946e-7,0.00003926532,0.00068110006],"genre_scores_gemma":[0.7637973,3.710585e-7,0.23605405,0.00008271519,0.000007506575,0.000031776544,1.5922312e-7,0.0000069835246,0.000019143305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994095,0.000010251327,0.00013853297,0.00015804083,0.00011459394,0.00016908033],"domain_scores_gemma":[0.99982125,0.000010638776,0.000025867685,0.00009026732,0.000010799404,0.00004118251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000353566,0.00007101893,0.00008433429,0.00013495162,0.000089710076,0.000044477074,0.000066735876,0.000023954306,0.000020542986],"category_scores_gemma":[0.0000049577397,0.00006717754,0.000013111876,0.0003437193,0.00005041705,0.000167427,0.000034995734,0.00005838867,3.04138e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045789453,0.0014488078,0.008435112,0.0021617238,0.0005194797,0.0003379763,0.02437566,0.7971499,0.018050896,0.008574219,0.0019746604,0.13692576],"study_design_scores_gemma":[0.0005615954,0.00009221051,0.0002726019,0.00001757058,0.00011417674,0.00006900142,0.011490015,0.98585474,0.0010291254,0.00011032783,0.0001947546,0.00019388607],"about_ca_topic_score_codex":0.000038572365,"about_ca_topic_score_gemma":0.000005103766,"teacher_disagreement_score":0.2647392,"about_ca_system_score_codex":0.000025692256,"about_ca_system_score_gemma":0.0000014796801,"threshold_uncertainty_score":0.27394205},"labels":[],"label_agreement":null},{"id":"W2043472740","doi":"10.3926/jiem.451","title":"Workforce scheduling: A new model incorporating human factors","year":2012,"lang":"en","type":"article","venue":"Journal of Industrial Engineering and Management","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Workforce; Overtime; Human resources; Originality; Workforce management; Scheduling (production processes); Human resource management; Engineering; Operations management; Operations research; Computer science; Labour economics; Economics; Knowledge management; Psychology; Management; Social psychology","score_opus":0.06319480430748918,"score_gpt":0.25411539664196403,"score_spread":0.19092059233447484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043472740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18569632,0.000488969,0.8103715,0.00005696017,0.00078927184,0.00019471018,5.0444197e-7,0.00017753296,0.0022242328],"genre_scores_gemma":[0.9393603,0.000056644567,0.060026634,0.000008160604,0.00039103342,0.0000014063867,9.334629e-7,0.000025953437,0.00012890344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999278,0.000004293057,0.0003374845,0.000044224314,0.0001408723,0.00019509932],"domain_scores_gemma":[0.99960095,0.000024167126,0.00007668275,0.00006208839,0.000015776197,0.00022031619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024412801,0.00012378219,0.0001745887,0.00014732579,0.000034669054,0.00005221419,0.000073004965,0.00006990624,0.000005504515],"category_scores_gemma":[0.000047351776,0.000108761145,0.000049393882,0.00012432797,0.0000067409637,0.0002361483,0.00003021252,0.00022094754,8.20246e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021520232,0.000016402426,0.00010956404,0.00006192312,0.00006700812,0.0000016604772,0.00018303757,0.9801194,0.00019439819,0.011841769,0.00040108213,0.0070016207],"study_design_scores_gemma":[0.0016035653,0.00007275584,0.000046739704,0.00058014423,0.00015775595,0.000017287295,0.00041982633,0.98978776,0.00074994616,0.00045663855,0.0057031317,0.00040444912],"about_ca_topic_score_codex":6.8061445e-7,"about_ca_topic_score_gemma":8.6408754e-8,"teacher_disagreement_score":0.753664,"about_ca_system_score_codex":0.000043166663,"about_ca_system_score_gemma":0.0000073808706,"threshold_uncertainty_score":0.4435151},"labels":[],"label_agreement":null},{"id":"W2046861175","doi":"10.1007/s10700-006-0021-0","title":"Acceptable optimality in linear fractional programming with fuzzy coefficients","year":2006,"lang":"en","type":"article","venue":"Fuzzy Optimization and Decision Making","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Linear programming; Mathematical optimization; Fuzzy logic; Fractional programming; Linear-fractional programming; Value (mathematics); Computer science; Nonlinear programming; Nonlinear system; Artificial intelligence; Statistics","score_opus":0.011760272251597197,"score_gpt":0.2648410122389339,"score_spread":0.2530807399873367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046861175","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010329387,0.00014770096,0.9810632,0.000016936292,0.00014740873,0.0003324452,0.0000031162472,0.0002398469,0.0077199633],"genre_scores_gemma":[0.45285696,0.000021292662,0.54694206,0.000026639415,0.000039334867,0.000020231775,0.000024191962,0.000029647186,0.000039636358],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861217,0.000020579899,0.00043855247,0.00028261254,0.00033579412,0.000310311],"domain_scores_gemma":[0.9994488,0.0001464895,0.00006949554,0.00015975643,0.00010239612,0.00007310399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026374403,0.00020071982,0.0002243308,0.00025017312,0.00014460427,0.0002014262,0.000089755995,0.000112206886,0.00019082034],"category_scores_gemma":[0.000060842405,0.00018074669,0.000033582266,0.000642065,0.000047203503,0.00037728457,0.000043054668,0.0001684201,0.000015704949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003041174,0.00008042266,0.001198984,0.000030096566,0.000005530374,0.00000562405,0.000032836506,0.96523064,0.000003844755,0.0009389769,0.000118775395,0.032323834],"study_design_scores_gemma":[0.0007868394,0.000027458667,0.0006314909,0.0001425511,0.0000103803095,0.000015013631,0.000085663996,0.9958003,0.000010544721,0.00086062436,0.0013852934,0.000243836],"about_ca_topic_score_codex":0.0000056875883,"about_ca_topic_score_gemma":0.00001601597,"teacher_disagreement_score":0.44252756,"about_ca_system_score_codex":0.000063630774,"about_ca_system_score_gemma":0.000016875209,"threshold_uncertainty_score":0.7370636},"labels":[],"label_agreement":null},{"id":"W2046874201","doi":"10.5539/jmr.v1n1p31","title":"An Improved Graph Method for Linear Goal Programming","year":2009,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Southwestern University of Finance and Economics","keywords":"Linear programming; Mathematics; Goal programming; Graph; Mathematical optimization; Discrete mathematics","score_opus":0.0699535536938029,"score_gpt":0.43169864414949793,"score_spread":0.36174509045569503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046874201","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031593272,0.0001300678,0.9953139,0.00019620397,0.00008815978,0.00047919765,0.0000015981465,0.000083099956,0.0005484762],"genre_scores_gemma":[0.046693414,0.000040601102,0.95293164,0.000016990482,0.00020403902,0.000013969347,0.0000015944023,0.00003737541,0.00006038026],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824506,0.00006778097,0.0006393965,0.0000968585,0.0005193048,0.00043160588],"domain_scores_gemma":[0.99830043,0.00047057556,0.0001099738,0.00022477173,0.00064684264,0.000247414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040513296,0.00012925154,0.00032650516,0.00036517036,0.00010584482,0.00014894574,0.00035651418,0.00010400949,0.00002337961],"category_scores_gemma":[0.00060258387,0.00010280295,0.00015740076,0.00035699032,0.00003617288,0.00024807535,0.00001447116,0.00046758048,0.0000050661993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013175051,0.0032917825,0.0000063884227,0.0032062766,0.0003686221,0.000043263764,0.0069951694,0.045516748,0.11051318,0.04700767,0.0033829962,0.7795361],"study_design_scores_gemma":[0.0006460216,0.00095867686,0.0000028497927,0.0001267952,0.000028174145,0.00006457309,0.00093615137,0.94785994,0.0055697323,0.040428936,0.0032271533,0.00015098063],"about_ca_topic_score_codex":4.3745828e-7,"about_ca_topic_score_gemma":6.6656906e-7,"teacher_disagreement_score":0.9023432,"about_ca_system_score_codex":0.000054709068,"about_ca_system_score_gemma":0.00004318273,"threshold_uncertainty_score":0.41921827},"labels":[],"label_agreement":null},{"id":"W2047707165","doi":"10.1007/s10957-007-9265-2","title":"Merit-Function Piecewise SQP Algorithm for Mathematical Programs with Equilibrium Constraints","year":2007,"lang":"en","type":"article","venue":"Journal of Optimization Theory and Applications","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Piecewise; Theory of computation; Mathematics; Sequential quadratic programming; Mathematical optimization; Complementarity theory; Constraint (computer-aided design); Complementarity (molecular biology); Stationary point; Point (geometry); Function (biology); Independence (probability theory); Piecewise linear function; Algorithm; Quadratic programming; Nonlinear system; Mathematical analysis","score_opus":0.008362994103040132,"score_gpt":0.2365910185940447,"score_spread":0.22822802449100454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047707165","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020820668,0.00013183671,0.9974306,0.00003800383,0.000043521115,0.0005709292,0.0000036158874,0.00009120907,0.0014820592],"genre_scores_gemma":[0.059077576,0.000045050278,0.9403702,0.00005141521,0.00018633793,0.000092860566,0.000023654662,0.000042318854,0.000110565314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991521,0.00001852734,0.00043874752,0.00009199426,0.00013051415,0.00016808328],"domain_scores_gemma":[0.99913347,0.0002449438,0.00015034765,0.00009816247,0.00023133322,0.00014177135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008640285,0.00012173947,0.00018758759,0.00010164766,0.00008270528,0.000068081696,0.000076283075,0.00007036137,0.00006619823],"category_scores_gemma":[0.000037327474,0.00009706991,0.00005322955,0.00021183361,0.00015002822,0.0002182071,0.000008279022,0.000106469415,0.0000021829853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010069493,0.0002612072,0.0000073643837,0.00019366918,0.00015032077,0.0000016422549,0.00021848826,0.16769515,0.00020594225,0.21510261,0.00005600086,0.6160069],"study_design_scores_gemma":[0.002465336,0.0006083931,0.00001084866,0.00020620144,0.00044745422,0.00038462042,0.0016917486,0.89220226,0.001304738,0.09235492,0.007814211,0.00050923787],"about_ca_topic_score_codex":2.1881538e-8,"about_ca_topic_score_gemma":6.759034e-8,"teacher_disagreement_score":0.72450715,"about_ca_system_score_codex":0.000020507081,"about_ca_system_score_gemma":0.000017623619,"threshold_uncertainty_score":0.3958396},"labels":[],"label_agreement":null},{"id":"W2056235974","doi":"10.1007/s10479-012-1168-4","title":"A cardinality constrained stochastic goal programming model with satisfaction functions for venture capital investment decision making","year":2012,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Venture capital; Portfolio; Investment (military); Cardinality (data modeling); Social venture capital; Theory of computation; Business; Capital investment; Resource (disambiguation); Selection (genetic algorithm); Goal programming; Economics; Microeconomics; Computer science; Finance; Industrial organization; Operations research; Mathematics; Artificial intelligence","score_opus":0.12974704198774223,"score_gpt":0.4075030370474891,"score_spread":0.27775599505974685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056235974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13706331,0.00017439884,0.8612029,0.0001125568,0.000057731326,0.000919852,0.00003217364,0.00007447657,0.00036257738],"genre_scores_gemma":[0.89493376,0.0000069871026,0.104520775,0.00001745816,0.0000516516,0.0003739639,0.000027529733,0.000021983884,0.000045902525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988292,0.00003858864,0.00024943956,0.00012709241,0.00036177906,0.00039391848],"domain_scores_gemma":[0.9990185,0.00016487944,0.000015084974,0.00017436343,0.0004978441,0.00012933364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074468117,0.00010697552,0.00015188902,0.00017472591,0.00028324535,0.00009029763,0.000063972824,0.000067157314,0.000027383792],"category_scores_gemma":[0.0002650134,0.00009158288,0.000058625195,0.0002968309,0.0001076181,0.0003537295,0.000024407622,0.00018146871,0.0000069395483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003499303,0.00010110431,0.00007394383,0.00010783111,0.00006496322,1.9472999e-7,0.00093242695,0.96461916,0.00028560552,0.013664198,0.0003920278,0.019723572],"study_design_scores_gemma":[0.0003182746,0.00016163595,0.00022569425,0.00012968534,0.00002262272,0.0000072754124,0.001123295,0.99659485,0.00024207661,0.00080624,0.00022496932,0.00014340157],"about_ca_topic_score_codex":0.000012534668,"about_ca_topic_score_gemma":0.000050847757,"teacher_disagreement_score":0.75787044,"about_ca_system_score_codex":0.000047978712,"about_ca_system_score_gemma":0.000065860026,"threshold_uncertainty_score":0.37346414},"labels":[],"label_agreement":null},{"id":"W2059718457","doi":"10.4236/jmf.2012.21012","title":"Analytical Hierarchy Process and Goal Programming Approach for Asset Allocation","year":2012,"lang":"en","type":"article","venue":"Journal of Mathematical Finance","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Laurentian University; Business Development Bank of Canada; Université du Québec à Montréal","funders":"","keywords":"Asset allocation; Goal programming; Portfolio; Scope (computer science); Variance (accounting); Hierarchy; Analytic hierarchy process; Project portfolio management; Financial market; Economics; Replicating portfolio; Black–Litterman model; Asset (computer security); Actuarial science; Modern portfolio theory; Computer science; Portfolio optimization; Financial economics; Operations research; Finance; Mathematical economics; Mathematics; Project management; Accounting","score_opus":0.019298364663407973,"score_gpt":0.27828319852628924,"score_spread":0.2589848338628813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059718457","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028518261,0.00036822638,0.969423,0.00010390869,0.000060865215,0.00029296492,0.0000012034518,0.000045231798,0.0011863619],"genre_scores_gemma":[0.5947045,0.000024899931,0.40505233,0.000018756233,0.000106434716,0.000026104319,0.0000016930943,0.000020809104,0.000044486762],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891996,0.000013765806,0.0004886788,0.0000730654,0.00021048973,0.00029401467],"domain_scores_gemma":[0.9993676,0.000173854,0.00010847163,0.00008648201,0.00011956563,0.00014400715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006783602,0.00012807515,0.00030340502,0.00007037103,0.000044905562,0.000053037686,0.00010521543,0.00008071979,0.000010537756],"category_scores_gemma":[0.00038406937,0.00010025669,0.00007851954,0.00014623473,0.000059820333,0.0003561967,0.000013499456,0.00017520107,0.0000034376928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018501202,0.0026188588,0.0009831082,0.013669716,0.00047914532,0.000008408637,0.0067299837,0.050083697,0.00037583738,0.6819136,0.003544961,0.23940763],"study_design_scores_gemma":[0.000657012,0.0001393475,0.0000842583,0.000183951,0.00010332244,0.00017480756,0.0002998401,0.97905016,0.00039479142,0.014243217,0.004417291,0.00025201542],"about_ca_topic_score_codex":2.8662956e-8,"about_ca_topic_score_gemma":1.2330163e-8,"teacher_disagreement_score":0.92896646,"about_ca_system_score_codex":0.00003708157,"about_ca_system_score_gemma":0.000017352764,"threshold_uncertainty_score":0.40883493},"labels":[],"label_agreement":null},{"id":"W2064042079","doi":"10.5539/ijsp.v4n2p95","title":"Comparative Study of the Quick Convergent Inflow Algorithm (QCIA) and the Modified Quick Convergent Inflow Algorithm (MQCIA)","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Algorithm; Inflow; Mathematics; Point (geometry); Set (abstract data type); Mathematical optimization; Computer science","score_opus":0.034473415459057376,"score_gpt":0.2870876548848006,"score_spread":0.25261423942574324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064042079","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29022402,0.00041466756,0.70584214,0.00042608517,0.0016905005,0.00090490404,0.00019192873,0.000021076828,0.00028471538],"genre_scores_gemma":[0.94741917,0.00009877429,0.052308112,0.000047725007,0.00007946932,0.000011347634,0.0000041799444,0.0000100269335,0.0000212029],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998377,0.00014522058,0.00069845974,0.000106705884,0.00055606576,0.00011654237],"domain_scores_gemma":[0.9982605,0.0003119163,0.00028387402,0.00012807231,0.0008894229,0.00012620009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008923603,0.00013941995,0.00033781223,0.000046093242,0.000055509998,0.000074152114,0.00024280352,0.000041316613,0.00002268056],"category_scores_gemma":[0.00027082497,0.00008517026,0.00004896167,0.00008897047,0.0002640035,0.00010760264,0.00010633284,0.00023181246,9.31545e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015604218,0.0045049665,0.0139219975,0.00070857076,0.005719399,0.000073267365,0.09309655,0.27776316,0.000084147236,0.15467693,0.005323866,0.44256672],"study_design_scores_gemma":[0.0048838207,0.00030216484,0.0037559173,0.000060274797,0.000118987366,0.000040925373,0.0019615893,0.9366331,0.00009205887,0.05118142,0.0007959675,0.00017377397],"about_ca_topic_score_codex":0.0000456693,"about_ca_topic_score_gemma":0.00003155873,"teacher_disagreement_score":0.6588699,"about_ca_system_score_codex":0.000078853314,"about_ca_system_score_gemma":0.00006671352,"threshold_uncertainty_score":0.3473142},"labels":[],"label_agreement":null},{"id":"W2065073968","doi":"10.5539/mas.v7n6p90","title":"On Solving Linear Fractional Programming Problems","year":2013,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"VIT University","keywords":"Fractional programming; Linear programming; Simplex algorithm; Decomposition method (queueing theory); Linear-fractional programming; Mathematical optimization; Decomposition; Mathematics; Computer science; Fuzzy logic; Algorithm; Nonlinear programming; Nonlinear system; Discrete mathematics; Artificial intelligence","score_opus":0.013430967590885997,"score_gpt":0.2232066346652236,"score_spread":0.20977566707433762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065073968","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068385415,0.000010430125,0.95863146,0.00005358753,0.000099608675,0.00042980976,2.584169e-7,0.0005332426,0.03340304],"genre_scores_gemma":[0.9192563,0.0000014580056,0.080375224,0.00007742485,0.000029885936,0.0001488876,0.0000012344633,0.000018186956,0.00009135687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989352,0.0000022436238,0.00015243156,0.00021380537,0.00035556933,0.0003407215],"domain_scores_gemma":[0.9996025,0.000039743776,0.000021835895,0.00016329666,0.000048011567,0.00012459514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019362215,0.0001083822,0.00008837419,0.000092835726,0.00019642833,0.00018306168,0.00019989657,0.000036634476,0.00014088338],"category_scores_gemma":[0.00003476649,0.000096266675,0.00002067334,0.00032205484,0.00012981237,0.00025463683,0.000036726706,0.00014832197,0.0004401772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017714593,0.000110695306,0.000010703263,0.00010198898,0.000008625741,5.6538613e-7,0.0008976264,0.63352954,0.12137105,0.041272577,0.0002786716,0.20241617],"study_design_scores_gemma":[0.00008417272,0.000009601762,0.000017268068,0.000013870268,0.0000015878705,0.0000011870968,0.00002913149,0.985683,0.0015356161,0.01183275,0.0006654365,0.00012636723],"about_ca_topic_score_codex":0.0000020671373,"about_ca_topic_score_gemma":4.818708e-7,"teacher_disagreement_score":0.9124178,"about_ca_system_score_codex":0.000057301873,"about_ca_system_score_gemma":0.000019476849,"threshold_uncertainty_score":0.5657734},"labels":[],"label_agreement":null},{"id":"W2065990064","doi":"10.1057/jors.2010.30","title":"Production planning with uncertainty in the quality of raw materials: a case in sawmills","year":2010,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Production planning; Time horizon; Production (economics); Computer science; Stochastic programming; Mathematical optimization; Product (mathematics); Capacity planning; Quality (philosophy); Operations research; Mathematics; Economics","score_opus":0.08852421422799558,"score_gpt":0.40937107740112694,"score_spread":0.3208468631731314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065990064","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978041,0.000024040815,0.0005039026,0.0012361956,0.00009905138,0.00020380493,0.0000019284498,0.0000027831484,0.00012420253],"genre_scores_gemma":[0.99600095,0.000013686786,0.0038310695,0.000026555206,0.000091002796,0.000007725545,8.8397485e-7,0.0000055486685,0.000022563318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987235,0.00022903507,0.00035069924,0.000049605613,0.0005260284,0.00012109568],"domain_scores_gemma":[0.99923795,0.0003167825,0.000059771464,0.00011177227,0.00025087487,0.000022864506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062808553,0.000048217986,0.00011326251,0.000038899278,0.00008743226,0.000059109374,0.00016276629,0.00004185547,0.000032016756],"category_scores_gemma":[0.0008283235,0.000023865903,0.00004457773,0.00031121582,0.00010603289,0.00015220171,0.00002178339,0.0005759714,4.1040624e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008728911,0.00020581413,0.0009625604,0.00020197927,0.00004202695,0.000027781698,0.01263243,0.8292822,0.14826338,0.0053772354,0.0025203186,0.0003969754],"study_design_scores_gemma":[0.013312671,0.0011819737,0.069796085,0.0036884076,0.000101031284,0.008146991,0.1259112,0.5458376,0.20290071,0.021553243,0.0059330813,0.0016370165],"about_ca_topic_score_codex":0.000034615892,"about_ca_topic_score_gemma":0.00010421292,"teacher_disagreement_score":0.28344464,"about_ca_system_score_codex":0.000054997203,"about_ca_system_score_gemma":0.00009035218,"threshold_uncertainty_score":0.2502341},"labels":[],"label_agreement":null},{"id":"W2066316339","doi":"10.1002/mcda.448","title":"Fuzzy goal programming model: an overview of the current state‐of‐the art","year":2009,"lang":"en","type":"article","venue":"Journal of Multi-Criteria Decision Analysis","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Laurentian University","funders":"","keywords":"Goal programming; Decision maker; Computer science; Fuzzy logic; State (computer science); Operations research; Management science; Artificial intelligence; Mathematics; Engineering; Programming language","score_opus":0.05753464246591396,"score_gpt":0.36068800222586256,"score_spread":0.3031533597599486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066316339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18717012,0.0027558045,0.808977,0.0001795675,0.00049172377,0.0002944398,0.000013503316,0.000039145085,0.00007869757],"genre_scores_gemma":[0.8922159,0.00040157055,0.10727931,0.00003358351,0.000025921192,0.0000012448809,0.0000014585652,0.000015433134,0.000025568754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978663,0.000091627786,0.0011519673,0.0001101978,0.00060401525,0.00017591302],"domain_scores_gemma":[0.99860024,0.00007041334,0.0004585395,0.00041302707,0.00033786998,0.000119934215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071177643,0.00015993556,0.00053521135,0.00027335962,0.000056585406,0.00006774698,0.0004915582,0.00003959026,0.000028936067],"category_scores_gemma":[0.00022798759,0.00009059356,0.0007130911,0.0011575614,0.000043559492,0.00022509252,0.000052531588,0.00025291072,0.0000018179785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017974939,0.00029540452,0.00009297705,0.00004770596,0.00016927363,8.4858067e-7,0.00029583424,0.47152865,0.00095243135,0.000050664872,0.00015556498,0.5263927],"study_design_scores_gemma":[0.0004950741,0.00005322953,0.0021038475,0.0002742838,0.0006294338,0.000005808119,0.00003242472,0.99250495,0.00067486655,0.0017974043,0.0013063747,0.00012231803],"about_ca_topic_score_codex":6.840375e-7,"about_ca_topic_score_gemma":0.000010881519,"teacher_disagreement_score":0.70504576,"about_ca_system_score_codex":0.00004523394,"about_ca_system_score_gemma":0.000031414416,"threshold_uncertainty_score":0.36942983},"labels":[],"label_agreement":null},{"id":"W2069540826","doi":"10.1016/j.apm.2012.01.034","title":"Adjacency based method for generating maximal efficient faces in multiobjective linear programming","year":2012,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Defence Research and Development Canada","funders":"","keywords":"Extreme point; Adjacency list; Simplex; Linear programming; Mathematical optimization; Mathematics; Face (sociological concept); Feasible region; Simplex algorithm; Set (abstract data type); Regular polygon; Algorithm; Point (geometry); Polyhedron; Computer science; Combinatorics; Geometry","score_opus":0.03284012179857119,"score_gpt":0.28042299222797684,"score_spread":0.24758287042940566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069540826","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063202716,0.00010167706,0.98966694,0.000016667345,0.00007276405,0.0014808684,0.0000030672381,0.00042541287,0.0019123607],"genre_scores_gemma":[0.38137195,0.0000013292658,0.61796397,0.000021217926,0.00006638973,0.00050469034,0.0000071408767,0.00005644775,0.000006874136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981654,0.000024346062,0.00058785063,0.00025827112,0.0002284179,0.0007357125],"domain_scores_gemma":[0.9989476,0.000569073,0.00006148066,0.00019395564,0.000046798184,0.00018108357],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008867837,0.00028635387,0.00041804326,0.00013749683,0.000106227635,0.000055477765,0.00012660505,0.0001513376,0.00003373404],"category_scores_gemma":[0.00009065887,0.00026626716,0.00010452479,0.00025935905,0.00003106941,0.00009319754,0.000030588362,0.000233247,0.000032066226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000104419205,0.00022413726,0.000002150141,0.0005688483,0.000015454896,2.8149643e-7,0.0011611981,0.9438359,0.0008912427,0.041749258,0.0000027338785,0.011538347],"study_design_scores_gemma":[0.0005894105,0.000014396317,2.766363e-7,0.00008434423,0.00003159892,0.0000014267791,0.00036939842,0.989708,0.005089179,0.0036827675,0.00011850985,0.00031069585],"about_ca_topic_score_codex":0.0000013839511,"about_ca_topic_score_gemma":4.7574963e-7,"teacher_disagreement_score":0.37505168,"about_ca_system_score_codex":0.000089632296,"about_ca_system_score_gemma":0.000013638772,"threshold_uncertainty_score":0.99997896},"labels":[],"label_agreement":null},{"id":"W2070738041","doi":"10.1016/s0308-521x(01)00082-8","title":"Livestock manure systems for swine finishing enterprises","year":2002,"lang":"en","type":"article","venue":"Agricultural Systems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Manure; Manure management; Agricultural science; Livestock; Environmental science; Agricultural engineering; Ammonia; Business; Mathematics; Agronomy; Engineering; Geography; Chemistry; Biology; Forestry","score_opus":0.019242282415613616,"score_gpt":0.19849670441755637,"score_spread":0.17925442200194275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070738041","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37475657,0.06333199,0.36975235,0.00095316966,0.022002244,0.014688282,0.00023061459,0.010727471,0.1435573],"genre_scores_gemma":[0.99399143,0.000085933425,0.00080793817,0.0000058358683,0.00043712708,0.00027027284,0.00002629024,0.000024883637,0.0043503135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991078,0.000017551894,0.0003200335,0.0001447359,0.00014125681,0.00026864774],"domain_scores_gemma":[0.9995791,0.00009197264,0.000050025617,0.000115273935,0.00006363261,0.000100004174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000070773036,0.00017203,0.00023976677,0.000028021657,0.0000903316,0.00025611464,0.0001274226,0.00009094592,0.00002298946],"category_scores_gemma":[0.000036291985,0.00011273723,0.00008442833,0.00011987562,0.00000971763,0.000209178,0.000014559906,0.00008038645,0.000112174115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000078028415,0.00023079664,0.00040444627,0.0071458607,0.0004518333,0.000014589293,0.0025436268,0.622292,0.0038240908,0.020924252,0.33919933,0.0029613771],"study_design_scores_gemma":[0.00068354263,0.00012118152,0.00043858,0.00071562984,0.00007154548,0.00011204687,0.002304496,0.87729734,0.000117002804,0.000023055378,0.117443465,0.0006721296],"about_ca_topic_score_codex":0.000014343772,"about_ca_topic_score_gemma":0.0000010216362,"teacher_disagreement_score":0.61923486,"about_ca_system_score_codex":0.000052583648,"about_ca_system_score_gemma":7.7736115e-7,"threshold_uncertainty_score":0.45972908},"labels":[],"label_agreement":null},{"id":"W2071287365","doi":"10.1115/detc2013-12668","title":"Mixed Discrete and Continuous Variable Optimization Based on Constraint Aggregation and Relative Sensitivity","year":2013,"lang":"en","type":"article","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mathematical optimization; Sensitivity (control systems); Constraint (computer-aided design); Mathematics; Differentiable function; Function (biology); Nonlinear programming; Variable (mathematics); Feasible region; Nonlinear system; Optimization problem; Constrained optimization; Computer science","score_opus":0.007625292604720208,"score_gpt":0.18766007823676184,"score_spread":0.18003478563204164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071287365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033591462,0.000009644117,0.97336847,0.0001480823,0.000035572546,0.00029807532,0.0000022535262,0.00020474248,0.022574008],"genre_scores_gemma":[0.7451109,0.000005703223,0.25468644,0.00006238301,0.0000062594686,0.000012450683,0.000010879518,0.000010885761,0.00009406437],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957794,0.00003069161,0.00011742261,0.00010766339,0.000062088926,0.00010418536],"domain_scores_gemma":[0.9996208,0.00018366695,0.000020411468,0.0000644212,0.000042579875,0.000068130044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011944112,0.000091957896,0.00011055234,0.00003857957,0.000046917714,0.000080096645,0.000009853454,0.000055685246,0.00012135834],"category_scores_gemma":[0.000108772685,0.000079426,0.000010010135,0.000067691006,0.000051643237,0.00020560542,0.0000085389065,0.00006479912,0.0000042334373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030778544,0.000016332211,0.000054044882,0.00005755951,0.000015577016,9.965039e-7,0.000093500326,0.9548846,0.00023644396,0.030322572,0.000118347656,0.014196942],"study_design_scores_gemma":[0.0002940392,0.000025613925,0.000052133124,0.00003710815,0.000009902987,0.000002914367,0.000080160346,0.99826276,0.0003662682,0.00073313416,0.000033437267,0.00010255261],"about_ca_topic_score_codex":0.000010478333,"about_ca_topic_score_gemma":0.0000013381168,"teacher_disagreement_score":0.7417518,"about_ca_system_score_codex":0.000013904015,"about_ca_system_score_gemma":0.0000035110925,"threshold_uncertainty_score":0.32388982},"labels":[],"label_agreement":null},{"id":"W2074827063","doi":"10.5430/ijba.v4n1p105","title":"Personnel Selection Method Based on Personnel-Job Matching","year":2013,"lang":"en","type":"article","venue":"International Journal of Business Administration","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Disadvantage; Selection (genetic algorithm); Computer science; Job satisfaction; Personnel selection; Seekers; Fuzzy logic; Human resource management; Operations research; Knowledge management; Artificial intelligence; Management; Economics; Mathematics; Statistics; Law","score_opus":0.014900174512581951,"score_gpt":0.27247925524831873,"score_spread":0.25757908073573677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074827063","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071869686,0.000010565023,0.9238691,0.0010983907,0.0008009497,0.00011470341,0.000002547882,0.000079425205,0.0021546367],"genre_scores_gemma":[0.9124989,0.0000074958007,0.08690908,0.00014944148,0.0003497034,0.000008692243,0.0000111954805,0.000021991535,0.000043497763],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989407,0.000029846118,0.00039514457,0.00008688426,0.0004329935,0.00011442208],"domain_scores_gemma":[0.99884003,0.0000999084,0.000158,0.000056561392,0.00077244005,0.00007306758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002180916,0.00012642423,0.00014370894,0.00023622344,0.000053186177,0.0002348661,0.00016717837,0.000066685505,0.0004070698],"category_scores_gemma":[0.0001329402,0.00011436662,0.00007960873,0.00017842508,0.000013462982,0.0005354662,0.0000063561038,0.00016771624,0.000030666204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078824974,0.00023390022,0.0001948585,0.000093700226,0.00013420892,0.00002515783,0.00092727086,0.95229703,0.00733048,0.00313803,0.0009482693,0.034598246],"study_design_scores_gemma":[0.0006392764,0.00011027314,0.0015318383,0.0001616582,0.000025742262,0.00016652027,0.0007843761,0.9927339,0.0020913885,0.0010746294,0.0004969758,0.00018340358],"about_ca_topic_score_codex":0.000011645255,"about_ca_topic_score_gemma":0.000005247528,"teacher_disagreement_score":0.8406292,"about_ca_system_score_codex":0.000112757785,"about_ca_system_score_gemma":0.00005835194,"threshold_uncertainty_score":0.46637353},"labels":[],"label_agreement":null},{"id":"W2075501806","doi":"10.1023/a:1013346202934","title":"Long-Term Planning of Waste Management System in the City of Regina – an Integrated Inexact Optimization Approach","year":2001,"lang":"en","type":"article","venue":"Environmental Modeling & Assessment","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Plan (archaeology); Municipal solid waste; Operations research; Solid waste management; Reliability (semiconductor); Stochastic programming; Computer science; Operations management; Waste management; Business; Engineering; Mathematical optimization; Mathematics; Geography","score_opus":0.023519670109304846,"score_gpt":0.2533160536532565,"score_spread":0.22979638354395165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075501806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2916261,0.000042787036,0.7064106,0.0000058043634,0.000026339001,0.00030270027,0.000002084767,0.000041101015,0.0015425215],"genre_scores_gemma":[0.8736283,0.000057793928,0.12614828,0.000004723239,0.000009343426,0.000037750186,0.0000834902,0.000020321193,0.000009974096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990212,0.000055,0.0003281094,0.00016055904,0.00027275996,0.00016239095],"domain_scores_gemma":[0.999622,0.000014808994,0.00007454678,0.0002477496,0.000005040866,0.00003587654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032565783,0.00014801978,0.0001987054,0.000100325924,0.000033447363,0.000021664062,0.0001780296,0.0000500675,0.000012117543],"category_scores_gemma":[0.0000013861679,0.00012056521,0.000040982068,0.00014503632,0.000027086508,0.00015644745,0.00003826747,0.00012964377,3.7485182e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007258914,0.00021235802,0.002782166,0.00022794235,0.000026802527,0.000005321317,0.00036521492,0.99413073,0.00007075295,0.0002791714,9.593155e-7,0.0018913329],"study_design_scores_gemma":[0.00033433773,0.000040894887,0.0004188256,0.00016297538,0.000029772877,0.000006224284,0.004091948,0.9947567,0.000039112198,0.000006049749,0.0000036701292,0.00010951972],"about_ca_topic_score_codex":0.000004083949,"about_ca_topic_score_gemma":3.855141e-7,"teacher_disagreement_score":0.5820022,"about_ca_system_score_codex":0.00017111543,"about_ca_system_score_gemma":0.0000035545374,"threshold_uncertainty_score":0.49165067},"labels":[],"label_agreement":null},{"id":"W2078978921","doi":"10.1155/2013/837919","title":"Bilevel Multiobjective Programming Applied to Water Resources Allocation","year":2013,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"National High-tech Research and Development Program; Ministry of Water Resources; National Natural Science Foundation of China","keywords":"Mathematical optimization; Bilevel optimization; Linear programming; Computer science; Analytic hierarchy process; Fuzzy logic; Hierarchy; Operations research; Mathematics; Optimization problem; Artificial intelligence","score_opus":0.008404664537964594,"score_gpt":0.19489940562292482,"score_spread":0.18649474108496022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078978921","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030386938,0.000021794734,0.96281797,0.00009687425,0.00007869076,0.0016693883,5.5217697e-7,0.0009616266,0.003966173],"genre_scores_gemma":[0.8579504,0.0000016845216,0.14062026,0.000026973095,0.000035799472,0.0012194386,0.0000045711513,0.000077968936,0.00006291959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984505,0.00000875884,0.0005134041,0.00024248229,0.0002160308,0.00056881533],"domain_scores_gemma":[0.9994339,0.000096644704,0.000016440905,0.0002288934,0.000043799253,0.00018032026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027114234,0.00026092102,0.0003007266,0.0002571657,0.00003560343,0.00014062595,0.00017927132,0.00011523471,0.00018471302],"category_scores_gemma":[0.00010524959,0.00020965717,0.000046112757,0.00032623817,0.00002053834,0.00018936465,0.000070331975,0.00023487212,0.00065549655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.785506e-7,0.00007898388,0.000009150606,0.00093083695,0.00002075793,7.8410267e-7,0.004510041,0.96527416,0.013917057,0.0023813893,0.000040455558,0.012835516],"study_design_scores_gemma":[0.0003869671,0.000030891733,0.000059487425,0.00033263527,0.000011274343,0.0000057525804,0.00025201592,0.9825677,0.0072590658,0.0070531787,0.0014881948,0.0005528205],"about_ca_topic_score_codex":0.0000055832948,"about_ca_topic_score_gemma":0.0000016886531,"teacher_disagreement_score":0.82756346,"about_ca_system_score_codex":0.00011114163,"about_ca_system_score_gemma":0.0000025979768,"threshold_uncertainty_score":0.85495716},"labels":[],"label_agreement":null},{"id":"W2080447297","doi":"10.5539/jmr.v1n2p47","title":"Single and Multi Container Maintenance Model: A Fuzzy Geometric Programming Approach","year":2008,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Container (type theory); Mathematics; Fuzzy logic; Mathematical optimization; Fuzzy number; Fuzzy set operations; Geometric programming; Operator (biology); Defuzzification; Fuzzy classification; Fuzzy set; Applied mathematics; Computer science; Artificial intelligence; Engineering","score_opus":0.14803862089933428,"score_gpt":0.33253712734571583,"score_spread":0.18449850644638155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080447297","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03890569,0.00075890153,0.9548515,0.00007074205,0.000040491374,0.0003296328,0.000001047745,0.000070949674,0.00497101],"genre_scores_gemma":[0.42675874,0.000233637,0.5725718,0.0000060062916,0.000031556654,0.000009304199,4.1921462e-7,0.000034366185,0.0003541742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818176,0.00004008398,0.0005564387,0.00010288202,0.00069619296,0.00042266576],"domain_scores_gemma":[0.9987524,0.0002770622,0.000101227095,0.00016355868,0.0004871915,0.0002185711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015992749,0.00013388484,0.000338875,0.00058381533,0.00012517684,0.00009029267,0.00021732226,0.00008659965,0.000007804111],"category_scores_gemma":[0.0008303967,0.00010294263,0.000078346886,0.0006543183,0.00016540424,0.00020578565,0.00006866006,0.00053597963,0.000006666663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015565853,0.014302081,0.00053726666,0.015687726,0.001294517,0.0009311723,0.04720224,0.61216927,0.015067373,0.052107576,0.024778415,0.21576668],"study_design_scores_gemma":[0.00070038287,0.00012475887,0.000005946126,0.00010825711,0.000012899494,0.00060217635,0.0013728567,0.9943208,0.00029385762,0.0016177268,0.0007011917,0.00013911312],"about_ca_topic_score_codex":6.8798926e-7,"about_ca_topic_score_gemma":3.5569116e-7,"teacher_disagreement_score":0.38785306,"about_ca_system_score_codex":0.00009647357,"about_ca_system_score_gemma":0.000040139013,"threshold_uncertainty_score":0.41978785},"labels":[],"label_agreement":null},{"id":"W2081516133","doi":"10.1007/s11269-008-9380-3","title":"A Two-Step Infinite α-Cuts Fuzzy Linear Programming Method in Determination of Optimal Allocation Strategies in Agricultural Irrigation Systems","year":2008,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Linear programming; Fuzzy logic; Mathematical optimization; Fuzzy number; Mathematics; Fuzzy set operations; Linear-fractional programming; Computer science; Fuzzy set; Artificial intelligence","score_opus":0.015241318940135213,"score_gpt":0.24995074344953497,"score_spread":0.23470942450939974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081516133","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.571615,0.00009271449,0.41193864,0.000039548042,0.000110912224,0.001351682,9.9883e-7,0.00025403537,0.014596505],"genre_scores_gemma":[0.84680134,0.000012317761,0.15285172,0.0000035141104,0.00002018059,0.00013442451,0.000032858232,0.00001615141,0.0001275176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988315,0.00006716634,0.00048754318,0.00016274948,0.00021428007,0.00023675634],"domain_scores_gemma":[0.9997292,0.00002131026,0.000059243346,0.00011962392,0.00003794632,0.00003266883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033159507,0.00014519691,0.00019561802,0.00028459643,0.000033608092,0.000060289338,0.000107781154,0.00005296913,0.000004211931],"category_scores_gemma":[0.000006149385,0.00011239749,0.000034621746,0.00028923986,0.000024972871,0.0002820844,0.0000449258,0.00009362026,0.000008187028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001167564,0.00008534198,0.000110893954,0.0009125296,0.000022811697,0.000018961024,0.009197188,0.9675292,0.0006640171,0.0028442915,0.000004335795,0.018598793],"study_design_scores_gemma":[0.0009243064,0.000053835236,0.0010093638,0.00027994238,0.000023388144,0.0000105313065,0.0058739297,0.98831034,0.0013590328,0.00017012891,0.0017199549,0.0002652488],"about_ca_topic_score_codex":0.000066900284,"about_ca_topic_score_gemma":0.000032220218,"teacher_disagreement_score":0.27518633,"about_ca_system_score_codex":0.00007530617,"about_ca_system_score_gemma":0.0000020186847,"threshold_uncertainty_score":0.45834365},"labels":[],"label_agreement":null},{"id":"W2089233175","doi":"10.1023/b:anor.0000039515.90453.1d","title":"Path Relinking, Cycle-Based Neighbourhoods and Capacitated Multicommodity Network Design","year":2004,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":125,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université de Montréal","funders":"","keywords":"Tabu search; Theory of computation; Path (computing); Mathematical optimization; Mathematics; Heuristic; Set (abstract data type); Local search (optimization); Network planning and design; Computer science; Algorithm","score_opus":0.18403810617067948,"score_gpt":0.3873745427847574,"score_spread":0.20333643661407794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089233175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062604606,0.00040222422,0.933629,0.0012770935,0.000048251142,0.0005529463,0.0000073634355,0.00016248834,0.0013160805],"genre_scores_gemma":[0.8560601,0.00013558782,0.14363569,0.00005484817,0.00003073171,0.000036061254,0.000011316987,0.000018987404,0.000016676502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989751,0.000112352114,0.00023425526,0.00011897518,0.0002499077,0.00030939656],"domain_scores_gemma":[0.99917066,0.00017913041,0.000008415477,0.00018946934,0.0003361105,0.00011620739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001006236,0.00008611563,0.00013438663,0.00011650598,0.0002255634,0.000101416605,0.0001059553,0.00007022643,0.00005051139],"category_scores_gemma":[0.00032062788,0.00008091242,0.000028400293,0.0003929953,0.000123126,0.00013625206,0.000025285994,0.00023583941,0.00001535855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005586074,0.00006284684,0.0000120693885,0.000057017634,0.0000140778375,0.0000017930056,0.00023780193,0.9895903,0.00034519215,0.0076601924,0.0003947604,0.0016183886],"study_design_scores_gemma":[0.00036846293,0.00009665495,0.000084753956,0.00009887445,0.0000032928574,0.0000010996647,0.00005348588,0.9905731,0.0042900904,0.0041548563,0.00017469392,0.00010065613],"about_ca_topic_score_codex":0.00005382003,"about_ca_topic_score_gemma":0.000022919805,"teacher_disagreement_score":0.7934555,"about_ca_system_score_codex":0.000017004448,"about_ca_system_score_gemma":0.000058913087,"threshold_uncertainty_score":0.3299513},"labels":[],"label_agreement":null},{"id":"W2092768552","doi":"10.1007/s10489-012-0368-6","title":"Method for solving unbalanced fully fuzzy multi-objective solid minimal cost flow problems","year":2012,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University Grants Commission; Ryerson University","keywords":"Computer science; Fuzzy logic; Mathematical optimization; Product (mathematics); Fuzzy number; Flow (mathematics); Fuzzy transportation; Fuzzy set; Mathematics; Artificial intelligence","score_opus":0.03533817306306876,"score_gpt":0.3079266794170878,"score_spread":0.27258850635401904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092768552","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015258585,0.00014877545,0.990673,0.000014199374,0.00023452935,0.001151805,0.00000811691,0.0003886894,0.0072282976],"genre_scores_gemma":[0.35684738,0.000020704054,0.6423264,0.000054980865,0.00009499102,0.0005277025,0.000011279554,0.000045098986,0.000071477545],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874896,0.000009260971,0.00034227787,0.00020774626,0.00012303492,0.0005687329],"domain_scores_gemma":[0.9992725,0.00026486863,0.000052005715,0.00018064214,0.000070583745,0.0001593906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038810645,0.00021525717,0.00025710513,0.00006241219,0.00009519876,0.000051475996,0.00017341229,0.000111040776,0.000064633496],"category_scores_gemma":[0.00008590036,0.00020902931,0.00007198474,0.00019974468,0.000039387076,0.00015205856,0.000041467432,0.00016389941,0.00013059394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004335156,0.00029721932,0.000036935333,0.0007477207,0.0001586052,5.566005e-7,0.0071941526,0.42070583,0.029602276,0.043496035,0.00051259797,0.49720472],"study_design_scores_gemma":[0.00019644911,0.000028382115,0.000011963201,0.000039891485,0.00002994153,0.0000039103807,0.0006599662,0.90648484,0.08669919,0.0020841558,0.0034050369,0.0003562669],"about_ca_topic_score_codex":0.0000019836257,"about_ca_topic_score_gemma":0.0000033055207,"teacher_disagreement_score":0.49684846,"about_ca_system_score_codex":0.000069654154,"about_ca_system_score_gemma":0.000011914701,"threshold_uncertainty_score":0.8523968},"labels":[],"label_agreement":null},{"id":"W2096219964","doi":"10.5267/j.msl.2014.7.031","title":"An approach for solving multi-objective assignment problem with interval parameters","year":2014,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Interval (graph theory); Computer science; Mathematical optimization; Mathematics; Combinatorics","score_opus":0.014660548658936498,"score_gpt":0.23083647211676425,"score_spread":0.21617592345782777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096219964","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008441752,0.0000010075013,0.9879625,0.0001428018,0.00005974965,0.00070336007,3.6845415e-7,0.00023859416,0.002449901],"genre_scores_gemma":[0.46828985,2.840792e-7,0.53119105,0.00034798542,0.0000073345486,0.00013349243,0.0000020260732,0.000012294929,0.000015649955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989769,0.0000098175215,0.00013136574,0.00029249975,0.0002397979,0.00034962443],"domain_scores_gemma":[0.99964964,0.000019056966,0.000028438444,0.00020562529,0.000011228198,0.0000860306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005209657,0.00012488394,0.00010604868,0.00013458956,0.00014097372,0.00021582181,0.0002899148,0.000013911594,0.0000027186322],"category_scores_gemma":[0.000008826263,0.00010143526,0.000026461856,0.00025697742,0.00015712349,0.00037405177,0.000038500013,0.00005574756,0.0000032934843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000062954964,0.0000924383,0.00017892696,0.00026080996,0.00003548482,7.7963387e-7,0.0006995413,0.971617,0.0027296024,0.00335113,0.00015383931,0.020874185],"study_design_scores_gemma":[0.00037542966,0.00006508557,0.00015728053,0.000026194346,0.000018994018,6.049387e-7,0.00045933042,0.99764574,0.0008409346,0.00006181498,0.00017328562,0.00017532721],"about_ca_topic_score_codex":0.0000014147154,"about_ca_topic_score_gemma":4.947637e-7,"teacher_disagreement_score":0.4598481,"about_ca_system_score_codex":0.00009032724,"about_ca_system_score_gemma":0.0000013474146,"threshold_uncertainty_score":0.413641},"labels":[],"label_agreement":null},{"id":"W2097717037","doi":"10.5430/ijfr.v4n4p83","title":"Financial Statement Management, Liability Reduction and Asset Accumulation: An Application of Goal Programming Model to a Nigerian Bank","year":2013,"lang":"en","type":"article","venue":"International Journal of Financial Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Financial statement; Liability; Statement (logic); Financial statement analysis; Business; Finance; Shareholder; Asset (computer security); Actuarial science; Goal programming; Bank statement; Financial ratio; Economics; Accounting; Computer science; Operations research; Corporate governance; Engineering","score_opus":0.060073445682017126,"score_gpt":0.3976336304549813,"score_spread":0.3375601847729642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097717037","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54638314,0.00002352326,0.451954,0.0003680676,0.00020078557,0.00062540546,0.0000063690236,0.000023962406,0.00041473945],"genre_scores_gemma":[0.9247155,0.00003160026,0.074941196,0.000014741767,0.0001675129,0.0000664687,0.000007984684,0.000012209183,0.000042794723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983612,0.000045420908,0.0005602877,0.0001320907,0.000704896,0.0001961062],"domain_scores_gemma":[0.9985236,0.000034176468,0.000104190396,0.00011788409,0.0010966621,0.00012348553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00111836,0.00008740176,0.00014645708,0.00032633392,0.000056386973,0.00011599603,0.00025466934,0.000060018825,0.000032252283],"category_scores_gemma":[0.00029729045,0.000085937434,0.000041569983,0.00022832077,0.000054010816,0.00053406245,0.000071702925,0.00020488029,0.000008421645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009338033,0.00022711398,0.00028441582,0.000116800264,0.00002581803,0.000003356103,0.00075848284,0.080847576,0.0033313434,0.013246597,0.0007453407,0.90031976],"study_design_scores_gemma":[0.0015767813,0.00066742074,0.028037013,0.00026624574,0.00002654756,0.0000333714,0.00034001758,0.9119147,0.0029109172,0.039634924,0.014200447,0.00039160112],"about_ca_topic_score_codex":0.000020300706,"about_ca_topic_score_gemma":0.000008077423,"teacher_disagreement_score":0.89992815,"about_ca_system_score_codex":0.00016112432,"about_ca_system_score_gemma":0.000065627006,"threshold_uncertainty_score":0.35044268},"labels":[],"label_agreement":null},{"id":"W2097978888","doi":"10.5539/mas.v6n12p22","title":"Type II Sensitivity Analysis in Solid Assignment Problems","year":2012,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"VIT University","keywords":"Sensitivity (control systems); Parametric statistics; Type (biology); Mathematical optimization; Computer science; Action (physics); Assignment problem; Mathematics; Algorithm; Statistics; Physics; Engineering","score_opus":0.018307689006117112,"score_gpt":0.2475322798865283,"score_spread":0.22922459088041117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097978888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053828336,0.000022427896,0.92889833,0.000016115524,0.000059090733,0.00014494838,3.9256463e-7,0.00013107511,0.0168993],"genre_scores_gemma":[0.98983616,0.0000024791282,0.010074552,0.000023835308,0.000011188178,0.000011672985,0.0000010763972,0.0000066780176,0.000032362677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991989,0.000005412712,0.00012120975,0.00012793549,0.00022035123,0.00032619803],"domain_scores_gemma":[0.9997026,0.000015898533,0.000014595151,0.00014724003,0.000016616013,0.000103098784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006599846,0.00007281131,0.00012088866,0.00015212252,0.00008456296,0.00003400664,0.000078235775,0.000025975456,0.00002962788],"category_scores_gemma":[0.00001611433,0.0000667348,0.000018490258,0.0011524553,0.00007072265,0.00014645429,0.000057757396,0.000071136164,0.000026944306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013530231,0.00008143751,0.00029431153,0.000018484514,0.000015164603,3.4816426e-7,0.00182449,0.8569103,0.13059199,0.0020358376,0.000010038731,0.00821628],"study_design_scores_gemma":[0.000049671282,0.0000031670177,0.00062240544,0.000002828019,0.000014687683,3.9015612e-7,0.000027051736,0.99490494,0.0037997141,0.00041499306,0.0000682833,0.00009187131],"about_ca_topic_score_codex":0.0000019009569,"about_ca_topic_score_gemma":0.0000062819445,"teacher_disagreement_score":0.9360078,"about_ca_system_score_codex":0.00007850329,"about_ca_system_score_gemma":0.000010582859,"threshold_uncertainty_score":0.27213663},"labels":[],"label_agreement":null},{"id":"W2098352464","doi":"10.5430/air.v2n2p109","title":"Interactive Fuzzy Programming for Stochastic Two-level Linear Programming Problems through Probability Maximization","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Linear programming; Vagueness; Mathematical optimization; Stochastic programming; Simplex algorithm; Fuzzy logic; Computer science; Linear-fractional programming; Mathematics; Artificial intelligence","score_opus":0.2473736253788599,"score_gpt":0.4067232692812456,"score_spread":0.15934964390238568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098352464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047012446,0.000075656695,0.9875934,0.0003380754,0.00022938244,0.005767466,0.000006585905,0.0005499526,0.0007382347],"genre_scores_gemma":[0.6458656,0.000012889786,0.35052484,0.000012919568,0.00019009005,0.0031744447,0.00003906982,0.00008070469,0.00009944877],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99691445,0.00012681045,0.00077196496,0.0005153217,0.0005783288,0.0010931435],"domain_scores_gemma":[0.99738336,0.00076302123,0.00007159864,0.0003832,0.0011883939,0.00021044823],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013614062,0.00027822857,0.00030598123,0.00017110423,0.0003833061,0.00049209106,0.00037834613,0.00014881125,0.00017071459],"category_scores_gemma":[0.0015678777,0.00026052078,0.000121866025,0.0010260749,0.0003131149,0.00081170653,0.00012065336,0.00058155175,0.00040027816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030730364,0.00038854152,0.000009026189,0.0005597762,0.000055782377,9.1930673e-7,0.0031649873,0.24919395,0.00095920236,0.033796567,0.000072514515,0.71176803],"study_design_scores_gemma":[0.00007834283,0.00021379982,0.0000021846377,0.00015529123,0.00001331482,0.000003438087,0.0020825225,0.7992594,0.00640576,0.1905627,0.00090347015,0.00031976064],"about_ca_topic_score_codex":0.00014184482,"about_ca_topic_score_gemma":0.00009022182,"teacher_disagreement_score":0.71144825,"about_ca_system_score_codex":0.00023048802,"about_ca_system_score_gemma":0.00006998202,"threshold_uncertainty_score":0.9999847},"labels":[],"label_agreement":null},{"id":"W2101205695","doi":"10.1016/s0191-2615(99)00021-1","title":"Optimization of the transportation expense of a firm with contractual supplies","year":2000,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Selkirk College","funders":"","keywords":"Transportation theory; Nonlinear system; Mathematical optimization; Computer science; Operations research; Mathematics","score_opus":0.19293871666370144,"score_gpt":0.3847738904818201,"score_spread":0.19183517381811865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101205695","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6626019,0.00007719104,0.3349361,0.00015489856,0.000039398208,0.0007467814,0.00005879677,0.00011837971,0.0012665482],"genre_scores_gemma":[0.8912685,0.00013046063,0.108337216,0.00001142604,0.000013259707,0.00007063767,0.0000579034,0.00001751401,0.000093108734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983144,0.00030387184,0.00048597573,0.00015339068,0.0005117574,0.00023061948],"domain_scores_gemma":[0.99859345,0.00090793037,0.000055767967,0.00018113713,0.00019424564,0.00006749538],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010019067,0.00011444189,0.00027485273,0.00006712889,0.00006845979,0.000010484218,0.00015279143,0.00010931537,0.0021492287],"category_scores_gemma":[0.00010523418,0.00007287677,0.00007910356,0.0004694901,0.00032297007,0.00011171436,0.0000010116241,0.0002296249,0.0000031753568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024721248,0.00012065629,0.0009926875,0.00020305168,0.00004057517,0.0000029481198,0.0018039644,0.986704,0.0010348362,0.004049372,0.00012558872,0.004675135],"study_design_scores_gemma":[0.010699117,0.0029168206,0.28167886,0.0013185118,0.0005758397,0.000011017458,0.008303772,0.4715484,0.19398607,0.009240044,0.017766641,0.0019549148],"about_ca_topic_score_codex":0.000021078802,"about_ca_topic_score_gemma":0.000058463193,"teacher_disagreement_score":0.51515555,"about_ca_system_score_codex":0.000011694239,"about_ca_system_score_gemma":0.000024988429,"threshold_uncertainty_score":0.99876297},"labels":[],"label_agreement":null},{"id":"W2103088687","doi":"10.1111/j.1475-3995.2012.00844.x","title":"Aggregate planning through the imprecise goal programming model: integration of the manager's preferences","year":2012,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Aggregate planning; Aggregate (composite); Time horizon; Computer science; Production planning; Production (economics); Operations research; Goal programming; Plan (archaeology); Set (abstract data type); Range (aeronautics); Industrial engineering; Mathematical optimization; Economics; Engineering; Mathematics; Microeconomics","score_opus":0.10835373592673947,"score_gpt":0.3908507517331907,"score_spread":0.28249701580645126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103088687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0372707,0.00030819947,0.937586,0.0014354375,0.00073885,0.0007705909,0.000022409842,0.000079691825,0.02178813],"genre_scores_gemma":[0.9827125,0.00006747239,0.01618238,0.000026693919,0.000076406664,0.00023384363,0.000012194958,0.000016294951,0.00067222526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845815,0.00009817947,0.00034114855,0.00011372212,0.00072669383,0.00026209973],"domain_scores_gemma":[0.99924535,0.00029545184,0.000029336663,0.0001616814,0.00023131784,0.00003686528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076752476,0.00010126622,0.0000904869,0.000134112,0.00021542344,0.00011254403,0.00040618543,0.00006604063,0.00019813114],"category_scores_gemma":[0.00013715008,0.00006450809,0.00006610328,0.00044080554,0.00015980864,0.0006476502,0.000024570823,0.00048973574,0.000010222689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014445695,0.00012200698,0.00023236386,0.00002449165,0.000044139662,2.4207557e-7,0.003638588,0.94405365,0.0006007714,0.026658684,0.00010508494,0.024505528],"study_design_scores_gemma":[0.00021922392,0.000012611609,0.0005457892,0.00014323217,0.000008082619,0.0000065162903,0.0013691382,0.98533857,0.0035926728,0.004457046,0.004199916,0.00010719784],"about_ca_topic_score_codex":0.000034132263,"about_ca_topic_score_gemma":0.000044954708,"teacher_disagreement_score":0.9454418,"about_ca_system_score_codex":0.00013268128,"about_ca_system_score_gemma":0.000049518534,"threshold_uncertainty_score":0.26305634},"labels":[],"label_agreement":null},{"id":"W2106102140","doi":"10.1111/j.1467-8276.2008.01149.x","title":"Economic Development Prospects of Forest‐Dependent Communities: Analyzing Trade‐offs Using a Compromise‐Fuzzy Programming Framework","year":2008,"lang":"en","type":"article","venue":"American Journal of Agricultural Economics","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University of British Columbia","funders":"","keywords":"Compromise; Weighting; Fuzzy logic; Diversification (marketing strategy); Rule of thumb; Economics; Computer science; Environmental economics; Natural resource economics; Business; Artificial intelligence; Marketing; Sociology; Algorithm","score_opus":0.016333784997525125,"score_gpt":0.21884270775520187,"score_spread":0.20250892275767673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106102140","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9900159,0.00013439011,0.009256903,0.000035324418,0.00017187143,0.00018544197,0.0000027332283,0.00004476506,0.0001526197],"genre_scores_gemma":[0.81555605,0.00017950333,0.18413316,0.000013132648,0.00008313786,0.000003915679,0.0000042772886,0.000023073944,0.0000037734073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985098,0.000029891895,0.0009838309,0.00009540256,0.0000890833,0.00029194832],"domain_scores_gemma":[0.9988786,0.00012138739,0.0006444118,0.00013220994,0.00005406283,0.00016931331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015517,0.00022624369,0.00064303714,0.00012815364,0.00012905839,0.00005387025,0.0002795239,0.00005690542,0.000012962204],"category_scores_gemma":[0.000014895331,0.00018244042,0.00017264696,0.00013925038,0.00018384683,0.00030840348,0.000041254672,0.00031340212,0.0000029816501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022199729,0.00010188032,0.006155402,0.000083858824,0.00040341428,0.0000058923815,0.0066413376,0.9688235,0.00015864617,0.00090235873,0.000021406773,0.016680107],"study_design_scores_gemma":[0.011569129,0.0052430565,0.24082458,0.0057159318,0.0015256514,0.010598541,0.17220467,0.5060752,0.02436792,0.0031250971,0.008977274,0.009772899],"about_ca_topic_score_codex":0.00002314802,"about_ca_topic_score_gemma":0.000033058892,"teacher_disagreement_score":0.46274826,"about_ca_system_score_codex":0.0003843943,"about_ca_system_score_gemma":0.00008060241,"threshold_uncertainty_score":0.74397045},"labels":[],"label_agreement":null},{"id":"W2106810399","doi":"10.5267/j.dsl.2015.1.004","title":"FFLP problem with symmetric trapezoidal fuzzy numbers","year":2015,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fuzzy logic; Mathematical optimization; Mathematics; Fuzzy number; Applied mathematics; Computer science; Fuzzy set; Artificial intelligence","score_opus":0.020390418193946026,"score_gpt":0.25034021813096147,"score_spread":0.22994979993701545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106810399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23109746,0.000034557106,0.73167026,0.00091395125,0.00036559015,0.00028919196,8.624939e-7,0.00046944493,0.035158675],"genre_scores_gemma":[0.7541592,0.0000041483786,0.24508692,0.0006716154,0.00002528577,0.000010900683,6.582526e-7,0.000017427019,0.000023814875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984892,0.0000073162314,0.00018656791,0.00021691404,0.00077768054,0.00032229786],"domain_scores_gemma":[0.9993358,0.00008122031,0.00002464493,0.00021316009,0.00006980731,0.00027533076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059925776,0.000112161666,0.00012009546,0.00053276535,0.00008024188,0.00020781305,0.00032181825,0.000027057182,0.000016933007],"category_scores_gemma":[0.00011017403,0.000082277176,0.000025355626,0.003284451,0.00020007337,0.0004819869,0.00003311284,0.00010425185,0.0001555055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000584899,0.00014432688,0.0014765027,0.00007575353,0.000033140892,0.00008694094,0.0025972754,0.59273744,0.010077374,0.011392259,0.06398633,0.3173342],"study_design_scores_gemma":[0.006809633,0.0005577836,0.0018178028,0.00044978215,0.000088013156,0.0003947843,0.002612499,0.8647522,0.009006024,0.016337177,0.09422889,0.0029454615],"about_ca_topic_score_codex":0.0000023867744,"about_ca_topic_score_gemma":0.0000011601013,"teacher_disagreement_score":0.52306175,"about_ca_system_score_codex":0.00006471389,"about_ca_system_score_gemma":0.00002456593,"threshold_uncertainty_score":0.33551657},"labels":[],"label_agreement":null},{"id":"W2109460885","doi":"10.1109/fuzzy.2011.6007355","title":"Multi-level multi-objective decision problem through fuzzy random regression based objective function","year":2011,"lang":"en","type":"article","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ryerson University; Universiti Tun Hussein Onn Malaysia","keywords":"Mathematical optimization; Computer science; Fuzzy logic; Pareto principle; Scheme (mathematics); Membership function; Fuzzy set; Function (biology); Mathematics; Artificial intelligence","score_opus":0.06129800702448464,"score_gpt":0.27025224479920223,"score_spread":0.2089542377747176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109460885","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012044571,0.000077911,0.9771195,0.0000071495274,0.00033378284,0.00076117745,0.0000044238714,0.00083563913,0.019655941],"genre_scores_gemma":[0.35735428,0.000014449927,0.6420462,0.000055439774,0.000020816824,0.00008459128,0.000009626507,0.000045655022,0.0003689022],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878097,0.000037541453,0.00036466416,0.0003041963,0.00021120453,0.00030144135],"domain_scores_gemma":[0.9992629,0.00016724732,0.00006246117,0.0002466359,0.0001572385,0.00010351082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022417134,0.00026776706,0.0002816398,0.0001076042,0.0001313263,0.00003930139,0.00011343381,0.00017477605,0.0004686507],"category_scores_gemma":[0.00015413883,0.00019947116,0.00012072338,0.00029180793,0.000043899745,0.00039932644,0.000037540012,0.00020742569,0.00016613032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005183887,0.0063585797,0.0013058642,0.0015983891,0.0011067586,0.000065837434,0.03609126,0.19400644,0.021729495,0.013939955,0.0071208808,0.71149266],"study_design_scores_gemma":[0.010991313,0.00019347208,0.001849308,0.0003616975,0.00009315753,0.0000052727805,0.0011413575,0.96052563,0.01905137,0.0046220208,0.00049093564,0.00067447126],"about_ca_topic_score_codex":0.000032447217,"about_ca_topic_score_gemma":0.000044716086,"teacher_disagreement_score":0.7665192,"about_ca_system_score_codex":0.00009012765,"about_ca_system_score_gemma":0.000020292555,"threshold_uncertainty_score":0.81341976},"labels":[],"label_agreement":null},{"id":"W2111830973","doi":"10.5539/ijsp.v3n4p54","title":"Modified Quick Convergent Inflow Algorithm for Solving Linear Programming Problems","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Maximization; Mathematical optimization; Variance (accounting); Mathematics; Linear programming; Inflow; Minification; Function (biology); Point (geometry); Interior point method; Algorithm; Convergence (economics); Penalty method; Computer science; Applied mathematics","score_opus":0.017870351110174407,"score_gpt":0.2623471607619417,"score_spread":0.2444768096517673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111830973","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030495245,0.00006195161,0.9958505,0.000095808406,0.0005486665,0.0002316076,0.000043217915,0.000029875488,0.00008883502],"genre_scores_gemma":[0.16534285,0.000056525103,0.834333,0.00003057723,0.00017756173,0.000014301812,0.000014163622,0.000015244327,0.000015784912],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990558,0.000014946452,0.00048857456,0.00008653037,0.00022233426,0.00013180851],"domain_scores_gemma":[0.9989134,0.00022722459,0.00013330378,0.000057969053,0.00056970835,0.000098400145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005965917,0.000098523175,0.0001727169,0.000054552933,0.000037816964,0.00008189821,0.00012407264,0.00004347685,0.000017189848],"category_scores_gemma":[0.00036389515,0.00008611193,0.000047387868,0.000034214696,0.000048281683,0.00010970718,0.000023700679,0.00011530979,8.9282287e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019493556,0.00011168868,0.0001612702,0.0003407676,0.00015133868,0.0000020516081,0.0003840402,0.052465837,0.00007669424,0.02882396,0.00022293978,0.9172399],"study_design_scores_gemma":[0.00057939475,0.00011390203,0.000054447955,0.000059902108,0.000021419226,0.000015296662,0.000015268688,0.9434393,0.00008955948,0.045243446,0.010266512,0.00010151568],"about_ca_topic_score_codex":0.000002598059,"about_ca_topic_score_gemma":0.0000030298004,"teacher_disagreement_score":0.9171384,"about_ca_system_score_codex":0.000046935336,"about_ca_system_score_gemma":0.00002069744,"threshold_uncertainty_score":0.35115427},"labels":[],"label_agreement":null},{"id":"W2116919268","doi":"","title":"Incorporating the Managers Preferences in Project Scheduling Problem through the Goal Programming Model","year":2007,"lang":"en","type":"article","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University; Université Laval; Defence Research and Development Canada","funders":"","keywords":"Computer science; Scheduling (production processes); Operations research; Goal programming; Compromise; Nurse scheduling problem; Automated planning and scheduling; Tabu search; Schedule; Mathematical optimization; Dynamic priority scheduling; Management science; Two-level scheduling; Engineering; Artificial intelligence; Mathematics","score_opus":0.031243506690162664,"score_gpt":0.2747929609617782,"score_spread":0.24354945427161553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116919268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011458314,0.0001094316,0.92136514,0.00024702522,0.00005360197,0.0011289773,2.801172e-7,0.00047555976,0.06516166],"genre_scores_gemma":[0.60451436,0.000008983981,0.39519906,0.000060739152,0.000024037303,0.000070039925,0.0000016553994,0.000019283849,0.000101828104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988786,0.000022860495,0.00037714318,0.00015292995,0.00020078718,0.00036765327],"domain_scores_gemma":[0.9995687,0.00014265711,0.000049912695,0.0001838116,0.000028915663,0.000025982681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008698021,0.00015427379,0.00012668199,0.00005246789,0.0001529132,0.00015883693,0.0002577318,0.00006365373,0.0000067866085],"category_scores_gemma":[0.000052302054,0.00008215633,0.000042698575,0.0005185978,0.000078532874,0.00025583094,0.000059356873,0.00029764653,0.000006637583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042698834,0.00003355844,0.0001247008,0.00014247232,0.000020724236,0.0000022002637,0.003520358,0.87277126,0.000048552047,0.06125058,0.000038941846,0.062042404],"study_design_scores_gemma":[0.00013824918,0.000012313476,0.000009581441,0.000059507824,0.000009226451,0.0000026254306,0.0044119637,0.98354995,0.00016297748,0.011018313,0.00047800693,0.00014726132],"about_ca_topic_score_codex":0.00004508347,"about_ca_topic_score_gemma":0.00021968878,"teacher_disagreement_score":0.593056,"about_ca_system_score_codex":0.00004066957,"about_ca_system_score_gemma":0.000021925765,"threshold_uncertainty_score":0.3350238},"labels":[],"label_agreement":null},{"id":"W2117619657","doi":"10.5539/mas.v7n7p59","title":"Reevaluating FMOLP Decision Variable Coefficients Using the SWAT Results for the Optimization of Sustainable Agricultural Land Use in Small Watershed","year":2013,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Watershed; Soil and Water Assessment Tool; SWAT model; Agriculture; Land use; Environmental science; Water resource management; Agricultural land; Agricultural engineering; Computer science; Hydrology (agriculture); Geography; Cartography; Ecology","score_opus":0.03145836592930898,"score_gpt":0.24454830527664023,"score_spread":0.21308993934733125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117619657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.106034435,0.000009935305,0.89237905,0.000024844985,0.000040886527,0.0010528518,0.0000012722014,0.000037348476,0.0004193777],"genre_scores_gemma":[0.82293063,0.0000017018265,0.17691213,0.0000147290275,0.0000079388265,0.00006518019,0.0000035224584,0.000008576112,0.000055560515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895084,0.000011436799,0.00029368597,0.00018099856,0.00023250408,0.00033052452],"domain_scores_gemma":[0.9991196,0.0003601322,0.000060605787,0.00021664289,0.00020549791,0.000037530408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001140341,0.00009527354,0.00010911398,0.000063755564,0.0002874639,0.00026240712,0.00030392158,0.000036499758,0.000004868583],"category_scores_gemma":[0.00039142466,0.000049875976,0.000018890676,0.0006202152,0.000110864596,0.00025911658,0.00008836015,0.00007249791,0.0000011253817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009033839,0.000013765707,0.000008380829,0.000021964024,0.0000019958788,5.116571e-8,0.0008859335,0.984231,0.0129437875,0.0010551497,0.000013604554,0.0008153171],"study_design_scores_gemma":[0.0004032901,0.000007314693,0.000076343786,0.000021785952,0.000008107505,6.37345e-7,0.0008293697,0.995986,0.0015787064,0.00100008,0.000012717688,0.00007568688],"about_ca_topic_score_codex":0.00007485056,"about_ca_topic_score_gemma":0.0000072681482,"teacher_disagreement_score":0.71689624,"about_ca_system_score_codex":0.00007991636,"about_ca_system_score_gemma":0.000025565783,"threshold_uncertainty_score":0.25303978},"labels":[],"label_agreement":null},{"id":"W2117803830","doi":"10.5267/j.ijiec.2014.12.003","title":"Designing a performance measurement system for supply chain using balanced scorecard, path analysis, cooperative game theory and evolutionary game theory: A Case Study","year":2015,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Balanced scorecard; Game theory; Supply chain; Path (computing); Computer science; Evolutionary game theory; Mathematical optimization; Mathematical economics; Mathematics; Process management; Engineering; Business; Marketing","score_opus":0.06035245931985457,"score_gpt":0.2777466659242099,"score_spread":0.21739420660435532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117803830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26962623,0.00016689177,0.7291292,0.000012815683,0.0006381038,0.00033546553,0.000013800747,0.00006644402,0.000011042533],"genre_scores_gemma":[0.9765605,0.0000041632725,0.023063987,0.000005079684,0.00030511638,0.00002411329,0.000007566815,0.00002692017,0.0000025723166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851894,0.00012556289,0.00059682707,0.00012463491,0.00046900625,0.00016503819],"domain_scores_gemma":[0.99820703,0.00035351756,0.00016916818,0.000073268755,0.001040156,0.00015686778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018703679,0.000176899,0.00033066637,0.0004933684,0.00005739309,0.00012752587,0.00014602323,0.000066801804,0.000002322073],"category_scores_gemma":[0.00059328374,0.0001665633,0.000100844736,0.00035946033,0.00002422691,0.00027069828,0.000030322539,0.00022587206,4.827031e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009207524,0.00006454151,0.0002946114,0.000021699027,0.0013114155,0.000092889415,0.0017102422,0.9931838,0.0000823859,0.0011034402,0.000016362663,0.002026569],"study_design_scores_gemma":[0.002274912,0.00018927752,0.000043688313,0.00026343565,0.00041918163,0.0007786644,0.0040850095,0.9915472,0.00011063461,0.00008815908,0.000028813241,0.00017101302],"about_ca_topic_score_codex":0.000004963955,"about_ca_topic_score_gemma":9.713895e-7,"teacher_disagreement_score":0.7069343,"about_ca_system_score_codex":0.0005223043,"about_ca_system_score_gemma":0.00013865886,"threshold_uncertainty_score":0.67922544},"labels":[],"label_agreement":null},{"id":"W2125734567","doi":"10.1002/mcda.492","title":"Group Decision Makers' Preferences Modelling within the Goal Programming Model: An Overview and a Typology","year":2012,"lang":"en","type":"article","venue":"Journal of Multi-Criteria Decision Analysis","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Typology; Goal programming; Articulation (sociology); Group decision-making; Preference; Management science; Decision model; Computer science; Decision maker; Decision analysis; Process (computing); Group (periodic table); Decision process; Operations research; Artificial intelligence; Psychology; Machine learning; Sociology; Economics; Engineering; Social psychology; Political science; Mathematical economics; Microeconomics","score_opus":0.0828717758745934,"score_gpt":0.3479270677816569,"score_spread":0.26505529190706345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125734567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22879264,0.003015562,0.7678148,0.00002120726,0.0001943803,0.00010589944,0.0000019969639,0.000037246988,0.00001623374],"genre_scores_gemma":[0.57767886,0.00053808745,0.421675,0.00003227309,0.000048841874,0.0000035008402,0.0000022012564,0.000016790376,0.00000444092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980726,0.0001143866,0.0009365758,0.0001589998,0.0004219445,0.00029544614],"domain_scores_gemma":[0.9986811,0.00033309634,0.00025859338,0.00026803312,0.00017257204,0.0002865934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017544735,0.00021518808,0.0005398245,0.000419481,0.00013696014,0.00023053314,0.00028285402,0.00013039351,0.00007141114],"category_scores_gemma":[0.00019158973,0.00013448187,0.00024665424,0.00063229125,0.000061702594,0.0006815553,0.0000693537,0.00028210678,0.0000042990964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087969565,0.00023671496,0.00047734368,0.000040995314,0.00046953245,0.0000040875843,0.0019361819,0.8554951,0.00018685657,0.0009814734,0.000041174833,0.14004259],"study_design_scores_gemma":[0.00049720594,0.00005617586,0.00015531336,0.00008015424,0.0006706263,0.000037856465,0.0006924849,0.9954781,0.000019636464,0.0018326429,0.00032053704,0.00015929902],"about_ca_topic_score_codex":0.0000059645427,"about_ca_topic_score_gemma":0.000032398697,"teacher_disagreement_score":0.34888625,"about_ca_system_score_codex":0.000039753606,"about_ca_system_score_gemma":0.000012114811,"threshold_uncertainty_score":0.5484012},"labels":[],"label_agreement":null},{"id":"W2131088269","doi":"10.1016/s0165-0114(03)00260-4","title":"Duality in linear programming with fuzzy parameters and matrix games with fuzzy pay-offs","year":2003,"lang":"en","type":"article","venue":"Fuzzy Sets and Systems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":126,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mathematics; Duality (order theory); Fuzzy logic; Dual (grammatical number); Linear programming; Mathematical optimization; Matrix (chemical analysis); Fuzzy number; Fuzzy set operations; Fuzzy classification; Fuzzy associative matrix; Fuzzy set; Algebra over a field; Discrete mathematics; Artificial intelligence; Computer science; Pure mathematics","score_opus":0.016235213090994398,"score_gpt":0.24477320793112886,"score_spread":0.22853799484013446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131088269","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9686347,0.0032088205,0.012878555,0.00013607222,0.000439941,0.0023703077,0.000019510504,0.00068665494,0.011625434],"genre_scores_gemma":[0.971408,0.000070381924,0.028141519,0.00001530536,0.000021849084,0.000086739514,0.000009037019,0.000046939786,0.00020024371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874276,0.00007559974,0.0003416792,0.00027030037,0.00019989703,0.0003697795],"domain_scores_gemma":[0.9994485,0.00008531891,0.000060062073,0.00019242128,0.00003952453,0.00017419456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004016779,0.00024371162,0.0003832357,0.000094302515,0.00006996389,0.00020046288,0.000052132935,0.0001000975,0.0000025488148],"category_scores_gemma":[0.000035834964,0.0001563602,0.00002195523,0.00024730366,0.000080484235,0.00016091179,0.000014003246,0.00018326758,0.0000041866465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006742558,0.001602864,0.24850504,0.03741912,0.0019613265,0.0010382431,0.0335066,0.3626986,0.00047871206,0.14022772,0.0023033782,0.16958413],"study_design_scores_gemma":[0.029158324,0.004832046,0.012408296,0.0111453775,0.000998083,0.0035793392,0.054155372,0.6527176,0.00072542275,0.0068773557,0.21232171,0.011081067],"about_ca_topic_score_codex":0.000064732725,"about_ca_topic_score_gemma":0.000051297793,"teacher_disagreement_score":0.29001898,"about_ca_system_score_codex":0.000027673152,"about_ca_system_score_gemma":0.00001687566,"threshold_uncertainty_score":0.63761836},"labels":[],"label_agreement":null},{"id":"W2133185406","doi":"10.1007/s00186-015-0516-y","title":"Extensions of the sequential stochastic assignment problem","year":2015,"lang":"en","type":"article","venue":"Mathematical Methods of Operations Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Air Force Office of Scientific Research","keywords":"Mathematical optimization; Extension (predicate logic); Computer science; Assignment problem; Generalized assignment problem; Mathematics","score_opus":0.26806749435509003,"score_gpt":0.48368437789569024,"score_spread":0.2156168835406002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133185406","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002409645,0.000057502315,0.98783094,0.0002805741,0.00006634943,0.0006535706,0.0000040642703,0.00004596592,0.008651414],"genre_scores_gemma":[0.27404693,0.0000019180259,0.7255207,0.000005014729,0.000015389536,0.00008954148,0.0000010607921,0.000018918863,0.00030056734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980637,0.00047355695,0.00049159396,0.00011290443,0.0006230111,0.000235255],"domain_scores_gemma":[0.9983378,0.0005984391,0.000018916708,0.00040210536,0.00050079427,0.00014191263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003205326,0.00009310263,0.00024337247,0.0001174489,0.00009433584,0.00003882975,0.00028402306,0.00006835101,0.00018674869],"category_scores_gemma":[0.0025954312,0.00006057669,0.0000730741,0.0005370527,0.000283706,0.00008120363,0.00016330977,0.00025849004,0.00002443907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000063366856,0.00036391744,0.0000013959203,0.00044297153,0.00006207035,5.219568e-7,0.0018589356,0.6200394,0.023440855,0.34592706,0.00071628817,0.007140208],"study_design_scores_gemma":[0.00023317909,0.00006446439,0.0000025766824,0.00014576972,0.000022362658,0.0000063180833,0.0006664865,0.90633994,0.01999123,0.072289854,0.00015001501,0.00008780635],"about_ca_topic_score_codex":0.000004477867,"about_ca_topic_score_gemma":0.000001999291,"teacher_disagreement_score":0.2863005,"about_ca_system_score_codex":0.00005484819,"about_ca_system_score_gemma":0.00009337642,"threshold_uncertainty_score":0.31071612},"labels":[],"label_agreement":null},{"id":"W2134787308","doi":"10.3138/infor.47.1.5","title":"A Discrete Stochastic Goal Program for Portfolio Selection: The Case of United Arab Emirates Equity Market","year":2009,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Portfolio; Equity (law); Stochastic programming; Goal programming; Normality; Selection (genetic algorithm); Economics; Computer science; Econometrics; Portfolio optimization; Modern portfolio theory; Actuarial science; Financial economics; Operations research; Mathematical optimization; Mathematics; Statistics; Machine learning","score_opus":0.04476132573014004,"score_gpt":0.36763829448810154,"score_spread":0.3228769687579615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134787308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059833605,0.0004167162,0.84430236,0.0010765576,0.0004286681,0.011953773,0.00028824012,0.0006324788,0.08106758],"genre_scores_gemma":[0.99744934,0.0000107989335,0.001748535,0.000041517633,0.00005793435,0.00037646486,0.000100842626,0.0000058829382,0.0002086569],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886745,0.000033436354,0.00050101185,0.00005861167,0.0003152609,0.00022423707],"domain_scores_gemma":[0.99874246,0.00025439434,0.000053208336,0.00009548718,0.00077204534,0.00008242631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013485117,0.0000943502,0.0001286186,0.00023914115,0.0003208322,0.0004529864,0.00008860685,0.0000700833,0.000032148502],"category_scores_gemma":[0.00031839154,0.000065528715,0.00003235929,0.00049301214,0.00006865355,0.0007958278,0.000027001986,0.0001516007,0.000005110105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012762108,0.00006263666,0.000019771647,0.0010691237,0.00009429983,0.0000029704322,0.0020673035,0.4005458,0.00004756147,0.49015653,0.012669929,0.093136445],"study_design_scores_gemma":[0.00034526567,0.000174657,0.00005101626,0.00004966137,0.0000057808406,0.00014247498,0.0010832639,0.98151135,0.000029360377,0.00037166997,0.01615146,0.00008404781],"about_ca_topic_score_codex":0.000031424377,"about_ca_topic_score_gemma":0.0000054530046,"teacher_disagreement_score":0.93761575,"about_ca_system_score_codex":0.000045470828,"about_ca_system_score_gemma":0.000065443426,"threshold_uncertainty_score":0.43681583},"labels":[],"label_agreement":null},{"id":"W2135362276","doi":"10.1002/9780470400531.eorms0411","title":"Instance Formats for Mathematical Optimization Models","year":2011,"lang":"en","type":"other","venue":"Wiley Encyclopedia of Operations Research and Management Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Solver; Computer science; Software; Set (abstract data type); Focus (optics); Mathematical software; Software engineering; Software package; Theoretical computer science; Programming language","score_opus":0.04739222563648038,"score_gpt":0.3122788937281135,"score_spread":0.26488666809163314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135362276","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000020688933,0.00019963669,0.4922287,0.000011591248,0.000052901283,0.0008937759,0.000012102365,0.00008776009,0.50651145],"genre_scores_gemma":[0.0004501074,0.026773626,0.8041377,0.000012781571,0.000051123672,0.00070367026,0.00003398209,0.00016538805,0.1676716],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986365,0.0000115705525,0.0002719553,0.00025801937,0.00045356518,0.0003683929],"domain_scores_gemma":[0.9993701,0.000031061205,0.000022249942,0.00029790247,0.00014353044,0.00013516207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058932696,0.00014674393,0.00019282135,0.0006708713,0.00017937017,0.00010544367,0.00034593343,0.000076744705,0.0003075593],"category_scores_gemma":[0.00006515301,0.00013279688,0.00002455334,0.0005746375,0.00047829846,0.00039886398,0.00013445946,0.00010430466,0.000016724818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061929345,0.00016521834,6.14147e-7,0.002866945,0.000049465987,0.0000013441244,0.00053753215,0.23230134,0.000007186132,0.66548586,0.08478517,0.013793123],"study_design_scores_gemma":[0.00020302173,0.000052247124,2.971351e-7,0.00036777506,0.000013368086,7.6741077e-7,0.0001229077,0.9458852,0.000016511452,0.006459083,0.046711087,0.00016773779],"about_ca_topic_score_codex":0.000005845294,"about_ca_topic_score_gemma":0.000015632868,"teacher_disagreement_score":0.7135838,"about_ca_system_score_codex":0.00003295304,"about_ca_system_score_gemma":0.000034657074,"threshold_uncertainty_score":0.54152995},"labels":[],"label_agreement":null},{"id":"W2136049019","doi":"10.1139/x2012-135","title":"Balancing equity and efficiency of goal programming for use in forest management planning","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Goal programming; Minimax; Decision maker; Computer science; Operations research; Set (abstract data type); Management by objectives; Equity (law); Multiple-criteria decision analysis; Mathematical optimization; Management science; Mathematics; Economics; Business; Marketing","score_opus":0.080473235801199,"score_gpt":0.3531321448333193,"score_spread":0.2726589090321203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136049019","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95434827,0.0008339035,0.04334117,0.000036170008,0.00012273369,0.00041357774,0.000002828477,0.000008057494,0.000893284],"genre_scores_gemma":[0.9764182,0.000012172905,0.023470731,0.0000039695483,0.0000469164,0.000010723664,0.0000012935467,0.000015886533,0.00002013467],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99877995,0.000025086896,0.0003090123,0.000057541143,0.00022604073,0.0006023548],"domain_scores_gemma":[0.9991327,0.00018727245,0.000040245104,0.00007837667,0.000121324614,0.0004401129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018313588,0.00007062597,0.00015441938,0.000544276,0.00007464926,0.0000901404,0.00014846321,0.000046990674,0.000005959223],"category_scores_gemma":[0.0002882735,0.0000656467,0.000035124423,0.00028705955,0.000083704006,0.00026473566,0.000037885333,0.0002220468,5.9528924e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032779255,0.000070835835,0.8171254,0.0016770024,0.00006270204,0.00007610756,0.002578484,0.098577514,0.000049176535,0.016350664,0.00048103096,0.06291829],"study_design_scores_gemma":[0.0060856408,0.0012848414,0.51778173,0.0047965236,0.00011229361,0.0003021656,0.008545183,0.41250387,0.0005179643,0.008928781,0.038015556,0.0011254108],"about_ca_topic_score_codex":0.000264588,"about_ca_topic_score_gemma":0.0057223174,"teacher_disagreement_score":0.31392637,"about_ca_system_score_codex":0.00014181051,"about_ca_system_score_gemma":0.00007929693,"threshold_uncertainty_score":0.31931874},"labels":[],"label_agreement":null},{"id":"W2138089115","doi":"10.5430/air.v2n4p75","title":"An interactive fuzzy satisficing method for random fuzzy multiobjective linear programming problems through fractile criteria optimization with possibility","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Satisficing; Mathematical optimization; Goal programming; Fuzzy logic; Linear programming; Pareto principle; Preference; Mathematics; Computer science; Artificial intelligence; Statistics","score_opus":0.09963363422740967,"score_gpt":0.4310779230352048,"score_spread":0.33144428880779514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138089115","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006986981,0.00003743316,0.9870921,0.00013203094,0.00013479119,0.003957785,0.0000080399195,0.0004060935,0.0012447431],"genre_scores_gemma":[0.47375488,0.000014362615,0.5250101,0.000012826681,0.00010663698,0.0009974957,0.000029045304,0.000056655506,0.000018009818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99715376,0.00037102,0.00064366014,0.00056636095,0.0004804606,0.0007847095],"domain_scores_gemma":[0.9962339,0.0017154202,0.00008545117,0.00041115173,0.001352414,0.00020170165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018516103,0.00027249518,0.00036629508,0.00020978916,0.0004280662,0.00057179114,0.00028006342,0.00016341876,0.00028036558],"category_scores_gemma":[0.0011879769,0.00023052315,0.00008590052,0.0008016377,0.00020047792,0.0015323965,0.000052932468,0.00056391594,0.000074884134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023233719,0.00034789732,0.000017904418,0.00026178578,0.00007319219,0.0000015321773,0.008714809,0.72914845,0.0040477137,0.0023080918,0.00003125087,0.25481504],"study_design_scores_gemma":[0.00017000435,0.00043228077,0.000005106946,0.00012008326,0.00001726876,0.000003837776,0.008138616,0.93901736,0.029512191,0.022210332,0.000099631354,0.0002733081],"about_ca_topic_score_codex":0.00072195637,"about_ca_topic_score_gemma":0.00017877406,"teacher_disagreement_score":0.4667679,"about_ca_system_score_codex":0.00019565377,"about_ca_system_score_gemma":0.000065840206,"threshold_uncertainty_score":0.94004613},"labels":[],"label_agreement":null},{"id":"W2139575211","doi":"10.1287/ijoc.12.3.223.12638","title":"A Simplex-Based Tabu Search Method for Capacitated Network Design","year":2000,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":167,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Tabu search; Mathematical optimization; Simplex; Integer programming; Set (abstract data type); Network planning and design; Integer (computer science); Mathematics; Column generation; Computer science; Fixed charge; Algorithm; Combinatorics","score_opus":0.04292685753506973,"score_gpt":0.30508229562650413,"score_spread":0.2621554380914344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139575211","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031438703,0.000021017391,0.994144,0.00006979647,0.0001586152,0.0002714217,9.416795e-7,0.00024315598,0.0019471584],"genre_scores_gemma":[0.088581465,0.0000055097603,0.9105452,0.00048788358,0.00027387275,0.0000043231253,0.0000035541866,0.000038594695,0.000059597518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877554,0.000034957367,0.00044377602,0.00008000048,0.0001936996,0.00047205194],"domain_scores_gemma":[0.99896103,0.00063997513,0.000050708866,0.000093879244,0.00008952881,0.00016488135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011523695,0.00014908993,0.00020594786,0.00009013067,0.0002619022,0.00020205087,0.00014124496,0.00006839224,0.00021501885],"category_scores_gemma":[0.000069638874,0.00012104607,0.00010561631,0.0002570475,0.000014848946,0.00013010402,0.0000067012365,0.0003402145,0.000039906314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020577298,0.000008673918,0.0000022565425,0.000028331726,0.000021056267,0.0000025772292,0.00013087316,0.7085003,0.000007874386,0.00029633337,0.000754249,0.2902269],"study_design_scores_gemma":[0.00063823414,0.00012053383,0.000004724255,0.000111204274,0.000009078032,0.00002819057,0.000030377121,0.9839568,0.0002513707,0.00096213084,0.013730781,0.00015653309],"about_ca_topic_score_codex":0.0000011247296,"about_ca_topic_score_gemma":2.639025e-7,"teacher_disagreement_score":0.29007035,"about_ca_system_score_codex":0.00006972288,"about_ca_system_score_gemma":0.00003356343,"threshold_uncertainty_score":0.49361154},"labels":[],"label_agreement":null},{"id":"W2145460126","doi":"10.1061/9780784413616.127","title":"Case Studies for the Planning and Monitoring of Unit- and Fixed-Price Contracts Using Project Scheduling Software","year":2014,"lang":"en","type":"article","venue":"Computing in Civil and Building Engineering (2014)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Cash flow; Software; Computer science; Activity-based costing; Schedule; Scheduling (production processes); Software project management; Operations research; Engineering management; Software development; Operations management; Business; Finance; Engineering; Software construction; Accounting","score_opus":0.0464893035019804,"score_gpt":0.3091626853596981,"score_spread":0.2626733818577177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145460126","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51095945,0.0038118223,0.48482558,0.0000045320908,0.00014252728,0.00014428097,6.838114e-7,0.00010739236,0.0000037523157],"genre_scores_gemma":[0.7861477,0.00010621491,0.21362117,0.000003667206,0.00008711449,0.00000584213,2.704833e-7,0.000027125043,8.834551e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992101,0.0000137090765,0.00027515407,0.00017274992,0.000069600006,0.00025864353],"domain_scores_gemma":[0.99819946,0.001552654,0.000055827077,0.00009685189,0.000048944552,0.000046267018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005424097,0.00017366784,0.00028118902,0.0001334471,0.0001463342,0.0000697041,0.000057767335,0.000061170234,1.6476262e-7],"category_scores_gemma":[0.0006242898,0.0001464803,0.000021522053,0.00011957087,0.00004323517,0.00009222209,0.00008303361,0.00017103067,3.525196e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000283024,0.000005534629,0.00293175,0.0012639638,0.00005057368,0.000009948894,0.001070421,0.9877997,0.0012427926,0.0003536289,0.0000033059373,0.005265553],"study_design_scores_gemma":[0.00034335165,0.000021401771,0.00034421144,0.00081506453,0.000034068955,0.0002002389,0.00041065982,0.9970392,0.0004652978,0.000064671716,0.00008898291,0.00017287437],"about_ca_topic_score_codex":0.0000067303627,"about_ca_topic_score_gemma":8.02388e-7,"teacher_disagreement_score":0.27518827,"about_ca_system_score_codex":0.00001939438,"about_ca_system_score_gemma":0.000006118593,"threshold_uncertainty_score":0.5973294},"labels":[],"label_agreement":null},{"id":"W2149139265","doi":"10.3138/infor.48.3.143","title":"Goal Programming Model for Fire and Emergency Service Facilities Site Selection","year":2010,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Decision maker; Operations research; Service (business); Process (computing); Computer science; Site selection; Selection (genetic algorithm); Goal programming; Function (biology); Decision-making; Operations management; Process management; Business; Engineering; Artificial intelligence; Marketing","score_opus":0.042454664129446706,"score_gpt":0.3167141210032354,"score_spread":0.2742594568737887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149139265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4450377,0.00020648442,0.542298,0.00047574204,0.00060869916,0.0036246334,0.00022884058,0.0004599635,0.0070599313],"genre_scores_gemma":[0.98585474,0.000051232764,0.0123145925,0.000026935206,0.000087608845,0.0005573957,0.00015923353,0.000011505252,0.0009367752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902195,0.000009811585,0.0003725336,0.000071482056,0.00030784114,0.0002163797],"domain_scores_gemma":[0.9990197,0.00006927164,0.000025033809,0.00006472321,0.000720246,0.00010105128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006937486,0.000093058785,0.000098913246,0.00012967539,0.00035654916,0.00045258718,0.000050738334,0.000100838275,0.00002427405],"category_scores_gemma":[0.00015255027,0.00008398039,0.000016775917,0.0001836611,0.000030356468,0.0015431476,0.000027839596,0.00019875697,0.000020981031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068417976,0.000042574648,0.0013483854,0.007135274,0.00008998405,1.6922037e-7,0.024408476,0.5307756,0.0016225938,0.31477422,0.0065597515,0.11317454],"study_design_scores_gemma":[0.0001988898,0.000021061125,0.000045227764,0.000014772167,0.0000018950885,0.0000067696556,0.0007545242,0.9343756,0.00002549196,0.0001203238,0.06434298,0.00009248348],"about_ca_topic_score_codex":0.00007230657,"about_ca_topic_score_gemma":0.00008528954,"teacher_disagreement_score":0.540817,"about_ca_system_score_codex":0.000021525375,"about_ca_system_score_gemma":0.00004402806,"threshold_uncertainty_score":0.43643087},"labels":[],"label_agreement":null},{"id":"W2151042303","doi":"10.5539/jas.v6n12p194","title":"Linear Programming-Based Optimization of Synthetic Fertilizers Formulation","year":2014,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fertilizer; Yield (engineering); Mathematics; Production (economics); Linear programming; Agricultural engineering; Economics; Agronomy; Mathematical optimization; Engineering","score_opus":0.009363871438292199,"score_gpt":0.22600297820826612,"score_spread":0.21663910676997392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151042303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22093454,0.000024138399,0.7778071,0.00015750142,0.00020311962,0.0001315584,2.316086e-7,0.000053754728,0.0006880641],"genre_scores_gemma":[0.86149067,0.0000027664803,0.13846254,0.000006756643,0.000026817548,8.312062e-7,5.9241586e-7,0.0000034467798,0.000005587072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913985,0.000011322716,0.00032603022,0.000056037985,0.00033300096,0.00013373878],"domain_scores_gemma":[0.999328,0.000044418968,0.00015656032,0.000056614714,0.000326895,0.000087483415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042006045,0.000068022004,0.00012724705,0.00008907939,0.000053234788,0.00003930412,0.00015765766,0.000028150625,0.000013407355],"category_scores_gemma":[0.0002574783,0.00004032066,0.000058128397,0.00050513726,0.00006920505,0.00037360424,0.000008743682,0.000067422785,0.0000013518127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025408494,0.000020728954,0.000039011575,0.000032873657,0.0000026258058,8.944404e-8,0.00006284248,0.98929894,0.005288656,0.000288851,0.000011067549,0.0049517984],"study_design_scores_gemma":[0.00021684449,0.00012242621,0.0017545699,0.00008912689,0.000017787355,0.000010373841,0.00008408917,0.98484373,0.012583722,0.000050869785,0.0001416808,0.00008477015],"about_ca_topic_score_codex":4.1840255e-7,"about_ca_topic_score_gemma":2.5833353e-7,"teacher_disagreement_score":0.64055616,"about_ca_system_score_codex":0.00004498026,"about_ca_system_score_gemma":0.000018114844,"threshold_uncertainty_score":0.16442288},"labels":[],"label_agreement":null},{"id":"W2152797042","doi":"10.5539/mas.v6n4p12","title":"Hybrid Two-Stage Algorithm for Solving Transportation Problem","year":2012,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Applied Science Private University","keywords":"Simplex algorithm; Mathematical optimization; Computer science; Algorithm; Genetic algorithm; Linear programming; Population; Multi stage; Hybrid algorithm (constraint satisfaction); Mathematics; Stochastic programming; Engineering","score_opus":0.01549142708471356,"score_gpt":0.24490145994498988,"score_spread":0.2294100328602763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152797042","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002511102,0.000028025772,0.991564,0.0000076059564,0.00009044601,0.0003745327,0.0000072621597,0.00027967352,0.005137389],"genre_scores_gemma":[0.62036955,0.000001179904,0.37943435,0.00001956758,0.000027302875,0.00007683894,0.000005602805,0.000013068359,0.000052549],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915665,9.944747e-7,0.00014590539,0.000134778,0.00018482041,0.00037683407],"domain_scores_gemma":[0.99970573,0.000021545005,0.00002101705,0.00010505393,0.00002755244,0.00011912293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034645453,0.00008784742,0.00008617902,0.000055688888,0.0001390655,0.00006082196,0.00013226464,0.000016009291,0.000016623455],"category_scores_gemma":[0.0000051677607,0.00008532052,0.000023273034,0.00014063492,0.00007023273,0.0003024238,0.0000065179433,0.000052807212,0.000013875774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002959467,0.00008946193,0.000015297648,0.00021901981,0.000009817893,3.9067552e-7,0.0033422853,0.12095425,0.1904692,0.053661283,0.00008937137,0.63114667],"study_design_scores_gemma":[0.00017534544,0.0000036580616,0.000009034711,0.000005233591,0.0000053595563,4.6389331e-7,0.000040836225,0.97586125,0.020285571,0.0027994646,0.00069540244,0.00011837295],"about_ca_topic_score_codex":7.9546595e-7,"about_ca_topic_score_gemma":5.4337005e-7,"teacher_disagreement_score":0.854907,"about_ca_system_score_codex":0.00003861289,"about_ca_system_score_gemma":0.000013298134,"threshold_uncertainty_score":0.34792697},"labels":[],"label_agreement":null},{"id":"W2153593711","doi":"10.3138/infor.50.3.117","title":"A Goal Programming Model with Satisfaction Function for Risk Management and Optimal Portfolio Diversification","year":2012,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Diversification (marketing strategy); Mathematical optimization; Portfolio; Computer science; Goal programming; Scalar (mathematics); Project portfolio management; Set (abstract data type); Portfolio optimization; Mathematics; Project management; Economics; Marketing; Management","score_opus":0.0357329195835477,"score_gpt":0.28867419863416377,"score_spread":0.2529412790506161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153593711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037482575,0.00016822542,0.9563864,0.000021653319,0.00009711983,0.0016294415,0.00002806992,0.00011463602,0.0040719104],"genre_scores_gemma":[0.9594353,0.00012413178,0.03948024,0.000013690836,0.000055226486,0.00057955616,0.00012033863,0.000010380889,0.00018116663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898726,0.000017016644,0.00028846902,0.00007228512,0.00038583355,0.00024911007],"domain_scores_gemma":[0.9994071,0.000060342743,0.00004802991,0.0000788342,0.00028460345,0.000121055215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090768567,0.000097235905,0.00009250095,0.00022548377,0.00037285083,0.0003967006,0.00003232181,0.00006431747,0.0000060232946],"category_scores_gemma":[0.00003469715,0.00008089907,0.000014661474,0.0001606974,0.000042556185,0.0023902336,0.000024801886,0.00011432814,0.000012364994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016239398,0.000030244943,0.0056762844,0.0012292282,0.00014764862,1.1079032e-7,0.002685613,0.5511238,0.000013828152,0.2697017,0.001745686,0.1674835],"study_design_scores_gemma":[0.000573587,0.0000761789,0.0027733918,0.00003662684,0.000020642543,0.000008267169,0.002183244,0.96342087,0.000009718424,0.000054163425,0.030717319,0.00012597964],"about_ca_topic_score_codex":0.000019483778,"about_ca_topic_score_gemma":0.0000017971767,"teacher_disagreement_score":0.9219527,"about_ca_system_score_codex":0.000067701454,"about_ca_system_score_gemma":0.000016556925,"threshold_uncertainty_score":0.3825393},"labels":[],"label_agreement":null},{"id":"W2154326504","doi":"10.1287/opre.1120.1041","title":"Fixed-Charge Transportation Problem: Facets of the Projection Polyhedron","year":2012,"lang":"en","type":"article","venue":"Operations Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Polyhedron; Heuristics; Fixed charge; Projection (relational algebra); Facet (psychology); Variable (mathematics); Mathematical optimization; Fixed cost; Computer science; Space (punctuation); Transportation theory; Facility location problem; Charge (physics); Unit (ring theory); Fixed point; Mathematics; Combinatorics; Algorithm; Physics; Mathematical analysis","score_opus":0.059166430063894146,"score_gpt":0.3412065012197392,"score_spread":0.28204007115584506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154326504","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7894611,0.0006966094,0.13605016,0.0015743058,0.00075584,0.0046528564,0.000055743538,0.0005061012,0.06624729],"genre_scores_gemma":[0.99247056,0.00002150036,0.0060350117,0.0000044324033,0.0000369874,0.00011206382,0.000014497445,0.000013819326,0.0012911169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992622,0.00005312695,0.0001636273,0.000057054345,0.00025704215,0.00020693197],"domain_scores_gemma":[0.999668,0.000025483932,0.0000061271676,0.00013549624,0.00012022485,0.00004464461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041903183,0.00005021646,0.00005866245,0.000077488534,0.00016940272,0.000031661173,0.000081921906,0.000045402216,0.00021358911],"category_scores_gemma":[0.000048284768,0.000036373167,0.000027825887,0.0004210939,0.00004235353,0.00024766912,0.00000748479,0.000164261,0.000049935734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002400217,0.0012872977,0.006873426,0.0016110418,0.00015132615,4.7777837e-7,0.030302469,0.366347,0.28621617,0.25063944,0.010421709,0.04612567],"study_design_scores_gemma":[0.00087547716,0.0001325777,0.00736142,0.00021436032,0.000040071936,0.000007092667,0.0018783101,0.6914419,0.2685079,0.00047001976,0.028594311,0.0004765774],"about_ca_topic_score_codex":0.000036124737,"about_ca_topic_score_gemma":0.000074483185,"teacher_disagreement_score":0.32509488,"about_ca_system_score_codex":0.000036427366,"about_ca_system_score_gemma":0.00002306974,"threshold_uncertainty_score":0.2338651},"labels":[],"label_agreement":null},{"id":"W2161805526","doi":"10.1017/cbo9781107282094.007","title":"Solving integer programs","year":2018,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Selection (genetic algorithm); Focus (optics); Point (geometry); Section (typography); Reading (process); Computer science; Integer (computer science); Mathematics education; Range (aeronautics); Management science; Mathematics; Engineering; Artificial intelligence; Programming language; Political science","score_opus":0.021380664321275427,"score_gpt":0.18634084016887525,"score_spread":0.16496017584759984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161805526","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000012163357,0.00006839128,0.028627388,0.0000024066421,0.00026705847,0.0003452833,0.000015993823,0.0008815602,0.96977973],"genre_scores_gemma":[0.000512048,0.000040857492,0.0027801634,0.000015472706,0.00013901507,0.0000010705897,0.000038999722,0.00009177529,0.9963806],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991348,0.0000074804116,0.00017000327,0.00025414742,0.00016363025,0.0002699403],"domain_scores_gemma":[0.99931115,0.000024903526,0.00006006264,0.00033720225,0.000108615415,0.00015804553],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00005886879,0.00030931973,0.00029443944,0.00011342262,0.000087379085,0.00007188785,0.00028548256,0.00031464334,0.00004857259],"category_scores_gemma":[0.000006625184,0.0003582228,0.00015543887,0.000007673159,0.00017323508,0.000076603,0.00015653431,0.00034152486,0.000092797534],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010745759,0.000009692029,1.816711e-7,0.00040336864,0.00019322336,0.00008019176,0.00012806083,0.000056564222,0.000013612053,0.94129896,0.045981146,0.01182425],"study_design_scores_gemma":[0.00019297241,0.00002881008,9.069658e-8,0.00027691695,0.00010745808,0.0000075123826,0.000030823347,0.007807588,0.00009233144,0.000031724387,0.99101704,0.00040672798],"about_ca_topic_score_codex":0.0000038611547,"about_ca_topic_score_gemma":6.639716e-7,"teacher_disagreement_score":0.9450359,"about_ca_system_score_codex":0.00012753048,"about_ca_system_score_gemma":0.000019137075,"threshold_uncertainty_score":0.999887},"labels":[],"label_agreement":null},{"id":"W2169662397","doi":"10.5267/j.msl.2014.1.019","title":"A fuzzy mixed integer programming for marketing planning","year":2014,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Integer programming; Fuzzy logic; Computer science; Marketing; Business; Artificial intelligence; Algorithm","score_opus":0.011221531366785269,"score_gpt":0.23531657291484306,"score_spread":0.2240950415480578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169662397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011539863,0.000007390624,0.9698988,0.0006421449,0.000354574,0.00044625762,1.8729853e-7,0.00044982354,0.016660916],"genre_scores_gemma":[0.7104787,9.147468e-7,0.28845584,0.0007624823,0.000057045112,0.00012843576,0.0000022610518,0.000023354869,0.00009097296],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895287,0.0000126767245,0.00016920622,0.00021896504,0.00022174892,0.00042456592],"domain_scores_gemma":[0.99964696,0.00008051257,0.000026947262,0.00016411301,0.000010491854,0.00007095613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013464015,0.00010975638,0.00009686274,0.0001838223,0.00018305132,0.00022817106,0.0002680052,0.000015978916,0.000005701729],"category_scores_gemma":[0.000111898786,0.00010446218,0.00003888547,0.00038880712,0.00010728891,0.00022029302,0.00006766938,0.00006028513,0.000014110012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017347562,0.000052761203,0.00074131106,0.0016538093,0.0000688638,0.000007492012,0.00091341033,0.32678762,0.004820506,0.039661676,0.0100161135,0.6152591],"study_design_scores_gemma":[0.00050914584,0.000024444937,0.00058696914,0.000223782,0.000034500117,0.000001906833,0.0006193974,0.91682416,0.00046705632,0.00072565966,0.07954597,0.0004369821],"about_ca_topic_score_codex":2.8115488e-7,"about_ca_topic_score_gemma":1.522075e-7,"teacher_disagreement_score":0.69893885,"about_ca_system_score_codex":0.000047324174,"about_ca_system_score_gemma":9.0508e-7,"threshold_uncertainty_score":0.4259844},"labels":[],"label_agreement":null},{"id":"W2190179764","doi":"","title":"OPTIMUM ALLOCATION OF COMPUTER RESOURCES THROUGH GOAL PROGRAMMING","year":2015,"lang":"en","type":"article","venue":"IJITR International Journal of Innovative Technology and Research - IJITR International Journal of Innovative Technology and Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Computer science; Implementation; Goal programming; Process (computing); Computer programming; Program Design Language; Software engineering; Distributed computing; Operations research; Programming language; Engineering","score_opus":0.06166204085384396,"score_gpt":0.40145543531103056,"score_spread":0.3397933944571866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2190179764","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86970085,0.0026810917,0.099824354,0.022883048,0.001728544,0.0005938725,0.00004419657,0.00012817104,0.0024158626],"genre_scores_gemma":[0.93242866,0.00087064196,0.06601584,0.00006350919,0.0004618757,0.000018026594,0.000012173346,0.000040072457,0.00008918815],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9943755,0.000302739,0.0019084915,0.00030975573,0.0025111502,0.0005923492],"domain_scores_gemma":[0.96156853,0.0006230461,0.00088824605,0.00022040746,0.03655654,0.00014322705],"candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0069319177,0.0003048469,0.00068761344,0.010940783,0.00019508472,0.00017769907,0.001905584,0.0006358182,0.000046161742],"category_scores_gemma":[0.0039570387,0.00024410193,0.000087488406,0.006976944,0.0036322856,0.00091922696,0.00088422734,0.0037037034,0.000004178031],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018570957,0.0016466549,0.014621229,0.00022080085,0.0048477193,0.0010377497,0.0059096003,0.00102071,0.023560442,0.63253903,0.008352323,0.30438665],"study_design_scores_gemma":[0.017605897,0.011986145,0.003592112,0.005315769,0.00007472078,0.010561924,0.05167763,0.008616626,0.20951517,0.5151138,0.16432741,0.0016128403],"about_ca_topic_score_codex":0.0000074548198,"about_ca_topic_score_gemma":0.000002344902,"teacher_disagreement_score":0.3027738,"about_ca_system_score_codex":0.0003821381,"about_ca_system_score_gemma":0.0004567494,"threshold_uncertainty_score":0.9990792},"labels":[],"label_agreement":null},{"id":"W2194624903","doi":"10.1007/s12351-015-0216-7","title":"Fuzzy chance-constrained geometric programming: the possibility, necessity and credibility approaches","year":2015,"lang":"en","type":"article","venue":"Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Credibility; Mathematical optimization; Computational intelligence; Ambiguity; Duality (order theory); Computer science; Credibility theory; Fuzzy logic; Optimization problem; Process (computing); Face (sociological concept); Mathematics; Artificial intelligence","score_opus":0.2686170978067754,"score_gpt":0.3633974369433609,"score_spread":0.09478033913658551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2194624903","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73270595,0.003297919,0.09963838,0.010808559,0.000459739,0.0050418973,0.00005019462,0.00086658425,0.14713079],"genre_scores_gemma":[0.9850414,0.000016290955,0.014322331,0.000020855441,0.00009683235,0.00011961364,0.000023853183,0.000012922281,0.00034590162],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847823,0.00013756087,0.00019940351,0.00022092472,0.0006513586,0.00031253128],"domain_scores_gemma":[0.99890214,0.0003450934,0.000010819309,0.00022933082,0.0003058003,0.00020679827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034167543,0.00009784315,0.00011691986,0.00012003346,0.00025891146,0.0003434916,0.00018325429,0.000067605324,0.000040245544],"category_scores_gemma":[0.0015119131,0.00006693561,0.000024419103,0.00076804037,0.00034626547,0.00021268548,0.00011680851,0.00033417932,0.000028417779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013687828,0.0012141607,0.0137893865,0.0010254645,0.00021484865,0.00001455838,0.0075496845,0.04571718,0.0003301946,0.27673513,0.012116853,0.64115566],"study_design_scores_gemma":[0.0014467123,0.00028976455,0.0059774746,0.000045824232,0.00001802523,0.000036707337,0.0036889531,0.92909664,0.00089441467,0.039444495,0.018530283,0.00053069275],"about_ca_topic_score_codex":0.000018094453,"about_ca_topic_score_gemma":0.000018869225,"teacher_disagreement_score":0.88337946,"about_ca_system_score_codex":0.000080653925,"about_ca_system_score_gemma":0.000115481685,"threshold_uncertainty_score":0.33122975},"labels":[],"label_agreement":null},{"id":"W223119275","doi":"10.1023/a:1026102724889","title":"A Tabu Search with Slope Scaling for the Multicommodity Capacitated Location Problem with Balancing Requirements","year":2003,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Tabu search; Initialization; Mathematical optimization; Heuristic; Integer programming; Theory of computation; Scaling; Mathematics; Integer (computer science); Computer science; Scale (ratio); Algorithm","score_opus":0.20830744040420834,"score_gpt":0.4026867677618781,"score_spread":0.19437932735766977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W223119275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08744001,0.00020390358,0.90612924,0.00091142376,0.000023160226,0.002264254,0.000010301276,0.000091374604,0.0029263613],"genre_scores_gemma":[0.9318562,0.000044574605,0.06758884,0.000020377847,0.000012207573,0.0002922134,0.000015192378,0.00002637825,0.0001440164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877626,0.00010410058,0.00022155301,0.00013678276,0.000421668,0.0003396107],"domain_scores_gemma":[0.9979867,0.00027720333,0.000011501492,0.00023984138,0.0014127791,0.0000720121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001623012,0.00009110458,0.000119132215,0.00012101874,0.00040924927,0.00013896888,0.00013750837,0.0000368862,0.000034652734],"category_scores_gemma":[0.00029265735,0.000057175814,0.000018988365,0.0006980064,0.00013714902,0.0002348877,0.000014191824,0.00019471107,0.000006658346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002517687,0.00008930233,0.00008760615,0.0002507283,0.0000761677,4.748069e-7,0.0012904044,0.9844324,0.0011626112,0.009915196,0.00025082304,0.0024191001],"study_design_scores_gemma":[0.0005245762,0.00017925561,0.00007716622,0.00018241501,0.000010485646,0.0000032265907,0.0016027516,0.976626,0.01971046,0.0001217275,0.00083920814,0.00012276124],"about_ca_topic_score_codex":0.00005278075,"about_ca_topic_score_gemma":0.0001326644,"teacher_disagreement_score":0.8444162,"about_ca_system_score_codex":0.000026753301,"about_ca_system_score_gemma":0.000104536,"threshold_uncertainty_score":0.3147656},"labels":[],"label_agreement":null},{"id":"W2249032529","doi":"10.1016/j.automatica.2015.09.013","title":"Local optimization of dynamic programs with guaranteed satisfaction of path constraints","year":2015,"lang":"en","type":"article","venue":"Automatica","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Karush–Kuhn–Tucker conditions; Path (computing); Solver; Mathematical optimization; Mathematics; Bounded function; Convergence (economics); Point (geometry); Interior point method; Computer science","score_opus":0.010686258319215164,"score_gpt":0.21893277117581308,"score_spread":0.20824651285659793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2249032529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027375406,0.00002072968,0.9692926,0.000011660375,0.000040610168,0.0002753904,0.0000026616874,0.0002855931,0.002695358],"genre_scores_gemma":[0.7794505,0.0000024452909,0.22050172,0.0000031485567,0.0000017540794,0.000010588032,0.000009174802,0.000015814938,0.0000048686825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993074,0.000015814854,0.0002994991,0.00007193215,0.0001882931,0.00011706183],"domain_scores_gemma":[0.99961233,0.000027194124,0.00007470239,0.000127983,0.000095298536,0.00006250192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011350064,0.000093928014,0.00019718199,0.00005199652,0.000010321279,0.000010365365,0.000048453603,0.000052453615,0.000043241387],"category_scores_gemma":[0.00003509197,0.00007786213,0.00002532963,0.0001924039,0.00016739732,0.0000847851,0.000008345098,0.000048594447,0.0000053633576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017934455,0.000121282945,0.00029533112,0.0007035284,0.000091581496,0.0000023779114,0.0011655601,0.8450446,0.00017281336,0.0037434015,0.000036336154,0.14860526],"study_design_scores_gemma":[0.00046968774,0.00012630949,0.00012480922,0.00014815343,0.000029866314,0.000010710549,0.00052529457,0.9979428,0.00027037997,0.00025700108,0.000007785986,0.0000871887],"about_ca_topic_score_codex":0.000005038193,"about_ca_topic_score_gemma":0.000003725298,"teacher_disagreement_score":0.7520751,"about_ca_system_score_codex":0.000031546675,"about_ca_system_score_gemma":0.000028092005,"threshold_uncertainty_score":0.31751254},"labels":[],"label_agreement":null},{"id":"W2250241007","doi":"10.3138/infor.52.3.138","title":"A Fuzzy Goal Programming Model for Venture Capital Investment Decision Making","year":2014,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Venture capital; Cardinality (data modeling); Investment (military); Fuzzy logic; Selection (genetic algorithm); Computer science; Operations research; Process (computing); Capital (architecture); Decision-making; Economics; Business; Actuarial science; Finance; Operations management; Mathematics; Artificial intelligence; Data mining","score_opus":0.03860612207624459,"score_gpt":0.32919156314355497,"score_spread":0.2905854410673104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250241007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010747898,0.00011393949,0.97608006,0.00006321373,0.00019867285,0.0015056806,0.000025222731,0.00014237492,0.011122933],"genre_scores_gemma":[0.947934,0.000012646367,0.05106881,0.00011943415,0.00009540393,0.0005512733,0.000096095006,0.000014013209,0.000108349035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998595,0.000019665174,0.0004690509,0.00008528734,0.0005500877,0.0002809279],"domain_scores_gemma":[0.99906904,0.00023084455,0.00003618075,0.00011487577,0.00043837365,0.000110678484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012396215,0.00011217262,0.00013643813,0.00022268218,0.00030366948,0.0007388027,0.00009863577,0.000100735226,0.0000047038748],"category_scores_gemma":[0.00037294585,0.00009563353,0.000035004672,0.00015633515,0.000041118747,0.0011526946,0.00004398983,0.00015126143,0.000034934073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018921293,0.00001063778,0.00002036444,0.000461922,0.000016090507,7.832903e-8,0.0020551244,0.54000604,0.0000123123,0.40377071,0.0017388955,0.051888857],"study_design_scores_gemma":[0.00037491767,0.00004789384,0.000017902968,0.00011295071,0.0000025797026,0.000005249165,0.000512889,0.9468177,0.000007706807,0.002218346,0.04977264,0.000109228466],"about_ca_topic_score_codex":0.0000038072078,"about_ca_topic_score_gemma":0.0000032339306,"teacher_disagreement_score":0.93718606,"about_ca_system_score_codex":0.000084318825,"about_ca_system_score_gemma":0.0000493161,"threshold_uncertainty_score":0.71242917},"labels":[],"label_agreement":null},{"id":"W2260140128","doi":"10.3138/infor.52.3.97","title":"Modelling Investment Optimization on Smallholder Farms through Multiple Criteria Decision Making and Goal Programming: A Case Study from Ethiopia","year":2014,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Portfolio; Goal programming; Computer science; Investment (military); Agriculture; Linear programming; Set (abstract data type); Decision maker; Operations research; Time horizon; Mathematical optimization; Economics; Management science; Mathematics; Finance; Geography","score_opus":0.09141171868760253,"score_gpt":0.359710019974783,"score_spread":0.26829830128718046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2260140128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15900707,0.000055213484,0.83660245,0.0000328193,0.00013176617,0.0013990997,0.000011457877,0.000116900745,0.0026432094],"genre_scores_gemma":[0.93631697,0.000017406172,0.06311192,0.00012588204,0.000073204166,0.00025796512,0.000064894775,0.00001714923,0.000014623755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998279,0.00009589635,0.00063082494,0.00016296805,0.0005779193,0.0002534135],"domain_scores_gemma":[0.99869806,0.00056745904,0.000054800035,0.00017494758,0.000389991,0.000114719944],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011739398,0.00017109806,0.00019418557,0.00022179933,0.0004400845,0.0014631406,0.000080479665,0.000120526514,0.000016303837],"category_scores_gemma":[0.0003162229,0.00014503207,0.0000210286,0.0002149889,0.00005174771,0.0016527271,0.00007382943,0.00025474117,0.000017807704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002447134,0.00003719398,0.000099484256,0.0001269185,0.000022874485,0.0000042847228,0.008378749,0.9731237,0.0000021607148,0.010361855,0.000072913936,0.0077453763],"study_design_scores_gemma":[0.00081240665,0.00014835558,0.000011086089,0.00014875818,0.00000603246,0.000045143064,0.0071112043,0.98529863,0.0000058954984,0.00021888711,0.006035076,0.00015851064],"about_ca_topic_score_codex":0.00022512379,"about_ca_topic_score_gemma":0.000014187607,"teacher_disagreement_score":0.7773099,"about_ca_system_score_codex":0.00007369056,"about_ca_system_score_gemma":0.000026091015,"threshold_uncertainty_score":0.9995734},"labels":[],"label_agreement":null},{"id":"W2270839823","doi":"10.1007/978-3-319-12307-3_3","title":"Solving the Linear Transportation Problem by Modified Vogel Method","year":2015,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Transportation theory; Simplex algorithm; Mathematical optimization; Intuition; Reduction (mathematics); Linear programming; Matrix (chemical analysis); Simplex; Computer science; Algorithm; Mathematics; Combinatorics","score_opus":0.02709272304401239,"score_gpt":0.26491603033691186,"score_spread":0.23782330729289947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2270839823","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000081719145,0.0005012596,0.7939498,0.000048302452,0.00014576252,0.001014626,0.00021748354,0.00039297287,0.20372161],"genre_scores_gemma":[0.0003630677,0.0004247272,0.9565134,0.00003167198,0.00011155141,0.000112181966,0.00013204075,0.00034218215,0.041969195],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973368,0.0000054882553,0.0011426543,0.00037037703,0.0006753414,0.00046934813],"domain_scores_gemma":[0.9986451,0.0002521745,0.00033982217,0.00024036311,0.00036287287,0.00015966536],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00094351923,0.0006013063,0.00070251344,0.00017553965,0.000091753056,0.00016109967,0.00042166372,0.00040314932,0.00010021389],"category_scores_gemma":[0.0001663334,0.00051530503,0.00009349825,0.000099879886,0.00010041453,0.00014887846,0.00004287385,0.0008878644,0.00006147255],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005857359,0.000067981724,0.0000014076339,0.003882833,0.00013452805,0.000009706508,0.0047970484,0.008189658,0.00010309976,0.95885324,0.015947022,0.008007586],"study_design_scores_gemma":[0.00037897355,0.00003722055,5.163941e-7,0.00085443654,0.00022189008,0.0000067431943,0.00024118615,0.44720376,0.00009516348,0.5064436,0.04375866,0.0007577935],"about_ca_topic_score_codex":0.0000041506228,"about_ca_topic_score_gemma":0.0000121904095,"teacher_disagreement_score":0.45240963,"about_ca_system_score_codex":0.00019933803,"about_ca_system_score_gemma":0.000048879207,"threshold_uncertainty_score":0.9997299},"labels":[],"label_agreement":null},{"id":"W2270848256","doi":"10.5267/j.uscm.2015.12.002","title":"Solving a fuzzy multi-objective products and time planning using hybrid meta-heuristic algorithm: Gas refinery case study","year":2016,"lang":"en","type":"article","venue":"Uncertain Supply Chain Management","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Refinery; Meta heuristic; Computer science; Fuzzy logic; Heuristic; Algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Engineering; Waste management","score_opus":0.03590131261841701,"score_gpt":0.26714954074713976,"score_spread":0.23124822812872275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2270848256","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06487891,0.0011405091,0.9263241,0.00030818296,0.00037614396,0.0038070132,0.000049653197,0.0011829757,0.0019324884],"genre_scores_gemma":[0.7823354,0.00003517248,0.21608791,0.0000480271,0.000087117434,0.00021911316,0.000009934108,0.00009588213,0.0010814414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830484,0.00009786684,0.00039620078,0.00048640097,0.00024866362,0.0004660517],"domain_scores_gemma":[0.99922967,0.00016216164,0.000069373964,0.00034641047,0.00006832833,0.00012404851],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063435215,0.00034375107,0.0004192515,0.00025320108,0.0001818423,0.00010931936,0.00012334077,0.000035288635,0.000070792725],"category_scores_gemma":[0.00010238081,0.00025464574,0.000064792264,0.0002530745,0.000055561457,0.00019209531,0.00017212653,0.0001123013,0.0000214797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011030728,0.0027180447,0.00087429356,0.0040545394,0.016475549,0.048609138,0.022291493,0.3339332,0.0026426255,0.002716556,0.0066009103,0.5589733],"study_design_scores_gemma":[0.0019139929,0.00013386057,0.000020084495,0.00020724212,0.0013294411,0.00051881117,0.0047357744,0.98846155,0.00017846431,0.0005954733,0.0011740439,0.00073124183],"about_ca_topic_score_codex":0.000047676192,"about_ca_topic_score_gemma":0.0000030387848,"teacher_disagreement_score":0.71745646,"about_ca_system_score_codex":0.00014968096,"about_ca_system_score_gemma":0.000008926789,"threshold_uncertainty_score":0.9999906},"labels":[],"label_agreement":null},{"id":"W2280827137","doi":"","title":"Implementation of a Modified Multi Objective Optimization GA Algorithm in A Stealth Assessment for School Readiness","year":2015,"lang":"en","type":"article","venue":"Global Learn","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Optimization algorithm; Algorithm; Mathematical optimization; Mathematics","score_opus":0.03249517123770223,"score_gpt":0.3514941461909186,"score_spread":0.3189989749532164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2280827137","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023167469,0.000026247413,0.995141,0.00002956536,0.000098310615,0.0006219619,0.000028873865,0.00009012607,0.001647168],"genre_scores_gemma":[0.34704924,0.000007808573,0.65269196,0.00001685533,0.000021332662,0.00010036902,0.00006134653,0.00001400824,0.000037093945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934447,0.000021602531,0.00025034696,0.000105798266,0.000119403725,0.00015836413],"domain_scores_gemma":[0.99964464,0.000015314175,0.000049589267,0.00007732544,0.00012568176,0.000087460765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021530979,0.00008397921,0.00014664122,0.00004780496,0.000015241556,0.00002051445,0.0000519509,0.00004702886,0.0000143304505],"category_scores_gemma":[0.00004855633,0.000086794986,0.0000262231,0.00022645395,0.000010195037,0.00012715702,0.000014109289,0.000051183753,0.0000025171635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008551588,0.00005284917,0.000542564,0.000074275944,0.000015724137,3.9147824e-7,0.00018749475,0.9580214,0.0000066682405,0.0013387563,0.000100375706,0.039650954],"study_design_scores_gemma":[0.001644503,0.00006389933,0.00068113074,0.00001899878,0.000010303469,8.968573e-7,0.0014689236,0.9954632,0.00004563789,0.00042729496,0.00008773646,0.00008745854],"about_ca_topic_score_codex":0.00014210943,"about_ca_topic_score_gemma":0.000066909066,"teacher_disagreement_score":0.3447325,"about_ca_system_score_codex":0.00031551192,"about_ca_system_score_gemma":0.000076162214,"threshold_uncertainty_score":0.35393968},"labels":[],"label_agreement":null},{"id":"W2313585617","doi":"10.1111/j.1475-3995.2000.tb00181.x","title":"Editorial","year":2000,"lang":"en","type":"editorial","venue":"International Transactions in Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"University of Portsmouth","keywords":"Citation; Computer science; Library science; Humanities; Philosophy","score_opus":0.031106467099299446,"score_gpt":0.37665995854916934,"score_spread":0.3455534914498699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2313585617","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000031293675,0.00007650633,0.010640678,0.00019731656,0.93566746,0.00028179405,0.00023327187,0.00014946624,0.052750394],"genre_scores_gemma":[0.0006917628,0.0012675497,0.0020722968,0.000008010861,0.98605794,0.00035623508,0.00072584103,0.000077799225,0.008742569],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99605787,0.000093229704,0.00051657675,0.00032833894,0.0026240416,0.00037993645],"domain_scores_gemma":[0.99776495,0.0011647361,0.00001523195,0.00020335338,0.00074943836,0.000102269594],"candidate_categories":["research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009136571,0.00021988987,0.0002231787,0.00079237425,0.00014059729,0.00034559044,0.00054916687,0.0007265964,0.0078098024],"category_scores_gemma":[0.00041308164,0.00023959321,0.00009651288,0.00044538477,0.00011152426,0.00034327476,0.000012250803,0.002388998,0.000608514],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023712435,0.000090319765,2.0566698e-7,0.000034826957,0.000047388545,0.0000037183745,0.00007337869,0.10118334,0.000009459483,0.0002640458,0.89345735,0.0048122597],"study_design_scores_gemma":[0.00039790283,0.0000185335,4.184544e-7,0.000092577706,0.0000042852644,0.000001075586,0.000023597635,0.04818922,0.000022528768,0.00071352586,0.95033675,0.00019955722],"about_ca_topic_score_codex":0.000053941883,"about_ca_topic_score_gemma":0.0001109199,"teacher_disagreement_score":0.05687943,"about_ca_system_score_codex":0.00062361214,"about_ca_system_score_gemma":0.0003807791,"threshold_uncertainty_score":0.99991256},"labels":[],"label_agreement":null},{"id":"W2315982544","doi":"10.15244/pjoes/28643","title":"A Simulation-Based Nonlinear Goal Programming Model for Groundwater Remediation Systems Design","year":2014,"lang":"en","type":"article","venue":"Polish Journal of Environmental Studies","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Program for New Century Excellent Talents in University; National Natural Science Foundation of China","keywords":"Environmental remediation; Groundwater remediation; Groundwater; Goal programming; Environmental science; Nonlinear system; Computer science; Engineering; Geotechnical engineering; Operations research; Ecology; Contamination","score_opus":0.039292019759066266,"score_gpt":0.2671689631769241,"score_spread":0.22787694341785786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2315982544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006551088,0.00054968277,0.9923183,0.0000352214,0.0001977536,0.00029253642,0.000003928696,0.000035796234,0.000015707734],"genre_scores_gemma":[0.87232,0.000041707222,0.1273547,0.000026759542,0.0001862905,0.000022156291,0.0000040824534,0.000024387307,0.00001992756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916035,0.000024686013,0.00038914316,0.00006765897,0.00018773908,0.00017040798],"domain_scores_gemma":[0.9993364,0.0003888133,0.00012373511,0.00005744126,0.000031319505,0.000062252315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035330758,0.00011799419,0.00022090747,0.000060062892,0.00007893212,0.000045102053,0.0000661363,0.000039389044,0.0000030706776],"category_scores_gemma":[0.00020522597,0.00009742346,0.000076616176,0.0000322595,0.000042426404,0.00016440048,0.0000118572725,0.00006878711,0.0000027792441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001263858,0.000056625842,0.0000372687,0.00009282867,0.000066476045,3.007624e-7,0.0003456096,0.9946996,0.00013964363,0.000029873567,0.000049185528,0.004469943],"study_design_scores_gemma":[0.00052949996,0.00011258002,0.000013028229,0.000045743785,0.000055519322,0.0000012384448,0.00022663968,0.9976942,0.00011435816,0.00015822361,0.0009450725,0.0001038927],"about_ca_topic_score_codex":2.8728263e-7,"about_ca_topic_score_gemma":2.842939e-7,"teacher_disagreement_score":0.8657689,"about_ca_system_score_codex":0.0001264944,"about_ca_system_score_gemma":0.000004490214,"threshold_uncertainty_score":0.39728135},"labels":[],"label_agreement":null},{"id":"W2317651347","doi":"10.5509/2014874809","title":"An Elusive Quest with Mixed Results","year":2014,"lang":"en","type":"article","venue":"Pacific Affairs","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Political science","score_opus":0.005566549228692413,"score_gpt":0.19898883943477283,"score_spread":0.19342229020608043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317651347","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005680819,0.000013324358,0.26897857,0.000044737048,0.00013265212,0.000120430035,0.0000043582922,0.0006999593,0.7294379],"genre_scores_gemma":[0.9712801,0.000004621593,0.028394056,0.0000027443677,0.000057486766,0.000010990019,0.00002247894,0.000028269582,0.00019922722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994424,0.000020062796,0.00013296166,0.00013060491,0.00010149618,0.00017246214],"domain_scores_gemma":[0.9995646,0.00004359228,0.000019725223,0.0002311907,0.00003086778,0.00011006444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013228077,0.0001022956,0.00011000846,0.000036596397,0.000041229883,0.000045164412,0.000075821714,0.000043819862,0.000028543194],"category_scores_gemma":[0.00004078639,0.00008254823,0.000017553812,0.00010827859,0.000035416582,0.00010374052,0.0000063571483,0.00007425655,0.00010894841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003261312,0.0007098272,0.00017375949,0.00096294796,0.00028828473,0.000048996873,0.023530383,0.54718035,0.0034788758,0.21334307,0.018794142,0.19116321],"study_design_scores_gemma":[0.0015043656,0.00036970308,0.00005178632,0.00010537049,0.00003680605,0.000017144419,0.031838097,0.9309031,0.0028149553,0.001342648,0.030331334,0.00068470027],"about_ca_topic_score_codex":0.0000011496444,"about_ca_topic_score_gemma":0.00000807428,"teacher_disagreement_score":0.97071207,"about_ca_system_score_codex":0.00001633822,"about_ca_system_score_gemma":0.0000042028732,"threshold_uncertainty_score":0.3366219},"labels":[],"label_agreement":null},{"id":"W2327057938","doi":"10.1504/ijor.2015.065937","title":"Coping with uncertainties in production planning through fuzzy mathematical programming: application to steel rolling industry","year":2014,"lang":"en","type":"article","venue":"International Journal of Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Mathematical optimization; Fuzzy logic; Production planning; Production (economics); Computer science; Fuzzy set; Membership function; Fuzzy number; Linear programming; Bilinear interpolation; Mathematics; Operations research; Economics; Artificial intelligence","score_opus":0.06193963089791538,"score_gpt":0.3851503910772,"score_spread":0.32321076017928463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327057938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17784779,0.000087164626,0.8096828,0.0064377277,0.0003380714,0.00062110287,0.0000015615294,0.0000647039,0.004919078],"genre_scores_gemma":[0.9193848,0.000010691321,0.07988188,0.000094914496,0.00044004447,0.0000632441,0.000006999046,0.000024643541,0.00009281336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977906,0.000078382014,0.0005313158,0.00015379694,0.0012014828,0.00024442948],"domain_scores_gemma":[0.99835575,0.00025942482,0.000067579895,0.00010344394,0.0011133134,0.000100490724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015659414,0.00011496739,0.00018083688,0.00041051378,0.000081220016,0.00023899079,0.00031557083,0.000101052865,0.000032572632],"category_scores_gemma":[0.00086028856,0.00009545465,0.000033007327,0.00040863283,0.00006319828,0.00049566466,0.00004392047,0.00069931336,0.00002066358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009292182,0.00012309423,0.00082341424,0.00006135768,0.00006026102,0.000010048712,0.0014230391,0.9491579,0.0013020078,0.037466973,0.00054257474,0.008936414],"study_design_scores_gemma":[0.0042321207,0.0013265668,0.0023578526,0.0039085746,0.000045260564,0.0007930396,0.006808255,0.8054972,0.015980965,0.05034919,0.10742215,0.0012787646],"about_ca_topic_score_codex":0.0000042495467,"about_ca_topic_score_gemma":0.000004929511,"teacher_disagreement_score":0.741537,"about_ca_system_score_codex":0.00023971814,"about_ca_system_score_gemma":0.000090192196,"threshold_uncertainty_score":0.38925275},"labels":[],"label_agreement":null},{"id":"W2344254384","doi":"10.3166/jesa.49.31-53","title":"Approches d’optimisation de parcours dans un hypermarché","year":2016,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Geology","score_opus":0.018159913038236757,"score_gpt":0.2396864679616077,"score_spread":0.22152655492337095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344254384","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052946284,0.0045899637,0.9278241,0.0039777104,0.0011612831,0.00030788517,0.000021013599,0.0006122458,0.00855955],"genre_scores_gemma":[0.6088077,0.0023606154,0.38025594,0.00008979411,0.00074327417,0.000024521649,0.0000035734886,0.00020256796,0.0075120595],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969573,0.00046235343,0.00091501436,0.00026186355,0.00047873912,0.00092473143],"domain_scores_gemma":[0.99822104,0.0003882788,0.00030225705,0.00030398578,0.00019779844,0.0005866257],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011368502,0.00041458898,0.00045793594,0.00019919655,0.00047338716,0.00055399555,0.00038757408,0.00023634908,0.0011447455],"category_scores_gemma":[0.0006468553,0.0003206951,0.0002560584,0.0003423638,0.00046010502,0.00079967757,0.00008323897,0.00041422015,0.00053699105],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010747788,0.00016323684,0.0006353562,0.0008690913,0.0002639433,0.00017134429,0.0044986256,0.011623623,0.006740712,0.011296055,0.0024064907,0.96132076],"study_design_scores_gemma":[0.004894263,0.00070363266,0.09229475,0.010740074,0.001079858,0.016485175,0.002662905,0.66982126,0.012544298,0.1402406,0.045736838,0.002796325],"about_ca_topic_score_codex":0.000018903871,"about_ca_topic_score_gemma":0.000006080812,"teacher_disagreement_score":0.95852447,"about_ca_system_score_codex":0.00088666624,"about_ca_system_score_gemma":0.00017858391,"threshold_uncertainty_score":0.99992454},"labels":[],"label_agreement":null},{"id":"W2345988899","doi":"10.5539/jmr.v8n3p1","title":"Heuristic Algorithms for Solving Multiobjective Transportation Problems","year":2016,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tardiness; Heuristic; Mathematical optimization; Mathematics; Algorithm; Multi-objective optimization; Null-move heuristic; Computer science; Job shop scheduling; Routing (electronic design automation)","score_opus":0.10347043435953183,"score_gpt":0.3701570154086054,"score_spread":0.26668658104907356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345988899","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008456251,0.00011649868,0.99030346,0.00014909366,0.000121073215,0.0003884287,0.000008003495,0.000042461852,0.00041475613],"genre_scores_gemma":[0.65779376,0.00020743332,0.34134173,0.0000042228016,0.0001734676,0.00005349366,0.0000013971189,0.00006476472,0.00035974526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986574,0.000019892139,0.00051931763,0.00006853109,0.0004659732,0.0002688705],"domain_scores_gemma":[0.9979961,0.0010368634,0.00009682253,0.00009981118,0.0006634642,0.00010696254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001568051,0.00008742062,0.00021113345,0.0002267114,0.000069119174,0.00004849405,0.00015558912,0.000060605227,0.000047278285],"category_scores_gemma":[0.00074092956,0.00005554069,0.00009526347,0.00016089012,0.0000536773,0.00019209077,0.000006101027,0.00017086085,0.000014220094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019313542,0.0035452368,0.00020984087,0.022005716,0.0016399095,0.00010949928,0.044389557,0.08180463,0.24204953,0.107079424,0.01989622,0.4770773],"study_design_scores_gemma":[0.0054528504,0.001121888,0.00013200713,0.0036712515,0.00013679574,0.00010503721,0.003971027,0.68938935,0.036753196,0.25122777,0.0073552607,0.000683547],"about_ca_topic_score_codex":4.699766e-7,"about_ca_topic_score_gemma":0.0000022020029,"teacher_disagreement_score":0.6493375,"about_ca_system_score_codex":0.00009784885,"about_ca_system_score_gemma":0.000034932465,"threshold_uncertainty_score":0.22648837},"labels":[],"label_agreement":null},{"id":"W2397154059","doi":"10.1080/03155986.2004.11732700","title":"Goal Programming Formulations For A Comparative Analysis Of Scalar Norms And Ordinal Vs. Ratio Data","year":2004,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ambiguity; Computer science; Ordinal data; Metric (unit); Stability (learning theory); Variety (cybernetics); Data mining; Econometrics; Machine learning; Artificial intelligence; Statistics; Mathematics; Engineering","score_opus":0.10783262454389553,"score_gpt":0.38710127594971316,"score_spread":0.27926865140581764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2397154059","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059886795,0.00017056038,0.93540764,0.00016911502,0.00007466329,0.0019996772,0.0003743636,0.00008301479,0.001834179],"genre_scores_gemma":[0.97453415,0.000021067935,0.024104305,0.000015783457,0.000028356386,0.00016248906,0.0010945835,0.0000052216815,0.000034048684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986625,0.000016739677,0.00059652934,0.00010050006,0.00042619358,0.00019758294],"domain_scores_gemma":[0.99877626,0.00020948064,0.000061575745,0.0001834992,0.00066921813,0.00009996537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010073474,0.00010039351,0.00025555858,0.000516921,0.00027383317,0.0004277967,0.00012871047,0.000069133115,0.000008386749],"category_scores_gemma":[0.0001893066,0.00008467175,0.00003163473,0.00069226435,0.00009552246,0.0016639915,0.00007432913,0.00010822272,0.000004789696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037962964,0.000028805707,0.0003250164,0.0005735887,0.00039186957,1.17425365e-7,0.0033717903,0.57562613,0.00003845019,0.41101658,0.00021665047,0.008373017],"study_design_scores_gemma":[0.0006321566,0.00007473429,0.000861246,0.000043274253,0.000052532672,0.0000034445236,0.00114261,0.97546655,0.000043845215,0.0001227912,0.021450996,0.00010583012],"about_ca_topic_score_codex":0.00007649726,"about_ca_topic_score_gemma":0.000051072326,"teacher_disagreement_score":0.91464734,"about_ca_system_score_codex":0.0000499083,"about_ca_system_score_gemma":0.00010031517,"threshold_uncertainty_score":0.41252536},"labels":[],"label_agreement":null},{"id":"W2399843333","doi":"10.1080/03155986.2004.11732710","title":"A Goal Programming Approach For A Multi Period Task Assignment Problem","year":2004,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Goal programming; Task (project management); Period (music); Computer science; Goal setting; Goal orientation; Operations research; Psychology; Mathematics; Engineering; Social psychology; Systems engineering","score_opus":0.061791170494660576,"score_gpt":0.3278699342839413,"score_spread":0.2660787637892807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2399843333","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007040166,0.000106847954,0.9905773,0.00008891217,0.000069371825,0.0025661602,0.000028988501,0.00015220494,0.005706205],"genre_scores_gemma":[0.7058971,0.000020169382,0.29058102,0.00004330229,0.00008896298,0.0027830133,0.0003116514,0.000020679336,0.00025411998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850386,0.000017960787,0.00050484575,0.00009858929,0.00053946604,0.00033525928],"domain_scores_gemma":[0.9992232,0.00004505447,0.0000356187,0.00010217728,0.0004530318,0.00014093507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001068375,0.00012547919,0.00014915816,0.00020865408,0.0003547186,0.0009154633,0.000100649,0.00009891689,0.0000058437836],"category_scores_gemma":[0.00011942391,0.00010598559,0.000036976166,0.00022388807,0.0000617672,0.001263281,0.000037255668,0.00017017398,0.000030418883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024877785,0.0000824756,0.00002815656,0.001794589,0.00005971733,4.210324e-7,0.0060659423,0.73730236,0.000110304805,0.23066258,0.0006069175,0.023261664],"study_design_scores_gemma":[0.0014334938,0.00010264559,0.000015325128,0.00007489812,0.000004070704,0.000022908745,0.0031462442,0.89102125,0.000060273785,0.000080778016,0.10385358,0.00018451706],"about_ca_topic_score_codex":0.00002091995,"about_ca_topic_score_gemma":0.0000016475626,"teacher_disagreement_score":0.70519304,"about_ca_system_score_codex":0.00016029233,"about_ca_system_score_gemma":0.00010554557,"threshold_uncertainty_score":0.8827834},"labels":[],"label_agreement":null},{"id":"W2406183637","doi":"10.1080/03155986.2003.11732678","title":"Development Of A Catch Allocation Tool Design For Production Planning At Js Mcmillan Fisheries","year":2003,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Fishing; Profit (economics); Production (economics); Fishery; Product (mathematics); Operations research; Plan (archaeology); Fisheries management; Decision support system; Computer science; Quality (philosophy); Business; Engineering; Economics; Geography; Microeconomics; Mathematics; Data mining","score_opus":0.10151170792959786,"score_gpt":0.32403000083703,"score_spread":0.22251829290743214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406183637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07318088,0.00016180554,0.9182714,0.000070613874,0.00028477257,0.0022198511,0.000010591044,0.00009730205,0.0057027717],"genre_scores_gemma":[0.9168695,0.000018978562,0.08197098,0.0000121461035,0.00003113513,0.0006144106,0.00011915297,0.000010396873,0.00035328223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988461,0.00003024473,0.00052441185,0.00006406163,0.0003711374,0.00016400624],"domain_scores_gemma":[0.9991346,0.00013032315,0.00004597755,0.000079740756,0.00056330964,0.000046054334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015454058,0.00007945778,0.00011200581,0.00016287719,0.00029461912,0.00014878315,0.00004606037,0.00006310571,0.000013867813],"category_scores_gemma":[0.0004905685,0.00007122584,0.000014311852,0.00016795505,0.00004037216,0.0007264443,0.000014317129,0.00006245008,0.000013939151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012392063,0.00004172253,0.00046003948,0.0031119885,0.0001259697,1.7877053e-7,0.034374565,0.7943374,0.0029055597,0.13181002,0.012081327,0.020627288],"study_design_scores_gemma":[0.00088188145,0.000092743736,0.00025587642,0.00023334318,0.000006832649,0.000022458034,0.007226592,0.5338987,0.02465153,0.00015984778,0.43222862,0.00034157702],"about_ca_topic_score_codex":0.0000028344691,"about_ca_topic_score_gemma":0.0000017470181,"teacher_disagreement_score":0.84368867,"about_ca_system_score_codex":0.000105857616,"about_ca_system_score_gemma":0.000112304086,"threshold_uncertainty_score":0.29045054},"labels":[],"label_agreement":null},{"id":"W2406724119","doi":"10.1139/cjce-2015-0144","title":"Conflicts resolution based construction temporary facilities layout planning in large-scale construction projects","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Sichuan University; National Natural Science Foundation of China","keywords":"Process (computing); Conflict resolution; Scale (ratio); Construction engineering; Resolution (logic); Construction management; Computer science; Operations research; Engineering; Transport engineering; Civil engineering; Artificial intelligence","score_opus":0.013588787148711066,"score_gpt":0.19489455783329587,"score_spread":0.1813057706845848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406724119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15572436,0.00060630566,0.83816457,0.00016144975,0.0012029416,0.00021657968,0.00003574723,0.0001541473,0.0037339036],"genre_scores_gemma":[0.9899036,0.000007194424,0.009941801,0.000010813391,0.00007771195,0.0000045621146,0.0000026620512,0.000024602841,0.000027050393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906003,0.000016317295,0.00039828912,0.000083630206,0.00012084091,0.0003208792],"domain_scores_gemma":[0.9994461,0.0000593835,0.00006663409,0.00008517159,0.00007383384,0.00026889716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002590862,0.00013308288,0.00020295227,0.0005514801,0.000041007395,0.000042137177,0.000077204306,0.00009889023,0.00010328157],"category_scores_gemma":[0.00015484243,0.0001189096,0.00005183023,0.00020711312,0.00004643861,0.0003379156,0.0000037293717,0.00018120283,0.0000031542295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000128151005,0.000009440665,0.012718654,0.00036577423,0.000039551862,0.00008920368,0.0017008424,0.97952557,0.0012254168,0.0014685177,0.0003983468,0.0024458887],"study_design_scores_gemma":[0.0034293835,0.00013383287,0.002401936,0.0032517514,0.000037757956,0.0005501171,0.0031222985,0.93712604,0.0027602876,0.00038526888,0.045943536,0.000857794],"about_ca_topic_score_codex":0.000052481333,"about_ca_topic_score_gemma":0.0032670118,"teacher_disagreement_score":0.8341792,"about_ca_system_score_codex":0.0002384864,"about_ca_system_score_gemma":0.0001791945,"threshold_uncertainty_score":0.48489928},"labels":[],"label_agreement":null},{"id":"W2407438826","doi":"10.1080/03155986.2003.11732666","title":"Revue Des Inégalités Valides Pertinentes Aux Problèmes Des Conception De Réseaux","year":2003,"lang":"fr","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal; Université du Québec à Montréal; Université de Montréal","funders":"","keywords":"Heuristic; Scale (ratio); Computer science; Mathematical optimization; Implementation; Inequality; Point (geometry); Mathematics; Theoretical computer science; Physics; Programming language","score_opus":0.12895941741366448,"score_gpt":0.3682386656143179,"score_spread":0.2392792482006534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2407438826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09544047,0.017502233,0.73173857,0.0030268636,0.0013698295,0.0041688657,0.00017126516,0.0004283254,0.14615355],"genre_scores_gemma":[0.9351525,0.001816301,0.05453564,0.00010221235,0.0002790092,0.00044040574,0.00013506123,0.000035448393,0.0075034504],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748814,0.00026729662,0.00089163403,0.00013608969,0.0006030548,0.00061380997],"domain_scores_gemma":[0.9975173,0.0003691206,0.00008069447,0.00016168175,0.0015787398,0.00029245557],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003097061,0.00021249292,0.00024352015,0.0003181019,0.000843276,0.001589189,0.00012841604,0.00023068894,0.00031098482],"category_scores_gemma":[0.0023699924,0.00020611616,0.000061008523,0.0004999373,0.00071815704,0.0036231729,0.000050141236,0.0003638837,0.00028280346],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012454342,0.000038065562,0.0010068917,0.0039916276,0.00005189154,0.0000014739045,0.012281529,0.10512795,0.00010758632,0.86099946,0.0018360409,0.01454504],"study_design_scores_gemma":[0.0009742171,0.00019735207,0.00045814243,0.0016142118,0.000022541166,0.00025426867,0.018794816,0.6797846,0.0007311669,0.009345997,0.2873533,0.00046935093],"about_ca_topic_score_codex":0.0005716415,"about_ca_topic_score_gemma":0.000035711746,"teacher_disagreement_score":0.85165346,"about_ca_system_score_codex":0.00043065092,"about_ca_system_score_gemma":0.0003973199,"threshold_uncertainty_score":0.9994473},"labels":[],"label_agreement":null},{"id":"W2482729627","doi":"10.1016/j.compenvurbsys.2016.07.002","title":"An improved Genetic Algorithm for spatial optimization of multi-objective and multi-site land use allocation","year":2016,"lang":"en","type":"article","venue":"Computers Environment and Urban Systems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":137,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Graduate Research and Innovation Projects of Jiangsu Province; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; National Natural Science Foundation of China","keywords":"Crossover; Genetic algorithm; Robustness (evolution); Land use; Computer science; Land-use planning; Mathematical optimization; Optimal allocation; Spatial planning; Set (abstract data type); Operations research; Geography; Engineering; Mathematics; Environmental planning; Artificial intelligence; Civil engineering","score_opus":0.011769854788763526,"score_gpt":0.19570478763242072,"score_spread":0.1839349328436572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2482729627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01389689,0.00022077565,0.98514324,0.000003754497,0.00012023094,0.00054509327,0.000018270528,0.000050693696,0.0000010455],"genre_scores_gemma":[0.5187804,0.00011519358,0.48093656,0.0000034739585,0.000044613287,0.00003928647,0.000017835207,0.000021902444,0.000040707982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947643,0.000020928794,0.00019141058,0.0001569683,0.000051178813,0.000103054466],"domain_scores_gemma":[0.99971277,0.00005288723,0.00005156735,0.00010259379,0.000012829181,0.00006733917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006104122,0.00010696585,0.0001442019,0.000041135983,0.000033140746,0.00004100401,0.000032668373,0.000053985106,0.000001578008],"category_scores_gemma":[0.0000044676253,0.000084537154,0.000017427948,0.00001719568,0.0000385629,0.00013860821,0.000015811034,0.000018215223,5.7424717e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015903683,0.00016858036,0.008917346,0.00033619866,0.00011287892,6.432311e-7,0.0013235722,0.7675256,0.007079061,0.000028293101,0.0000376408,0.21445428],"study_design_scores_gemma":[0.0011135701,0.00008510069,0.0018991826,0.000043726126,0.00002025274,0.0000016731561,0.000021223606,0.99641377,0.00017325638,0.0000010387291,0.00011083375,0.00011639343],"about_ca_topic_score_codex":0.000015431631,"about_ca_topic_score_gemma":0.0000012229642,"teacher_disagreement_score":0.5048835,"about_ca_system_score_codex":0.00002562488,"about_ca_system_score_gemma":0.0000017688565,"threshold_uncertainty_score":0.34473252},"labels":[],"label_agreement":null},{"id":"W2485301704","doi":"10.5539/mas.v10n10p133","title":"An Optimized Mathematical Model for Items Supplies Planning of a Logistic System","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Purchasing; Computer science; Operations research; Minification; Total cost; Function (biology); Mathematical optimization; Operations management; Business; Mathematics; Economics","score_opus":0.04362831973685467,"score_gpt":0.2839014280683566,"score_spread":0.24027310833150195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2485301704","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027270105,0.000009976328,0.99167573,0.000010832821,0.000029771858,0.00039125595,0.000010372693,0.00034241154,0.004802663],"genre_scores_gemma":[0.7329947,5.4735466e-7,0.266841,0.0000048376637,0.000009352729,0.00009728968,9.145637e-7,0.00001776755,0.000033603017],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889386,0.0000034872135,0.00029875166,0.00022586754,0.0002591044,0.00031890726],"domain_scores_gemma":[0.99936014,0.00013296821,0.000046077064,0.00027856164,0.000056500463,0.00012576317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045600053,0.00012580243,0.00024040107,0.00009754595,0.00009219005,0.000053182226,0.0003273,0.000049449784,0.0000069690564],"category_scores_gemma":[0.000051967105,0.00008629296,0.000033842556,0.00014374741,0.0002752374,0.00015734271,0.00003186266,0.000036393994,0.000007823724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014691293,0.00003099273,8.2419757e-7,0.00030641045,0.0000047895796,2.2744098e-7,0.0007589255,0.6884332,0.17379291,0.1343057,0.000012338748,0.0023390083],"study_design_scores_gemma":[0.00038035706,0.000013424543,4.9979184e-7,0.00007284242,0.000010065326,0.000001817484,0.00013510059,0.97348523,0.0066421623,0.0191287,0.0000030830033,0.00012670599],"about_ca_topic_score_codex":1.3469872e-7,"about_ca_topic_score_gemma":7.2176356e-8,"teacher_disagreement_score":0.7302677,"about_ca_system_score_codex":0.000060010872,"about_ca_system_score_gemma":0.000030344867,"threshold_uncertainty_score":0.35189247},"labels":[],"label_agreement":null},{"id":"W2488073955","doi":"","title":"EFFICIENT AND LOCAL EFFICIENT SOLUTIONS FOR ASSIGNMENT TYPE PROBLEMS","year":2001,"lang":"en","type":"article","venue":"French digital mathematics library (Numdam)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Computer science; Type (biology); Linear programming; Assignment problem; Multiobjective programming; Mathematics; Multi-objective optimization","score_opus":0.018577208902918762,"score_gpt":0.2014252444063325,"score_spread":0.18284803550341375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2488073955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020329654,0.00052477315,0.9525605,0.00017738061,0.00023135988,0.00091237563,0.00004842101,0.0007708985,0.024444664],"genre_scores_gemma":[0.95709705,0.000032644693,0.04129697,0.000044954635,0.00006378321,0.00011355729,0.000069842055,0.000088691115,0.0011925255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872005,0.0000057237153,0.00040161962,0.00020839398,0.00020001816,0.0004641976],"domain_scores_gemma":[0.99933743,0.00017124032,0.000050377534,0.00022825782,0.000030840718,0.00018185553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009006157,0.00023167602,0.0002483651,0.00009478957,0.00011760516,0.00033082365,0.00014276388,0.00008803337,0.00008573695],"category_scores_gemma":[0.00005744974,0.00020450259,0.00007392632,0.00028298443,0.000092663955,0.00021490022,0.00011213677,0.000098736826,0.00007241092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059418794,0.00097594253,0.000046460802,0.0014564337,0.00010623157,0.0000073848582,0.0011528339,0.88974077,0.00011132946,0.086224236,0.006607835,0.013564581],"study_design_scores_gemma":[0.00033961775,0.00008519192,0.000010646111,0.00014164319,0.000021637807,0.00002251673,0.00016156494,0.976642,0.000098861674,0.010685872,0.011515577,0.00027488632],"about_ca_topic_score_codex":2.785741e-7,"about_ca_topic_score_gemma":1.2727189e-7,"teacher_disagreement_score":0.9367674,"about_ca_system_score_codex":0.000041835916,"about_ca_system_score_gemma":0.000021026353,"threshold_uncertainty_score":0.83393735},"labels":[],"label_agreement":null},{"id":"W2517686855","doi":"","title":"Stochastic Goal Programming and a Metaheuristic for Scheduling of Operating Rooms","year":2015,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Metaheuristic; Computer science; Scheduling (production processes); Constraint programming; Goal programming; Stochastic programming; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.029643684499557673,"score_gpt":0.23338476670503885,"score_spread":0.20374108220548118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2517686855","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7222671,0.00034683917,0.2763499,0.000051413877,0.00009040381,0.00045569448,0.00001384492,0.00013335192,0.0002914691],"genre_scores_gemma":[0.84322274,0.0000032598325,0.15660487,0.00000537247,0.000020402771,0.0000016529515,0.000011307391,0.000025508634,0.000104914114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990483,0.000034966244,0.00022672005,0.00020138423,0.00022205188,0.00026658855],"domain_scores_gemma":[0.9991828,0.00014187116,0.00009771425,0.00016988584,0.00019071093,0.00021701766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005850588,0.00015982594,0.0003425022,0.00015104716,0.00014487117,0.00003347434,0.00018853901,0.00012033419,0.000019678937],"category_scores_gemma":[0.00035206953,0.00019078942,0.00009190134,0.00021496801,0.00012530596,0.00037648116,0.00010546656,0.0001732772,0.0000054646116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070750766,0.0007587656,0.014671199,0.0067114546,0.0014559518,0.000041593194,0.03255987,0.81843054,0.032910608,0.014347703,0.00019998045,0.07720484],"study_design_scores_gemma":[0.007995281,0.00073325477,0.0024194263,0.000808699,0.0007683197,0.00004990789,0.015084502,0.96293306,0.003492438,0.0030942317,0.0013744069,0.0012464436],"about_ca_topic_score_codex":0.0000069188786,"about_ca_topic_score_gemma":0.000013228066,"teacher_disagreement_score":0.14450257,"about_ca_system_score_codex":0.000053941814,"about_ca_system_score_gemma":0.000034817982,"threshold_uncertainty_score":0.7780167},"labels":[],"label_agreement":null},{"id":"W2522731561","doi":"10.5539/mas.v10n10p289","title":"A Model to Determine Optimal Composition of Production to Obtain Maximum Profit &amp; Reduce Overhead Costs by Linear Programming","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Linear programming; Production (economics); Profit (economics); Computer science; Overhead (engineering); Production planning; Operations research; Production rate; Dynamic programming; Mathematical optimization; Operations management; Manufacturing engineering; Economics; Microeconomics; Mathematics; Algorithm","score_opus":0.031159433034354454,"score_gpt":0.2744968192462074,"score_spread":0.24333738621185294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2522731561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21860377,0.0000054807574,0.7796612,0.00024717173,0.000051538398,0.0008078153,0.000005604477,0.00020881038,0.000408633],"genre_scores_gemma":[0.6694367,0.0000010957169,0.33024386,0.000029356117,0.000017601307,0.00013090915,0.000002550956,0.000019399786,0.00011854185],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984949,0.0000060753587,0.0002883141,0.0003987513,0.00041088945,0.00040104685],"domain_scores_gemma":[0.9992911,0.00001767171,0.000043192995,0.0003043808,0.000114268165,0.00022935441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039190875,0.00015694357,0.00017705551,0.00015371275,0.00010469757,0.000050443836,0.0002602969,0.000045995803,0.0000055066207],"category_scores_gemma":[0.000066462766,0.0001289948,0.000025293635,0.0005575267,0.00012326906,0.00021286432,0.00008666897,0.0000652906,0.00003967716],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019812998,0.000034724122,0.000002248138,0.00003727797,0.0000022833347,1.0234038e-7,0.00047554311,0.13195802,0.7771021,0.00026145173,0.00010519288,0.09000123],"study_design_scores_gemma":[0.00016786212,0.000035278907,0.0000033192234,0.00008055481,0.0000059800855,0.000002798792,0.000019629571,0.73755896,0.26140654,0.000300434,0.00021857019,0.0002000472],"about_ca_topic_score_codex":0.000001612791,"about_ca_topic_score_gemma":0.0000018836678,"teacher_disagreement_score":0.60560095,"about_ca_system_score_codex":0.00020125588,"about_ca_system_score_gemma":0.00003472481,"threshold_uncertainty_score":0.52602553},"labels":[],"label_agreement":null},{"id":"W2525966964","doi":"10.1080/03155986.2016.1214448","title":"An approach to determine unsupported non-dominated solutions in bicriteria integer linear programs","year":2016,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Nuclear Fuel Cycle and Supply Chain; Fundação para a Ciência e a Tecnologia","keywords":"Integer (computer science); Chebyshev filter; Set (abstract data type); Point (geometry); Mathematical optimization; Computer science; Algorithm; Phase (matter); Implementation; Integer programming; Minification; Linear programming; Mathematics; Geometry","score_opus":0.07522440007004182,"score_gpt":0.335503778932631,"score_spread":0.2602793788625892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2525966964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15748416,0.0000363451,0.81529087,0.0003062948,0.00031005734,0.0033931911,0.00007167128,0.00031655954,0.022790842],"genre_scores_gemma":[0.9922951,0.000012834877,0.006714004,0.000032847835,0.000062307554,0.0005115677,0.00015332029,0.00001280952,0.00020522269],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984988,0.000043966487,0.00058378576,0.00010667374,0.0004087252,0.00035803005],"domain_scores_gemma":[0.9990317,0.000072389084,0.000023792274,0.00016627705,0.0004948312,0.00021101566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012496623,0.000118720716,0.0001564109,0.0005274473,0.00013892491,0.0003728638,0.00013599705,0.00010195923,0.000022291182],"category_scores_gemma":[0.00014418566,0.000083701794,0.00001857261,0.00052476925,0.000058962254,0.0018791382,0.00004680664,0.00013352919,0.00013770022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030263283,0.0008181855,0.0022392164,0.0027820545,0.0001647668,0.000008181648,0.0212429,0.22475572,0.014290642,0.1668383,0.007101987,0.5594554],"study_design_scores_gemma":[0.0007296697,0.00012733253,0.0003737642,0.00014887695,0.0000017019621,0.000015365687,0.0010506841,0.974908,0.00013631268,0.00003014676,0.022293698,0.00018448032],"about_ca_topic_score_codex":0.000044780532,"about_ca_topic_score_gemma":0.000008465117,"teacher_disagreement_score":0.8348109,"about_ca_system_score_codex":0.00010236292,"about_ca_system_score_gemma":0.00006344936,"threshold_uncertainty_score":0.35955343},"labels":[],"label_agreement":null},{"id":"W2555683065","doi":"10.1139/cjfr-2016-0299","title":"Strategic planning in a forest supply chain: a multigoal and multiproduct approach","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Consejo Nacional de Investigaciones Científicas y Técnicas","keywords":"Maximization; Sustainability; Minification; Work (physics); Supply chain; Schedule; Production (economics); Yield (engineering); Forest management; Operations research; Computer science; Environmental economics; Business; Economics; Environmental science; Mathematics; Agroforestry; Ecology; Microeconomics; Engineering","score_opus":0.07447308180755687,"score_gpt":0.30189949200278876,"score_spread":0.2274264101952319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555683065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9901994,0.0008744345,0.004958585,0.00036434576,0.000076688186,0.00023026922,0.0000046401337,0.000013515836,0.0032781519],"genre_scores_gemma":[0.99421966,0.00003369988,0.0055241818,0.000005138052,0.00008951639,0.0000075542416,0.000001060937,0.000022673372,0.00009649123],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998834,0.000052416224,0.0002721088,0.00010889795,0.00021244903,0.0005201198],"domain_scores_gemma":[0.999005,0.0001431836,0.000025845215,0.00011077104,0.00013486875,0.0005803343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009744176,0.0000940712,0.00016631531,0.0006593214,0.00007354333,0.000094919684,0.00017464223,0.00007128859,0.000033022006],"category_scores_gemma":[0.0003245306,0.00006655771,0.000029501976,0.00029024435,0.00016108074,0.00017948706,0.000012261368,0.00035702682,0.0000050304197],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096142125,0.000121699326,0.7645944,0.0008752012,0.00012954454,0.0014578092,0.0069283983,0.14076804,0.0009811433,0.033748645,0.0023302368,0.047968753],"study_design_scores_gemma":[0.011786822,0.001280048,0.28407732,0.004049372,0.00003577234,0.0017971648,0.010195542,0.63402784,0.0005443555,0.03862984,0.011933073,0.0016428839],"about_ca_topic_score_codex":0.00047864488,"about_ca_topic_score_gemma":0.011904644,"teacher_disagreement_score":0.4932598,"about_ca_system_score_codex":0.00014772374,"about_ca_system_score_gemma":0.00026343722,"threshold_uncertainty_score":0.6643071},"labels":[],"label_agreement":null},{"id":"W2587219924","doi":"10.4236/jsea.2017.102007","title":"An Evolutionary Firefly Algorithm, Goal Programming Optimization Approach for Setting the Osmotic Dehydration Parameters of Papaya","year":2017,"lang":"en","type":"article","venue":"Journal of Software Engineering and Applications","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Firefly algorithm; Converse; Mathematical optimization; Sensitivity (control systems); Key (lock); Metaheuristic; Set (abstract data type); Linear programming; Range (aeronautics); Mathematics; Evolutionary algorithm; Process (computing); Computer science; Optimization problem; Algorithm; Particle swarm optimization; Engineering","score_opus":0.00903675254457508,"score_gpt":0.23456372427101738,"score_spread":0.2255269717264423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587219924","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015489487,0.00021579581,0.9976482,0.00004030995,0.000056443405,0.0003918302,0.000007672615,0.000080558006,0.000010260418],"genre_scores_gemma":[0.1883465,0.0000364021,0.81138104,0.0000046890154,0.00009708994,0.00009381062,0.000015475447,0.000022057888,0.0000029466614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992945,0.000008078307,0.00035792845,0.000086658925,0.00012055287,0.00013227068],"domain_scores_gemma":[0.9992163,0.00013922993,0.00021770089,0.0002069161,0.00014627197,0.00007353547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028070225,0.00010743414,0.00015781536,0.00006470182,0.00025921452,0.000113673865,0.00019636784,0.000059079666,9.0690696e-7],"category_scores_gemma":[0.00018490043,0.000087105735,0.000064291984,0.00007127282,0.000047141064,0.0003064818,0.000014690588,0.00012684733,9.97953e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018242147,0.000037740603,0.000083005165,0.00015730175,0.000032869546,1.09659226e-7,0.00011567571,0.91432965,0.000082499704,0.00023155122,0.000014188501,0.08491359],"study_design_scores_gemma":[0.00020532904,0.000047073747,0.00024756903,0.00004565286,0.000057822235,0.000018665667,0.00010507001,0.9985882,0.00012151464,0.000091744514,0.00037642164,0.00009494908],"about_ca_topic_score_codex":0.000001329959,"about_ca_topic_score_gemma":8.4647326e-8,"teacher_disagreement_score":0.18679756,"about_ca_system_score_codex":0.000026642634,"about_ca_system_score_gemma":0.000015064822,"threshold_uncertainty_score":0.35520688},"labels":[],"label_agreement":null},{"id":"W2613081544","doi":"","title":"New Branch-and-Cut Algorithm for Bilevel Linear Programming","year":2004,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Transpose; Branch and bound; Branch and cut; Linear programming; Algorithm; Integer programming; Bilevel optimization; Mathematics; Mathematical optimization; Branching (polymer chemistry); Set (abstract data type); Exploit; Criss-cross algorithm; Branch and price; Computer science; Linear-fractional programming; Optimization problem","score_opus":0.0112541236968237,"score_gpt":0.23225245431071825,"score_spread":0.22099833061389457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613081544","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010366624,0.00072601176,0.99426985,0.00075589656,0.00011268373,0.0010668265,0.000015542917,0.0018332654,0.0001832476],"genre_scores_gemma":[0.039706036,0.000112274036,0.95873576,0.00033310868,0.00018890167,0.0004401705,0.000022292406,0.00010898926,0.00035243755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860215,0.000010760864,0.00036576044,0.000264553,0.00016910673,0.0005876966],"domain_scores_gemma":[0.9991543,0.00006664315,0.000054023236,0.00029887905,0.000058719434,0.00036741595],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023330866,0.00027952233,0.00029377078,0.00019431079,0.00013789324,0.00014800922,0.00018864391,0.00021294937,0.00001568301],"category_scores_gemma":[0.00009557142,0.00028262025,0.00012041306,0.00030251595,0.000040248415,0.00024009586,0.00005728828,0.00022461539,0.000011047653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007542727,0.00006951486,0.00005312091,0.00012943153,0.00004924247,0.0000057133657,0.00024933522,0.026646532,0.00074075407,0.017269839,0.0004187416,0.95436025],"study_design_scores_gemma":[0.0016190782,0.0001584025,0.00014012617,0.00012593599,0.000060233993,0.00008358841,0.00006444656,0.9460492,0.013352426,0.01791306,0.019778404,0.00065506855],"about_ca_topic_score_codex":0.0004089563,"about_ca_topic_score_gemma":0.00018311996,"teacher_disagreement_score":0.9537052,"about_ca_system_score_codex":0.0001797258,"about_ca_system_score_gemma":0.00008318063,"threshold_uncertainty_score":0.99996257},"labels":[],"label_agreement":null},{"id":"W2618247029","doi":"","title":"Time-Indexed Formulations and the Total Weighted Tardiness Problem","year":2005,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Tardiness; Mathematical optimization; Mathematics; Time horizon; Integer programming; Computer science; Job shop scheduling","score_opus":0.005148404545507928,"score_gpt":0.1998810159891852,"score_spread":0.19473261144367726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618247029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028495276,0.0006785248,0.9579729,0.0036198942,0.000041044044,0.0011177756,0.000010267433,0.0016845445,0.0063797757],"genre_scores_gemma":[0.76618785,0.000063702784,0.23164536,0.00029421208,0.00010043746,0.00037556014,0.000016244512,0.000057713707,0.0012589294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989695,0.000035401506,0.0003210511,0.0001553093,0.00016159964,0.000357157],"domain_scores_gemma":[0.99934405,0.00012017773,0.00004844906,0.0002947706,0.000050798888,0.00014172857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031959073,0.0001985413,0.00023555424,0.00012694277,0.00018689179,0.00014002367,0.00014760587,0.00013001554,0.00007894241],"category_scores_gemma":[0.000050950235,0.00014951342,0.000081559156,0.0002900822,0.000104785795,0.00028292878,0.000068550806,0.00021744832,0.00003387072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011816683,0.00024073868,0.0003441944,0.00020843142,0.00023909341,0.000008243278,0.0022117167,0.53848016,0.0028758924,0.29844186,0.0052172597,0.15161422],"study_design_scores_gemma":[0.00076991256,0.000012060418,0.00026184466,0.000021190903,0.000029654571,0.0000442849,0.000028537535,0.9923674,0.00071552175,0.003553155,0.0020005645,0.00019589947],"about_ca_topic_score_codex":0.0000759976,"about_ca_topic_score_gemma":0.00006489863,"teacher_disagreement_score":0.73769253,"about_ca_system_score_codex":0.0001041806,"about_ca_system_score_gemma":0.000020475925,"threshold_uncertainty_score":0.609698},"labels":[],"label_agreement":null},{"id":"W2623014745","doi":"10.1287/trsc.2016.0728","title":"The Value of Flexibility in Robust Location–Transportation Problems","year":2017,"lang":"en","type":"article","venue":"Transportation Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Flexibility (engineering); Mathematical optimization; Exploit; Set (abstract data type); Computer science; Facility location problem; Robust optimization; Linear programming; Mathematics; Statistics","score_opus":0.0347453042944062,"score_gpt":0.2828744584522935,"score_spread":0.24812915415788728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2623014745","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.704364,0.00008054214,0.29223877,0.00023591834,0.0002577976,0.0005161948,0.000008428079,0.00012315794,0.0021752145],"genre_scores_gemma":[0.99408686,0.000029688788,0.0058172173,0.0000049619402,0.000004818553,0.00002095292,0.0000058358005,0.0000062105014,0.000023478806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990822,0.00000589819,0.00034346074,0.0001346713,0.00027916706,0.00015459499],"domain_scores_gemma":[0.9993642,0.00004133758,0.00008949654,0.00031382596,0.00014786448,0.00004326178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069797464,0.000065916094,0.00008595457,0.000052395684,0.00028494454,0.00008027878,0.00034470105,0.00002664466,0.000008975892],"category_scores_gemma":[0.00008559106,0.00005228261,0.00002166842,0.00031254673,0.00042734283,0.0005329338,0.0000012299538,0.00006620407,0.0000033853025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034703055,0.000024460282,0.007839345,0.00012635611,0.0000022028446,3.194314e-7,0.0014729789,0.94310606,0.0010482023,0.041738458,0.0000037762018,0.0046343636],"study_design_scores_gemma":[0.00028351668,0.000014460816,0.63626444,0.000078906385,0.000008550235,1.3561619e-7,0.0002098914,0.35364136,0.004610988,0.004590878,0.00017039583,0.00012646345],"about_ca_topic_score_codex":0.00006664783,"about_ca_topic_score_gemma":0.0009166611,"teacher_disagreement_score":0.6284251,"about_ca_system_score_codex":0.000025491172,"about_ca_system_score_gemma":0.000050183113,"threshold_uncertainty_score":0.21915919},"labels":[],"label_agreement":null},{"id":"W2625106248","doi":"","title":"Row-Reduced Column Generation for Degenerate Master Problems","year":2013,"lang":"en","type":"article","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Column generation; Degeneracy (biology); Simplex algorithm; Linear programming; Mathematical optimization; Degenerate energy levels; Column (typography); Reduction (mathematics); Mathematics; Simplex; Row; Function (biology); Computer science; Combinatorics","score_opus":0.042709259604935254,"score_gpt":0.21430721584588325,"score_spread":0.17159795624094798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2625106248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030779408,0.00003080355,0.9552877,0.00021909823,0.00021455393,0.00090442743,0.0000011507491,0.00038456143,0.0121782785],"genre_scores_gemma":[0.7548786,0.0000060412235,0.23837808,0.00018197004,0.0001569851,0.00065115007,0.00003595947,0.000039890452,0.005671367],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995831,0.00000404779,0.00014405856,0.000080365266,0.000046064375,0.00014236708],"domain_scores_gemma":[0.9997938,0.000011063447,0.000009985758,0.00007870808,0.000054234704,0.000052185194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000045161607,0.0000704167,0.00007193926,0.000022857921,0.00003443006,0.000107822714,0.000036972546,0.000040072802,0.00062100304],"category_scores_gemma":[0.000010934513,0.000061665065,0.000025967252,0.00005131338,0.0000061092164,0.0001450144,0.0000063370494,0.000025143687,0.00015932177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014872779,0.00007727602,0.000020606349,0.00040116394,0.00006822369,1.7914044e-7,0.0003534363,0.36285257,0.4604633,0.02337264,0.09834536,0.054043774],"study_design_scores_gemma":[0.00016905913,0.000014231047,0.000002933785,0.000004193397,0.000004889148,5.373846e-7,0.00001371194,0.9726707,0.02241853,0.00043448896,0.0041690446,0.00009772474],"about_ca_topic_score_codex":0.0000029935165,"about_ca_topic_score_gemma":0.000007244789,"teacher_disagreement_score":0.72409916,"about_ca_system_score_codex":0.000013810012,"about_ca_system_score_gemma":0.0000034553366,"threshold_uncertainty_score":0.67995477},"labels":[],"label_agreement":null},{"id":"W2735614447","doi":"10.21474/ijar01/4450","title":"TOWARDS ALTERNATIVE IBFS USING GUI TO OBTAIN SOLUTION TO TRANSPORTATION PROBLEM.","year":2017,"lang":"en","type":"article","venue":"International Journal of Advanced Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Transportation theory; Software engineering; Mathematical optimization; Mathematics","score_opus":0.0971918259297353,"score_gpt":0.44589345241379236,"score_spread":0.34870162648405706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2735614447","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15844774,0.000050649734,0.8330619,0.0031476396,0.0011103968,0.00040666072,0.000015880325,0.000038005965,0.0037211266],"genre_scores_gemma":[0.8042797,0.000042265932,0.19522023,0.000038267142,0.0002742845,0.000009974658,0.0000024939313,0.000021690179,0.00011111193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982731,0.000031991658,0.00039284056,0.000112408845,0.00094440236,0.00024525894],"domain_scores_gemma":[0.9980256,0.000054516942,0.00010837799,0.0001437763,0.0014642791,0.00020346486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079208944,0.0000922789,0.00014556713,0.0004082407,0.00015253879,0.0002191268,0.00066721224,0.000039693223,0.000054215343],"category_scores_gemma":[0.0005527768,0.00008600218,0.000064829255,0.00011972957,0.000041019597,0.0005633778,0.00004700672,0.0002782236,0.000022379993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015144891,0.00007414917,0.000057693796,0.000038652088,0.000118780255,0.000069070025,0.0018380897,0.80869114,0.04245981,0.0042520976,0.0004740972,0.14177498],"study_design_scores_gemma":[0.007648507,0.0019434452,0.0061136787,0.004254566,0.000085556465,0.00024066334,0.0036692368,0.5268298,0.24344385,0.0997005,0.10435671,0.0017135253],"about_ca_topic_score_codex":0.000026836953,"about_ca_topic_score_gemma":0.000025619744,"teacher_disagreement_score":0.64583194,"about_ca_system_score_codex":0.0003233739,"about_ca_system_score_gemma":0.000061617175,"threshold_uncertainty_score":0.35070673},"labels":[],"label_agreement":null},{"id":"W2743724002","doi":"10.5539/jmr.v9n5p1","title":"A Study on the Minimum and Maximum Sum of C2 Problem in IMO2014","year":2017,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Mathematical induction; Maximum principle; Maximum cut; Set (abstract data type); Sequence (biology); State (computer science); Mathematical optimization; Discrete mathematics; Combinatorics; Applied mathematics; Graph; Computer science; Algorithm; Optimal control","score_opus":0.1382243470362173,"score_gpt":0.39490593583439915,"score_spread":0.2566815887981818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743724002","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98552597,0.00007691773,0.00204649,0.00076598086,0.000052316023,0.00057652936,8.540472e-7,0.000009036129,0.010945907],"genre_scores_gemma":[0.9910457,0.00004923374,0.008771915,0.0000026657176,0.000022876931,0.000008132225,3.6129478e-8,0.000017268534,0.00008218692],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986658,0.000072000184,0.00046847606,0.000054662283,0.00055171724,0.00018734703],"domain_scores_gemma":[0.99867696,0.00062165485,0.00015179197,0.0003114296,0.00017481987,0.00006335391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003739254,0.000075929966,0.00024433242,0.00019850358,0.00009799316,0.00013120487,0.0003923168,0.000039580464,0.000023295448],"category_scores_gemma":[0.00093427865,0.000045806224,0.000037970312,0.000088666246,0.000110254616,0.00009689527,0.00009556403,0.00042153324,0.0000063081034],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007550109,0.040928133,0.03927609,0.027902866,0.0033025467,0.0016949286,0.32542583,0.027493192,0.035407312,0.35247448,0.033547778,0.11179186],"study_design_scores_gemma":[0.010909405,0.0061167735,0.01415327,0.007058211,0.00017519279,0.00032882905,0.1182469,0.32309157,0.011046879,0.5059109,0.0018351251,0.0011269687],"about_ca_topic_score_codex":0.0000023284053,"about_ca_topic_score_gemma":0.000005948165,"teacher_disagreement_score":0.29559836,"about_ca_system_score_codex":0.00002623639,"about_ca_system_score_gemma":0.000021854685,"threshold_uncertainty_score":0.18679236},"labels":[],"label_agreement":null},{"id":"W2750313416","doi":"10.1155/2017/2139791","title":"Modified Approach for Optimization of Real Life Transportation Problem in Neutrosophic Environment","year":2017,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Transportation theory; Minification; Transshipment (information security); Computer science; Mathematical optimization; Operations research; Mathematics","score_opus":0.021767164339711438,"score_gpt":0.21945711652749128,"score_spread":0.19768995218777985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2750313416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039048826,0.000030293342,0.99279326,0.000024862964,0.000029278248,0.001018887,0.000005666859,0.000114634786,0.002078245],"genre_scores_gemma":[0.7047664,0.000043472806,0.2947826,0.0000015614711,0.000012695281,0.0003235448,0.000018572564,0.00004299598,0.000008181184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986943,0.0000071899917,0.00067115785,0.00018875614,0.0001538361,0.00028475898],"domain_scores_gemma":[0.99945974,0.00007026835,0.000089312205,0.00028600934,0.000016035714,0.00007862483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035013127,0.00018797876,0.0003687276,0.0001447176,0.000034172244,0.000042468855,0.00020439997,0.00013145356,0.00001271994],"category_scores_gemma":[0.00009054629,0.00019324225,0.000063153195,0.00007828459,0.000036227368,0.00021663478,0.000014715056,0.00015014735,0.0000011216288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040020736,0.00012110495,0.00005492725,0.0029593976,0.000011273199,4.2566143e-7,0.00039181078,0.97624654,0.0003420972,0.019668067,0.0000017967336,0.00019854576],"study_design_scores_gemma":[0.00072693813,0.000022705442,0.00011815496,0.00020936187,0.0000121855055,4.555315e-7,0.000022393222,0.9948471,0.00014381116,0.003708328,0.000004543437,0.00018401856],"about_ca_topic_score_codex":0.000004367168,"about_ca_topic_score_gemma":0.0000011340545,"teacher_disagreement_score":0.7008615,"about_ca_system_score_codex":0.000059616123,"about_ca_system_score_gemma":0.000007668657,"threshold_uncertainty_score":0.788019},"labels":[],"label_agreement":null},{"id":"W2754617386","doi":"10.1109/tfuzz.2017.2751006","title":"A New Possibilistic Optimization Model for Multiple Criteria Assignment Problem","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Mathematical optimization; Optimization problem; Mathematics","score_opus":0.03912850296693645,"score_gpt":0.2756797137835551,"score_spread":0.23655121081661865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754617386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000473176,0.000022929631,0.99552584,0.000051109753,0.00091759843,0.0011087812,0.000045763125,0.0003741752,0.0019064858],"genre_scores_gemma":[0.8070976,0.000009648548,0.190197,0.000008910368,0.000059966173,0.00032943045,0.000006016598,0.000053129526,0.0022382773],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908704,0.000013387146,0.00031891195,0.00019875866,0.00015015058,0.00023173995],"domain_scores_gemma":[0.9992532,0.00006407219,0.00006802906,0.00040542407,0.00006141754,0.00014782776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013306491,0.000172763,0.00020972271,0.000072000956,0.0003560695,0.00035814205,0.00017212603,0.00010241773,0.000023187411],"category_scores_gemma":[0.000015717547,0.0001676949,0.00008841881,0.00004048448,0.000019164934,0.00025273662,8.9659756e-7,0.000089777895,0.000018900364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014375771,0.000048196413,4.8499066e-7,0.0002763043,0.000034731223,3.5364843e-7,0.00021085601,0.9954192,0.00023990513,0.000246322,0.00052604295,0.0029832018],"study_design_scores_gemma":[0.0007028168,0.000041351515,4.0940006e-7,0.00011833404,0.000042635005,0.000002558543,0.00004160375,0.9981899,0.0003002877,0.00023535032,0.00013831485,0.00018639148],"about_ca_topic_score_codex":0.000025599402,"about_ca_topic_score_gemma":0.000015123682,"teacher_disagreement_score":0.8070503,"about_ca_system_score_codex":0.00009836913,"about_ca_system_score_gemma":0.000027485781,"threshold_uncertainty_score":0.6838399},"labels":[],"label_agreement":null},{"id":"W27583426","doi":"","title":"Locating Post Offices Using Fuzzy Goal Programming and Geographical Information System (GIS)","year":2011,"lang":"en","type":"article","venue":"Americas Conference on Information Systems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Megacity; Geographic information system; Fuzzy logic; Computer science; Service (business); Operations research; Order (exchange); Information system; Goal programming; Facility location problem; Geography; Business; Engineering; Artificial intelligence; Marketing; Cartography; Economics","score_opus":0.030570053798958267,"score_gpt":0.23565644987445458,"score_spread":0.2050863960754963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W27583426","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10505233,0.00008076286,0.79037917,0.000030993895,0.0009760122,0.001858309,0.000033134947,0.0022701635,0.099319115],"genre_scores_gemma":[0.9879668,0.000010578543,0.011798434,0.000054218362,0.000030030058,0.000079211124,0.000041768897,0.000015061181,0.000003930273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983566,0.0000418152,0.0008411954,0.00010643128,0.0003277566,0.00032619815],"domain_scores_gemma":[0.9989401,0.000040489092,0.0003030246,0.00021288739,0.00033768773,0.00016583063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030878323,0.00024297374,0.00029963892,0.00034036962,0.00017430248,0.0005003859,0.00014679435,0.00012618302,0.000012740458],"category_scores_gemma":[0.00005193004,0.00022300034,0.000048109392,0.0004041621,0.000101945145,0.0025916952,0.000038870785,0.0001903006,0.00013616636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110351684,0.000091344096,0.0017103511,0.007890295,0.0002514682,0.000004802976,0.03792918,0.039575767,0.00015823905,0.5755939,0.00010475577,0.33657953],"study_design_scores_gemma":[0.0004574459,0.00016181142,0.00028340798,0.00049859466,0.00003835603,0.00008973367,0.036051538,0.95873487,0.000061945015,0.000015741341,0.0031124721,0.00049408386],"about_ca_topic_score_codex":0.00017420705,"about_ca_topic_score_gemma":0.0000010654122,"teacher_disagreement_score":0.9191591,"about_ca_system_score_codex":0.00006904844,"about_ca_system_score_gemma":0.000031785246,"threshold_uncertainty_score":0.90936905},"labels":[],"label_agreement":null},{"id":"W2770226579","doi":"","title":"A Thermodynamic Investigation of Commercial Kitchen Operations and the Implementation of a Waste Heat Recovery System","year":2017,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"McMaster University","keywords":"Waste heat recovery unit; Waste management; Engineering; Environmental science; Process engineering; Mechanical engineering; Heat exchanger","score_opus":0.009886170478921787,"score_gpt":0.2144405540544898,"score_spread":0.20455438357556802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770226579","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53891754,0.00037416042,0.017133718,0.000119666176,0.0007605027,0.0024233586,0.00015143458,0.00022179274,0.43989784],"genre_scores_gemma":[0.98336685,0.00008042205,0.0010342783,0.000005767404,0.000028590248,0.000006357256,0.00023750022,0.000036245532,0.015203989],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932474,0.00006759644,0.00026337372,0.000121027406,0.00012295309,0.00010028415],"domain_scores_gemma":[0.9995251,0.000047285466,0.000118411546,0.0001868305,0.00008511833,0.00003721214],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010991398,0.00015330818,0.00030328176,0.00009837419,0.00012132574,0.00004487994,0.00017695344,0.00012170735,0.0009737572],"category_scores_gemma":[0.000007696805,0.00013573682,0.000080005804,0.00011261009,0.00008796583,0.00019159462,0.000023736768,0.0001149337,0.0000016969634],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007844571,0.00006302184,0.0005698527,0.013801982,0.0016358107,0.000010666362,0.03631758,0.03370937,0.008688497,0.07664787,0.000303025,0.82746786],"study_design_scores_gemma":[0.01695574,0.00039016586,0.0037287811,0.004826753,0.0034183,0.000015723754,0.26612547,0.6774968,0.018851548,0.0011397186,0.005073813,0.0019771713],"about_ca_topic_score_codex":0.00029858795,"about_ca_topic_score_gemma":0.0015766963,"teacher_disagreement_score":0.8254907,"about_ca_system_score_codex":0.00006471285,"about_ca_system_score_gemma":0.000040611383,"threshold_uncertainty_score":0.9999395},"labels":[],"label_agreement":null},{"id":"W2774074136","doi":"10.1007/bf03398807","title":"Revisiting Dinkelbach-type algorithms for generalized fractional programs","year":2008,"lang":"en","type":"article","venue":"OPSEARCH","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Iterated function; Computer science; Type (biology); Mathematical optimization; Algorithm; Fractional programming; Applied mathematics; Mathematics; Nonlinear programming; Nonlinear system","score_opus":0.09134289677039446,"score_gpt":0.3276415467877081,"score_spread":0.23629865001731365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774074136","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030057926,0.0005678822,0.9550681,0.00038535788,0.00048489321,0.001164382,0.000006650607,0.0010355113,0.011229295],"genre_scores_gemma":[0.2911163,0.00024364017,0.7040289,0.00007470795,0.0007668411,0.00022804375,0.00015422695,0.00009945797,0.0032878672],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992566,0.0000127744825,0.00016251841,0.00012059268,0.00018199165,0.00026550572],"domain_scores_gemma":[0.9996015,0.000075926284,0.0000137887,0.00010419349,0.00011533693,0.00008925033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018088758,0.00008719804,0.000121362646,0.000044995373,0.00015131528,0.000040765673,0.000079624035,0.000054708376,0.00016191832],"category_scores_gemma":[0.000085624335,0.00008370205,0.000055201857,0.00019267207,0.000034923654,0.00009503938,0.00001777113,0.00011932178,0.000062183826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072977826,0.00029232822,0.0013745571,0.0014278863,0.00024761487,0.000036534413,0.001134257,0.039508358,0.0037999188,0.027273504,0.009779994,0.91505206],"study_design_scores_gemma":[0.00077745435,0.00007197916,0.00014015588,0.000049877413,0.000015828991,0.0000465055,0.00004969117,0.9136517,0.00086054194,0.0010116372,0.083031245,0.0002933705],"about_ca_topic_score_codex":0.0000025959755,"about_ca_topic_score_gemma":1.971123e-7,"teacher_disagreement_score":0.9147587,"about_ca_system_score_codex":0.00003344166,"about_ca_system_score_gemma":0.000018814546,"threshold_uncertainty_score":0.34132704},"labels":[],"label_agreement":null},{"id":"W2786155106","doi":"10.1016/j.ejor.2018.01.022","title":"Matheuristics based on iterative linear programming and slope scaling for multicommodity capacitated fixed charge network design","year":2018,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"European Regional Development Fund; Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport; European Commission","keywords":"Heuristics; Mathematical optimization; Linear programming; Integer programming; Mathematics; Heuristic; Limit (mathematics); Fixed charge; Scaling; Iterative method; Set (abstract data type); Integer (computer science); Algorithm; Computer science","score_opus":0.12431254137681133,"score_gpt":0.3508453785029433,"score_spread":0.22653283712613195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786155106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047920863,0.000051515162,0.9931784,0.00023338679,0.00014728606,0.00043696314,0.000007702317,0.00003596823,0.001116677],"genre_scores_gemma":[0.52421856,0.000009684091,0.47502267,0.000059281803,0.0005754879,0.000007175177,0.0000080788595,0.000039125338,0.00005997158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820423,0.00051898824,0.00041292812,0.00011877762,0.0004138197,0.00033127863],"domain_scores_gemma":[0.9976513,0.0008968973,0.000056255467,0.00009515485,0.0011336302,0.00016678218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050106933,0.00012001208,0.00016627758,0.00014438322,0.00039716932,0.00023261452,0.00014904291,0.000027437121,0.000072556],"category_scores_gemma":[0.001296439,0.000098084995,0.000043777534,0.0002099157,0.00014531071,0.0001366241,0.000026946955,0.00035295248,0.000029661505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006275302,0.0003390806,0.000053692675,0.00025076384,0.0001467675,0.00007445057,0.0025387986,0.93329054,0.0023994534,0.0069237426,0.010544193,0.04281096],"study_design_scores_gemma":[0.0007669917,0.000652897,0.00004273642,0.00018550361,0.000008099491,0.000009473472,0.00004877141,0.9909299,0.0009508124,0.00010503305,0.0061920234,0.000107737935],"about_ca_topic_score_codex":1.525297e-7,"about_ca_topic_score_gemma":1.8282067e-7,"teacher_disagreement_score":0.51942647,"about_ca_system_score_codex":0.00004668917,"about_ca_system_score_gemma":0.00005316998,"threshold_uncertainty_score":0.399979},"labels":[],"label_agreement":null},{"id":"W2789391008","doi":"10.5539/jmr.v10n2p77","title":"Efficiency of MOMA-plus Method to Solve Some Fully Fuzzy L-R Triangular Multiobjective Linear Programs","year":2018,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Deutscher Akademischer Austauschdienst","keywords":"Mathematics; Multi-objective optimization; Mathematical optimization; Fuzzy logic; Pareto principle; Linear programming; Computer science; Artificial intelligence","score_opus":0.09067810224268658,"score_gpt":0.4141740766849413,"score_spread":0.3234959744422547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789391008","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03729388,0.00015947208,0.95798314,0.0001340531,0.00018076545,0.00072978146,0.0000026886826,0.000059988957,0.003456207],"genre_scores_gemma":[0.11973102,0.000021874604,0.8796697,0.0000109948905,0.00032437887,0.000017849903,7.149502e-7,0.00005597771,0.00016744909],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972188,0.00010728704,0.0009283426,0.00013157222,0.0011412277,0.00047277965],"domain_scores_gemma":[0.99716973,0.0005551136,0.00018478936,0.00028974493,0.0015167481,0.0002838496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00480751,0.00016141495,0.0005265352,0.00060536055,0.00009534896,0.00007290263,0.00045695793,0.00011982778,0.00006993447],"category_scores_gemma":[0.0013815115,0.00012698068,0.00017354387,0.0008236216,0.00015286377,0.0001829152,0.000108981985,0.00048600064,0.000084966436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009425785,0.010494457,0.000041680756,0.0142484205,0.0025981802,0.00033632893,0.1369731,0.15332486,0.17270827,0.17186482,0.008454624,0.3280127],"study_design_scores_gemma":[0.002542292,0.0031225244,0.000011332803,0.0010896382,0.00009857975,0.00016807838,0.0077969516,0.8586288,0.08374749,0.040122434,0.0022188937,0.00045297015],"about_ca_topic_score_codex":0.000003180289,"about_ca_topic_score_gemma":0.0000016709126,"teacher_disagreement_score":0.70530397,"about_ca_system_score_codex":0.00015081374,"about_ca_system_score_gemma":0.000120489145,"threshold_uncertainty_score":0.5178122},"labels":[],"label_agreement":null},{"id":"W2804809316","doi":"10.1155/2018/4949565","title":"Decision-Support Framework for Selecting the Optimal Road Toll Collection System","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multiple-criteria decision analysis; SWOT analysis; Ranking (information retrieval); Toll; Operations research; Selection (genetic algorithm); Decision support system; Computer science; Decision analysis; Evidential reasoning approach; Management science; Risk analysis (engineering); Transport engineering; Data mining; Engineering; Machine learning; Business decision mapping; Mathematics; Business","score_opus":0.009411637542008968,"score_gpt":0.26692301491793263,"score_spread":0.25751137737592367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804809316","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071415156,0.000047691825,0.92709595,0.000040441813,0.00096797524,0.00022258061,0.0000020567045,0.0000724367,0.00013573184],"genre_scores_gemma":[0.57568014,0.00001376785,0.42406657,0.000012933594,0.00018675804,0.000009392608,0.0000022431718,0.000016479167,0.000011716774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911475,0.000008370004,0.0004927109,0.00006650161,0.0001800701,0.00013759613],"domain_scores_gemma":[0.99920386,0.00018521545,0.00018011018,0.00006873474,0.0003098917,0.000052214462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027681454,0.00008998881,0.00015882726,0.000071987844,0.00014752294,0.000033485703,0.00008238752,0.00006344785,0.000019166891],"category_scores_gemma":[0.00009606173,0.000066670735,0.00009667622,0.0002466773,0.0000196831,0.00024687502,7.405914e-7,0.0001408728,0.0000030582526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025901644,0.000036542857,0.000042415413,0.00021212462,0.00009312493,0.0000034921677,0.0038556627,0.88380814,0.0009661821,0.0047883033,0.00031079128,0.105624184],"study_design_scores_gemma":[0.007250437,0.004649666,0.009536018,0.0033936088,0.0011055529,0.00041984319,0.027682437,0.841611,0.039618786,0.024636539,0.038501468,0.001594621],"about_ca_topic_score_codex":3.4781297e-7,"about_ca_topic_score_gemma":0.0000061257597,"teacher_disagreement_score":0.50426495,"about_ca_system_score_codex":0.00006648608,"about_ca_system_score_gemma":0.000023361217,"threshold_uncertainty_score":0.27187535},"labels":[],"label_agreement":null},{"id":"W2886657640","doi":"10.5539/jmr.v10n5p60","title":"A New Method for Optimal Solutions of Transportation Problems in LPP","year":2018,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Arithmetic function; Simple (philosophy); Mathematics; Transportation theory; Mathematical optimization; Linear programming; Feature (linguistics); Code (set theory); Algorithm; Computer science; Programming language","score_opus":0.14162479296987449,"score_gpt":0.4299329605513355,"score_spread":0.28830816758146105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886657640","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040133703,0.000089734385,0.99476796,0.00010653734,0.000051403684,0.00027988714,0.0000031321472,0.000011812973,0.0006761505],"genre_scores_gemma":[0.0846942,0.000028560511,0.91506696,0.0000017362181,0.00007276951,0.000009116022,0.0000010177005,0.00001994969,0.00010569994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987139,0.000032078613,0.00061300595,0.000048956765,0.00036391,0.00022812502],"domain_scores_gemma":[0.9988335,0.00042212522,0.000095713694,0.00009141919,0.00046946123,0.00008783121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002494001,0.000062143474,0.00022467083,0.000333495,0.00003389447,0.000022628252,0.00015377076,0.000059748883,0.00007471909],"category_scores_gemma":[0.00029751266,0.000052639494,0.00007617727,0.00034750058,0.00004200099,0.000118610486,0.000007318927,0.0001999086,0.0000040138952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015405688,0.0022756415,0.00008465728,0.013240713,0.00056975026,0.000014434352,0.068671435,0.5076176,0.06298535,0.21576497,0.026642544,0.10197882],"study_design_scores_gemma":[0.0011548328,0.00050696864,0.000028536604,0.00060257973,0.00003043743,0.000017418002,0.0015487273,0.9371899,0.0072248676,0.049612775,0.0019668478,0.00011614688],"about_ca_topic_score_codex":0.0000040948125,"about_ca_topic_score_gemma":0.000022850314,"teacher_disagreement_score":0.42957225,"about_ca_system_score_codex":0.00004497706,"about_ca_system_score_gemma":0.00007871501,"threshold_uncertainty_score":0.21465763},"labels":[],"label_agreement":null},{"id":"W2890533656","doi":"10.4236/ajor.2018.85019","title":"Fast Computation of Pareto Set for Bicriteria Linear Programs with Application to a Diet Formulation Problem","year":2018,"lang":"en","type":"article","venue":"American Journal of Operations Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Swine Innovation Porc","keywords":"Set (abstract data type); Mathematical optimization; Pareto principle; Solver; Linear programming; Computation; Simple (philosophy); Computer science; Decision maker; Pareto interpolation; Space (punctuation); Mathematics; Algorithm; Operations research; Statistics; Programming language","score_opus":0.04879858949140138,"score_gpt":0.38852608958476437,"score_spread":0.339727500093363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890533656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1529416,0.0000061082847,0.84589875,0.00023534842,0.000017194243,0.0007521462,0.0000053871827,0.000022510978,0.00012098084],"genre_scores_gemma":[0.6832806,0.0000029982211,0.3165184,0.000008217506,0.00006951966,0.0000793212,0.00001642583,0.000015939193,0.000008578459],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989943,0.000046124653,0.00038366424,0.00008905747,0.00030330993,0.00018358146],"domain_scores_gemma":[0.9979832,0.000058388334,0.00005625984,0.00010094436,0.001693718,0.00010745571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058693107,0.0000741696,0.00017630232,0.00025764274,0.00011722883,0.000070109265,0.000113048554,0.000021137526,0.000005459139],"category_scores_gemma":[0.00006900431,0.000059051457,0.00003149469,0.0008150821,0.00012932305,0.00020267269,0.000015247747,0.00010281541,0.0000056587924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044640075,0.00022961317,0.00023120371,0.00022126839,0.00011073807,6.2500095e-7,0.005964666,0.6337084,0.010424015,0.0022762702,0.0003023458,0.34608448],"study_design_scores_gemma":[0.00040772156,0.005152408,0.000083802996,0.00013016244,0.000014836816,0.00001056983,0.0014485258,0.98902893,0.0022689665,0.00017631413,0.0011734195,0.00010436406],"about_ca_topic_score_codex":0.00001616027,"about_ca_topic_score_gemma":0.00004918349,"teacher_disagreement_score":0.530339,"about_ca_system_score_codex":0.000053733325,"about_ca_system_score_gemma":0.00005898779,"threshold_uncertainty_score":0.24080487},"labels":[],"label_agreement":null},{"id":"W2896636203","doi":"10.33423/jabe.v20i6.380","title":"Quality Dollar Cost Averaging Investing Versus Quality Index Investing","year":2018,"lang":"en","type":"article","venue":"Journal of Applied Business and Economics","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Liberian dollar; Dividend; Independence (probability theory); Volatility (finance); Index (typography); Quality (philosophy); Economics; Investment (military); Financial independence; Capital (architecture); Business; Finance; Mathematics; Statistics","score_opus":0.05351454684516536,"score_gpt":0.2749090275210119,"score_spread":0.22139448067584652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896636203","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92605,0.000043660246,0.057485584,0.00009286356,0.00058108807,0.00010067725,0.0000015894291,0.00005958573,0.015584962],"genre_scores_gemma":[0.9772996,0.00007418792,0.022074573,0.000119981545,0.00039934795,0.0000020159132,0.0000014783332,0.000024223085,0.0000046379528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989368,0.000011170161,0.00068893045,0.000103854196,0.00006825231,0.00019101867],"domain_scores_gemma":[0.99916965,0.00015315063,0.0002994027,0.000105724255,0.00014067745,0.00013138405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081562536,0.00013477562,0.00032459424,0.00008462372,0.00011357371,0.00014899012,0.000100461606,0.00007502987,0.000017316821],"category_scores_gemma":[0.00017214255,0.00013180033,0.00003487088,0.00014442368,0.0000952289,0.00024592772,0.000047093432,0.00016575266,0.0000052155674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008914946,0.00017947072,0.007317155,0.00204866,0.0005535619,0.000009470471,0.004899931,0.5487115,0.00308991,0.11303244,0.00055669615,0.3187097],"study_design_scores_gemma":[0.016698962,0.00018874869,0.033151764,0.00061362196,0.00023739354,0.00013958155,0.0070040817,0.8461665,0.0040007043,0.053020883,0.03580187,0.0029759107],"about_ca_topic_score_codex":0.000010173065,"about_ca_topic_score_gemma":0.000021851181,"teacher_disagreement_score":0.3157338,"about_ca_system_score_codex":0.00006905146,"about_ca_system_score_gemma":0.00003677613,"threshold_uncertainty_score":0.53746617},"labels":[],"label_agreement":null},{"id":"W2912136880","doi":"10.1007/s12351-019-00451-x","title":"A light robust model for aggregate production planning with consideration of environmental impacts of machines","year":2019,"lang":"en","type":"article","venue":"Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Aggregate planning; Production (economics); Aggregate (composite); Computer science; Production planning; Environmental pollution; Computational intelligence; Sensitivity (control systems); Variable (mathematics); Product (mathematics); Operations research; Control (management); Function (biology); Risk analysis (engineering); Mathematical optimization; Environmental science; Artificial intelligence; Engineering; Economics; Mathematics; Business; Microeconomics","score_opus":0.05105892803399259,"score_gpt":0.3152119319201446,"score_spread":0.264153003886152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912136880","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8897254,0.00013820421,0.10758912,0.0002579453,0.000038615322,0.00096162834,0.000019430614,0.000026003956,0.0012436503],"genre_scores_gemma":[0.9639847,0.000011801429,0.035559557,0.0000035697462,0.000018971143,0.000034772434,0.000029371442,0.000013766518,0.00034344717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934334,0.00001570454,0.00015556625,0.00009334478,0.00027946426,0.00011259248],"domain_scores_gemma":[0.99969304,0.00007100203,0.000020616333,0.00008196831,0.00010261645,0.00003075036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030870922,0.00005429363,0.00009340493,0.00008428989,0.000048412243,0.000021996655,0.00003723442,0.000029510335,0.00006106524],"category_scores_gemma":[0.00008656527,0.00004359979,0.000016363598,0.00007502271,0.000027688871,0.00017274341,0.000011062502,0.00007103988,0.0000043108976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038694667,0.000036561316,0.00029874683,0.00016697355,0.000016533755,1.08537996e-7,0.00036163122,0.8916233,0.10462126,0.0023538386,0.00014326756,0.00033906387],"study_design_scores_gemma":[0.00024522634,0.000077104494,0.00008706977,0.000066871144,0.0000030275028,0.000002265204,0.00007403175,0.9584705,0.04066987,0.00022344179,0.000033069828,0.000047512945],"about_ca_topic_score_codex":0.0000014274325,"about_ca_topic_score_gemma":0.000002594738,"teacher_disagreement_score":0.07425934,"about_ca_system_score_codex":0.000029490833,"about_ca_system_score_gemma":0.0000400244,"threshold_uncertainty_score":0.17779478},"labels":[],"label_agreement":null},{"id":"W2936226829","doi":"10.1111/itor.12694","title":"Special issue on “Transportation and Logistics with Autonomous Technologies”","year":2019,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Norwegian; Citation; Schools of economic thought; Sociology; Library science; Operations research; Computer science; Linguistics; Philosophy; Economics; Mathematics; Neoclassical economics","score_opus":0.034537682451647886,"score_gpt":0.3313023377556271,"score_spread":0.2967646553039792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2936226829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0542688,0.000083045285,0.47037783,0.009867975,0.0027855868,0.002395233,0.00019031855,0.0009970899,0.45903412],"genre_scores_gemma":[0.98766196,0.00011201104,0.009224879,0.00003149766,0.0002889043,0.00008401896,0.00004242167,0.000020046597,0.0025342668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990611,0.000016054213,0.00017577686,0.00016571044,0.00042607734,0.00015529389],"domain_scores_gemma":[0.99954545,0.00017703499,0.000008217251,0.00009020022,0.00015021485,0.00002888709],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020021378,0.00008432671,0.00008290113,0.00032073812,0.000060525254,0.00008713041,0.00011921471,0.000074254334,0.0016807374],"category_scores_gemma":[0.000032831605,0.000076644974,0.000014263394,0.00024343589,0.00008732799,0.00017902226,0.0000020435552,0.0003571338,0.0001561144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007335345,0.00015505133,0.0004469471,0.00004832117,0.000043809065,0.000012271456,0.00033386273,0.8847899,0.00020572978,0.055048842,0.0005869629,0.058254976],"study_design_scores_gemma":[0.0018799213,0.0003743579,0.0021608833,0.00020046943,0.000008478869,0.00002279388,0.0016598285,0.6874172,0.0027201995,0.003595932,0.2995006,0.000459324],"about_ca_topic_score_codex":0.0000097349985,"about_ca_topic_score_gemma":0.00008331221,"teacher_disagreement_score":0.9333932,"about_ca_system_score_codex":0.00013835604,"about_ca_system_score_gemma":0.000037730457,"threshold_uncertainty_score":0.9992319},"labels":[],"label_agreement":null},{"id":"W2938267440","doi":"10.1287/ijoc.2018.0838","title":"Solving Large Batches of Linear Programs","year":2019,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematical proof; Parametric statistics; Linear programming; Geometric programming; Basis (linear algebra); Computer science; Set (abstract data type); Space (punctuation); Mathematics; Sensitivity (control systems); Mathematical optimization; Algorithm; Geometry","score_opus":0.011727803759225532,"score_gpt":0.23535209128610612,"score_spread":0.2236242875268806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2938267440","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.708581,0.00007213927,0.26884168,0.000031292155,0.0007821047,0.00023024953,5.899639e-7,0.00029038414,0.02117055],"genre_scores_gemma":[0.97068894,0.000008282343,0.029086312,0.00004634177,0.00009697684,3.6217997e-7,0.0000015113366,0.0000191487,0.00005214275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908906,0.000005202595,0.00040936575,0.000050404804,0.00019248387,0.00025347486],"domain_scores_gemma":[0.9995891,0.00006377427,0.000107384774,0.00009319884,0.00006763528,0.00007895061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003705341,0.000101308004,0.00018196607,0.000087426015,0.000064549014,0.00006517939,0.00012242516,0.00005020224,0.00006534136],"category_scores_gemma":[0.000045634173,0.00007856871,0.000084427826,0.00013873333,0.000009799612,0.00017710525,0.000032969972,0.00028641638,0.000081800936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012828608,0.0001239351,0.002930645,0.00054634333,0.000112311194,0.000008264006,0.0025535875,0.6255936,0.00030373567,0.006654452,0.00013738748,0.36102292],"study_design_scores_gemma":[0.0004598883,0.00011625361,0.000087438675,0.0003734488,0.000005468504,0.00003510199,0.00035341474,0.99321544,0.000649374,0.0002100073,0.0043556155,0.00013856965],"about_ca_topic_score_codex":2.7245756e-7,"about_ca_topic_score_gemma":1.4714755e-7,"teacher_disagreement_score":0.3676218,"about_ca_system_score_codex":0.000027789438,"about_ca_system_score_gemma":0.000010261958,"threshold_uncertainty_score":0.3203939},"labels":[],"label_agreement":null},{"id":"W2944062520","doi":"10.3233/jifs-181527","title":"Commentary on “A reply to a Note on the paper “A simplified novel technique for solving fully fuzzy linear programming problems””","year":2019,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Simplex algorithm; Fuzzy logic; Mathematics; Linear programming; Mathematical theory; Fuzzy set; Applied mathematics; Calculus (dental); Computer science; Mathematical economics; Mathematical optimization; Artificial intelligence","score_opus":0.02530461734579143,"score_gpt":0.26802147681863486,"score_spread":0.24271685947284344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944062520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004148176,0.00021523953,0.9761333,0.0061448705,0.0020130519,0.006877212,0.000015124389,0.00022600344,0.0042270063],"genre_scores_gemma":[0.93039566,0.00004946237,0.060611013,0.0067553455,0.0010444334,0.0005664578,0.000009303272,0.00019979458,0.00036853208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978494,0.000040168597,0.0010699223,0.0001960695,0.00044135033,0.0004030954],"domain_scores_gemma":[0.9982837,0.00057193416,0.0003064556,0.0003763464,0.0002784577,0.00018314586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014977047,0.00028087432,0.0004573656,0.00021649808,0.00010376519,0.00017206398,0.00037515294,0.000110195724,0.000022002969],"category_scores_gemma":[0.00024823807,0.00018614651,0.0002477011,0.00024721728,0.000018278146,0.00014424603,0.00004222364,0.00045288607,0.000062768624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037669772,0.0007542296,0.000038270315,0.0025524017,0.00050380494,0.000014296154,0.002656541,0.87210476,0.05080861,0.01659935,0.03991619,0.013674857],"study_design_scores_gemma":[0.0013819045,0.0043324954,0.0000053834938,0.0075876745,0.00014749866,0.00022062294,0.0030809378,0.119057655,0.03409088,0.0008771919,0.82812613,0.0010916013],"about_ca_topic_score_codex":0.000009811334,"about_ca_topic_score_gemma":0.0000022768613,"teacher_disagreement_score":0.9262475,"about_ca_system_score_codex":0.00020318614,"about_ca_system_score_gemma":0.000026798529,"threshold_uncertainty_score":0.75908345},"labels":[],"label_agreement":null},{"id":"W2952129533","doi":"10.4230/lipics.socg.2019.15","title":"Preconditioning for the Geometric Transportation Problem","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Business; Transport engineering; Engineering","score_opus":0.05221844124841421,"score_gpt":0.17338697592397498,"score_spread":0.12116853467556077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952129533","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013515558,0.00009341788,0.9826468,0.000020015905,0.00032425756,0.00090995646,0.00004341608,0.00029291943,0.0021536215],"genre_scores_gemma":[0.9948346,0.00013281977,0.003763923,0.000013667464,0.000038357834,0.000010124755,0.00012730563,0.000032260417,0.0010469562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994064,0.000009363946,0.00014297878,0.00023463517,0.000036251575,0.00017037391],"domain_scores_gemma":[0.99938744,0.00019541806,0.00006423436,0.00024326448,0.00006691634,0.000042715503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000113825496,0.00014850915,0.00015676781,0.00014495787,0.000081176564,0.00005183859,0.00022238694,0.00014860547,0.00008567545],"category_scores_gemma":[0.00001617123,0.00014078105,0.00013658336,0.00030790913,0.000024384246,0.00010490219,0.000022276738,0.00022779395,0.000034435605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003911337,0.0000119296565,0.000071578324,0.00045457017,0.0000755665,8.3285636e-7,0.00008542292,0.98344487,0.0000018516726,0.01463067,0.00031969842,0.0008991251],"study_design_scores_gemma":[0.00029276405,0.000014675054,0.00025358368,0.00007026765,0.00016358867,2.7828602e-7,0.00010212393,0.9862998,0.000046719484,0.010952013,0.0015922075,0.00021196863],"about_ca_topic_score_codex":0.000005443042,"about_ca_topic_score_gemma":0.000009097519,"teacher_disagreement_score":0.981319,"about_ca_system_score_codex":0.00007037357,"about_ca_system_score_gemma":0.000020930845,"threshold_uncertainty_score":0.57408845},"labels":[],"label_agreement":null},{"id":"W2952556332","doi":"10.3233/jifs-181541","title":"A novel method for solving the fully neutrosophic linear programming problems: Suggested modifications","year":2019,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Linear programming; Mathematical optimization; Algorithm; Mathematics","score_opus":0.03529797426391277,"score_gpt":0.2852922435176206,"score_spread":0.24999426925370785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952556332","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001539883,0.0012112232,0.9936309,0.0003425329,0.0010883828,0.00152592,0.0000049388827,0.00010646382,0.00054979254],"genre_scores_gemma":[0.62153554,0.00014720518,0.37648612,0.000075277436,0.00075243606,0.0002302738,0.000008420601,0.00013447658,0.00063026085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818146,0.000040781575,0.0010276712,0.00013289948,0.00030527436,0.0003119073],"domain_scores_gemma":[0.9981691,0.00055891235,0.00037569477,0.0002642311,0.0005113477,0.0001207057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013762276,0.00019101905,0.0003882737,0.0001609401,0.00009829632,0.00020319423,0.00034631233,0.0001009285,0.000009730463],"category_scores_gemma":[0.00022512193,0.00012860035,0.0002408819,0.0002977295,0.0000223344,0.00018984602,0.000023821673,0.00031015472,0.000027323234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017249416,0.00013810134,0.000027759377,0.0011880442,0.0002378425,0.0000010137081,0.0012851543,0.966956,0.011145871,0.011400915,0.0003730671,0.0072289347],"study_design_scores_gemma":[0.00039009744,0.00018887599,0.000005275485,0.0005368175,0.000094789415,0.00019948295,0.0015406716,0.9602228,0.00085075444,0.0002713774,0.035493582,0.00020551881],"about_ca_topic_score_codex":0.000007325777,"about_ca_topic_score_gemma":0.0000022518298,"teacher_disagreement_score":0.61999565,"about_ca_system_score_codex":0.00010032698,"about_ca_system_score_gemma":0.000047764013,"threshold_uncertainty_score":0.524417},"labels":[],"label_agreement":null},{"id":"W2952728573","doi":"10.48550/arxiv.1208.5172","title":"An iterative scheme for solving the optimal transportation problem","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pacific Institute for the Mathematical Sciences; University of British Columbia","funders":"","keywords":"Measure (data warehouse); Scheme (mathematics); Mathematical optimization; Transportation theory; Function (biology); Upper and lower bounds; Iterative method; Computer science; Mathematics","score_opus":0.05555063535320044,"score_gpt":0.19627993579545322,"score_spread":0.1407293004422528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952728573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07907027,0.000047561316,0.9188748,0.00001887852,0.0001622949,0.000674663,0.00003030317,0.00032958743,0.0007916158],"genre_scores_gemma":[0.96031106,0.000026939377,0.039224926,0.000015326194,0.00007923549,0.000010408141,0.00012908092,0.00003802188,0.00016502701],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992848,0.000017611736,0.00015637696,0.00025333356,0.000039891507,0.00024799857],"domain_scores_gemma":[0.99941576,0.00006227718,0.00006481765,0.00027258473,0.000087228276,0.00009731006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015251825,0.00019902356,0.0001740618,0.00005864158,0.00012125058,0.0000738821,0.00025864138,0.00016888382,0.000050088704],"category_scores_gemma":[0.000007838341,0.00018379968,0.00011666056,0.0001227041,0.000045161472,0.000286883,0.000024315346,0.0002731022,0.000010024492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010873613,0.00004065524,0.000066874076,0.00027150678,0.00006891332,0.000002126247,0.0013127374,0.9474329,0.00011017432,0.050251566,0.00006383367,0.000367876],"study_design_scores_gemma":[0.0002285152,0.0000189025,0.000043949898,0.000059419515,0.000103373495,3.442634e-7,0.0003385865,0.99431276,0.00021824476,0.003894374,0.0005292205,0.0002522946],"about_ca_topic_score_codex":0.000004420584,"about_ca_topic_score_gemma":0.000011174192,"teacher_disagreement_score":0.8812408,"about_ca_system_score_codex":0.000066057684,"about_ca_system_score_gemma":0.00002037016,"threshold_uncertainty_score":0.7495133},"labels":[],"label_agreement":null},{"id":"W2955649334","doi":"10.1007/s10479-019-03295-y","title":"Goal programming approach for political districting in Santa Catarina State: Brazil","year":2019,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Contiguity; Legislation; Redistricting; Politics; Context (archaeology); Population; State (computer science); Distribution (mathematics); Public administration; Operations research; Computer science; Political science; Economics; Sociology; Geography; Mathematics; Law; Demography","score_opus":0.11756106179337812,"score_gpt":0.42744367272472567,"score_spread":0.30988261093134756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955649334","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64882356,0.00036382335,0.31879914,0.00091542973,0.00010820512,0.004107918,0.00007273311,0.00023594566,0.026573254],"genre_scores_gemma":[0.958575,0.000023180255,0.040822737,0.000015951968,0.000023006734,0.00016284181,0.00007197878,0.000022595337,0.00028270902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986905,0.000046458335,0.00030830098,0.00015963992,0.00025652783,0.0005385789],"domain_scores_gemma":[0.9992497,0.00018444574,0.0000070555193,0.0001860413,0.00026770047,0.00010503517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086647493,0.00008209429,0.00016140593,0.00022168536,0.00008390315,0.00010773468,0.00015692084,0.000054806897,0.00003271209],"category_scores_gemma":[0.00033901312,0.000078917896,0.000044573117,0.0004828384,0.000059344664,0.0001998604,0.0000503489,0.00022889793,0.000016177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007058042,0.001241059,0.0041835126,0.002868309,0.0001095176,0.000004265264,0.0026635642,0.6690401,0.0035323454,0.24852838,0.0019761352,0.06578225],"study_design_scores_gemma":[0.00037246797,0.00012696469,0.00024456703,0.000047426933,0.0000021568599,0.0000020568814,0.0010988439,0.99191475,0.0036913292,0.0004532556,0.0018973,0.00014889581],"about_ca_topic_score_codex":0.00010466185,"about_ca_topic_score_gemma":0.00003171976,"teacher_disagreement_score":0.32287464,"about_ca_system_score_codex":0.000032267206,"about_ca_system_score_gemma":0.000051713057,"threshold_uncertainty_score":0.32181785},"labels":[],"label_agreement":null},{"id":"W2972051872","doi":"10.1007/s00521-019-04466-5","title":"Derivation of personalized numerical scales from distribution linguistic preference relations: an expected consistency-based goal programming approach","year":2019,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Consistency (knowledge bases); Preference; Statement (logic); Computer science; Semantics (computer science); Problem statement; Linguistics; Natural language processing; Mathematics; Artificial intelligence; Management science; Statistics","score_opus":0.01990908669235801,"score_gpt":0.24722783032678733,"score_spread":0.22731874363442933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972051872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25535375,0.00011160123,0.74344873,0.00001613052,0.000024733452,0.00044575825,0.000018363346,0.00030351314,0.00027739536],"genre_scores_gemma":[0.9117339,0.0000018621095,0.0875236,0.000009569118,0.000038799168,0.000055639142,0.00061675627,0.0000139848225,0.000005874261],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992107,0.00003400004,0.00028886797,0.00021201602,0.000119304605,0.00013511356],"domain_scores_gemma":[0.9994142,0.00017489165,0.000078429126,0.00016597436,0.00009610131,0.000070378424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006702651,0.00011617683,0.0001719422,0.000033417917,0.00012037487,0.000050657934,0.000080194455,0.00006333329,0.000010516715],"category_scores_gemma":[0.000058739213,0.00011634292,0.00003563217,0.00023836219,0.00006963081,0.000060428327,0.00001713623,0.00012225343,0.000004474124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006865936,0.0012468727,0.020802625,0.0013215219,0.00014211143,7.96947e-7,0.0030675654,0.5601464,0.010387608,0.1262927,0.000063589716,0.27645957],"study_design_scores_gemma":[0.00029452177,0.000028328051,0.0021856811,0.000033626835,0.000022881572,0.000001226052,0.00016623261,0.9961397,0.00016135466,0.00043006527,0.00040481813,0.00013158208],"about_ca_topic_score_codex":0.00001026078,"about_ca_topic_score_gemma":6.0389726e-7,"teacher_disagreement_score":0.6563802,"about_ca_system_score_codex":0.000022376387,"about_ca_system_score_gemma":0.000013096522,"threshold_uncertainty_score":0.47443265},"labels":[],"label_agreement":null},{"id":"W2988733330","doi":"10.1007/978-3-030-26676-9_7","title":"New Methods for Solving Fully Fuzzy Solid Transportation Problems with LR Fuzzy Parameters","year":2019,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Fuzzy transportation; Fuzzy logic; Mathematical optimization; Computer science; Mathematics; Fuzzy set operations; Fuzzy set; Artificial intelligence","score_opus":0.04444471830262737,"score_gpt":0.3205293188917555,"score_spread":0.27608460058912815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988733330","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019688637,0.006135548,0.9708898,0.000047738773,0.00070746767,0.0012684695,0.000007764257,0.00031994455,0.020426355],"genre_scores_gemma":[0.01883247,0.001289451,0.97153443,0.00006762928,0.0002456534,0.000054614706,0.00006560963,0.00025695105,0.007653171],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984739,0.000010376276,0.0006035322,0.0003970254,0.0001389575,0.0003762215],"domain_scores_gemma":[0.99884593,0.0006059738,0.00016375596,0.00016963134,0.00013613304,0.00007854929],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038671753,0.00044716583,0.00086655485,0.00015847912,0.00012815392,0.000069698996,0.000114248454,0.00018661428,0.0000034888774],"category_scores_gemma":[0.00005864292,0.00038940934,0.000099572695,0.000076933364,0.000086099026,0.000097536096,0.000037150592,0.00029344726,0.0000019051477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004187179,0.000019265884,0.000069189606,0.009938567,0.0008621618,0.000009138358,0.008273908,0.39244598,0.00002657372,0.05444915,0.0003770522,0.53348714],"study_design_scores_gemma":[0.00713771,0.00091138115,0.00011601658,0.021787008,0.0013795336,0.00003872162,0.0046143727,0.729106,0.00009912233,0.19874237,0.030587886,0.005479877],"about_ca_topic_score_codex":0.000004460115,"about_ca_topic_score_gemma":0.000031538228,"teacher_disagreement_score":0.52800727,"about_ca_system_score_codex":0.0000617663,"about_ca_system_score_gemma":0.000032049906,"threshold_uncertainty_score":0.99985576},"labels":[],"label_agreement":null},{"id":"W2994661168","doi":"10.35629/9795-05020101","title":"A Simulation-Optimization Algorithm for Generating Sets of Alternatives Using Population-Based Metaheuristic Procedures","year":2019,"lang":"en","type":"article","venue":"Journal of Software Engineering and Simulation","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Metaheuristic; Computer science; Mathematical optimization; Population; Parallel metaheuristic; Algorithm; Mathematics; Meta-optimization; Medicine","score_opus":0.012728386387827409,"score_gpt":0.264325938099421,"score_spread":0.2515975517115936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994661168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1780389,0.00015708759,0.82137954,0.0000017163899,0.00015886148,0.00020308593,0.0000055111273,0.00005437926,9.089775e-7],"genre_scores_gemma":[0.5536324,0.0000025579425,0.44629085,0.0000029274283,0.000039145267,0.0000014391296,0.000008736445,0.000020073421,0.0000019272154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991203,0.000011298554,0.0005073174,0.00008036654,0.00017131884,0.0001094139],"domain_scores_gemma":[0.99866086,0.00074603833,0.00021479327,0.000059234113,0.00026860766,0.000050458028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018834679,0.00012699876,0.00025401355,0.00021108094,0.00003546271,0.000037452326,0.000043234577,0.00006335197,0.000009804867],"category_scores_gemma":[0.00074753346,0.00012295565,0.00007147466,0.00013587666,0.0000059011295,0.00025344724,0.000005439604,0.000074621,1.6591272e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000058333576,0.000013573689,0.0007635432,0.00042019523,0.000040885105,2.3681619e-7,0.000096580035,0.99399483,0.00015826648,0.000013097032,4.176903e-7,0.0044925516],"study_design_scores_gemma":[0.0005378616,0.000046989157,0.00021772225,0.00020389243,0.000052679898,0.0000017520941,0.000013660005,0.99862796,0.00013084475,0.000036556594,0.000009024917,0.00012104996],"about_ca_topic_score_codex":0.0000011285097,"about_ca_topic_score_gemma":1.0297383e-7,"teacher_disagreement_score":0.37559345,"about_ca_system_score_codex":0.00004414845,"about_ca_system_score_gemma":0.000021302925,"threshold_uncertainty_score":0.5013986},"labels":[],"label_agreement":null},{"id":"W3006806717","doi":"10.1155/2020/7570686","title":"Green and Reliable Freight Routing Problem in the Road-Rail Intermodal Transportation Network with Uncertain Parameters: A Fuzzy Goal Programming Approach","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Shandong Province; Ministry of Education of the People's Republic of China; Kungliga Tekniska Högskolan; National Natural Science Foundation of China","keywords":"Mathematical optimization; Routing (electronic design automation); Fuzzy logic; Fuzzy transportation; Fuzzy set; Computer science; Linearization; Operations research; Fuzzy number; Integer programming; Engineering; Nonlinear system; Mathematics","score_opus":0.012931384612039243,"score_gpt":0.21574504847273177,"score_spread":0.20281366386069252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006806717","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5555191,0.0004104905,0.44213063,0.0005428861,0.0000696447,0.0009319047,0.0000048030056,0.00011112154,0.00027941374],"genre_scores_gemma":[0.7223235,0.00003680319,0.27745515,0.0000623706,0.00004933444,0.000021783782,0.000027474198,0.000021959824,0.0000016332665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987233,0.000027027021,0.0006208889,0.00014006722,0.00025973647,0.00022895704],"domain_scores_gemma":[0.9995213,0.000048155754,0.00020952475,0.00006168173,0.000071111666,0.000088238885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028143794,0.00016603291,0.00026639254,0.000054030264,0.00005330079,0.000054921362,0.00011266101,0.00005989491,0.0000021142337],"category_scores_gemma":[0.00001030679,0.000117583324,0.000059127935,0.00037805573,0.000034693996,0.00053352595,8.042568e-7,0.0003407324,2.569424e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013661252,0.00004162035,0.0010628304,0.0003327377,0.000038003487,0.000028326951,0.015187309,0.9567191,0.00009004339,0.00074117,0.000007753422,0.025614465],"study_design_scores_gemma":[0.020745028,0.0050584846,0.09889512,0.004556794,0.001342625,0.00022828045,0.060658306,0.7861294,0.00058769924,0.014382741,0.0045894366,0.002826061],"about_ca_topic_score_codex":0.0000121091725,"about_ca_topic_score_gemma":0.0000520516,"teacher_disagreement_score":0.1705897,"about_ca_system_score_codex":0.000025960919,"about_ca_system_score_gemma":0.000018279525,"threshold_uncertainty_score":0.47949088},"labels":[],"label_agreement":null},{"id":"W3037228688","doi":"10.1504/ijor.2021.10024058","title":"Computing Pareto set in the criterion space for bicriteria linear programs using a single criterion software","year":2019,"lang":"en","type":"article","venue":"International Journal of Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Simple (philosophy); Set (abstract data type); Pareto principle; Mathematical optimization; Solver; Computer science; Linear programming; Space (punctuation); Linear space; Software; Algorithm; Mathematics; Discrete mathematics","score_opus":0.14810578597622853,"score_gpt":0.4278482001933418,"score_spread":0.27974241421711327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037228688","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.772821,0.0001673484,0.22347534,0.0015973536,0.0009392008,0.00071063614,0.000016519609,0.000030259285,0.00024235898],"genre_scores_gemma":[0.9155687,0.000013994959,0.08379191,0.000098412434,0.00041593565,0.000010548181,0.0000390622,0.000023897952,0.000037527396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980732,0.00012237225,0.00049573893,0.0001198038,0.0009401823,0.0002486984],"domain_scores_gemma":[0.99802274,0.0004661307,0.00006429698,0.000096797754,0.0012936429,0.00005637501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018200318,0.000106086365,0.00015977805,0.00032441286,0.00007989112,0.00043940297,0.00043959773,0.00006319349,0.00007821796],"category_scores_gemma":[0.00055282074,0.000081984595,0.00008514531,0.00024197255,0.000045822006,0.0004247044,0.000056922796,0.00032944194,0.000012196891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010101584,0.002000719,0.010762653,0.0011525687,0.0005783058,0.00024690258,0.020301897,0.6931024,0.14563337,0.020871805,0.0047618966,0.09957729],"study_design_scores_gemma":[0.0011644757,0.000359885,0.00030473343,0.00058348756,0.0000077415625,0.00022836622,0.0013194917,0.9808997,0.0012059738,0.0011859853,0.012556355,0.00018380095],"about_ca_topic_score_codex":0.000008009153,"about_ca_topic_score_gemma":0.0000057083853,"teacher_disagreement_score":0.28779727,"about_ca_system_score_codex":0.0002079562,"about_ca_system_score_gemma":0.00008786852,"threshold_uncertainty_score":0.4237173},"labels":[],"label_agreement":null},{"id":"W3046875461","doi":"","title":"Entrepreneur 2019: Application of linear programming to semi-commercial arable and fishery","year":2020,"lang":"en","type":"paratext","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fishery; Arable land; Business; Fishing; Agriculture; Ecology; Biology","score_opus":0.00980613457138608,"score_gpt":0.23739455282605776,"score_spread":0.2275884182546717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046875461","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003212895,0.00061602204,0.90184164,0.00043301395,0.00048834947,0.0014977405,0.000040780003,0.00042933397,0.09433185],"genre_scores_gemma":[0.084570214,0.0025088382,0.76031935,0.0017463287,0.0024727099,0.0012901354,0.0022327318,0.00083839893,0.14402127],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99902624,0.000012066012,0.0003696725,0.0002516312,0.00013662536,0.00020378489],"domain_scores_gemma":[0.9994735,0.000050909228,0.000056723416,0.00020174154,0.00004889014,0.00016822336],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006279082,0.00021912929,0.00038690562,0.00008048182,0.000031447085,0.000046125442,0.00012852954,0.00019015117,0.00044845563],"category_scores_gemma":[0.000035850004,0.00021752746,0.000058228237,0.0002266615,0.000020570362,0.00005857685,0.000073339215,0.00019505202,0.00085413735],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021060101,0.00012144153,0.000025918438,0.0037495731,0.0001578624,0.0000014588818,0.00076681125,0.098292984,0.00083735236,0.00048817587,0.55712545,0.3384119],"study_design_scores_gemma":[0.00020429394,0.000054366617,0.000004920847,0.000119126766,0.000048910686,0.000002032868,0.00004334685,0.1434759,0.0021078624,0.000028162469,0.85352445,0.00038664238],"about_ca_topic_score_codex":0.000019708024,"about_ca_topic_score_gemma":0.000004473391,"teacher_disagreement_score":0.33802527,"about_ca_system_score_codex":0.000019628325,"about_ca_system_score_gemma":0.0000138686955,"threshold_uncertainty_score":0.9999238},"labels":[],"label_agreement":null},{"id":"W3047295571","doi":"10.1016/j.jclepro.2020.123566","title":"A stochastic network design problem for hazardous waste management","year":2020,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Norges Forskningsråd","keywords":"Hazardous waste; Time horizon; Stochastic programming; Network planning and design; Population; Flow network; Linear programming; Operations research; Computer science; Engineering; Risk analysis (engineering); Mathematical optimization; Waste management; Business; Mathematics","score_opus":0.027007497033502217,"score_gpt":0.22327363851022056,"score_spread":0.19626614147671834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047295571","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00035852537,0.00011957064,0.996995,0.0013988757,0.00043728392,0.00042702697,1.7292918e-7,0.00005974534,0.00020385647],"genre_scores_gemma":[0.33413017,0.000040499075,0.6629701,0.00012713025,0.002387797,0.000022607204,0.0000011655759,0.000051196002,0.00026934274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992235,0.000019699866,0.0003663703,0.00008434535,0.00016931955,0.00013677309],"domain_scores_gemma":[0.9995983,0.000027988184,0.0001157373,0.000066469824,0.000111739755,0.000079756916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043023602,0.00007973009,0.00015586802,0.000044223216,0.000037348153,0.000041377763,0.00007881973,0.000026149133,0.00000959317],"category_scores_gemma":[0.000096952324,0.00006710321,0.00006576339,0.00013725585,0.000009491844,0.00014379005,0.000009865523,0.00010147928,0.00000539006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050479885,0.000015486012,3.3339842e-7,0.00017663122,0.0000592261,0.000001775969,0.00020584566,0.94854546,0.00009994981,0.0002480851,0.020125635,0.03047111],"study_design_scores_gemma":[0.0008114466,0.0004255246,0.0000027412473,0.00015216686,0.00019009347,0.00007395674,0.00046361718,0.9643132,0.0011108764,0.008341457,0.023898577,0.00021631563],"about_ca_topic_score_codex":2.0382148e-8,"about_ca_topic_score_gemma":5.2849728e-8,"teacher_disagreement_score":0.33402485,"about_ca_system_score_codex":0.000026950418,"about_ca_system_score_gemma":0.0000058847936,"threshold_uncertainty_score":0.27363896},"labels":[],"label_agreement":null},{"id":"W3087366400","doi":"10.1002/cjce.23886","title":"A two‐step coordinated optimization model for a dewatering process","year":2020,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Foundation for Innovative Research Groups of the National Natural Science Foundation of China; National Natural Science Foundation of China","keywords":"Dewatering; Production (economics); Process (computing); Mathematical optimization; Genetic algorithm; Computer science; Filter (signal processing); Process engineering; Engineering; Mathematics; Economics","score_opus":0.015275831380019074,"score_gpt":0.20905668478659445,"score_spread":0.1937808534065754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087366400","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008916669,0.00008673851,0.9900961,0.0005368409,0.000069974885,0.00013790048,0.0000038533635,0.00006976759,0.00008217327],"genre_scores_gemma":[0.929916,8.4816634e-7,0.06984406,0.000087899876,0.00009694291,0.000008484114,0.0000026326675,0.000039606657,0.0000034866712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993585,0.0000026579478,0.00026997473,0.000057057998,0.00008056094,0.00023128398],"domain_scores_gemma":[0.9993736,0.000034642686,0.0000391794,0.00005427967,0.00011139383,0.0003868931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010505886,0.000110867884,0.00016480894,0.000053848446,0.000038391212,0.00006955865,0.00019131193,0.00004846518,0.000012632428],"category_scores_gemma":[0.00021498838,0.00009056067,0.00006404497,0.00016730855,0.000018018427,0.00011403339,0.0000055705214,0.00018055424,8.8029304e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004244187,0.0000013819637,8.170426e-7,0.00012606227,0.000023517208,0.0000022496868,0.00047266806,0.9956253,0.003164767,0.00030237072,0.00007639302,0.00020021055],"study_design_scores_gemma":[0.00031633806,0.000010524085,4.151377e-8,0.000048271344,0.000024752579,0.000018051476,0.000014835087,0.9919916,0.0073085646,0.000075651886,0.00008445291,0.00010690789],"about_ca_topic_score_codex":0.00000844039,"about_ca_topic_score_gemma":0.0000063869297,"teacher_disagreement_score":0.92099935,"about_ca_system_score_codex":0.00007470478,"about_ca_system_score_gemma":0.00008272,"threshold_uncertainty_score":0.36929572},"labels":[],"label_agreement":null},{"id":"W3104709940","doi":"10.3390/jrfm13110280","title":"A New Application for the Goal Programming—The Target Decision Rule for Uncertain Problems","year":2020,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Goal programming; Analogy; Decision maker; Computer science; Certainty; Interpretation (philosophy); Domain (mathematical analysis); Order (exchange); Decision rule; Basis (linear algebra); Mathematics; Artificial intelligence; Management science; Operations research; Programming language; Engineering","score_opus":0.01010883456407226,"score_gpt":0.2271718912013876,"score_spread":0.21706305663731532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104709940","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020510996,0.0006905771,0.9971959,0.0007400431,0.000107561245,0.001005333,0.000002871101,0.000019930076,0.00003265468],"genre_scores_gemma":[0.11125889,0.0010295543,0.8865491,0.00024669315,0.00061763136,0.00021926947,0.000003151642,0.00002784587,0.00004787825],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994776,0.0000049619416,0.00024392239,0.00006222097,0.00010142881,0.000109837834],"domain_scores_gemma":[0.9996146,0.0001375426,0.00009115791,0.000057084806,0.0000433628,0.000056277793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031022407,0.000069994734,0.00010786332,0.00002134602,0.00012118567,0.000055987955,0.00012155452,0.000025127405,0.0000023003333],"category_scores_gemma":[0.000110714675,0.000039987663,0.00007396561,0.00010910064,0.000012278286,0.00005228807,0.00002066981,0.000077561584,9.538554e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048019327,0.000012181369,0.000019358567,0.0001223716,0.000017070975,2.6759884e-7,0.000431276,0.03852188,0.000003854464,0.006010972,0.0036893173,0.9511234],"study_design_scores_gemma":[0.0007244645,0.00011651731,0.0001222846,0.000027393773,0.00009091468,0.0000013266481,0.00015092203,0.16586354,0.000013849832,0.018327866,0.81449676,0.0000641485],"about_ca_topic_score_codex":0.0000014867113,"about_ca_topic_score_gemma":0.0000017657088,"teacher_disagreement_score":0.9510593,"about_ca_system_score_codex":0.000012510295,"about_ca_system_score_gemma":0.0000086691825,"threshold_uncertainty_score":0.16306496},"labels":[],"label_agreement":null},{"id":"W3115020944","doi":"10.1287/trsc.2020.1022","title":"Partial Benders Decomposition: General Methodology and Application to Stochastic Network Design","year":2020,"lang":"en","type":"article","venue":"Transportation Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada; Université du Québec à Montréal","funders":"","keywords":"Benders' decomposition; Mathematical optimization; Decomposition; Network planning and design; Computer science; Stochastic programming; Class (philosophy); Dual (grammatical number); Decomposition method (queueing theory); Relaxation (psychology); Process (computing); Mathematics","score_opus":0.07043514893512383,"score_gpt":0.32223355932867853,"score_spread":0.2517984103935547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115020944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075899404,0.00001600923,0.991501,0.0004124071,0.000052222975,0.00023013072,0.0000011314,0.00015197438,0.000045180943],"genre_scores_gemma":[0.60578674,0.0000014679174,0.39388725,0.00026208864,0.00003050181,0.00002255922,0.0000036387007,0.0000047609733,9.739981e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994114,0.00001433834,0.00014551533,0.00015962035,0.000120449535,0.00014865524],"domain_scores_gemma":[0.99967694,0.000053773605,0.00001521807,0.00005069435,0.00003197025,0.00017138112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024262581,0.000060336857,0.00008083867,0.000029151546,0.00008692636,0.000029599127,0.00007476128,0.000020849504,0.000016884485],"category_scores_gemma":[0.0000218039,0.00006268213,0.000010658453,0.00043700935,0.00006814102,0.000132858,0.000002193843,0.000039631457,0.000011461441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057404777,0.0000021970216,0.0000092496975,0.000009905347,0.0000018018989,2.547726e-7,0.0009293638,0.9817807,0.0051554013,0.0072352546,0.00003039415,0.004839753],"study_design_scores_gemma":[0.00010308864,0.000031128035,0.0004934666,0.000003540902,0.0000106950265,6.99864e-7,0.000052029598,0.9966104,0.0016078833,0.00088816247,0.00011108378,0.000087818975],"about_ca_topic_score_codex":0.0000012127753,"about_ca_topic_score_gemma":0.0000019357547,"teacher_disagreement_score":0.5981968,"about_ca_system_score_codex":0.000009964473,"about_ca_system_score_gemma":0.000013348553,"threshold_uncertainty_score":0.2556103},"labels":[],"label_agreement":null},{"id":"W3120798890","doi":"","title":"Estimating Technical Efficiency and Bootstrapping Malmquist Indices: Analysis of Malaysian Preschool Sector.","year":2016,"lang":"en","type":"article","venue":"Early childhood education","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bootstrapping (finance); Malmquist index; Econometrics; Economics; Statistics; Mathematics; Macroeconomics; Productivity; Total factor productivity","score_opus":0.005866134191896754,"score_gpt":0.22349340283050073,"score_spread":0.217627268638604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120798890","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76936936,0.00011373945,0.22756389,0.00006005035,0.00012657046,0.00019677688,0.000004148387,0.00018745096,0.0023780232],"genre_scores_gemma":[0.97855103,0.000007214284,0.021303639,0.000011142852,0.000055288067,0.00002095709,0.0000068221325,0.000015876203,0.000028026117],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992845,0.000014848319,0.00028036695,0.00014884202,0.00012238903,0.00014905901],"domain_scores_gemma":[0.99957764,0.00006370478,0.000070788934,0.00016795931,0.000033977114,0.000085937296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012715862,0.000102863385,0.00017465533,0.00027055433,0.00004516957,0.000042205516,0.00008758923,0.00006228669,0.00009398078],"category_scores_gemma":[0.00012484103,0.00008238712,0.000053457166,0.00053244526,0.000040392828,0.00017851655,0.000017536158,0.00007152997,0.0000065130116],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014299631,0.0024953997,0.12965061,0.0011325963,0.002787158,0.0000023173054,0.04086203,0.15725939,0.03583076,0.02138431,0.0008312117,0.60774994],"study_design_scores_gemma":[0.00041492176,0.00008853842,0.938661,0.0004956417,0.0006431683,0.00000756301,0.00029985592,0.056675192,0.0010742119,0.0011110582,0.00007759158,0.00045126386],"about_ca_topic_score_codex":0.0000082325705,"about_ca_topic_score_gemma":0.0000021753197,"teacher_disagreement_score":0.8090104,"about_ca_system_score_codex":0.000030774467,"about_ca_system_score_gemma":0.00003932732,"threshold_uncertainty_score":0.33596495},"labels":[],"label_agreement":null},{"id":"W3134091450","doi":"10.1007/s12652-021-02975-7","title":"Interactive multilevel programming approaches in neutrosophic environments","year":2021,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Fuzzy logic; Computational intelligence; Measure (data warehouse); Set (abstract data type); Mathematical optimization; Artificial intelligence; Data mining; Mathematics","score_opus":0.06276874478871781,"score_gpt":0.265828406481373,"score_spread":0.2030596616926552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134091450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34351835,0.0008142775,0.65481246,0.00004446914,0.00027705287,0.00013374718,3.012073e-7,0.00003078556,0.00036853622],"genre_scores_gemma":[0.95234686,0.000110164554,0.04742991,0.000024611576,0.000055550885,0.0000011034008,8.436476e-7,0.000014276617,0.000016649718],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902064,0.000030526073,0.0005146764,0.00010927617,0.00014309648,0.00018179217],"domain_scores_gemma":[0.99960357,0.00010175119,0.00012263164,0.00006728059,0.000036819052,0.00006797038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024994803,0.00011430787,0.00023306608,0.000114189876,0.000050685707,0.00009317851,0.0000913178,0.000041326246,0.00002005389],"category_scores_gemma":[0.00006098809,0.00010847126,0.000059766495,0.00010976191,0.000028406708,0.0001727764,0.00005828602,0.00029680406,0.0000034414277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033451157,0.000543626,0.0008608131,0.00030403904,0.00014921311,0.00020190254,0.013010086,0.22759306,0.002665047,0.0074954256,0.000011972575,0.74713135],"study_design_scores_gemma":[0.0005675157,0.0001243677,0.00072649156,0.00064671674,0.00003584276,0.00022102377,0.0051142643,0.9722061,0.016722457,0.002375893,0.00095776026,0.00030156117],"about_ca_topic_score_codex":7.6806634e-7,"about_ca_topic_score_gemma":0.0000014723184,"teacher_disagreement_score":0.7468298,"about_ca_system_score_codex":0.000054679225,"about_ca_system_score_gemma":0.00001078706,"threshold_uncertainty_score":0.44233295},"labels":[],"label_agreement":null},{"id":"W3140227716","doi":"10.1007/978-3-642-11474-8_11","title":"Chance Constrained Programming","year":2010,"lang":"en","type":"book-chapter","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":175,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Decision maker; Computer science; Constraint (computer-aided design); Constraint programming; Mathematical optimization; Stochastic programming; Mathematical economics; Operations research; Mathematics","score_opus":0.011685107020817279,"score_gpt":0.20469234023917043,"score_spread":0.19300723321835317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3140227716","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.995611e-7,0.00010359486,0.08607303,0.000033429318,0.00029146805,0.00028163072,0.0000031190896,0.0011556417,0.9120574],"genre_scores_gemma":[0.0012280379,0.00008745541,0.24602532,0.00007617924,0.00022361407,0.000030114265,0.000050355702,0.00016752785,0.7521114],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99930394,9.23482e-7,0.00022763085,0.0001469363,0.00012333077,0.00019721757],"domain_scores_gemma":[0.99960226,0.000027630393,0.00003199092,0.00019833806,0.000038384787,0.00010140217],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000056568617,0.0002365295,0.0002380003,0.000064774205,0.00003406736,0.00005486369,0.000103419545,0.00034244164,0.0027969577],"category_scores_gemma":[0.00001293785,0.00022216105,0.00008500663,0.000015810305,0.00008154457,0.000041731175,0.000021155174,0.00043249558,0.00037883752],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.566302e-7,0.0000073097776,6.8123775e-8,0.0002924934,0.00005853213,0.000009922148,0.00004858542,0.000106875144,0.0001390171,0.79444396,0.00046280702,0.20442966],"study_design_scores_gemma":[0.000114294984,0.000012731202,3.7109903e-8,0.00009648239,0.000025709345,0.000012502453,0.000005143176,0.006412175,0.00021679788,0.011101348,0.9816219,0.0003808617],"about_ca_topic_score_codex":2.8395482e-7,"about_ca_topic_score_gemma":0.000005281585,"teacher_disagreement_score":0.9811591,"about_ca_system_score_codex":0.000015980098,"about_ca_system_score_gemma":0.0000095562455,"threshold_uncertainty_score":0.99811465},"labels":[],"label_agreement":null},{"id":"W3157126773","doi":"10.1111/itor.12989","title":"An extended ϵ‐constraint method for a multiobjective finite‐horizon Markov decision process","year":2021,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kwantlen Polytechnic University; University of British Columbia","funders":"","keywords":"Mathematical optimization; Markov decision process; Computer science; Pareto principle; Constraint (computer-aided design); Scheduling (production processes); Markov process; Selection (genetic algorithm); Class (philosophy); Mathematics; Artificial intelligence","score_opus":0.05930180064658388,"score_gpt":0.45211477209029133,"score_spread":0.39281297144370747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157126773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016887954,0.000032092197,0.9919105,0.000446895,0.00034893636,0.0004354781,0.000089360765,0.00008187783,0.0049660765],"genre_scores_gemma":[0.6195868,0.000040277508,0.3792629,0.000030461899,0.00008161372,0.00052600325,0.00011199647,0.000024875702,0.00033506055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833167,0.00010849284,0.00035685845,0.00030737478,0.0006381172,0.00025750484],"domain_scores_gemma":[0.9971187,0.0013947636,0.000013043672,0.00014553983,0.0012284335,0.0000995132],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00096826535,0.00011785709,0.00013304027,0.00039357325,0.00017486225,0.00021052107,0.00020851284,0.0001056616,0.0015003522],"category_scores_gemma":[0.00071690034,0.00012529077,0.000069613925,0.00049788476,0.00005499607,0.00046027504,0.000006763464,0.00037407302,0.000017642511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073334,0.00042142763,0.000011238901,0.000042827123,0.000056435463,0.000009746017,0.0005402301,0.76691306,0.0018854125,0.011368355,0.00003940956,0.21863851],"study_design_scores_gemma":[0.00076336565,0.00006821577,0.00007834776,0.000058586906,0.000004436718,0.000020422343,0.0011567916,0.9796461,0.0065030046,0.009751357,0.0018114571,0.00013794782],"about_ca_topic_score_codex":0.000009135992,"about_ca_topic_score_gemma":0.00014198557,"teacher_disagreement_score":0.617898,"about_ca_system_score_codex":0.000257088,"about_ca_system_score_gemma":0.00022293373,"threshold_uncertainty_score":0.9994124},"labels":[],"label_agreement":null},{"id":"W3159678997","doi":"10.18280/mmep.080203","title":"A Multi-Objective Risk Return Trade off Models for Banks: Fuzzy Programming Approach","year":2021,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Kalyani; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Liquidity risk; Market liquidity; Business; Interest rate risk; Fuzzy logic; Liquidity crisis; Interest rate; Economics; Actuarial science; Computer science; Finance","score_opus":0.029274081314496325,"score_gpt":0.21818791871341683,"score_spread":0.1889138373989205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159678997","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015438582,0.0016226289,0.99431145,0.000031058407,0.00008093042,0.0007239452,0.000013878756,0.000927618,0.0007446082],"genre_scores_gemma":[0.21018904,0.00027152553,0.78889906,0.000009136459,0.000051644765,0.0003628709,0.000021954691,0.00011954787,0.00007523087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982605,0.000020337111,0.00052942353,0.00041294226,0.00019801677,0.0005787768],"domain_scores_gemma":[0.9991426,0.00024254239,0.00004936233,0.00026917717,0.00006482873,0.00023148634],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040301916,0.00036640227,0.0004981052,0.00009556337,0.00012846041,0.000192076,0.0001165967,0.00020985042,0.000004553398],"category_scores_gemma":[0.000119986646,0.00034956288,0.00015613393,0.00025190675,0.000039119856,0.00021321628,0.000041359694,0.00038132537,0.0000029572298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029615885,0.0001386666,8.027911e-7,0.002396471,0.00009809858,0.0000017047951,0.0017392564,0.9790449,0.00014661778,0.010404495,0.000013001082,0.006012998],"study_design_scores_gemma":[0.00055857806,0.000028908447,4.1430454e-7,0.00027692158,0.00009456172,0.000031708132,0.00016269104,0.9799554,0.00022775198,0.017744927,0.0004978465,0.00042031115],"about_ca_topic_score_codex":0.000001193063,"about_ca_topic_score_gemma":3.7775396e-7,"teacher_disagreement_score":0.20864518,"about_ca_system_score_codex":0.000047534875,"about_ca_system_score_gemma":0.000015116182,"threshold_uncertainty_score":0.99989563},"labels":[],"label_agreement":null},{"id":"W3181866333","doi":"10.1016/j.agsy.2021.103208","title":"Operations research for environmental assessment of crop-livestock production systems","year":2021,"lang":"en","type":"article","venue":"Agricultural Systems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Sustainability; Life-cycle assessment; Computer science; Livestock; Agriculture; Production (economics); Strengths and weaknesses; Benchmarking; Decision support system; Decision tree; Data envelopment analysis; Management science; Environmental economics; Environmental resource management; Operations research; Business; Economics; Engineering; Mathematics; Artificial intelligence","score_opus":0.045861096815315104,"score_gpt":0.3107810420867116,"score_spread":0.2649199452713965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181866333","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92127365,0.0030908599,0.060504757,0.0002463828,0.0032876888,0.0038652094,0.00006783194,0.00033013677,0.007333499],"genre_scores_gemma":[0.99272585,0.000040334966,0.003183088,4.564582e-7,0.00019217287,0.0003572838,0.000101398626,0.000011749987,0.003387669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909437,0.00007203788,0.00028577712,0.00014359743,0.00023528362,0.00016893417],"domain_scores_gemma":[0.9996188,0.000047129284,0.000018342358,0.0001243449,0.00013931777,0.000052061867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024799112,0.000082808045,0.00015961955,0.000028772194,0.00011960381,0.000097825854,0.000059578782,0.000053592168,0.000009487551],"category_scores_gemma":[0.00004136951,0.00005869095,0.00004351273,0.00016239403,0.000023832554,0.00013170019,0.000018935372,0.00008659527,0.0000090526755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.1277193e-7,0.00010217781,0.00008142398,0.00080795016,0.00006948138,9.2496055e-7,0.00016337719,0.8563803,0.1276104,0.0116583025,0.0028087397,0.0003161163],"study_design_scores_gemma":[0.0006796129,0.0001978126,0.0055899275,0.00058568694,0.00007433251,0.0001467061,0.015339773,0.9375325,0.0149286315,0.000026118316,0.02434362,0.0005552867],"about_ca_topic_score_codex":0.0000129308555,"about_ca_topic_score_gemma":0.00000475711,"teacher_disagreement_score":0.11268177,"about_ca_system_score_codex":0.00012801606,"about_ca_system_score_gemma":0.000012571073,"threshold_uncertainty_score":0.23933475},"labels":[],"label_agreement":null},{"id":"W3192147792","doi":"10.5267/j.dsl.2021.7.002","title":"A mixed-integer linear programming model for the selective full-truckload multi-depot vehicle routing problem with time windows","year":2021,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Truck; Vehicle routing problem; Integer programming; Mathematical optimization; Computer science; Context (archaeology); Linear programming; Profit (economics); Set (abstract data type); Operations research; Routing (electronic design automation); Transport engineering; Mathematics; Engineering; Automotive engineering; Economics","score_opus":0.024612636865152672,"score_gpt":0.2633155510898738,"score_spread":0.23870291422472115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192147792","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028854212,0.000028960465,0.96937406,0.0007000328,0.00008478728,0.00063297106,0.000002235337,0.00024409175,0.00007865079],"genre_scores_gemma":[0.40295497,0.0000018438926,0.59638804,0.00043635524,0.000029577923,0.00010241147,0.0000016607765,0.000030406327,0.000054758173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822885,0.000016000964,0.00030573117,0.00040085337,0.00052510685,0.0005234317],"domain_scores_gemma":[0.99885875,0.00040798075,0.000059238755,0.0003036641,0.0002490313,0.000121340716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076289865,0.00018169435,0.00019003032,0.00009603183,0.00044007116,0.0003389462,0.00036050103,0.000046335263,0.0000090281865],"category_scores_gemma":[0.00039843313,0.00012004311,0.00007822449,0.0010775449,0.000201581,0.0003944492,0.0000867732,0.00020575798,0.000031340278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018109566,0.000035964546,0.000024980636,0.000018540579,0.00001768665,0.0000053369586,0.00088414026,0.92160153,0.030298386,0.00017772694,0.00023380418,0.04668381],"study_design_scores_gemma":[0.0004755602,0.000024196368,0.000014753071,0.00007706111,0.000018679288,0.000021582971,0.00020919903,0.99333584,0.005205629,0.000079287296,0.00035233743,0.00018586809],"about_ca_topic_score_codex":0.0000013449292,"about_ca_topic_score_gemma":0.000007710601,"teacher_disagreement_score":0.37410074,"about_ca_system_score_codex":0.000088940724,"about_ca_system_score_gemma":0.0000710187,"threshold_uncertainty_score":0.4895216},"labels":[],"label_agreement":null},{"id":"W3192861312","doi":"10.1155/2021/9994853","title":"Optimization Model for Integrated Municipal Solid Waste System Using Stochastic Chance-Constraint Programming under Uncertainty: A Case Study in Qazvin, Iran","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Constraint (computer-aided design); Municipal solid waste; Mathematical optimization; Stochastic programming; Pareto principle; Reliability (semiconductor); Operations research; Environmental pollution; Computer science; Waste disposal; Engineering; Waste management; Environmental science; Mathematics","score_opus":0.038339066551994726,"score_gpt":0.3009948872528982,"score_spread":0.2626558207009035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192861312","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3840951,0.000057244113,0.61520123,0.0000053433023,0.00014251894,0.00044674525,0.0000064096225,0.00004321892,0.000002175767],"genre_scores_gemma":[0.7609582,0.000005405656,0.23891075,0.0000052871487,0.000028000937,0.00003125776,0.000023648048,0.000034606186,0.000002844716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984495,0.000032448905,0.0009230141,0.00015323015,0.0001982066,0.00024357621],"domain_scores_gemma":[0.9991033,0.00006896561,0.00023999433,0.00010619689,0.0003882205,0.00009333576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027720563,0.0001879141,0.00039052553,0.00020780762,0.00007058629,0.000048789436,0.00006482812,0.00007546478,0.0000037204366],"category_scores_gemma":[0.000043049535,0.00018549716,0.00010640378,0.00041317593,0.000021259339,0.00039102044,0.0000022520098,0.00021368895,8.995506e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006174631,0.00016326856,0.000007015371,0.00028140657,0.00006725666,0.00032110937,0.007470737,0.98819506,0.00041914373,0.00016086322,1.9779237e-7,0.002852176],"study_design_scores_gemma":[0.002054679,0.00010107401,0.0000026310834,0.00045367907,0.00013417195,0.00028227508,0.07227092,0.92438596,0.000071853814,0.00007981773,8.0543913e-7,0.00016214608],"about_ca_topic_score_codex":0.00000931381,"about_ca_topic_score_gemma":0.0005069946,"teacher_disagreement_score":0.3768631,"about_ca_system_score_codex":0.00024308602,"about_ca_system_score_gemma":0.00008832461,"threshold_uncertainty_score":0.75643545},"labels":[],"label_agreement":null},{"id":"W3193442282","doi":"10.1002/cjce.24298","title":"A two‐layer chance‐constrained optimization model for a thickening‐dewatering process with uncertain variables","year":2021,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Mathematical optimization; Monte Carlo method; Optimization problem; Process optimization; Computer science; Process (computing); Control theory (sociology); Engineering; Mathematics; Statistics","score_opus":0.01595565825402903,"score_gpt":0.2189171203586657,"score_spread":0.20296146210463667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193442282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013220821,0.00015164571,0.98591125,0.00025520346,0.00008566493,0.00012192392,0.0000053054596,0.000053727814,0.00019444256],"genre_scores_gemma":[0.82287204,0.0000019155807,0.17692207,0.00004071207,0.0000835379,0.00001552665,0.000004931417,0.000041248593,0.000018012495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992068,0.0000049479263,0.00027406856,0.00008386371,0.00012341283,0.00030690266],"domain_scores_gemma":[0.9992983,0.00006684128,0.000050373634,0.00009937132,0.0002296589,0.0002555024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018834486,0.00014410388,0.00020564995,0.000074732,0.00006170039,0.00010574011,0.00014980484,0.00006323822,0.0000210737],"category_scores_gemma":[0.00017022679,0.00010942291,0.000056676552,0.00021140084,0.000033372005,0.00013935602,0.0000059634835,0.00021976486,2.8405591e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053667623,0.0000034245982,0.0000013942475,0.0001475432,0.00005186754,0.000012004314,0.00055295083,0.9908157,0.0069370023,0.0013199757,0.000011048032,0.00014172688],"study_design_scores_gemma":[0.00040673962,0.000010133125,6.807657e-8,0.00019838546,0.000041523002,0.00013029824,0.00004683923,0.97445107,0.024159797,0.00037386868,0.00003678554,0.00014448009],"about_ca_topic_score_codex":0.000013229054,"about_ca_topic_score_gemma":0.000036856676,"teacher_disagreement_score":0.80965126,"about_ca_system_score_codex":0.000107590946,"about_ca_system_score_gemma":0.00031515947,"threshold_uncertainty_score":0.44621366},"labels":[],"label_agreement":null},{"id":"W3195931591","doi":"10.2139/ssrn.3813822","title":"Target-oriented Robust Location-transportation Problem with Service-level Measure","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Measure (data warehouse); Computer science; Transport engineering; Business; Engineering; Data mining","score_opus":0.01046493887560354,"score_gpt":0.1933830260038875,"score_spread":0.18291808712828395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195931591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039903265,0.0009363928,0.99315894,0.00049371,0.00007361014,0.00011865833,0.0000022039017,0.0001571429,0.001069012],"genre_scores_gemma":[0.9053449,0.00042717246,0.09310838,0.00012531423,0.00011531766,0.00001996638,0.000090023525,0.00007474702,0.0006941734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985284,0.000020264295,0.00023883562,0.00011883712,0.00024650118,0.00084713835],"domain_scores_gemma":[0.99930364,0.000013218089,0.00004963255,0.00010276352,0.00044753036,0.00008319729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003041989,0.00013272431,0.00013191217,0.00004936784,0.000108738495,0.00005428244,0.00007986449,0.000060175935,0.00006699388],"category_scores_gemma":[0.00001596625,0.00011636781,0.000034696528,0.00053129764,0.000010513728,0.0002164993,0.0000028515076,0.00072097825,0.000019718058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027721624,0.00013775611,0.0004892401,0.00026081665,0.00040064775,0.000015130372,0.0009466772,0.8331714,0.00045173115,0.15442617,0.00012967957,0.009543001],"study_design_scores_gemma":[0.012823406,0.000979984,0.0020051587,0.0019167393,0.0010418157,0.0032204622,0.03207461,0.635676,0.009816425,0.26854604,0.027884608,0.004014697],"about_ca_topic_score_codex":0.0000052325904,"about_ca_topic_score_gemma":0.0008652334,"teacher_disagreement_score":0.90135455,"about_ca_system_score_codex":0.00027041615,"about_ca_system_score_gemma":0.0007203872,"threshold_uncertainty_score":0.47453415},"labels":[],"label_agreement":null},{"id":"W3199848312","doi":"","title":"On the approximation of separable non-convex optimization programs to an arbitrary numerical precision","year":2021,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation Mathématique Jacques Hadamard","keywords":"Solver; Mathematical optimization; Mathematics; Convergence (economics); Computer science; Applied mathematics","score_opus":0.014169942672300257,"score_gpt":0.22581024265843938,"score_spread":0.21164029998613912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199848312","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02812013,0.00009478962,0.94610953,0.0010484084,0.0001434367,0.0010142148,0.000008199551,0.00031370186,0.023147594],"genre_scores_gemma":[0.53302824,0.00007732628,0.46565533,0.00006916552,0.000012738019,0.00021201692,0.00053987064,0.000064328546,0.00034096718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99707115,0.0012072297,0.0005737136,0.0004575804,0.00043393858,0.00025636677],"domain_scores_gemma":[0.9965102,0.0007044015,0.00020859882,0.0014166454,0.0009870501,0.00017311267],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002279827,0.0002839117,0.00037221287,0.00013871795,0.00014691736,0.00039026403,0.0006672603,0.00024186635,0.00018974965],"category_scores_gemma":[0.0008682139,0.00025191845,0.00014546042,0.0005356143,0.00007074895,0.00016993147,0.0003520602,0.0004757239,0.000015043069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018875227,0.0013170512,0.000044113134,0.00070935016,0.00010901049,0.0000019449662,0.011838817,0.8515165,0.00072545133,0.062247265,0.00040689303,0.07106477],"study_design_scores_gemma":[0.00015231923,0.0000014259974,0.000030097759,0.0013722698,0.000026241472,0.0000017378013,0.00016306849,0.9794704,0.016043602,0.0022334415,0.0002512263,0.0002541869],"about_ca_topic_score_codex":0.000049715105,"about_ca_topic_score_gemma":0.000025088519,"teacher_disagreement_score":0.50490814,"about_ca_system_score_codex":0.000076431126,"about_ca_system_score_gemma":0.00008278702,"threshold_uncertainty_score":0.9999933},"labels":[],"label_agreement":null},{"id":"W3200566607","doi":"10.1016/j.trb.2021.08.010","title":"Target-oriented robust location–transportation problem with service-level measure","year":2021,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Mathematical optimization; Computer science; Profit (economics); Measure (data warehouse); Operations research; Quadratic equation; Affine transformation; Mathematics; Data mining; Economics","score_opus":0.3869647240031798,"score_gpt":0.39254138874970707,"score_spread":0.005576664746527238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200566607","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014642875,0.00018608164,0.9812315,0.0009541079,0.00008970248,0.00062154414,0.00005426949,0.0005336377,0.0016862635],"genre_scores_gemma":[0.16371705,0.000082297025,0.83411425,0.00015123517,0.00007332629,0.00033992867,0.0011274598,0.000059973463,0.00033446445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969959,0.0005541242,0.00054266263,0.00043486728,0.00092506065,0.00054733315],"domain_scores_gemma":[0.9970854,0.00063259236,0.000048984748,0.0002751731,0.001695577,0.00026229236],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019740558,0.00022577166,0.00034514788,0.00012957372,0.0002054232,0.00006758318,0.00015597092,0.00021364528,0.0009889225],"category_scores_gemma":[0.00024795823,0.00018751393,0.00006553084,0.0019217196,0.000116844465,0.00025443544,0.0000033369697,0.0005893267,0.00006431966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024002233,0.0004970373,0.0037078601,0.0017316322,0.00023958788,0.0002037561,0.0031822873,0.92541546,0.0029670808,0.053797543,0.0021038423,0.0059138904],"study_design_scores_gemma":[0.015389826,0.0018174513,0.22448556,0.003192219,0.000832913,0.00007663215,0.02084637,0.34298176,0.10658873,0.039814036,0.23782,0.006154493],"about_ca_topic_score_codex":0.000021212514,"about_ca_topic_score_gemma":0.0005093495,"teacher_disagreement_score":0.5824337,"about_ca_system_score_codex":0.000055932935,"about_ca_system_score_gemma":0.00013802823,"threshold_uncertainty_score":0.9999243},"labels":[],"label_agreement":null},{"id":"W3210181913","doi":"10.3390/jrfm14110519","title":"A Neutrosophic Fuzzy Optimisation Model for Optimal Sustainable Closed-Loop Supply Chain Network during COVID-19","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fuzzy logic; Supply chain; Computer science; Supply chain network; Operations research; Transmission (telecommunications); Mathematical optimization; Supply chain management; Business; Engineering; Artificial intelligence; Mathematics","score_opus":0.009570188888174623,"score_gpt":0.22475847817449235,"score_spread":0.21518828928631772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210181913","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04961735,0.000824633,0.9485636,0.00021000681,0.00015070237,0.00029056467,0.000004626938,0.00004241932,0.0002961464],"genre_scores_gemma":[0.7866469,0.0034015013,0.2083891,0.0001778592,0.00038070296,0.000037286238,0.0000077158165,0.00003730721,0.0009216334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905205,0.000019384594,0.00037488967,0.00011608638,0.00013954885,0.00029804738],"domain_scores_gemma":[0.9994804,0.00004862364,0.00011498561,0.000092641385,0.00010772652,0.00015562068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041742253,0.00012582724,0.00022884867,0.00010059673,0.00021913553,0.000091471054,0.000075592354,0.00006170848,0.000009077275],"category_scores_gemma":[0.00014843218,0.00012580959,0.00010081834,0.00018796026,0.000018544297,0.00017043602,0.000055592238,0.00014596409,6.093454e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006343675,0.00003729866,0.000062689,0.00062604103,0.000025128724,0.000073990865,0.0004368676,0.9603373,0.000008773746,0.03020518,0.0006931344,0.0074301763],"study_design_scores_gemma":[0.002405319,0.00008025541,0.00047589157,0.00008171255,0.00018084401,0.000036273705,0.000789934,0.9550843,0.00004405124,0.028707167,0.011856464,0.00025776765],"about_ca_topic_score_codex":0.0000012904594,"about_ca_topic_score_gemma":0.000002325934,"teacher_disagreement_score":0.7401745,"about_ca_system_score_codex":0.000097874254,"about_ca_system_score_gemma":0.0000456877,"threshold_uncertainty_score":0.5130366},"labels":[],"label_agreement":null},{"id":"W3213824768","doi":"10.5267/j.uscm.2021.9.004","title":"Contribution of robust optimization on handling agricultural processed products supply chain problem during Covid-19 pandemic","year":2021,"lang":"en","type":"article","venue":"Uncertain Supply Chain Management","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Padjadjaran","keywords":"Robust optimization; Supply chain; Agriculture; Linear programming; Social distance; Computer science; Pandemic; Mathematical optimization; Environmental economics; Coronavirus disease 2019 (COVID-19); Operations research; Business; Economics; Mathematics; Marketing; Geography","score_opus":0.016977448448530597,"score_gpt":0.23293531743460197,"score_spread":0.21595786898607136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213824768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042903796,0.00063398987,0.9446044,0.0028075792,0.00039268986,0.0037884894,0.00003708646,0.0014295696,0.0034024282],"genre_scores_gemma":[0.9486497,0.00036496,0.048901036,0.00015022325,0.000093287264,0.0002899471,0.0006089242,0.000050473438,0.0008914586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800485,0.000081443046,0.0005716292,0.00045352746,0.00041495645,0.0004735894],"domain_scores_gemma":[0.99904454,0.00008489326,0.00013395897,0.00029249446,0.0002772798,0.00016684795],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045481182,0.00030502715,0.00035171898,0.00018866468,0.00019319524,0.00011818872,0.00018118371,0.00010926741,0.0001430926],"category_scores_gemma":[0.00032054726,0.0002788233,0.000077945115,0.000838729,0.000045235996,0.00019881074,0.0000815133,0.00017520998,0.000008822443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029861685,0.0001314636,0.00028550057,0.0024410724,0.000102775695,0.000022828382,0.0005973217,0.9901248,0.001403501,0.0037957272,0.00029733244,0.0007678125],"study_design_scores_gemma":[0.004351233,0.00012053435,0.0004439763,0.00090078724,0.00018042828,0.000034373148,0.0036360289,0.97080386,0.015086596,0.0007510547,0.002707825,0.0009832857],"about_ca_topic_score_codex":0.000009362208,"about_ca_topic_score_gemma":0.000016004911,"teacher_disagreement_score":0.9057459,"about_ca_system_score_codex":0.0004315578,"about_ca_system_score_gemma":0.00004479569,"threshold_uncertainty_score":0.9999664},"labels":[],"label_agreement":null},{"id":"W381406155","doi":"10.1023/a:1018935031580","title":"Scheduling the flying squad nurses of a hospital using a multi-objective programming model","year":2000,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Theory of computation; Computer science; Programming language","score_opus":0.20011221527984288,"score_gpt":0.44096406525828014,"score_spread":0.24085184997843725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W381406155","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8051301,0.00043085462,0.19206384,0.00023755788,0.000039432638,0.0008402589,0.000008188994,0.00007946955,0.001170268],"genre_scores_gemma":[0.87182415,0.000099346806,0.12782829,0.000007534266,0.000020567466,0.000057696725,0.0000031225838,0.000023008974,0.00013628963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988147,0.00006961549,0.0003249168,0.00013014665,0.0003448502,0.00031578972],"domain_scores_gemma":[0.9990393,0.00009668869,0.000014390264,0.00022471196,0.0005643254,0.000060566923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067717244,0.000096445525,0.00016113851,0.00015141365,0.00032075445,0.00009157518,0.00019586715,0.00005655774,0.00007785032],"category_scores_gemma":[0.00029004013,0.0000763191,0.00007277326,0.0005911337,0.00018811498,0.0002650075,0.000033156746,0.00024568988,0.000008610014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043699165,0.00015331482,0.000015073473,0.0000651413,0.000036494133,4.873653e-7,0.003938305,0.9633105,0.0013282329,0.0013158559,0.000009858473,0.029822396],"study_design_scores_gemma":[0.00012550777,0.00005476073,0.000010518316,0.00009331265,0.0000060913526,8.9445166e-7,0.0016352573,0.9921304,0.0056803813,0.000115243216,0.00006643812,0.00008118794],"about_ca_topic_score_codex":0.000068447334,"about_ca_topic_score_gemma":0.0000136010385,"teacher_disagreement_score":0.066694014,"about_ca_system_score_codex":0.000017329086,"about_ca_system_score_gemma":0.000067642584,"threshold_uncertainty_score":0.31122023},"labels":[],"label_agreement":null},{"id":"W4200384955","doi":"10.18280/mmep.080603","title":"Stochastic Multi-Objective Programming Problem: A Two-Phase Weighted Coefficient Approach","year":2021,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Stochastic programming; Linear programming; Mathematics; Random variable; Computer science; Statistics","score_opus":0.024102404491334495,"score_gpt":0.2394865567359958,"score_spread":0.21538415224466131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200384955","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032146652,0.000691246,0.99331594,0.000020952126,0.00007933204,0.0006313645,0.0000045676206,0.0011705396,0.00087142346],"genre_scores_gemma":[0.3778103,0.000021831847,0.6216964,0.000007896489,0.000034785116,0.000210901,0.000021790433,0.0000956477,0.00010040643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980898,0.000021600248,0.0005644147,0.00042810282,0.00027327763,0.00062281475],"domain_scores_gemma":[0.9991349,0.0001366141,0.000042429823,0.00027122197,0.000116020565,0.00029882],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030742862,0.00039444354,0.00049119693,0.00013303218,0.00011916717,0.00022167902,0.000117957476,0.00013811633,0.000021470254],"category_scores_gemma":[0.000058147903,0.00037134235,0.00010051075,0.00043715996,0.00005658512,0.00013461,0.00007020376,0.0003901976,0.000021100139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027386034,0.00043900264,2.5315896e-7,0.0016431122,0.00007521359,0.0000070379765,0.0013314603,0.97981197,0.00026409287,0.013301174,0.0000053563263,0.0031185786],"study_design_scores_gemma":[0.0012277644,0.000038963997,9.244427e-8,0.00038687998,0.00006913037,0.000083929604,0.00013999248,0.9946263,0.00021648107,0.0025638267,0.00020187847,0.00044474873],"about_ca_topic_score_codex":0.0000016965216,"about_ca_topic_score_gemma":3.0006981e-7,"teacher_disagreement_score":0.37459564,"about_ca_system_score_codex":0.00006043918,"about_ca_system_score_gemma":0.000020610461,"threshold_uncertainty_score":0.9998739},"labels":[],"label_agreement":null},{"id":"W4206262260","doi":"10.3390/math10020283","title":"Transformation and Linearization Techniques in Optimization: A State-of-the-Art Survey","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":229,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; Dalhousie University","funders":"","keywords":"Linearization; Transformation (genetics); Mathematical optimization; Optimization problem; Multiplication (music); Mathematics; Computer science; Piecewise linear function; Nonlinear system","score_opus":0.012969401093111307,"score_gpt":0.21966500171240808,"score_spread":0.20669560061929676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206262260","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013708087,0.00003335589,0.98284775,0.00006225329,0.000059590344,0.00048271698,0.000015594667,0.00014594277,0.0026446986],"genre_scores_gemma":[0.79626584,0.00005839647,0.20317517,0.000038767936,0.0000063991483,0.00012414723,0.000053767497,0.000045081702,0.00023240776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939203,0.000037770795,0.00029727412,0.00005057652,0.00014172576,0.0000806057],"domain_scores_gemma":[0.99974954,0.000060851613,0.00004479956,0.000102333746,0.000025883153,0.00001661815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037642542,0.000066626526,0.000113517955,0.000071737755,0.000051549632,0.00001748712,0.00007614922,0.00001885149,0.000050308045],"category_scores_gemma":[0.000053584612,0.0000594046,0.000017531294,0.00036423822,0.000020129317,0.00008050596,0.00003325708,0.000092077746,8.2809987e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024901765,0.00007990367,0.0001794107,0.00037442127,0.000007000674,2.1229397e-7,0.0029783177,0.9895857,0.00008030826,0.0017668683,0.00010797159,0.004837425],"study_design_scores_gemma":[0.000115745686,0.00001443591,0.00007679644,0.000024656445,0.0000045176776,0.0000036322679,0.00010016663,0.9968161,0.0010657074,0.0012623811,0.0004417632,0.000074100695],"about_ca_topic_score_codex":0.0000019201843,"about_ca_topic_score_gemma":0.000009353498,"teacher_disagreement_score":0.7825578,"about_ca_system_score_codex":0.000029592687,"about_ca_system_score_gemma":0.000008225524,"threshold_uncertainty_score":0.24224493},"labels":[],"label_agreement":null},{"id":"W4206573161","doi":"10.3233/jifs-212909","title":"Mehar approach to solve fuzzy linear fractional minimal cost flow problems","year":2022,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Fuzzy logic; Mathematics; Fuzzy number; Mathematical optimization; Minimum-cost flow problem; Linear programming; Flow (mathematics); Fractional programming; Computer science; Fuzzy set; Flow network; Nonlinear programming; Nonlinear system; Artificial intelligence","score_opus":0.033001413822481375,"score_gpt":0.25258896620584487,"score_spread":0.2195875523833635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206573161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027755634,0.0014287555,0.9662338,0.00018598555,0.0036989846,0.0011579291,0.000034448127,0.0001624012,0.02432217],"genre_scores_gemma":[0.8709734,0.00013551347,0.12511761,0.00021401112,0.0017937343,0.00033124562,0.00004191279,0.00017300638,0.0012195292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976815,0.000059598413,0.0010172437,0.00015519075,0.00076737785,0.00031909466],"domain_scores_gemma":[0.99895483,0.0001002703,0.00023805362,0.00017113531,0.000243112,0.00029260857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009163623,0.00020603425,0.0004172518,0.0002964307,0.00015384267,0.00011091721,0.00032784516,0.00006622884,0.00015153903],"category_scores_gemma":[0.00008474365,0.0001853048,0.00020903692,0.00037922262,0.00001831783,0.00017658177,0.00007518823,0.0005553041,0.00008443808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003063998,0.00024069284,0.000017485634,0.00023989321,0.00014571533,0.000010810507,0.00091877603,0.98488283,0.00023916285,0.0017314985,0.009927085,0.0016154082],"study_design_scores_gemma":[0.00038814428,0.00036790365,0.0000073227416,0.00014810298,0.00006451257,0.0006742853,0.0037302894,0.7316078,0.00028491297,0.00020177003,0.26215714,0.0003678124],"about_ca_topic_score_codex":0.000005494216,"about_ca_topic_score_gemma":5.131009e-7,"teacher_disagreement_score":0.86819786,"about_ca_system_score_codex":0.000331829,"about_ca_system_score_gemma":0.00005563489,"threshold_uncertainty_score":0.75565106},"labels":[],"label_agreement":null},{"id":"W4206915070","doi":"10.5539/mas.v16n1p30","title":"New Approach to Obtain the Maximum Flow in a Network and Optimal Solution for the Transportation Problems","year":2022,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Rajarata University of Sri Lanka","keywords":"Maximum flow problem; Minimum-cost flow problem; Flow network; Out-of-kilter algorithm; Multi-commodity flow problem; Computer science; Mathematical optimization; Flow (mathematics); Heuristic; Algorithm; Mathematics; Theoretical computer science; Graph","score_opus":0.01929774037166811,"score_gpt":0.21547171086062333,"score_spread":0.1961739704889552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206915070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000714579,0.0000483792,0.9971085,0.0002545155,0.000043592772,0.0009155975,0.0000015226128,0.00005416422,0.0008591263],"genre_scores_gemma":[0.7921388,0.0000023094826,0.20711552,0.00009065429,0.000019836523,0.00058463356,0.000002946543,0.000009371831,0.000035888042],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933547,0.0000050793706,0.00011012625,0.0001543138,0.00017651825,0.00021852188],"domain_scores_gemma":[0.99978584,0.000043146676,0.00001260379,0.000106842694,0.00000768291,0.000043872937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006703438,0.000059825466,0.000058064285,0.000030034154,0.0003701648,0.00006626068,0.00020163479,0.000011670292,0.0000054783477],"category_scores_gemma":[0.000006064332,0.000041369174,0.000012040379,0.00045152236,0.00004631788,0.000052483272,0.000024611027,0.000089961584,7.9436575e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052434475,0.0000072575563,0.0000016045291,0.0000121360345,0.0000012224671,2.227728e-8,0.0029088547,0.95062405,0.000636836,0.0059981616,0.00013303476,0.039671585],"study_design_scores_gemma":[0.00015874467,0.0000075092153,0.000044485892,0.0000018774939,0.0000040119753,4.9471276e-7,0.00023732785,0.991855,0.00001551023,0.006602325,0.0010145007,0.00005821606],"about_ca_topic_score_codex":0.000006506022,"about_ca_topic_score_gemma":0.000013779054,"teacher_disagreement_score":0.7914243,"about_ca_system_score_codex":0.000040575196,"about_ca_system_score_gemma":0.000023024422,"threshold_uncertainty_score":0.2847046},"labels":[],"label_agreement":null},{"id":"W4211146157","doi":"10.1017/cbo9781107282094.003","title":"Solving linear programs","year":2018,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Focus (optics); Reading (process); Selection (genetic algorithm); Point (geometry); Section (typography); Computer science; Mathematics education; Range (aeronautics); Linear algebra; Engineering; Mathematics; Artificial intelligence","score_opus":0.023670972194085354,"score_gpt":0.19271296849255318,"score_spread":0.16904199629846783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211146157","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016368425,0.000066683555,0.02099609,0.0000021921815,0.00023573113,0.00035594273,0.000020118032,0.0009448807,0.977362],"genre_scores_gemma":[0.00024054441,0.000050595492,0.00584663,0.000013746738,0.00019070729,8.969536e-7,0.00004707271,0.00009485167,0.99351496],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99913406,0.0000071461122,0.0001683477,0.0002514126,0.00016910072,0.00026993672],"domain_scores_gemma":[0.99930704,0.000023540773,0.00006044029,0.00033867435,0.000106131854,0.00016417401],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000057277244,0.00030158545,0.0002910422,0.00010266718,0.00009776502,0.000050604707,0.00027446216,0.0003190482,0.00003495656],"category_scores_gemma":[0.000006325387,0.00035769207,0.00015130389,0.000007748927,0.00015717602,0.00006851294,0.00014454081,0.00031653405,0.000093873605],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014790996,0.000013935227,2.473432e-7,0.00061068416,0.00024962518,0.000120221855,0.00013767094,0.00015584749,0.000020396827,0.94764185,0.037807193,0.013227556],"study_design_scores_gemma":[0.00021095475,0.000031626336,8.610711e-8,0.00022621013,0.00010886858,0.0000066091316,0.000019675468,0.015670376,0.00011004759,0.000018402698,0.98320156,0.00039558194],"about_ca_topic_score_codex":0.0000035227583,"about_ca_topic_score_gemma":5.2409916e-7,"teacher_disagreement_score":0.94762343,"about_ca_system_score_codex":0.00010346996,"about_ca_system_score_gemma":0.000019956038,"threshold_uncertainty_score":0.9998875},"labels":[],"label_agreement":null},{"id":"W4212892245","doi":"10.23952/jano.1.2019.3.04","title":"Solving bilevel problems with polyhedral constraint set","year":2019,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bilevel optimization; Constraint (computer-aided design); Set (abstract data type); Mathematical optimization; Computer science; Mathematics; Optimization problem; Geometry","score_opus":0.007189926153251263,"score_gpt":0.19216286614363703,"score_spread":0.18497293999038578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212892245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009285288,0.000043620763,0.9833565,0.00005830907,0.00006700218,0.00012155485,7.712626e-7,0.0000447981,0.007022203],"genre_scores_gemma":[0.8015997,0.00003337845,0.19826727,0.00004202056,0.000027742402,0.000001958866,0.000002336089,0.000016399523,0.000009238156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999442,0.0000045752986,0.00024353755,0.00006264602,0.00013165624,0.00011559151],"domain_scores_gemma":[0.9996953,0.00003514165,0.00009175257,0.000047093727,0.00004535999,0.00008533407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009497478,0.00008589401,0.00016459411,0.0000585032,0.00002481232,0.00005039221,0.00004322736,0.000045432236,0.00012668147],"category_scores_gemma":[0.0000070518245,0.000064352666,0.000024119845,0.00011096146,0.000026678692,0.00012175707,0.000009282039,0.0001203382,0.0000043649347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002173754,0.00001644997,0.00003234637,0.00006361975,0.000026947215,0.0000011566833,0.00020156887,0.99191356,0.00036972293,0.0025701404,0.000032509877,0.0047502606],"study_design_scores_gemma":[0.0006415194,0.00012940346,0.000021750488,0.000058285244,0.000022932543,0.000054328175,0.00016323352,0.99781245,0.00030579054,0.0003671963,0.0003000287,0.00012305527],"about_ca_topic_score_codex":2.381471e-7,"about_ca_topic_score_gemma":2.782456e-8,"teacher_disagreement_score":0.79231435,"about_ca_system_score_codex":0.0000143543475,"about_ca_system_score_gemma":0.000013977203,"threshold_uncertainty_score":0.26242253},"labels":[],"label_agreement":null},{"id":"W4226080534","doi":"10.21203/rs.3.rs-1499946/v1","title":"Mehar approach to solve neutrosophic linear programming problems using possibilistic mean","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Decision maker; Linear programming; Mathematical optimization; Computer science; Mathematics; Operations research","score_opus":0.14082245928453252,"score_gpt":0.3880737058554663,"score_spread":0.24725124657093378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226080534","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044762526,0.002994629,0.89517194,0.0003492916,0.0014713202,0.020468678,0.00025873224,0.0045227613,0.030000092],"genre_scores_gemma":[0.4644081,0.00010792657,0.5300353,0.000035375462,0.0007099553,0.003146331,0.0005482238,0.0005532363,0.00045552637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953334,0.00021842295,0.0006744789,0.0009026135,0.0015738173,0.0012973193],"domain_scores_gemma":[0.9976984,0.00019476618,0.00006885734,0.0010574957,0.0004166668,0.00056378776],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0022062773,0.00047576943,0.0006034066,0.00074726826,0.00047758702,0.00050776097,0.00086525164,0.00033549214,0.00023039636],"category_scores_gemma":[0.00040774996,0.00049039273,0.00024309594,0.0012400908,0.00011808863,0.00011440102,0.0018868207,0.0029053472,0.00006282444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010734338,0.00029182757,0.000019639796,0.007935251,0.00008685443,0.00001709011,0.002816219,0.98245984,0.00023342606,0.0028208406,0.00016551536,0.0031427878],"study_design_scores_gemma":[0.0002557459,0.00013323357,0.00001114106,0.0006727805,0.000041502106,0.000010136956,0.0012844459,0.9884028,0.00007624787,0.0022511152,0.0062017865,0.0006590845],"about_ca_topic_score_codex":0.00007635338,"about_ca_topic_score_gemma":0.000008924611,"teacher_disagreement_score":0.41964558,"about_ca_system_score_codex":0.0007447159,"about_ca_system_score_gemma":0.00021595444,"threshold_uncertainty_score":0.9997548},"labels":[],"label_agreement":null},{"id":"W4231104372","doi":"10.1090/stml/080/14","title":"Linear programming","year":2016,"lang":"en","type":"book-chapter","venue":"Student mathematical library","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science","score_opus":0.012866221153574865,"score_gpt":0.23109158227397755,"score_spread":0.2182253611204027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231104372","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000036750127,0.00037564075,0.03106119,0.0001850745,0.00021203654,0.00067013584,0.000022721617,0.0021764562,0.96529305],"genre_scores_gemma":[0.00036377998,0.00026473674,0.07517236,0.00012727032,0.0005659503,0.000085529275,0.000046649984,0.00048444094,0.9228893],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99809295,0.0000084739395,0.00068216835,0.00031746412,0.00047913878,0.00041980698],"domain_scores_gemma":[0.9989588,0.00019434416,0.000082264865,0.00047300788,0.000021629605,0.00026991585],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000090402835,0.0005183552,0.0006252113,0.00013827063,0.000045662648,0.00014236565,0.0004206776,0.00036860458,0.006269418],"category_scores_gemma":[0.000021577795,0.00038667384,0.00026436572,0.00004156009,0.00012013891,0.00030202774,0.0002413251,0.000380417,0.00422799],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024536928,0.000050660106,0.0000012296292,0.0008505938,0.00016505466,0.00003325344,0.00009999447,0.000019728357,0.0000031518166,0.97043645,0.0060044355,0.02233298],"study_design_scores_gemma":[0.00029968304,0.00005377508,4.6800753e-7,0.0013082131,0.000111593734,0.00001643123,0.00001676972,0.0015845664,0.000040873863,0.34701356,0.64876854,0.000785534],"about_ca_topic_score_codex":7.4059794e-9,"about_ca_topic_score_gemma":2.7199091e-8,"teacher_disagreement_score":0.6427641,"about_ca_system_score_codex":0.000032990683,"about_ca_system_score_gemma":0.000020336398,"threshold_uncertainty_score":0.9998585},"labels":[],"label_agreement":null},{"id":"W4233677790","doi":"10.1007/978-1-4614-0577-1_3","title":"Multiperiod Problems","year":2011,"lang":"en","type":"book-chapter","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science","score_opus":0.027581027859793116,"score_gpt":0.18775659554684365,"score_spread":0.16017556768705055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233677790","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.3053874e-7,0.00021559958,0.06831272,0.0000046188457,0.00012132282,0.0001673459,0.0000015770387,0.0007204194,0.9304563],"genre_scores_gemma":[0.000386844,0.00020064638,0.024047201,0.000028169765,0.000044759494,0.000011542144,0.000012545049,0.00009856954,0.9751697],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99956447,6.294614e-7,0.0001649119,0.00009372681,0.0000670662,0.000109201064],"domain_scores_gemma":[0.99974775,0.000007940067,0.000015857573,0.00014826903,0.00001984804,0.00006035684],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000024422658,0.00016201698,0.0001596587,0.000044408913,0.000014266876,0.000019520368,0.000071007424,0.00017215649,0.006836324],"category_scores_gemma":[0.0000032405346,0.00014169356,0.000060990176,0.0000066582725,0.000020126572,0.00003150056,0.000017891278,0.00013723002,0.0012470513],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.6456327e-7,0.000009082166,1.7226971e-7,0.0004508277,0.00007836525,0.000003770555,0.00019682688,0.0012382376,0.000009635043,0.9334978,0.0051447763,0.059369955],"study_design_scores_gemma":[0.00008438337,0.000011150332,9.274629e-8,0.00009416424,0.000020373322,0.0000032128091,0.0000018555278,0.012158134,0.000029090326,0.02738565,0.9599125,0.0002993873],"about_ca_topic_score_codex":6.9782794e-7,"about_ca_topic_score_gemma":0.000002627862,"teacher_disagreement_score":0.9547677,"about_ca_system_score_codex":0.000014824834,"about_ca_system_score_gemma":0.0000035557932,"threshold_uncertainty_score":0.9995306},"labels":[],"label_agreement":null},{"id":"W4239181871","doi":"10.23952/jano.2.2020.3.08","title":"A multi-view on the CQ algorithm for split feasibility problems: From optimization lens","year":2020,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Lens (geology); Optimization algorithm; Computer science; Algorithm; Mathematical optimization; Mathematics; Optics; Physics","score_opus":0.039975166744278846,"score_gpt":0.24303638211610884,"score_spread":0.20306121537183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239181871","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013159185,0.00013675226,0.9976747,0.0010200447,0.000071887385,0.00057450344,0.00001198103,0.00007098732,0.00030756558],"genre_scores_gemma":[0.040011223,0.0003056876,0.95869356,0.00074292487,0.00015450368,0.000027174174,0.00002313807,0.000038190876,0.0000036072654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990045,0.000022526376,0.0005001456,0.00014993717,0.00017552983,0.00014738688],"domain_scores_gemma":[0.9992939,0.00019685081,0.00017672514,0.000090350884,0.00011145889,0.00013067693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020198694,0.00015888084,0.00030394003,0.00003351432,0.00008853715,0.00008576021,0.00010951364,0.00008131663,0.00008004979],"category_scores_gemma":[0.00011380243,0.00010957132,0.000082442406,0.00018900033,0.000033401455,0.00014316135,0.000017861808,0.0001844211,0.0000028102372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003503592,0.000066941015,0.0000018392934,0.00005068011,0.00003865078,3.4458057e-7,0.00029410084,0.9827835,0.00006188106,0.00043410034,0.00015759715,0.016075352],"study_design_scores_gemma":[0.0008149957,0.00012808836,0.000005227599,0.000036882328,0.000053288248,0.0000013759527,0.00008874729,0.9973684,0.00012292819,0.00024779607,0.0010077137,0.00012456824],"about_ca_topic_score_codex":4.5647917e-7,"about_ca_topic_score_gemma":4.2035335e-8,"teacher_disagreement_score":0.03987963,"about_ca_system_score_codex":0.000035785288,"about_ca_system_score_gemma":0.000014673636,"threshold_uncertainty_score":0.4468189},"labels":[],"label_agreement":null},{"id":"W4281555956","doi":"","title":"A stochastic production-distribution problem with a choice of transportation lead time","year":2022,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Production (economics); Distribution (mathematics); Computer science; Lead (geology); Mathematical optimization; Mathematics; Economics; Geology; Microeconomics","score_opus":0.01028203721408631,"score_gpt":0.21079417490003977,"score_spread":0.20051213768595347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281555956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032308355,0.0002718912,0.9567721,0.0011999897,0.000114162474,0.0010144091,0.00020068858,0.00062167575,0.0074967556],"genre_scores_gemma":[0.89673066,0.000062602005,0.09587343,0.000007698777,0.0000175543,0.00033612587,0.0038957628,0.00008154589,0.0029946147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980729,0.00056254724,0.00044854157,0.00036658734,0.0003482065,0.00020118777],"domain_scores_gemma":[0.9978114,0.00031508243,0.00025317684,0.00071790523,0.000826676,0.00007579073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013018359,0.0002279215,0.00029410573,0.0001003415,0.00013645481,0.00007569754,0.00034670698,0.00011945558,0.0002333344],"category_scores_gemma":[0.00030770857,0.00024090125,0.00009361749,0.0003948276,0.00010615876,0.00010553766,0.00008375007,0.0004363614,0.000009930001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051353814,0.0016320104,0.00030367557,0.0047321315,0.0004926822,0.000003977938,0.02081119,0.8767379,0.004719633,0.047682278,0.002618806,0.040214382],"study_design_scores_gemma":[0.0014749254,0.0000043154755,0.0011878227,0.0053956006,0.00045115812,0.000013204515,0.0004116216,0.94689506,0.026075633,0.0049414122,0.011618066,0.0015311582],"about_ca_topic_score_codex":0.00010112272,"about_ca_topic_score_gemma":0.0002101403,"teacher_disagreement_score":0.8644223,"about_ca_system_score_codex":0.00011437371,"about_ca_system_score_gemma":0.00009413135,"threshold_uncertainty_score":0.9823668},"labels":[],"label_agreement":null},{"id":"W4284879750","doi":"10.1007/s00500-022-07242-1","title":"Mehar approach to solve neutrosophic linear programming problems using possibilistic mean","year":2022,"lang":"en","type":"article","venue":"Soft Computing","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Decision maker; Linear programming; Mathematical optimization; Computer science; Mathematics; Operations research","score_opus":0.033560888133010836,"score_gpt":0.252161515888293,"score_spread":0.21860062775528216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284879750","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04700129,0.00012000723,0.9488046,0.000027955646,0.00035572727,0.0008214141,0.0000042138113,0.0012010597,0.0016637237],"genre_scores_gemma":[0.6328908,3.0591625e-7,0.36677223,0.000072379655,0.00012889247,0.000031592554,0.000017144868,0.00007035324,0.000016299251],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983223,0.000035927562,0.00042823225,0.00034377736,0.0003233386,0.0005463989],"domain_scores_gemma":[0.9993761,0.00007595931,0.00006299584,0.00025411253,0.000053585725,0.00017725103],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005118652,0.00022498073,0.00027078038,0.00014663232,0.0005073294,0.00011740181,0.0002720985,0.00004456263,0.000024691977],"category_scores_gemma":[0.000066262364,0.00024931354,0.00009227967,0.0006625588,0.00002744243,0.00008097708,0.0003059114,0.00038851998,0.000013380594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002020424,0.000085757005,0.000027250215,0.00024812145,0.000023364077,0.0000027807935,0.0024016604,0.98601407,0.0005602772,0.0015013651,0.000024303648,0.0091090435],"study_design_scores_gemma":[0.00020281841,0.000043770076,0.0000051564793,0.000039479797,0.00002410082,0.000029373676,0.00055599445,0.9969672,0.00005278316,0.00030607797,0.0014776969,0.00029556287],"about_ca_topic_score_codex":0.000008910098,"about_ca_topic_score_gemma":7.081779e-7,"teacher_disagreement_score":0.5858895,"about_ca_system_score_codex":0.00016252804,"about_ca_system_score_gemma":0.000026141433,"threshold_uncertainty_score":0.9999959},"labels":[],"label_agreement":null},{"id":"W4292696703","doi":"10.19044/esj.2022.v8n0p181","title":"A Goal Programming Model for Dispatching Trucks in an Underground Gold Mine","year":2022,"lang":"en","type":"article","venue":"European Scientific Journal ESJ","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Truck; Flexibility (engineering); Goal programming; Underground mining (soft rock); Operations research; Programming paradigm; Computer science; Gold mining; Engineering; Coal mining; Waste management; Economics","score_opus":0.031034356539463903,"score_gpt":0.2600064596675246,"score_spread":0.2289721031280607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292696703","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32298502,0.00010539233,0.67323357,0.00007884495,0.0009929906,0.00031748108,0.000009188458,0.00018086714,0.0020966723],"genre_scores_gemma":[0.9398696,0.0000018799344,0.05797679,0.000036747457,0.000088885005,0.00001872313,0.000020988204,0.00006755008,0.0019188815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853677,0.00011066838,0.00040899872,0.00022404637,0.0003187566,0.000400789],"domain_scores_gemma":[0.99948305,0.000039238275,0.00006930714,0.00018407941,0.00004580971,0.00017850951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002039578,0.00013250849,0.00014260516,0.00024041651,0.00046652133,0.0007308325,0.00035176362,0.00001361479,0.00004870632],"category_scores_gemma":[0.00006320631,0.00013154805,0.00008502755,0.00038272585,0.000043329113,0.0003912451,0.00009506081,0.0003908734,0.000007739734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011152997,0.00012405464,0.00004172975,0.000043217566,0.0000097426455,0.000030071256,0.0081824,0.94847155,0.0007924126,0.00055988313,0.0008435619,0.040890243],"study_design_scores_gemma":[0.0005131927,0.000050604383,0.000013377705,0.00002279631,0.000009467007,0.00006141838,0.0025179156,0.98765737,0.000005356274,0.0014416911,0.0075319377,0.00017486392],"about_ca_topic_score_codex":5.645208e-7,"about_ca_topic_score_gemma":0.000014859789,"teacher_disagreement_score":0.6168845,"about_ca_system_score_codex":0.00013735685,"about_ca_system_score_gemma":0.00003058875,"threshold_uncertainty_score":0.70474344},"labels":[],"label_agreement":null},{"id":"W4293458129","doi":"10.19044/esipreprint.8.2022.p181","title":"A Goal Programming Model for Dispatching Trucks in an Underground Gold Mine","year":2022,"lang":"en","type":"article","venue":"European Scientific Journal ESJ","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Truck; Flexibility (engineering); Underground mining (soft rock); Goal programming; Gold mining; Operations research; Programming paradigm; Engineering; Computer science; Mining engineering; Transport engineering; Coal mining; Waste management; Economics; Automotive engineering","score_opus":0.031034356539463903,"score_gpt":0.2600064596675246,"score_spread":0.2289721031280607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293458129","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32298502,0.00010539233,0.67323357,0.00007884495,0.0009929906,0.00031748108,0.000009188458,0.00018086714,0.0020966723],"genre_scores_gemma":[0.9398696,0.0000018799344,0.05797679,0.000036747457,0.000088885005,0.00001872313,0.000020988204,0.00006755008,0.0019188815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853677,0.00011066838,0.00040899872,0.00022404637,0.0003187566,0.000400789],"domain_scores_gemma":[0.99948305,0.000039238275,0.00006930714,0.00018407941,0.00004580971,0.00017850951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002039578,0.00013250849,0.00014260516,0.00024041651,0.00046652133,0.0007308325,0.00035176362,0.00001361479,0.00004870632],"category_scores_gemma":[0.00006320631,0.00013154805,0.00008502755,0.00038272585,0.000043329113,0.0003912451,0.00009506081,0.0003908734,0.000007739734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011152997,0.00012405464,0.00004172975,0.000043217566,0.0000097426455,0.000030071256,0.0081824,0.94847155,0.0007924126,0.00055988313,0.0008435619,0.040890243],"study_design_scores_gemma":[0.0005131927,0.000050604383,0.000013377705,0.00002279631,0.000009467007,0.00006141838,0.0025179156,0.98765737,0.000005356274,0.0014416911,0.0075319377,0.00017486392],"about_ca_topic_score_codex":5.645208e-7,"about_ca_topic_score_gemma":0.000014859789,"teacher_disagreement_score":0.6168845,"about_ca_system_score_codex":0.00013735685,"about_ca_system_score_gemma":0.00003058875,"threshold_uncertainty_score":0.70474344},"labels":[],"label_agreement":null},{"id":"W4296870219","doi":"10.1007/978-3-031-12616-1_6","title":"Solving Problems with More Than One Constraint","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in mathematics","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Mathematics; Constraint (computer-aided design); Calculus (dental); Applied mathematics; Mathematical optimization; Algebra over a field; Pure mathematics; Geometry; Medicine; Orthodontics","score_opus":0.01771667953531084,"score_gpt":0.2129451409049437,"score_spread":0.19522846136963287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296870219","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000027234286,0.0005476682,0.67579037,0.00016095444,0.000113727045,0.00097143056,0.000022099126,0.0005868952,0.32177958],"genre_scores_gemma":[0.049818594,0.00047532984,0.9362728,0.00024822348,0.00024862334,0.0002737265,0.00021781988,0.0009497394,0.011495159],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985156,0.0000070360697,0.0004958159,0.0002514349,0.00040236546,0.0003277401],"domain_scores_gemma":[0.9990433,0.00030942875,0.00013000701,0.0003986375,0.00003979314,0.00007885847],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017260374,0.00044362125,0.0005999685,0.0001871847,0.00006480789,0.00006801917,0.00022447479,0.0002781672,0.0016839338],"category_scores_gemma":[0.00008728446,0.00039331522,0.0000952433,0.000089500405,0.00011479301,0.000055454588,0.000075373355,0.00090668036,0.000025722924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074427517,0.00018295457,0.0000067485803,0.008427858,0.00043363374,0.000097919605,0.008301722,0.6405483,0.00009289827,0.32514724,0.00007437995,0.016678918],"study_design_scores_gemma":[0.000928478,0.00020481336,9.327772e-7,0.004482272,0.00033368665,0.00018116154,0.00013046681,0.36293185,0.00026132457,0.61111623,0.017344903,0.0020838836],"about_ca_topic_score_codex":0.0000015008171,"about_ca_topic_score_gemma":0.00003098001,"teacher_disagreement_score":0.31028444,"about_ca_system_score_codex":0.00015550423,"about_ca_system_score_gemma":0.000034090604,"threshold_uncertainty_score":0.9998519},"labels":[],"label_agreement":null},{"id":"W4297836588","doi":"10.48550/arxiv.1305.5610","title":"Integrating tabu search and VLSN search to develop enhanced algorithms:\\n A case study using bipartite boolean quadratic programs","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tabu search; Generalization; Guided Local Search; Quadratic equation; Benchmark (surveying); Heuristic; Computer science; Mathematical optimization; Search algorithm; Bipartite graph; Hill climbing; Algorithm; Quadratic programming; Mathematics; Theoretical computer science","score_opus":0.1277943539698882,"score_gpt":0.24703315292513642,"score_spread":0.11923879895524822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297836588","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59545016,0.000018027888,0.4027798,0.0000059405684,0.000082740975,0.0012317278,0.0000025221616,0.0002649234,0.00016418996],"genre_scores_gemma":[0.95953125,0.000017507631,0.040057342,0.0000089531495,0.000053503907,0.00001334778,0.000010694261,0.00007308673,0.00023433339],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980456,0.0001490937,0.00036585308,0.0007212259,0.00014531804,0.0005729106],"domain_scores_gemma":[0.9985848,0.000088928005,0.000050656596,0.0004825758,0.00037897864,0.00041405807],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004333187,0.0004172935,0.00046668554,0.0003587437,0.00025233574,0.00034560976,0.00030320187,0.00020672026,0.00004711713],"category_scores_gemma":[0.00007550086,0.00045146377,0.000066186905,0.0010707608,0.00008683325,0.00023382904,0.00078668207,0.00074989285,0.000050367635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012838549,0.0002423445,0.0004982786,0.00082034647,0.0002756369,0.0014749893,0.014225603,0.95373225,0.0003587135,0.0011815429,0.000008984617,0.027168455],"study_design_scores_gemma":[0.00036826776,0.000104382125,0.000011591859,0.00026450833,0.000093715564,0.00005492816,0.018018868,0.980073,0.00033820135,0.00015135163,0.000013163302,0.0005080489],"about_ca_topic_score_codex":0.00089071196,"about_ca_topic_score_gemma":0.00037501508,"teacher_disagreement_score":0.36408108,"about_ca_system_score_codex":0.00025879443,"about_ca_system_score_gemma":0.00011414185,"threshold_uncertainty_score":0.9997937},"labels":[],"label_agreement":null},{"id":"W4303986377","doi":"10.1111/itor.13188","title":"Preface to the Special Issue on Developments in Metaheuristics","year":2022,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Metaheuristic; Computer science; Operations research; Management science; Artificial intelligence; Engineering","score_opus":0.06975319769106123,"score_gpt":0.38050630152011944,"score_spread":0.3107531038290582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303986377","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025885152,0.000080413134,0.26818395,0.046961766,0.008679073,0.004296737,0.00036770265,0.0002724546,0.64527273],"genre_scores_gemma":[0.9843862,0.00002629152,0.0069237584,0.00028682622,0.00048451283,0.0007974526,0.000033323133,0.0000213949,0.007040257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99845016,0.00010310906,0.0002419564,0.00014381486,0.0008758319,0.00018514598],"domain_scores_gemma":[0.9994905,0.0002714396,0.0000058084147,0.00009287722,0.000097982665,0.000041392475],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00086670904,0.00006886277,0.00006644562,0.0004408479,0.00021404079,0.000081413185,0.0003277696,0.000021371308,0.005675967],"category_scores_gemma":[0.00013681917,0.00006329837,0.000021093947,0.00063370756,0.000022924763,0.000086457076,0.000027384394,0.0005467291,0.00021803907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020651623,0.00012971024,0.000030661926,0.000003670309,0.000013489536,0.000006073002,0.000773101,0.9753516,0.00002102245,0.008471846,0.0026229513,0.012555229],"study_design_scores_gemma":[0.00033636804,0.0000392492,0.0006474186,0.0000138049845,0.0000010806299,0.000005560765,0.00053029635,0.2145647,0.00022078429,0.0005481027,0.7829887,0.00010393183],"about_ca_topic_score_codex":0.000016377267,"about_ca_topic_score_gemma":0.00014935879,"teacher_disagreement_score":0.95850104,"about_ca_system_score_codex":0.0005229762,"about_ca_system_score_gemma":0.00007218653,"threshold_uncertainty_score":0.995233},"labels":[],"label_agreement":null},{"id":"W4307536930","doi":"10.48550/arxiv.2210.15409","title":"Constrained Differential Dynamic Programming: A primal-dual augmented Lagrangian approach","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Willow Biosciences (Canada)","funders":"","keywords":"Augmented Lagrangian method; Mathematical optimization; Solver; Optimal control; Lagrangian relaxation; Dynamic programming; Optimization problem; Trajectory optimization; Nonlinear programming; Computer science; Mathematics; Nonlinear system","score_opus":0.03669263410485704,"score_gpt":0.18071600376036198,"score_spread":0.14402336965550494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307536930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09266908,0.00005800915,0.8892417,0.00002090041,0.00056915433,0.0012592009,0.00007959279,0.0018160148,0.014286342],"genre_scores_gemma":[0.9876637,0.000046016285,0.010318378,0.0000112376065,0.000034312598,0.000015013785,0.00053078023,0.000079155085,0.0013013905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983845,0.00007668012,0.00028689677,0.0006412852,0.00013298781,0.00047765058],"domain_scores_gemma":[0.99907494,0.000044339366,0.000110306806,0.000505026,0.000052720305,0.00021266702],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012208898,0.00042478566,0.00043751442,0.0002472595,0.00016041817,0.000114584655,0.00043811242,0.0002709635,0.0007288829],"category_scores_gemma":[0.00002053263,0.00050687767,0.00027683386,0.00042935222,0.00014610615,0.000095287745,0.0005697488,0.0008379124,0.000021515007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047196296,0.00047907385,0.00008708073,0.0011338969,0.0005155783,0.00014729332,0.0004727429,0.95906436,0.000074331896,0.034657594,0.00011932909,0.003201506],"study_design_scores_gemma":[0.00084410363,0.0000397797,0.00003597014,0.00004359848,0.00023073725,0.0000074583704,0.00057041104,0.9950029,0.00001182979,0.0013133236,0.0013215698,0.00057831325],"about_ca_topic_score_codex":0.000010346995,"about_ca_topic_score_gemma":0.000005952027,"teacher_disagreement_score":0.8949946,"about_ca_system_score_codex":0.0003231981,"about_ca_system_score_gemma":0.0000622694,"threshold_uncertainty_score":0.9997383},"labels":[],"label_agreement":null},{"id":"W4308531534","doi":"10.1007/s40313-022-00964-5","title":"A Note on “A New Ranking Approach for Solving Fully Fuzzy Transportation Problem in Intuitionistic Fuzzy Environment”","year":2022,"lang":"en","type":"article","venue":"Journal of Control Automation and Electrical Systems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mathematics; Ranking (information retrieval); Fuzzy number; Fuzzy logic; Fuzzy set operations; Mathematical optimization; Fuzzy set; Algebra over a field; Computer science; Artificial intelligence; Pure mathematics","score_opus":0.007466345645980507,"score_gpt":0.20386185221830666,"score_spread":0.19639550657232616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308531534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024304201,0.00042517355,0.99566996,0.00011830628,0.00010258429,0.00064286945,0.0000041149638,0.000058565816,0.00054802676],"genre_scores_gemma":[0.9881655,0.000021501644,0.011546404,0.000041421503,0.000085078485,0.000080585865,0.000011006591,0.000017638302,0.00003086215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877226,0.000052528914,0.0006305335,0.00009007585,0.00028004596,0.00017456072],"domain_scores_gemma":[0.999485,0.00016615077,0.0001956512,0.00004284934,0.000026785161,0.00008351991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054171536,0.000104779676,0.0002844033,0.0002066064,0.00010772716,0.00007677094,0.00006299557,0.00004769723,0.000008968135],"category_scores_gemma":[0.00004322834,0.00009697695,0.00006879484,0.00017147297,0.0000068453187,0.00012929311,0.0000017319074,0.00021666755,7.444726e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009613938,0.000070346396,0.0000355599,0.00017138862,0.000033729142,0.000002474252,0.0003691767,0.9752918,0.0014682957,0.008089109,0.00017463212,0.014197384],"study_design_scores_gemma":[0.002572833,0.0003032601,0.00016135098,0.000041781404,0.000042390548,0.000023144608,0.00006423206,0.99322915,0.000013129452,0.002346433,0.0010870958,0.00011522004],"about_ca_topic_score_codex":0.000003358764,"about_ca_topic_score_gemma":3.9048325e-7,"teacher_disagreement_score":0.98573506,"about_ca_system_score_codex":0.00021351808,"about_ca_system_score_gemma":0.000028500306,"threshold_uncertainty_score":0.39546055},"labels":[],"label_agreement":null},{"id":"W4310533879","doi":"10.19044/esj.2022.v18n36p1","title":"A Goal Programming Model for Dispatching Trucks in an Underground Gold Mine","year":2022,"lang":"en","type":"article","venue":"European Scientific Journal ESJ","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Truck; Flexibility (engineering); Operations research; Underground mining (soft rock); Goal programming; Computer science; Programming paradigm; Transport engineering; Engineering; Waste management; Coal mining; Economics","score_opus":0.031034356539463903,"score_gpt":0.2600064596675246,"score_spread":0.2289721031280607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310533879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32298502,0.00010539233,0.67323357,0.00007884495,0.0009929906,0.00031748108,0.000009188458,0.00018086714,0.0020966723],"genre_scores_gemma":[0.9398696,0.0000018799344,0.05797679,0.000036747457,0.000088885005,0.00001872313,0.000020988204,0.00006755008,0.0019188815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853677,0.00011066838,0.00040899872,0.00022404637,0.0003187566,0.000400789],"domain_scores_gemma":[0.99948305,0.000039238275,0.00006930714,0.00018407941,0.00004580971,0.00017850951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002039578,0.00013250849,0.00014260516,0.00024041651,0.00046652133,0.0007308325,0.00035176362,0.00001361479,0.00004870632],"category_scores_gemma":[0.00006320631,0.00013154805,0.00008502755,0.00038272585,0.000043329113,0.0003912451,0.00009506081,0.0003908734,0.000007739734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011152997,0.00012405464,0.00004172975,0.000043217566,0.0000097426455,0.000030071256,0.0081824,0.94847155,0.0007924126,0.00055988313,0.0008435619,0.040890243],"study_design_scores_gemma":[0.0005131927,0.000050604383,0.000013377705,0.00002279631,0.000009467007,0.00006141838,0.0025179156,0.98765737,0.000005356274,0.0014416911,0.0075319377,0.00017486392],"about_ca_topic_score_codex":5.645208e-7,"about_ca_topic_score_gemma":0.000014859789,"teacher_disagreement_score":0.6168845,"about_ca_system_score_codex":0.00013735685,"about_ca_system_score_gemma":0.00003058875,"threshold_uncertainty_score":0.70474344},"labels":[],"label_agreement":null},{"id":"W4311904686","doi":"10.5540/03.2022.009.01.0297","title":"Resolução de problemas de otimização com restrições de igualdade e desigualdade utilizando a Inicialização Global Topográfica","year":2022,"lang":"pt","type":"article","venue":"Proceeding Series of the Brazilian Society of Computational and Applied Mathematics","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Discovery Air (Canada)","funders":"","keywords":"Physics; Humanities; Mathematics; Philosophy","score_opus":0.02174058757971176,"score_gpt":0.2547258001903118,"score_spread":0.23298521261060007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311904686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11113413,0.0011196514,0.8759499,0.0047208555,0.00018268634,0.002531108,0.00029903837,0.00042488542,0.003637742],"genre_scores_gemma":[0.55431116,0.00007829776,0.4449404,0.00020948624,0.000042239782,0.000092111244,0.000016481725,0.000062139414,0.00024770777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975624,0.000047573172,0.0009358896,0.00028327896,0.00062458834,0.00054628536],"domain_scores_gemma":[0.99859816,0.00034507198,0.0005523171,0.0001949568,0.00014176399,0.0001677172],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010053987,0.00036011834,0.00062531966,0.00004554827,0.0005947691,0.000099027435,0.0005505216,0.00016091681,0.00006275258],"category_scores_gemma":[0.00013164089,0.00035280111,0.00029742863,0.0006442478,0.00043473075,0.0001223177,0.00051870756,0.00041988504,7.882018e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011119702,0.00068415253,0.00057274307,0.008539144,0.00046927837,7.0297233e-7,0.039708953,0.59801465,0.0009465224,0.3443277,0.004759393,0.0018655875],"study_design_scores_gemma":[0.0009978506,0.00012209396,0.0001623025,0.0004279512,0.00024999122,0.00008351742,0.023489984,0.6368735,0.0018407496,0.3350286,0.0003187586,0.00040471694],"about_ca_topic_score_codex":0.000007704904,"about_ca_topic_score_gemma":9.743354e-7,"teacher_disagreement_score":0.44317704,"about_ca_system_score_codex":0.00024879197,"about_ca_system_score_gemma":0.00043798823,"threshold_uncertainty_score":0.9998924},"labels":[],"label_agreement":null},{"id":"W4315752436","doi":"10.1016/j.procs.2022.12.325","title":"ANFIS Model for Cost Analysis in a Dual Source Multi-Destination System","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"University of Johannesburg","keywords":"Computer science; Dual (grammatical number); Focus (optics); Product (mathematics); Mathematical optimization; Face (sociological concept); Operations research; Algorithm","score_opus":0.03316031751886156,"score_gpt":0.26871931573658003,"score_spread":0.23555899821771847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315752436","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012181406,0.0000043319114,0.98675436,0.000026010845,0.00008590959,0.0003053927,0.000001773158,0.00059587276,0.000044935292],"genre_scores_gemma":[0.72343886,9.0054334e-7,0.27641696,0.000011106871,0.000016819617,0.00007716952,0.000004010142,0.000007355913,0.000026827382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992315,0.0000036014255,0.00016535766,0.00019324129,0.00016692217,0.00023935926],"domain_scores_gemma":[0.999673,0.000056724464,0.00002099634,0.00009534703,0.00008657692,0.00006736071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043833803,0.00007219513,0.000116044626,0.0004408136,0.00007126389,0.000102989805,0.00012594635,0.000024009112,4.7381292e-7],"category_scores_gemma":[0.00006435696,0.00007076958,0.00003240568,0.0026457778,0.00003997357,0.00020122904,0.000048385886,0.000040889638,0.000010975357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.2795264e-7,0.00000940456,0.000115632,0.00012517163,0.0000047565277,5.573623e-7,0.0008052749,0.9893541,0.000048853537,0.0006474807,0.000022913562,0.008865467],"study_design_scores_gemma":[0.00015407479,0.0000052735545,0.00024116831,0.000020358788,0.000012438103,0.0000013076445,0.00003401473,0.9993196,0.000069340575,0.00004380593,0.000013678772,0.00008491417],"about_ca_topic_score_codex":0.0000011786339,"about_ca_topic_score_gemma":0.0000066797375,"teacher_disagreement_score":0.71125746,"about_ca_system_score_codex":0.00007089657,"about_ca_system_score_gemma":0.000025849033,"threshold_uncertainty_score":0.28858995},"labels":[],"label_agreement":null},{"id":"W4320039432","doi":"10.1007/s10107-023-01930-y","title":"A novel reformulation for the single-sink fixed-charge transportation problem","year":2023,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université de Montréal","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Transportation theory; Fixed charge; Leverage (statistics); Mathematical optimization; Knapsack problem; Mathematics; Linear programming; Integer programming; Heuristic; Algorithm","score_opus":0.03791356835714092,"score_gpt":0.25676339012631094,"score_spread":0.21884982176917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320039432","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014909065,0.00004463468,0.99287885,0.0004913536,0.00012819571,0.0017347311,0.000011714804,0.0019237004,0.001295923],"genre_scores_gemma":[0.47168154,0.00001478221,0.52507406,0.000074827745,0.00020647272,0.0017048944,0.0002458432,0.00018999186,0.0008075744],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985188,0.000008017371,0.00052391924,0.00019790359,0.00027067328,0.00048067342],"domain_scores_gemma":[0.99904865,0.00048466423,0.00006289323,0.00023060017,0.000077286655,0.000095880074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005209277,0.00020910616,0.00022914546,0.00009384452,0.00021765953,0.00016110862,0.00016951273,0.00011337669,0.00005909645],"category_scores_gemma":[0.00016651157,0.00014874837,0.00014802905,0.00055712904,0.000055113433,0.0002018054,0.000012887756,0.00014345524,0.00015697013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032693468,0.0005022732,0.00001310572,0.00440874,0.00022918513,0.000004128788,0.005004775,0.044926386,0.009014077,0.2641077,0.001649166,0.6701078],"study_design_scores_gemma":[0.00059588754,0.00007102522,0.000022364895,0.00015464652,0.00009665472,0.0000057074885,0.0003551334,0.94964,0.0010110913,0.01887086,0.028851295,0.00032537465],"about_ca_topic_score_codex":0.0000011457231,"about_ca_topic_score_gemma":0.0000041959775,"teacher_disagreement_score":0.9047136,"about_ca_system_score_codex":0.000051347684,"about_ca_system_score_gemma":0.0000074118575,"threshold_uncertainty_score":0.60657823},"labels":[],"label_agreement":null},{"id":"W4321089801","doi":"10.21203/rs.3.rs-2525294/v1","title":"A note on “An algorithmic approach to solve unbalanced triangular fuzzy transportation problems”","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Fuzzy logic; Fuzzy transportation; Fuzzy set operations; Fuzzy number; Mathematical optimization; Point (geometry); Transportation theory; Mathematics; Computer science; Fuzzy set; Defuzzification; Artificial intelligence","score_opus":0.08824923857840812,"score_gpt":0.3712656601399721,"score_spread":0.283016421561564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321089801","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004557726,0.00013645556,0.9749731,0.000459029,0.0005035887,0.0070469016,0.00029471354,0.0026942631,0.009334227],"genre_scores_gemma":[0.61191714,0.00039375806,0.370752,0.00007955669,0.0011139321,0.007922256,0.0051311334,0.0008618851,0.0018283573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971139,0.000101931284,0.0004369005,0.0006126737,0.0010263983,0.00070818374],"domain_scores_gemma":[0.998513,0.00014886403,0.000035106983,0.00066369685,0.00026491508,0.00037441435],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012417354,0.00030660274,0.0004070896,0.00060349185,0.00012111277,0.00022194872,0.00042671544,0.00038452965,0.000027435277],"category_scores_gemma":[0.0001941639,0.00030322917,0.00014418965,0.0007645732,0.0000409139,0.000087010994,0.00007184361,0.0013157432,0.0003081855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030452731,0.00022364604,0.0000032654737,0.00432627,0.000057443736,0.0000152208495,0.0037804116,0.9727573,0.00021331264,0.0044596405,0.0009958737,0.013137205],"study_design_scores_gemma":[0.00093760923,0.0003590738,0.0002844835,0.0015876162,0.000030775303,0.0000010183431,0.00083287916,0.9752521,0.00045896185,0.016233481,0.0032268313,0.00079517585],"about_ca_topic_score_codex":0.000045559653,"about_ca_topic_score_gemma":0.000035019446,"teacher_disagreement_score":0.6073594,"about_ca_system_score_codex":0.00024543365,"about_ca_system_score_gemma":0.00008621517,"threshold_uncertainty_score":0.999942},"labels":[],"label_agreement":null},{"id":"W4322747773","doi":"10.1007/978-3-031-23876-5_7","title":"Using Goal Programming to Locate a New Fire Hall","year":2023,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Aeronautics; Architectural engineering; Engineering","score_opus":0.1356848343971156,"score_gpt":0.41841664377551013,"score_spread":0.28273180937839454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322747773","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001857161,0.00011613719,0.027623326,0.013863689,0.005235461,0.008517308,0.00012315487,0.00079747575,0.9418663],"genre_scores_gemma":[0.061344836,0.0037565595,0.16531323,0.00017622473,0.0005682978,0.001816878,0.0002873207,0.00026696155,0.7664697],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9860483,0.0001244048,0.0015275483,0.001996303,0.008262961,0.002040508],"domain_scores_gemma":[0.9959106,0.00014111494,0.00005376766,0.0012326764,0.0020200254,0.00064181746],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["sts","insufficient_payload"],"category_scores_codex":[0.009642711,0.0006471042,0.00048056088,0.010701763,0.0020835141,0.0054441243,0.0060553057,0.00020073785,0.001198317],"category_scores_gemma":[0.0010651044,0.0007047347,0.00013002465,0.0066966047,0.0036566136,0.005346865,0.004625746,0.0014956064,0.000984242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038101014,0.00008952545,0.000014073803,0.00009212581,0.000071698574,0.00014609878,0.0005860884,0.48575437,0.00017819392,0.50211346,0.0010895433,0.009826736],"study_design_scores_gemma":[0.00093230285,0.00018374893,0.0002791803,0.001740806,0.00002137626,0.000034875455,0.004133047,0.7697612,0.00023256034,0.02302708,0.19837789,0.0012759366],"about_ca_topic_score_codex":0.000653251,"about_ca_topic_score_gemma":0.0052756043,"teacher_disagreement_score":0.47908637,"about_ca_system_score_codex":0.0049691354,"about_ca_system_score_gemma":0.0006453843,"threshold_uncertainty_score":0.9997936},"labels":[],"label_agreement":null},{"id":"W4322761032","doi":"10.18280/mmep.100122","title":"Fixed Charge Solid Transportation Problem Based on Carbon Emission with Budget Constraints in Uncertain Environment (UFSTPCEBC)","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Carbon fibers; Fixed charge; Charge (physics); Environmental science; Mathematical optimization; Computer science; Physics; Mathematics; Chemical physics; Algorithm","score_opus":0.01516157294511676,"score_gpt":0.19856249082451238,"score_spread":0.1834009178793956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322761032","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05249735,0.00002346878,0.9446914,0.00012775579,0.000030067138,0.0006021451,0.000008567331,0.0008408507,0.0011784037],"genre_scores_gemma":[0.90715045,0.0000549423,0.092396386,0.000011885438,0.000013916813,0.00018615591,0.000047176716,0.00008136199,0.00005770061],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998736,0.000010119315,0.00037370247,0.00024656163,0.0002443763,0.00038922776],"domain_scores_gemma":[0.99955183,0.00011945054,0.000027185832,0.00014799512,0.000010756548,0.00014275349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028526937,0.00025547057,0.0002922707,0.00018755523,0.00003568402,0.00004017187,0.000066493405,0.000111691574,0.00003107069],"category_scores_gemma":[0.00001051129,0.00021686136,0.000033269487,0.00022145761,0.000036757057,0.000053133506,0.0000056221747,0.00018685467,0.000017953409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064455007,0.00005212086,0.000013345309,0.0012455668,0.0000111717945,0.000008564384,0.0006006449,0.9953191,0.00078783627,0.0013656596,0.0000050833287,0.00058441143],"study_design_scores_gemma":[0.00048176208,0.00006909275,0.0000067005612,0.0007936595,0.000014091286,0.0000020255397,0.00003126555,0.996334,0.00047569288,0.001467756,0.0000556487,0.00026828764],"about_ca_topic_score_codex":0.0000017551909,"about_ca_topic_score_gemma":2.4954443e-7,"teacher_disagreement_score":0.8546531,"about_ca_system_score_codex":0.000050452196,"about_ca_system_score_gemma":0.000008612127,"threshold_uncertainty_score":0.884335},"labels":[],"label_agreement":null},{"id":"W4324269802","doi":"10.1007/978-981-19-6406-0_11","title":"A Fuzzy Logic-Based Approach to Solve Interval Multi-objective Non-linear Transportation Problem: Suggested Modifications","year":2022,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Interval (graph theory); Fuzzy logic; Transportation theory; Mathematical optimization; Computer science; Mathematics; Artificial intelligence; Combinatorics","score_opus":0.03340645949066068,"score_gpt":0.2589579112234587,"score_spread":0.225551451732798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324269802","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002560374,0.000040140454,0.8283626,0.000026580645,0.00012405172,0.0022437565,0.00044198844,0.0004494412,0.16828586],"genre_scores_gemma":[0.0021435046,0.000059860184,0.9905677,0.00005435214,0.000052905394,0.00080293405,0.00040488303,0.0003361212,0.00557773],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996956,0.00000426517,0.0012790867,0.0006180877,0.00061730645,0.00052525656],"domain_scores_gemma":[0.998609,0.00014446206,0.00034762858,0.00029456205,0.00038610311,0.00021821965],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004392114,0.00070308277,0.00085029856,0.00059785857,0.0001402652,0.00012793476,0.000457851,0.0003291112,0.00012475818],"category_scores_gemma":[0.00015203306,0.0007801863,0.00014631367,0.00024914579,0.00009663583,0.00014707142,0.00006328997,0.0009819552,0.000067148016],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017936056,0.00055693125,0.0000040742634,0.005946093,0.00014454348,0.000012334169,0.008534527,0.10154873,0.00010068772,0.8813562,0.0006952792,0.0010826935],"study_design_scores_gemma":[0.0010678344,0.00016959204,0.000017471124,0.00089523173,0.00032763227,0.0000066615726,0.0009151358,0.8656567,0.00009994753,0.12303469,0.0062610605,0.0015480807],"about_ca_topic_score_codex":0.0000073699766,"about_ca_topic_score_gemma":0.000018659217,"teacher_disagreement_score":0.76410794,"about_ca_system_score_codex":0.0004479603,"about_ca_system_score_gemma":0.00008270037,"threshold_uncertainty_score":0.9994649},"labels":[],"label_agreement":null},{"id":"W4360978753","doi":"10.1007/s10878-023-01002-z","title":"An augmented Lagrangian approach with general constraints to solve nonlinear models of the large-scale reliable inventory systems","year":2023,"lang":"en","type":"article","venue":"Journal of Combinatorial Optimization","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Augmented Lagrangian method; Theory of computation; Mathematical optimization; Nonlinear system; Computer science; Nonlinear programming; Lagrange multiplier; Minification; Scale (ratio); Lagrangian relaxation; Mathematics; Algorithm","score_opus":0.011695456112597062,"score_gpt":0.2239217104963334,"score_spread":0.21222625438373632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360978753","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017208096,0.000028842986,0.9792688,0.000040024053,0.0016696284,0.0004105006,0.000008799998,0.00010032165,0.0012650103],"genre_scores_gemma":[0.62025577,0.000042109048,0.3790375,0.000034195702,0.00040477212,0.000018363005,0.00004067719,0.000086892505,0.00007972879],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881107,0.000038723734,0.00047764202,0.00008702275,0.0003950749,0.0001904849],"domain_scores_gemma":[0.9991452,0.000017832715,0.00019046935,0.000172626,0.00033911623,0.00013474925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048046676,0.00012029076,0.00026336842,0.00015559004,0.000065371874,0.0000584756,0.00020300745,0.000085734486,0.000008605412],"category_scores_gemma":[0.000028985813,0.000086139575,0.000068026486,0.0006272228,0.00003710173,0.0002983044,0.000022244729,0.00014645368,0.000001376013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002954948,0.00012404559,0.000026167287,0.00008704686,0.00004320819,0.0000010770046,0.0005364257,0.9930788,0.00012327507,0.0055394294,0.0003763477,0.00003458007],"study_design_scores_gemma":[0.001111207,0.00013569037,0.0000040731557,0.00010763341,0.000042216812,0.000008439668,0.0004267468,0.99741405,0.00023005198,0.00026300628,0.00015696809,0.000099911966],"about_ca_topic_score_codex":0.0000023243358,"about_ca_topic_score_gemma":2.70324e-7,"teacher_disagreement_score":0.60304767,"about_ca_system_score_codex":0.00006110763,"about_ca_system_score_gemma":0.000055214077,"threshold_uncertainty_score":0.351267},"labels":[],"label_agreement":null},{"id":"W4366808570","doi":"10.5267/j.jpm.2023.3.001","title":"Credibility based chance constrained programming for parallel machine scheduling under linear deterioration and learning effects with considering setup times dependent on past sequences","year":2023,"lang":"en","type":"article","venue":"Journal of Project Management","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tardiness; Mathematical optimization; Job shop scheduling; Fuzzy logic; Pareto optimal; Computer science; Scheduling (production processes); Heuristic; Constraint (computer-aided design); Constraint programming; Pareto principle; Multi-objective optimization; Mathematics; Artificial intelligence","score_opus":0.022471793127047307,"score_gpt":0.2742429177143532,"score_spread":0.2517711245873059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366808570","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070950456,0.00009684418,0.92694044,0.00020462401,0.00013169419,0.0012465583,0.000001323637,0.00024163003,0.00018645097],"genre_scores_gemma":[0.70771843,0.000032675944,0.29204234,0.00002953841,0.00004541402,0.0000624319,0.0000055648643,0.000023133718,0.000040442028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990439,0.000049627313,0.0003175064,0.00014689771,0.00022395964,0.00021815584],"domain_scores_gemma":[0.99944484,0.00021521677,0.00014406526,0.00007456967,0.00006617869,0.000055132266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007574183,0.00015308385,0.00022177865,0.00021150598,0.000121844896,0.000109270826,0.000061392275,0.000036659265,0.000003314879],"category_scores_gemma":[0.00008021138,0.0001192339,0.00004578825,0.00020591766,0.000039893446,0.00014392515,0.000021882252,0.000184547,0.0000013243603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011673649,0.000048784343,0.00019396035,0.0020887775,0.00018174984,0.000049061942,0.0003160958,0.9692779,0.0003975548,0.0005426406,0.00001655137,0.026770169],"study_design_scores_gemma":[0.0015833054,0.0006364559,0.000102097416,0.00059101277,0.00009056633,0.000013223569,0.00091353693,0.9946903,0.0005560477,0.00024936805,0.00038689532,0.00018720067],"about_ca_topic_score_codex":0.0000012493023,"about_ca_topic_score_gemma":0.0000035299877,"teacher_disagreement_score":0.636768,"about_ca_system_score_codex":0.000051976516,"about_ca_system_score_gemma":0.000019485884,"threshold_uncertainty_score":0.48622173},"labels":[],"label_agreement":null},{"id":"W4380486067","doi":"10.1007/978-3-031-30337-1_1","title":"Introduction","year":2023,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Management science; Engineering","score_opus":0.04378420860433334,"score_gpt":0.2769502726568363,"score_spread":0.23316606405250295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380486067","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00083513424,0.022679703,0.07176244,0.0014562348,0.016478254,0.0014255462,0.000014213895,0.005419938,0.8799285],"genre_scores_gemma":[0.16990262,0.039290704,0.04087543,0.00028895805,0.018050756,0.00009307576,0.00018027176,0.0014250294,0.72989315],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992997,0.0000034998056,0.00026969658,0.00018500171,0.00008646998,0.00015565191],"domain_scores_gemma":[0.9996742,0.00012434399,0.000035143166,0.00009436441,0.000047400084,0.000024550922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015849975,0.00018396972,0.00033919504,0.00011542736,0.000070120856,0.000023426786,0.00004560984,0.00009254551,0.0000101620235],"category_scores_gemma":[0.00007995922,0.00017731093,0.00003210698,0.000052719304,0.000079079786,0.000028943206,0.0001160727,0.00021115385,0.000026457707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006262912,0.000016185872,0.00004221633,0.0063677263,0.0005662093,0.000068203844,0.0043212073,0.11011217,0.000005151148,0.4805646,0.0183671,0.37956297],"study_design_scores_gemma":[0.0011826473,0.000082543826,0.0000757062,0.0042899954,0.0002049518,0.000044376226,0.0025876095,0.5639034,0.000008863248,0.16310674,0.26205716,0.0024560231],"about_ca_topic_score_codex":3.2325232e-7,"about_ca_topic_score_gemma":0.000003042125,"teacher_disagreement_score":0.45379123,"about_ca_system_score_codex":0.000036027952,"about_ca_system_score_gemma":0.0000030082838,"threshold_uncertainty_score":0.723053},"labels":[],"label_agreement":null},{"id":"W4380486168","doi":"10.1007/978-3-031-30337-1_4","title":"Mehar Method-III to Find All More-for-Less Solutions of Symmetric Intuitionistic Fuzzy Transportation Problems with Mixed Constraints","year":2023,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mathematics; Fuzzy number; Fuzzy logic; Transportation theory; Mathematical optimization; Fuzzy set operations; Discrete mathematics; Algebra over a field; Applied mathematics; Fuzzy set; Pure mathematics; Computer science; Artificial intelligence","score_opus":0.08852911648091881,"score_gpt":0.31171080202478174,"score_spread":0.22318168554386292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380486168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00063090597,0.0008296498,0.9871424,0.00009682002,0.0004486711,0.0013124472,0.00011475015,0.0002826699,0.009141696],"genre_scores_gemma":[0.7088267,0.0009900432,0.2838036,0.000088484834,0.00025320193,0.00033256024,0.00040662457,0.00036703053,0.0049317027],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864584,0.000011071266,0.0006032373,0.00028334095,0.00017537871,0.00028115208],"domain_scores_gemma":[0.99879205,0.00065994455,0.00013049046,0.00011471107,0.00023803303,0.00006477214],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004142187,0.00028686714,0.0006788476,0.0003447459,0.00013280487,0.000026170275,0.00008648683,0.00012348357,0.0000024485857],"category_scores_gemma":[0.00011505039,0.00026613433,0.000064675594,0.00023171629,0.00020206827,0.000036807218,0.00004056118,0.0001816989,0.000001261251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024520357,0.000042270716,0.000020484642,0.008469161,0.00090958015,0.000014223321,0.008575114,0.43014476,0.000021561596,0.44845667,0.00028282573,0.10303881],"study_design_scores_gemma":[0.006842558,0.0007771277,0.0009667679,0.023003818,0.0020791735,0.00003705512,0.016431367,0.76840657,0.000051866245,0.17386177,0.0032649,0.0042770063],"about_ca_topic_score_codex":0.000006712278,"about_ca_topic_score_gemma":0.000056651174,"teacher_disagreement_score":0.7081958,"about_ca_system_score_codex":0.00005199452,"about_ca_system_score_gemma":0.000019025001,"threshold_uncertainty_score":0.9999791},"labels":[],"label_agreement":null},{"id":"W4380486174","doi":"10.1007/978-3-031-30337-1_3","title":"Mehar Method-II to Find All More-For-Less Solutions of Symmetric Fuzzy Transportation Problems with Mixed Constraints","year":2023,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Fuzzy logic; Transportation theory; Mathematical optimization; Mathematics; Fuzzy transportation; Variable (mathematics); Fuzzy number; Fuzzy set; Computer science; Artificial intelligence; Mathematical analysis","score_opus":0.09174104285345736,"score_gpt":0.3115398983233454,"score_spread":0.21979885546988803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380486174","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031412141,0.0018725886,0.971166,0.00025429187,0.0009027944,0.002617891,0.00019835246,0.00055800524,0.019288853],"genre_scores_gemma":[0.5341517,0.0013663518,0.4549879,0.000111392794,0.0003133536,0.00032782357,0.00032986753,0.000501926,0.007909695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867517,0.000009658087,0.0005659642,0.00028305766,0.00017414324,0.00029201715],"domain_scores_gemma":[0.9989732,0.00051168056,0.00012233299,0.00011762602,0.00021180742,0.00006334493],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042847922,0.00029143674,0.00068033027,0.0003446195,0.0001399653,0.000016660037,0.000091733644,0.00012775986,0.0000022983127],"category_scores_gemma":[0.000100062396,0.00026155304,0.00006852884,0.00023050563,0.00015787694,0.000037229926,0.000055706754,0.0001765899,7.623076e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000251006,0.000054905795,0.00004169807,0.0092732245,0.001215472,0.000017860557,0.0152405305,0.56567514,0.000030518328,0.25542185,0.00033601173,0.15266766],"study_design_scores_gemma":[0.0063763293,0.0009735553,0.0009940942,0.018634083,0.0018676458,0.000037503196,0.014889328,0.86226064,0.000085360494,0.08228916,0.0069673345,0.0046249544],"about_ca_topic_score_codex":0.0000047354383,"about_ca_topic_score_gemma":0.00004874472,"teacher_disagreement_score":0.53101045,"about_ca_system_score_codex":0.00003978239,"about_ca_system_score_gemma":0.000016178261,"threshold_uncertainty_score":0.99998367},"labels":[],"label_agreement":null},{"id":"W4380519660","doi":"10.1007/978-3-031-30337-1_5","title":"Mehar Method-IV to Find All More-For-Less Solutions of Symmetric Intuitionistic Fuzzy Linear Fractional Transportation Problems with Mixed Constraints","year":2023,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mathematics; Applied mathematics; Fuzzy logic; Mathematical optimization; Computer science; Artificial intelligence","score_opus":0.0935773476194735,"score_gpt":0.32199276111957253,"score_spread":0.22841541350009903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380519660","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004476438,0.0004998815,0.9928374,0.00011736129,0.00043901286,0.00092443463,0.00014750895,0.00021525777,0.004371456],"genre_scores_gemma":[0.49583247,0.0010061556,0.4964222,0.00012275517,0.0005126303,0.00036079562,0.000746004,0.0004068417,0.0045901462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986667,0.000011693178,0.0005824564,0.00028003665,0.00020525142,0.00025383008],"domain_scores_gemma":[0.9985852,0.0008219642,0.0001382686,0.00009999537,0.0002902029,0.000064357795],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003929885,0.00027377767,0.00059529603,0.00034314228,0.00014792781,0.000021573347,0.000075213706,0.00013295542,0.000004235449],"category_scores_gemma":[0.00016009681,0.00025817318,0.00006785405,0.00021706306,0.00018467098,0.000045527264,0.0000342826,0.00022436153,0.0000020481716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022855607,0.00004227972,0.00002712128,0.004963124,0.00072951725,0.000009603183,0.003223759,0.70725375,0.000015831958,0.24087791,0.00020436903,0.042629898],"study_design_scores_gemma":[0.002785909,0.0003628533,0.0008541159,0.008413599,0.00089499075,0.000024179233,0.005130756,0.9049386,0.000020137291,0.07165435,0.0029573264,0.0019632252],"about_ca_topic_score_codex":0.000005022329,"about_ca_topic_score_gemma":0.00003476348,"teacher_disagreement_score":0.49641526,"about_ca_system_score_codex":0.000057898007,"about_ca_system_score_gemma":0.000024969788,"threshold_uncertainty_score":0.99998707},"labels":[],"label_agreement":null},{"id":"W4380519670","doi":"10.1007/978-3-031-30337-1_2","title":"Mehar Method-I to Find All More-For-Less Solutions of Symmetric Fuzzy Balanced Transportation Problems","year":2023,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Fuzzy logic; Transportation theory; Mathematics; Mathematical optimization; Fuzzy transportation; Fuzzy number; Applied mathematics; Fuzzy set; Computer science; Artificial intelligence","score_opus":0.10417114160114994,"score_gpt":0.3306300957529654,"score_spread":0.2264589541518155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380519670","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005979279,0.0030916673,0.98107606,0.00017102707,0.0007980381,0.0015396076,0.00009753519,0.00042620936,0.012201923],"genre_scores_gemma":[0.31982934,0.0040318603,0.6553701,0.00024074307,0.0007382329,0.0005552021,0.00049064745,0.0007849768,0.017958868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986674,0.000009570593,0.0006093844,0.00026960223,0.0001582331,0.00028580174],"domain_scores_gemma":[0.99897164,0.0005707176,0.00011002989,0.00011980696,0.00017199123,0.000055804005],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046939024,0.00026678835,0.0006707326,0.00034352246,0.00009181182,0.00002068635,0.00010058715,0.00013706529,0.0000013890291],"category_scores_gemma":[0.000103017795,0.00026416386,0.00008907845,0.0002292948,0.000060953116,0.000044757493,0.000053558073,0.00017054139,0.0000014686933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010582291,0.000026429087,0.000023672126,0.008190785,0.00053999264,0.000006691595,0.008584585,0.6708122,0.000046654037,0.22008891,0.000450316,0.09121921],"study_design_scores_gemma":[0.0018712169,0.00017315704,0.0005245919,0.0064433315,0.00053462264,0.000005103561,0.004307604,0.885767,0.00002556234,0.09086939,0.0076691043,0.0018093616],"about_ca_topic_score_codex":0.0000057065986,"about_ca_topic_score_gemma":0.000023454822,"teacher_disagreement_score":0.32570595,"about_ca_system_score_codex":0.000044960645,"about_ca_system_score_gemma":0.000011739872,"threshold_uncertainty_score":0.99998105},"labels":[],"label_agreement":null},{"id":"W4380519743","doi":"10.1007/978-3-031-30337-1","title":"More-for-Less Solutions in Fuzzy Transportation Problems","year":2023,"lang":"en","type":"book","venue":"Studies in fuzziness and soft computing","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Fuzzy logic; Fuzzy transportation; Computer science; Transportation theory; Operations research; Fuzzy control system; Engineering; Mathematics; Artificial intelligence; Mathematical optimization; Fuzzy set operations","score_opus":0.07585036192058145,"score_gpt":0.3010582730100483,"score_spread":0.22520791108946683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380519743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015869148,0.064025916,0.7459233,0.0016628221,0.012828327,0.013396614,0.0004283305,0.007297593,0.13856794],"genre_scores_gemma":[0.7530252,0.024028108,0.07477785,0.00033862639,0.0031755029,0.0025086082,0.002369901,0.0018546379,0.13792159],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987946,0.000009016973,0.0005145245,0.0002414263,0.00010952448,0.00033086457],"domain_scores_gemma":[0.9993965,0.0003475934,0.000059548907,0.00009163342,0.000072156465,0.000032557327],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003137995,0.00024027607,0.00050336425,0.00021782769,0.00011157396,0.000027192382,0.00007939124,0.00014625805,6.0203706e-7],"category_scores_gemma":[0.00007572962,0.0002452369,0.00005563595,0.00020948169,0.00009856807,0.00005093703,0.00003739994,0.00024081256,0.0000017698038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060213333,0.00004305882,0.0002697136,0.013186969,0.00020184375,0.000021616223,0.015265133,0.8920802,0.0000026316297,0.023734415,0.0035122978,0.051676124],"study_design_scores_gemma":[0.0018295236,0.000049322985,0.0008825991,0.0076002567,0.00012204781,0.0000031840825,0.0065834126,0.9078477,0.0000012200632,0.06702009,0.0067359735,0.001324682],"about_ca_topic_score_codex":0.0000039484744,"about_ca_topic_score_gemma":0.00020489882,"teacher_disagreement_score":0.73715603,"about_ca_system_score_codex":0.0001081639,"about_ca_system_score_gemma":0.000022310658,"threshold_uncertainty_score":1},"labels":[],"label_agreement":null},{"id":"W4381885510","doi":"10.1016/j.eswa.2023.120899","title":"Developing a fuzzy optimized model for selecting a maintenance strategy in the paper industry: An integrated FGP-ANP-FMEA approach","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Failure mode and effects analysis; Risk analysis (engineering); Preventive maintenance; Fuzzy logic; Predictive maintenance; Reliability engineering; Proactive maintenance; Optimal maintenance; Operations research; Business; Engineering; Artificial intelligence","score_opus":0.05490420174599783,"score_gpt":0.29411638113663324,"score_spread":0.2392121793906354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381885510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005510524,0.000077481,0.99357337,0.0001821982,0.000019784291,0.002343165,0.000009034232,0.0006694909,0.00257441],"genre_scores_gemma":[0.6651715,0.000019559788,0.3114074,0.00014580584,0.00009717323,0.022408485,0.00019829138,0.00010059402,0.00045117823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878776,0.00004330139,0.0003685086,0.00027104223,0.00015387741,0.00037548106],"domain_scores_gemma":[0.999335,0.000110828136,0.000057577432,0.00031946463,0.0001060167,0.00007110361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004487676,0.00019619052,0.0002384739,0.00011684138,0.0001808441,0.00016252154,0.00027003032,0.00016368799,0.0000017537848],"category_scores_gemma":[0.000032507,0.00013406388,0.000030862997,0.0011532825,0.000029761733,0.00019459921,0.0000137843035,0.00028190328,0.000007057206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007634077,0.00003890319,0.0000041776834,0.000121209254,0.000022252025,3.556324e-7,0.0023211404,0.9642897,0.00012105945,0.031166269,0.00078593293,0.0011213381],"study_design_scores_gemma":[0.0004468994,0.000013682013,0.0000030972953,0.000071769224,0.000006709114,0.000011825553,0.009341703,0.9857465,0.000011528642,0.0003576628,0.0037853457,0.00020325837],"about_ca_topic_score_codex":0.000036902507,"about_ca_topic_score_gemma":0.00002023541,"teacher_disagreement_score":0.682166,"about_ca_system_score_codex":0.00010196026,"about_ca_system_score_gemma":0.0000776693,"threshold_uncertainty_score":0.54669666},"labels":[],"label_agreement":null},{"id":"W4383110686","doi":"10.37278/insearch.v21i2.552","title":"Penjadwalan Housekeepers Hotel Pada Era Pandemi COVID-19 dengan Pendekatan Goal Programming","year":2023,"lang":"id","type":"article","venue":"In Search","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hotel Dieu Hospital","funders":"","keywords":"Humanities; Philosophy","score_opus":0.05682866554647322,"score_gpt":0.3333757203942045,"score_spread":0.2765470548477313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383110686","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8339913,0.0024834038,0.110654905,0.008140968,0.0033841992,0.009706899,0.00016285489,0.011560688,0.019914767],"genre_scores_gemma":[0.9856639,0.0005326491,0.010280538,0.00019879552,0.0002118506,0.0001523879,0.00012081087,0.00021359854,0.0026254547],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99613565,0.00025065552,0.00068880507,0.0006210326,0.0008279567,0.0014758706],"domain_scores_gemma":[0.99772257,0.00079012354,0.00005570597,0.00052855635,0.000075463526,0.0008275979],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018770954,0.00041339517,0.0005105149,0.0005909009,0.00028032457,0.00041551288,0.00053120067,0.00035435657,0.000489453],"category_scores_gemma":[0.000961334,0.0004531731,0.00016314525,0.0019268644,0.00018463652,0.00024459852,0.0002856045,0.0013195736,0.0012424868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003215909,0.00168109,0.056691866,0.016823184,0.00080516335,0.0047116415,0.09642866,0.35273668,0.005275896,0.009473924,0.013664636,0.44138566],"study_design_scores_gemma":[0.009193284,0.00073362404,0.010335359,0.0013132215,0.0002152721,0.00022424669,0.04071192,0.7304215,0.0013655344,0.0018144394,0.19936635,0.0043052547],"about_ca_topic_score_codex":0.00041520895,"about_ca_topic_score_gemma":0.00035382586,"teacher_disagreement_score":0.43708038,"about_ca_system_score_codex":0.00054933544,"about_ca_system_score_gemma":0.00033723356,"threshold_uncertainty_score":0.999792},"labels":[],"label_agreement":null},{"id":"W4383621633","doi":"10.1007/s13198-023-02010-2","title":"A method to solve Pythagorean fuzzy transportation problems","year":2023,"lang":"en","type":"article","venue":"International Journal of Systems Assurance Engineering and Management","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Pythagorean theorem; Fuzzy logic; Transportation theory; Product (mathematics); Fuzzy transportation; Mathematics; Computer science; Fuzzy set; Mathematical optimization; Operations research; Fuzzy number; Artificial intelligence","score_opus":0.009992202908025945,"score_gpt":0.24629236472761198,"score_spread":0.23630016181958605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383621633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055587706,0.00019478105,0.9917265,0.00022843033,0.0012137264,0.00021526706,0.000006199485,0.00020581922,0.0006504947],"genre_scores_gemma":[0.87723243,0.0005755564,0.12159912,0.000025173884,0.00025278074,0.00005427409,0.000009587044,0.000045170018,0.0002058767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914676,0.0000049008436,0.00036024328,0.00007576952,0.00028578495,0.0001265401],"domain_scores_gemma":[0.9996807,0.000038805996,0.000054149023,0.000050324357,0.00009468733,0.00008130588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037073338,0.00009732784,0.00015628227,0.0003152258,0.000013531384,0.00007918282,0.00012664976,0.00002540771,0.0000021677608],"category_scores_gemma":[0.000010038309,0.00009109791,0.000043706637,0.0001918123,0.0000033927126,0.00012989697,0.000009555267,0.00006822414,0.000010093372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034040802,0.0000075478033,0.000020826656,0.00037928004,0.0002017932,0.00003739027,0.00034726236,0.9805834,0.0003763224,0.006938227,0.0010560598,0.010048486],"study_design_scores_gemma":[0.0011118635,0.00008215872,0.0036618854,0.0018317233,0.00007976321,0.00008812876,0.0006961248,0.8321618,0.00026826776,0.0003543006,0.15924029,0.0004236849],"about_ca_topic_score_codex":0.000003160724,"about_ca_topic_score_gemma":0.0000010404947,"teacher_disagreement_score":0.8716737,"about_ca_system_score_codex":0.00003885986,"about_ca_system_score_gemma":0.0000026689088,"threshold_uncertainty_score":0.37148649},"labels":[],"label_agreement":null},{"id":"W4385483004","doi":"10.3390/math11153383","title":"An Investigation of Linear Diophantine Fuzzy Nonlinear Fractional Programming Problems","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Diophantine equation; Fractional programming; Mathematics; Nonlinear programming; Mathematical optimization; Fuzzy logic; Linear-fractional programming; Fuzzy set; Linear programming; Degree (music); Fuzzy number; Nonlinear system; Set (abstract data type); Computer science; Discrete mathematics; Artificial intelligence","score_opus":0.025302934982939822,"score_gpt":0.2637586048968325,"score_spread":0.2384556699138927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385483004","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6013175,0.00006116853,0.39130554,0.00015470505,0.00036363187,0.0010682893,0.000021955466,0.003422669,0.0022844977],"genre_scores_gemma":[0.35287607,0.00003181773,0.6464468,0.000016573005,0.00014668923,0.000069821646,0.00014745902,0.000090008456,0.00017472793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989865,0.000012301764,0.000427949,0.00011320051,0.00025295882,0.00020712483],"domain_scores_gemma":[0.9994432,0.000081032325,0.00008171209,0.00021253085,0.00008764524,0.000093886556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003168775,0.000133601,0.00020616509,0.00014060023,0.000056453075,0.000032495598,0.00011899996,0.00008028042,0.00003654208],"category_scores_gemma":[0.00007500757,0.00012588249,0.000050601513,0.0005657324,0.000049095837,0.00018335886,0.00002124593,0.00011205737,0.000106076775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000098319415,0.0008635821,0.0013430706,0.013439936,0.00028875342,0.000015141378,0.018955495,0.84156,0.05339655,0.0356448,0.0012041222,0.033278693],"study_design_scores_gemma":[0.00017823103,0.000043000986,0.000045951125,0.00012142643,0.0000209022,0.0000045753595,0.00033099292,0.98891574,0.0039511104,0.0055101337,0.00072322396,0.00015470124],"about_ca_topic_score_codex":0.0000012816912,"about_ca_topic_score_gemma":0.0000012887264,"teacher_disagreement_score":0.2551413,"about_ca_system_score_codex":0.00001462953,"about_ca_system_score_gemma":0.0000132960495,"threshold_uncertainty_score":0.5133339},"labels":[],"label_agreement":null},{"id":"W4386129051","doi":"10.46254/in02.20220030","title":"The Integrated Combined Compromise Solution Method and Distance-Based MCDM Model with Application","year":2022,"lang":"en","type":"article","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Multiple-criteria decision analysis; Closeness; Mathematical optimization; Compromise; Computer science; Variance (accounting); Fuzzy logic; Mathematics; Artificial intelligence","score_opus":0.008651163309683596,"score_gpt":0.2201563115818744,"score_spread":0.2115051482721908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386129051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00068449794,0.000028158536,0.99737257,0.0003051492,0.00001282074,0.00028200145,0.0000027324131,0.00026615348,0.0010459092],"genre_scores_gemma":[0.8006666,0.000002826096,0.19883607,0.00004608852,0.0000019832803,0.0002165599,0.000022378343,0.000013510441,0.00019403973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996206,0.000022145363,0.00009649681,0.00007946875,0.0000895408,0.00009174672],"domain_scores_gemma":[0.999766,0.00006086248,0.000016764852,0.000105595806,0.000019759516,0.00003104968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016151441,0.00006331846,0.00006521289,0.000016528367,0.00018795818,0.000033418186,0.000055774395,0.000011966947,0.000014123553],"category_scores_gemma":[0.0000056867907,0.00004124534,0.000010742384,0.00013043595,0.000024312847,0.000030966567,0.000015693704,0.00008852174,8.8684726e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027893759,0.000021886877,0.0000051975726,0.000018757662,0.000007781719,9.0230735e-8,0.000059412356,0.9647741,0.0004519289,0.024779255,0.00017817214,0.009675562],"study_design_scores_gemma":[0.00035959695,0.000025406003,0.0000030871295,0.0000022783229,0.000008168975,7.1118103e-7,0.00008753161,0.9952755,0.00020794122,0.0014272234,0.002537624,0.00006491141],"about_ca_topic_score_codex":0.0000038617536,"about_ca_topic_score_gemma":0.000009209295,"teacher_disagreement_score":0.7999821,"about_ca_system_score_codex":0.00004169076,"about_ca_system_score_gemma":0.000008601763,"threshold_uncertainty_score":0.16819361},"labels":[],"label_agreement":null},{"id":"W4386246550","doi":"10.18280/mmep.100445","title":"Application of a Modified Gauss Elimination Technique for Separable Fuzzy Nonlinear Programming Problems","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Separable space; Fuzzy logic; Gauss; Nonlinear system; Nonlinear programming; Mathematics; Mathematical optimization; Applied mathematics; Computer science; Algorithm; Artificial intelligence; Mathematical analysis; Physics","score_opus":0.023771462264889808,"score_gpt":0.23836023626193079,"score_spread":0.214588773997041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386246550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022409663,0.0001119103,0.9946106,0.000040774896,0.00003743372,0.001621936,0.0000069389566,0.0010779209,0.00025148943],"genre_scores_gemma":[0.5352095,0.00008107516,0.4626714,0.0000024548553,0.000033605862,0.0018030987,0.00004585441,0.00009357957,0.00005944728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866664,0.0000066374228,0.00053563213,0.00023188813,0.00017888642,0.00038032906],"domain_scores_gemma":[0.9993719,0.0001780468,0.000059504808,0.00019825813,0.00008738293,0.000104941755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055394106,0.00022490745,0.00034072943,0.00020997014,0.000062536266,0.000050926184,0.00011061868,0.00015572895,0.0000015661801],"category_scores_gemma":[0.000060705697,0.0002181369,0.00007594331,0.00044319459,0.00003116588,0.0001185978,0.0000292233,0.00014266948,0.000007696592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002370089,0.000044517263,8.275095e-7,0.0054936945,0.000025169627,1.7157392e-7,0.00049343106,0.96368474,0.004238087,0.022899756,0.000007914953,0.0031093333],"study_design_scores_gemma":[0.00022530416,0.000044562774,4.5706867e-7,0.0003415327,0.000031459014,0.0000044369935,0.000036012687,0.9741343,0.002249031,0.022136554,0.0005659293,0.00023040161],"about_ca_topic_score_codex":0.0000036841527,"about_ca_topic_score_gemma":3.3175215e-7,"teacher_disagreement_score":0.5329685,"about_ca_system_score_codex":0.00002650739,"about_ca_system_score_gemma":0.000007349897,"threshold_uncertainty_score":0.8895365},"labels":[],"label_agreement":null},{"id":"W4387544842","doi":"10.2139/ssrn.4598689","title":"Problemiterative Matheuristic for the Biomedical Sample Transportation Problem","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Sample (material); Business; Computer science; Chemistry; Chromatography","score_opus":0.01848583465514994,"score_gpt":0.25979674619971643,"score_spread":0.2413109115445665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387544842","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023003551,0.00082899956,0.99613494,0.0008790651,0.0004759943,0.00096985523,0.00008937309,0.00034580778,0.000045955596],"genre_scores_gemma":[0.73182786,0.021035297,0.23370874,0.00015926582,0.0029471999,0.00340872,0.0024123995,0.0009432768,0.0035572548],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977564,0.000025385956,0.0004995174,0.00018838885,0.0002473013,0.0012829627],"domain_scores_gemma":[0.99910986,0.00043494286,0.00012395234,0.00015914816,0.000095627445,0.00007644293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011812003,0.00023769912,0.0002545211,0.00010573315,0.00018548878,0.00015350925,0.00030839728,0.0001869457,0.00001834636],"category_scores_gemma":[0.0001259221,0.00016699327,0.00019618399,0.00014680132,0.000049562623,0.000057895137,0.000020465604,0.0021657338,0.000013777473],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054588138,0.00017818676,0.000022855476,0.0022690021,0.0020545218,0.0000048703682,0.0042211716,0.39523923,0.00006380639,0.49441114,0.0035646698,0.09791597],"study_design_scores_gemma":[0.00033062923,0.000088339446,0.000006242218,0.00015424678,0.00016082979,0.000016074258,0.0005068005,0.1872877,0.000008963338,0.8064418,0.0047780774,0.00022032665],"about_ca_topic_score_codex":0.000010447868,"about_ca_topic_score_gemma":0.00016479568,"teacher_disagreement_score":0.7624262,"about_ca_system_score_codex":0.0003897314,"about_ca_system_score_gemma":0.0005363527,"threshold_uncertainty_score":0.94091564},"labels":[],"label_agreement":null},{"id":"W4388660517","doi":"10.3390/axioms12111048","title":"Multi-Objective Non-Linear Programming Problems in Linear Diophantine Fuzzy Environment","year":2023,"lang":"en","type":"article","venue":"Axioms","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Diophantine equation; Mathematical optimization; Linear programming; Mathematics; Linear-fractional programming; Fuzzy logic; Nonlinear programming; Nonlinear system; Computer science; Discrete mathematics; Artificial intelligence","score_opus":0.018929520212169537,"score_gpt":0.24766774846455866,"score_spread":0.22873822825238913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388660517","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07741776,0.00019543002,0.9167243,0.00012498163,0.0005303062,0.001720359,0.0000070953784,0.0018433233,0.001436414],"genre_scores_gemma":[0.9303092,0.00013509359,0.0684603,0.000017813445,0.00008077384,0.00022695745,0.000039502047,0.00008102441,0.0006493277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989173,0.00001361096,0.00029580347,0.00021324551,0.00016083018,0.00039920266],"domain_scores_gemma":[0.99965763,0.00003922624,0.0000279969,0.00017590311,0.000012705059,0.00008651502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000222998,0.0001713791,0.0002014378,0.00016427424,0.000042727046,0.000024011782,0.00010859224,0.000085740896,0.000031495576],"category_scores_gemma":[0.000040344865,0.00016524627,0.0000553091,0.0005147176,0.000038084676,0.00010754877,0.000046762365,0.00017665283,0.0006552585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000514904,0.00021380569,0.00037502183,0.00042927085,0.00005959982,0.000039225357,0.0029562123,0.95319635,0.008444673,0.00018111632,0.000052814987,0.034046758],"study_design_scores_gemma":[0.00069016113,0.0000411095,0.0005354904,0.00007684288,0.0000088870565,0.0000018943697,0.00026640293,0.99458694,0.0012286846,0.0000645495,0.002258398,0.00024062289],"about_ca_topic_score_codex":0.0000093612125,"about_ca_topic_score_gemma":0.000005489243,"teacher_disagreement_score":0.85289145,"about_ca_system_score_codex":0.000051125804,"about_ca_system_score_gemma":0.0000060306797,"threshold_uncertainty_score":0.8422241},"labels":[],"label_agreement":null},{"id":"W4390095668","doi":"10.59254/sbpo-2018-85285","title":"Meta-heurística híbrida aplicada ao Problema das Sequências Justas Ponderadas","year":2018,"lang":"pt","type":"article","venue":"Anais do Simpósio Brasileiro de Pesquisa Operacional","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science","score_opus":0.06508167927139824,"score_gpt":0.32079279767299956,"score_spread":0.2557111184016013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390095668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23045093,0.016301794,0.62998974,0.012549688,0.006448474,0.009133964,0.001667661,0.005383738,0.08807401],"genre_scores_gemma":[0.94858855,0.00024887227,0.040757265,0.0024022341,0.0014991981,0.00026659784,0.00020891597,0.0004023903,0.0056259693],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9931941,0.00030690193,0.0016813044,0.0014092227,0.0013529947,0.0020554813],"domain_scores_gemma":[0.99581456,0.0006813329,0.00029956148,0.0012806169,0.00068928645,0.0012346592],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.001166799,0.0013321805,0.0015816491,0.00045093813,0.0009110945,0.0009301008,0.0010840199,0.0007297807,0.019977354],"category_scores_gemma":[0.0007414395,0.0012568897,0.0008597186,0.001171349,0.00055723026,0.00086258334,0.000526152,0.0011647515,0.0028496927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012509017,0.009299454,0.0075034937,0.009120209,0.036225926,0.0015409914,0.030441575,0.08997388,0.012102401,0.43417087,0.30947393,0.058896348],"study_design_scores_gemma":[0.006688249,0.0019776055,0.0055331206,0.0011114036,0.012405694,0.0012233872,0.0025675702,0.68452567,0.0059778932,0.008555898,0.26197964,0.0074538826],"about_ca_topic_score_codex":0.000103401966,"about_ca_topic_score_gemma":0.0000930076,"teacher_disagreement_score":0.7181376,"about_ca_system_score_codex":0.0004970612,"about_ca_system_score_gemma":0.00047636064,"threshold_uncertainty_score":0.99994296},"labels":[],"label_agreement":null},{"id":"W4390670687","doi":"10.28924/2291-8639-22-2024-11","title":"New Approach to Solving Fuzzy Multiobjective Linear Fractional Optimization Problems","year":2024,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Mathematical optimization; Fuzzy logic; Parametric statistics; Fuzzy number; Fractional programming; Fuzzy set; Nonlinear programming; Nonlinear system; Computer science; Artificial intelligence; Statistics","score_opus":0.011678148226831954,"score_gpt":0.2756985981748825,"score_spread":0.2640204499480506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390670687","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000088000845,0.00024954247,0.9939657,0.00030480008,0.000107437605,0.00011742724,0.00000738859,0.00006148577,0.005098221],"genre_scores_gemma":[0.4143005,0.00033501867,0.5839953,0.000077366545,0.00073440146,0.00004291159,0.00004910911,0.000025891262,0.00043950035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991967,0.000008696687,0.00034489422,0.000119936834,0.0002528992,0.000076890436],"domain_scores_gemma":[0.9994305,0.00006806666,0.00005737692,0.00006415896,0.00025784236,0.00012209667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016213644,0.00008568292,0.00014448477,0.00054495904,0.00004108163,0.0001898545,0.00013608769,0.00003934342,0.00009687056],"category_scores_gemma":[0.000025870519,0.00007629,0.00012745721,0.00066349667,0.000011861218,0.00023960035,0.000021217353,0.00012942926,0.000015943791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022835386,0.000034501176,0.000027570873,0.000015280142,0.0008441538,6.015598e-7,0.00022522961,0.973979,0.00010425877,0.0064322636,0.00042496776,0.01790992],"study_design_scores_gemma":[0.00010051685,0.000008492162,0.000058640246,0.00002757923,0.00027681576,0.000021229778,0.00009763562,0.98269767,0.000060060294,0.0010438613,0.015517383,0.00009011506],"about_ca_topic_score_codex":0.0000067493565,"about_ca_topic_score_gemma":0.000002163563,"teacher_disagreement_score":0.4142125,"about_ca_system_score_codex":0.00006372436,"about_ca_system_score_gemma":0.000030001702,"threshold_uncertainty_score":0.3111016},"labels":[],"label_agreement":null},{"id":"W4390842838","doi":"10.1504/wrstsd.2024.136010","title":"Optimal value determination using traditional and newly developed method based on using initial basic feasible solution of a transportation problem using northwest and Russell method","year":2024,"lang":"en","type":"article","venue":"World Review of Science Technology and Sustainable Development","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Value (mathematics); Mathematical optimization; Computer science; Mathematics; Machine learning","score_opus":0.03183875111031938,"score_gpt":0.3175507197879726,"score_spread":0.28571196867765325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390842838","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08101456,0.0028614427,0.91529757,0.00010324989,0.000033908753,0.0005143207,0.0000029959901,0.00007708077,0.00009486932],"genre_scores_gemma":[0.22775762,0.00023423761,0.77194816,0.000019802304,0.0000031978757,0.000014386402,0.000005658526,0.000009772532,0.0000071836603],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988073,0.0000346972,0.0004428795,0.00025596493,0.0002058467,0.0002533077],"domain_scores_gemma":[0.999556,0.000077759796,0.00008567079,0.00006861327,0.00016037279,0.000051596573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015638849,0.00014399542,0.00027451018,0.0012224538,0.00025039705,0.000034646277,0.000073107614,0.000065752094,0.0000062643317],"category_scores_gemma":[0.00005732848,0.00013438833,0.000020470368,0.0022318792,0.0003427264,0.0003285143,0.000019342764,0.0001220112,6.509232e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003893848,0.00021726148,0.00040572815,0.08753754,0.00008997993,0.000075515396,0.0025825554,0.27296278,0.01820241,0.20979846,0.000006283009,0.40808257],"study_design_scores_gemma":[0.00018446184,0.00003446905,0.00012424734,0.0040370114,0.0000718704,0.00003570879,0.0003687178,0.9838449,0.00962844,0.0012323662,0.00026321894,0.00017458721],"about_ca_topic_score_codex":0.000008482265,"about_ca_topic_score_gemma":0.0000044520875,"teacher_disagreement_score":0.7108821,"about_ca_system_score_codex":0.00016245278,"about_ca_system_score_gemma":0.00041723868,"threshold_uncertainty_score":0.5480197},"labels":[],"label_agreement":null},{"id":"W4391531701","doi":"10.28924/2291-8639-22-2024-26","title":"Single-Valued Neutrosophic Ideal Approximation Spaces","year":2024,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Majmaah University","keywords":"Mathematics; Ideal (ethics); Pure mathematics; Calculus (dental); Mathematical economics; Epistemology; Medicine; Orthodontics; Philosophy","score_opus":0.012100444504641797,"score_gpt":0.26832263493364267,"score_spread":0.25622219042900085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391531701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009277848,0.0010005918,0.9868925,0.00068406516,0.00012419888,0.00006142437,0.000006266213,0.00007130812,0.0018817963],"genre_scores_gemma":[0.98490864,0.00021921039,0.014516853,0.000027192045,0.00024470856,0.000010421191,0.000013739201,0.000009356645,0.000049878352],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935657,0.000008207809,0.00030474234,0.00007048335,0.00020060163,0.00005937051],"domain_scores_gemma":[0.9996371,0.000049236005,0.000056650457,0.0000545494,0.00014796127,0.000054504886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014470649,0.000065391156,0.00012557174,0.00038559866,0.000025087382,0.00026549157,0.000113355534,0.000029207667,0.000053627395],"category_scores_gemma":[0.000011755721,0.000055181994,0.00012376532,0.000413552,0.000024858235,0.00018365208,0.000012999685,0.000088388806,0.000008233693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015092717,0.0002863217,0.00049626007,0.00021945899,0.0076099196,0.000025485357,0.0009555083,0.16172722,0.014061724,0.41918582,0.00116775,0.39424944],"study_design_scores_gemma":[0.00021228251,0.000031911586,0.0003174214,0.00006760347,0.0009955665,0.00006580628,0.00018797335,0.9344469,0.0014950747,0.016158188,0.045834888,0.00018642812],"about_ca_topic_score_codex":0.0000013892208,"about_ca_topic_score_gemma":0.0000018283561,"teacher_disagreement_score":0.9756308,"about_ca_system_score_codex":0.00003237437,"about_ca_system_score_gemma":0.000008592686,"threshold_uncertainty_score":0.25601414},"labels":[],"label_agreement":null},{"id":"W4391890089","doi":"10.1007/978-3-031-49295-2_8","title":"MATLAB Codes of Metaheuristics Methods","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadore College","funders":"","keywords":"Metaheuristic; MATLAB; Computer science; Algorithm; Programming language","score_opus":0.027378496765449314,"score_gpt":0.2993723518208891,"score_spread":0.2719938550554398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391890089","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.950711e-8,0.00082545244,0.4000712,0.000007944494,0.00016106709,0.000061050414,0.000008231369,0.00027116897,0.5985939],"genre_scores_gemma":[0.000039672344,0.00020007517,0.46046117,0.000008187427,0.000026860162,0.0000023075181,0.00000983297,0.00006450428,0.5391874],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994129,0.0000030009128,0.00030213522,0.00009784287,0.00009842632,0.00008568965],"domain_scores_gemma":[0.9996135,0.00011255782,0.000026049544,0.00016556816,0.000038513877,0.000043757645],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013277463,0.00016805611,0.00033414533,0.00009153044,0.0000065712393,0.000023855424,0.00007914769,0.00015992929,0.0017919961],"category_scores_gemma":[0.00002160677,0.00014040063,0.00011087926,0.000021918457,0.00003398419,0.00001575372,0.00003587527,0.00016907734,0.00020361053],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.875124e-7,0.0000022076683,6.6277357e-9,0.0009953461,0.0001479044,0.0000024676838,0.000020987094,0.0009722182,0.000023079918,0.9838238,0.0023691594,0.011642545],"study_design_scores_gemma":[0.00003567239,0.000011080133,1.3044162e-8,0.0001866932,0.00026711845,0.0000038074945,0.0000075748767,0.09697004,0.0006681664,0.27558997,0.6260303,0.00022955688],"about_ca_topic_score_codex":4.2071161e-7,"about_ca_topic_score_gemma":4.8099434e-7,"teacher_disagreement_score":0.70823383,"about_ca_system_score_codex":0.000015675347,"about_ca_system_score_gemma":0.0000058591104,"threshold_uncertainty_score":0.9991205},"labels":[],"label_agreement":null},{"id":"W4392191459","doi":"10.18280/mmep.110228","title":"Transportation of Materials Under Fuzzy Environment Using Expected Monetary Value Strategy","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fuzzy transportation; Mathematical optimization; Travelling salesman problem; Fuzzy number; Fuzzy logic; Transportation theory; Computer science; Ranking (information retrieval); Fuzzy set operations; Fuzzy set; Set (abstract data type); Representation (politics); Value (mathematics); Mathematics; Artificial intelligence; Machine learning","score_opus":0.02677653344588662,"score_gpt":0.20830485493736997,"score_spread":0.18152832149148335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392191459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12483414,0.0009439098,0.87347543,0.00000804617,0.00006642109,0.0001712027,0.000008504227,0.00037424132,0.000118074],"genre_scores_gemma":[0.8566787,0.00015501906,0.14303903,0.0000015117081,0.000022936316,0.000018241964,0.000013220594,0.000056209374,0.000015096529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990245,0.000007464099,0.00043964735,0.00016703979,0.00014885033,0.00021249743],"domain_scores_gemma":[0.9997162,0.00006446795,0.000018277542,0.000111207104,0.000009287381,0.00008054598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014396997,0.00019042454,0.00026497588,0.00009671958,0.00002236582,0.00006686505,0.000050126073,0.000096928095,0.00004377484],"category_scores_gemma":[0.000003147909,0.0001754348,0.000045619836,0.000102456535,0.000026038011,0.000120562036,0.0000049904797,0.00011043624,0.000007804094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001022607,0.000015884923,1.511418e-7,0.0026182332,0.000049803428,0.0000017879586,0.00039507783,0.9396906,0.022882406,0.03417872,0.0000014249148,0.00016490446],"study_design_scores_gemma":[0.000078523175,0.000017614211,9.247167e-7,0.00048694684,0.000057388763,0.000005009203,0.000040297495,0.9658403,0.005082847,0.028192725,0.000018066285,0.00017937641],"about_ca_topic_score_codex":0.000003891355,"about_ca_topic_score_gemma":4.251967e-8,"teacher_disagreement_score":0.7318446,"about_ca_system_score_codex":0.000025099213,"about_ca_system_score_gemma":0.0000050094573,"threshold_uncertainty_score":0.71540236},"labels":[],"label_agreement":null},{"id":"W4395452180","doi":"10.1007/s10479-024-05903-y","title":"Multi-item order quantity optimization through stochastic goal programing","year":2024,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Qatar University","keywords":"Order (exchange); Computer science; Stochastic optimization; Stochastic programming; Mathematical optimization; Mathematics; Economics","score_opus":0.23686216625472759,"score_gpt":0.4645346067948513,"score_spread":0.22767244054012373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395452180","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049875923,0.001042587,0.99156415,0.0004514192,0.00012395016,0.0005103125,0.000010099998,0.0003272954,0.0009825608],"genre_scores_gemma":[0.7306111,0.00023180539,0.2684081,0.000017507755,0.000057095254,0.000118438584,0.000048610847,0.000044637785,0.00046274223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987817,0.000066275104,0.00030077226,0.00017938342,0.00035659038,0.0003152923],"domain_scores_gemma":[0.9989485,0.0001718562,0.000005478963,0.00018999161,0.0006146053,0.0000695249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062307,0.00010333467,0.00013592464,0.00021862841,0.00017337895,0.00031609056,0.00013837687,0.00007706256,0.0002131233],"category_scores_gemma":[0.000590971,0.00009539467,0.000048448008,0.0011261195,0.000108051165,0.0005091166,0.00004757937,0.00027309108,0.00007941331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018623366,0.000080706115,0.000002785467,0.00023428578,0.000036351947,0.0000021266023,0.0006691761,0.9840409,0.00041111713,0.01031902,0.00049113255,0.00371051],"study_design_scores_gemma":[0.00008069588,0.00004352689,0.000008132578,0.0001225798,0.000004955914,0.00000233899,0.0002225229,0.99794185,0.0006332514,0.00009099211,0.00075145764,0.00009770272],"about_ca_topic_score_codex":0.00004359426,"about_ca_topic_score_gemma":0.000031428757,"teacher_disagreement_score":0.7256235,"about_ca_system_score_codex":0.000020927962,"about_ca_system_score_gemma":0.000067757646,"threshold_uncertainty_score":0.3890082},"labels":[],"label_agreement":null},{"id":"W4396620742","doi":"10.2139/ssrn.4815864","title":"Multi-Objective Frequentistic Model Averaging with an Application to Economic Growth","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Economic model; Economics; Econometrics; Growth model; Computer science; Mathematical economics; Macroeconomics","score_opus":0.007867524894305682,"score_gpt":0.24069813255578137,"score_spread":0.2328306076614757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396620742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0108401505,0.00050210836,0.9869024,0.000086849126,0.00019938241,0.0004329591,0.000010197228,0.00036118296,0.00066475273],"genre_scores_gemma":[0.9231832,0.0004556342,0.07565812,0.00002667506,0.0001951675,0.00010785589,0.000024954235,0.00013455514,0.0002138265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980783,0.000020688814,0.00031415385,0.00034049668,0.00013415134,0.0011122118],"domain_scores_gemma":[0.9994578,0.0000151799895,0.000068292764,0.00022836188,0.00006367736,0.00016669385],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00046800703,0.00029130306,0.00026790533,0.00022408277,0.00008330237,0.000245218,0.00027665228,0.00013650511,0.000007783129],"category_scores_gemma":[0.000012262069,0.00027202175,0.000080959515,0.0000843286,0.000017460088,0.00010833012,0.00010948498,0.002635243,0.000101596095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008609836,0.000023665592,0.0000041456974,0.00013013098,0.0001986612,0.0000018496487,0.0003245117,0.9253329,0.0000682762,0.06796399,0.000011870829,0.005931404],"study_design_scores_gemma":[0.00014030679,0.000047659498,0.0000016577751,0.00007653312,0.00007250623,0.00006807988,0.00012965317,0.7214245,0.000038909133,0.2777465,0.000011648777,0.00024203128],"about_ca_topic_score_codex":0.00003119328,"about_ca_topic_score_gemma":0.00040888862,"teacher_disagreement_score":0.9123431,"about_ca_system_score_codex":0.0023392513,"about_ca_system_score_gemma":0.0008319062,"threshold_uncertainty_score":0.9999732},"labels":[],"label_agreement":null},{"id":"W4396721056","doi":"10.1016/j.cie.2024.110196","title":"Iterative matheuristic for the biomedical sample transportation problem","year":2024,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval; HEC Montréal; Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Sample (material); Computer science; Transport engineering; Operations research; Mathematical optimization; Engineering; Mathematics; Chemistry; Chromatography","score_opus":0.025151116018447047,"score_gpt":0.2328359100456205,"score_spread":0.20768479402717344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396721056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003951657,0.00023110672,0.9961178,0.00020357326,0.001721315,0.0004556748,0.000043440585,0.00080777536,0.00002418248],"genre_scores_gemma":[0.77204823,0.000037023972,0.22475907,0.00005105978,0.002148373,0.00041008546,0.00031245413,0.00016943595,0.00006424142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993181,0.000004582639,0.00023889237,0.00012858966,0.00010679352,0.00020301364],"domain_scores_gemma":[0.9990225,0.00079735514,0.000010020284,0.00008526767,0.000017200347,0.000067680645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014116248,0.00013767999,0.00012950826,0.00008113891,0.00004892972,0.00015923832,0.00011254645,0.00009061072,0.000019492965],"category_scores_gemma":[0.000057505626,0.00010568399,0.00007752397,0.0002503961,0.000019800838,0.00009587419,0.000006165463,0.00020488698,0.0000065420804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046465716,0.000014062614,8.676779e-7,0.00035118626,0.00014136996,0.0000049121013,0.00091260683,0.87876374,0.0001965423,0.021255715,0.004915825,0.09343851],"study_design_scores_gemma":[0.00021303701,0.000025932566,0.0000019083095,0.00014862552,0.00003474718,0.0000020726743,0.00001812553,0.89883393,0.00009769958,0.0002643966,0.10024365,0.00011584638],"about_ca_topic_score_codex":0.0000020717498,"about_ca_topic_score_gemma":3.344128e-7,"teacher_disagreement_score":0.7716531,"about_ca_system_score_codex":0.000043334963,"about_ca_system_score_gemma":0.000015763246,"threshold_uncertainty_score":0.43096682},"labels":[],"label_agreement":null},{"id":"W4399398532","doi":"10.46254/an14.20240232","title":"Goal Programming Model for Sustainability and Circular Economy Evaluation","year":2024,"lang":"en","type":"article","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Sustainability; Circular economy; Computer science","score_opus":0.020450278632781538,"score_gpt":0.28493437295055807,"score_spread":0.26448409431777653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399398532","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028333426,0.0003741033,0.9916384,0.00014876325,0.000041342802,0.0008382111,7.132268e-7,0.0004677212,0.0036573787],"genre_scores_gemma":[0.9358451,0.000003470064,0.06366513,0.000012758065,0.000019776682,0.00028735452,0.000007781512,0.000017178822,0.00014147966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959135,0.0000048218944,0.00011667315,0.00011681266,0.00004654869,0.00012378006],"domain_scores_gemma":[0.9997766,0.00003988499,0.000003766565,0.00006615373,0.000071318296,0.000042267173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035179264,0.00006396678,0.00006614817,0.00003613193,0.000029055771,0.00013156082,0.000022642498,0.00003553475,0.000022268996],"category_scores_gemma":[0.000061595674,0.000058911817,0.000030858835,0.000053502223,0.00001526794,0.00014600338,0.000008731754,0.000036977824,0.000002060337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015084861,0.00001787713,0.000011281983,0.0030554652,0.000038254606,3.8368438e-7,0.00059221755,0.47868153,0.000019130868,0.084307276,0.0001823073,0.43309277],"study_design_scores_gemma":[0.00008875784,0.0000066411126,7.650604e-7,0.000007874047,0.000024574596,9.946855e-7,0.00011131739,0.91956574,0.000032644453,0.077009305,0.0030812968,0.000070092654],"about_ca_topic_score_codex":6.287246e-7,"about_ca_topic_score_gemma":6.389269e-7,"teacher_disagreement_score":0.9330117,"about_ca_system_score_codex":0.00008535939,"about_ca_system_score_gemma":0.000028882372,"threshold_uncertainty_score":0.24023542},"labels":[],"label_agreement":null},{"id":"W4400821237","doi":"10.28924/2291-8639-22-2024-117","title":"Prediction of Stochastic Transportation Problem with Fixed Charge in Multi-Objective Rough Interval Environment","year":2024,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fixed charge; Mathematical optimization; Interval (graph theory); Probabilistic logic; Mathematics; Transportation theory; Constraint (computer-aided design); Weibull distribution; Stochastic programming; Operator (biology); Fuzzy logic; Linear programming; Computer science; Statistics; Artificial intelligence","score_opus":0.011288998990371786,"score_gpt":0.23730941928522958,"score_spread":0.2260204202948578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400821237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018166915,0.00019383554,0.9813212,0.00006906557,0.00002186806,0.00010795418,0.000036835703,0.000014894662,0.00006742749],"genre_scores_gemma":[0.987337,0.00010578306,0.012440057,0.0000034216139,0.00002627549,0.0000288255,0.000029946174,0.000006944679,0.000021740967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936837,0.000006782579,0.00034715407,0.00007132209,0.00016029703,0.000046101035],"domain_scores_gemma":[0.99975705,0.00003303617,0.000071696195,0.000036995236,0.00007244628,0.000028768418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009211969,0.000059246784,0.0001353609,0.0003163359,0.000009594397,0.000027167907,0.000062123756,0.00002314675,0.000037560465],"category_scores_gemma":[0.0000028867837,0.00004776407,0.00006767888,0.00025301662,0.000023324,0.00013729274,0.0000032709095,0.000084600964,0.0000012954466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034327593,0.00033324747,0.00259547,0.00012355667,0.0027481918,0.000006782598,0.002251566,0.9474618,0.0020134475,0.0061594006,0.000023202256,0.036249008],"study_design_scores_gemma":[0.0005746023,0.0000790662,0.008150381,0.0001982356,0.00069692894,0.000012811772,0.00034581506,0.98797137,0.00059055886,0.00063847756,0.000633677,0.00010805923],"about_ca_topic_score_codex":0.0000055511514,"about_ca_topic_score_gemma":0.000015950538,"teacher_disagreement_score":0.9691701,"about_ca_system_score_codex":0.000044485656,"about_ca_system_score_gemma":0.000008942806,"threshold_uncertainty_score":0.19477622},"labels":[],"label_agreement":null},{"id":"W4402842213","doi":"10.5267/j.dsl.2024.7.004","title":"Financial optimization modeling on asset liability management with weighted goal programming","year":2024,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi","keywords":"Asset management; Liability; Business; Finance; Goal programming; Actuarial science; Asset (computer security); Financial management; Computer science; Operations research; Mathematics","score_opus":0.01019774755117064,"score_gpt":0.24986888764094092,"score_spread":0.23967114008977028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402842213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059064012,0.000014684558,0.9384793,0.00033725437,0.00034930508,0.00030648676,0.0000010796265,0.0005160058,0.0009318743],"genre_scores_gemma":[0.5399132,0.0000069287094,0.45971122,0.0002652303,0.000034566972,0.000034153196,0.0000037613036,0.000020061383,0.000010906724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838,0.000009342456,0.00024066358,0.00038341724,0.00066456187,0.00032203144],"domain_scores_gemma":[0.99953645,0.000073112395,0.00001365257,0.00024697065,0.000041707466,0.00008812012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005988965,0.00014670078,0.000115203904,0.00031066962,0.00015705777,0.00051397213,0.00023756716,0.00003556037,0.000031161322],"category_scores_gemma":[0.000050697137,0.00011102101,0.000038647286,0.0013175125,0.000106587686,0.00047519474,0.000044200886,0.00014880649,0.000044921595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070229244,0.000015843021,0.0000038254225,0.000042436484,0.0000037536531,0.000015105935,0.000081034486,0.8850407,0.000076314565,0.0019607826,0.00018594491,0.11256725],"study_design_scores_gemma":[0.00011944729,0.000027067337,0.0000072927664,0.00018427802,0.00000920125,0.0000034658237,0.00002714117,0.9974424,0.000102871614,0.00037547323,0.0015489087,0.00015244859],"about_ca_topic_score_codex":5.69909e-7,"about_ca_topic_score_gemma":4.3391688e-7,"teacher_disagreement_score":0.48084915,"about_ca_system_score_codex":0.00013356541,"about_ca_system_score_gemma":0.000017739841,"threshold_uncertainty_score":0.4956245},"labels":[],"label_agreement":null},{"id":"W4404131643","doi":"10.1137/1.9781611978162.appc","title":"Appendix C: Mathematical Programming","year":2024,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Appendix; Computer science; Biology; Paleontology","score_opus":0.0414711881697485,"score_gpt":0.2370834395002066,"score_spread":0.19561225133045812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404131643","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013537733,0.00029891043,0.048153855,0.000046433182,0.0003386343,0.0029109523,0.000109671106,0.0013066314,0.9468214],"genre_scores_gemma":[0.00040620333,0.00009786999,0.37046146,0.00008282245,0.0016192017,0.00097176986,0.00027127573,0.00088212633,0.62520725],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980392,0.0000010084666,0.0007725403,0.00040267073,0.00031622156,0.0004683867],"domain_scores_gemma":[0.99908555,0.00023990747,0.00012462499,0.00029800218,0.00003889501,0.00021304998],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034763408,0.000640135,0.00082384184,0.00006971151,0.00015679415,0.00029501648,0.00018933056,0.0010293525,0.00015296614],"category_scores_gemma":[0.000015729944,0.0005710135,0.0006371094,0.000027839993,0.00021552203,0.00002837582,0.00012473011,0.0007813522,0.00023427319],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035611376,0.000017516099,2.8508274e-9,0.0036948575,0.00044834017,0.0000010838594,0.000845274,0.000030275782,0.000034968347,0.9498877,0.007075956,0.03796045],"study_design_scores_gemma":[0.0005295516,0.000037829617,3.846879e-10,0.00070425903,0.00057555543,0.000013475595,0.00030853887,0.010120579,0.0001609045,0.6345722,0.35234818,0.00062892],"about_ca_topic_score_codex":9.2185466e-8,"about_ca_topic_score_gemma":2.7230172e-7,"teacher_disagreement_score":0.3452722,"about_ca_system_score_codex":0.000076340286,"about_ca_system_score_gemma":0.00003660328,"threshold_uncertainty_score":0.99967414},"labels":[],"label_agreement":null},{"id":"W4406195342","doi":"10.1016/j.trpro.2024.12.046","title":"A multi-objective optimization model of a closed-loop supply chain for supplier selection and order allocation under uncertainty: A case study of retail stores for protein products","year":2025,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fundação para a Ciência e a Tecnologia","keywords":"Supply chain; Selection (genetic algorithm); Order (exchange); Supply chain management; Closed loop; Loop (graph theory); Computer science; Business; Operations research; Reliability engineering; Engineering; Marketing; Control engineering; Mathematics; Artificial intelligence","score_opus":0.05669124496196061,"score_gpt":0.3399467096532582,"score_spread":0.2832554646912976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406195342","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33615643,0.00002920496,0.6580834,0.000056607907,0.00001066943,0.005547402,0.00003275326,0.00007625465,0.0000073101764],"genre_scores_gemma":[0.8087663,0.000010094727,0.18823533,0.0000032820951,0.000008240451,0.0027046155,0.00008466298,0.000032720734,0.00015472641],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988017,0.000036844034,0.0004258565,0.00027897718,0.00023469298,0.00022191604],"domain_scores_gemma":[0.99789524,0.00010833027,0.000070996124,0.00010918157,0.0017700476,0.000046188597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006182967,0.00013355848,0.00022774018,0.00034810882,0.00011567723,0.000021760694,0.0000618,0.000096583,0.0000031553386],"category_scores_gemma":[0.00028777754,0.00013292451,0.000028192178,0.00079120224,0.00005995674,0.0001703764,0.0000039597303,0.0001262816,6.19334e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018002871,0.00047989184,0.00008875073,0.002923581,0.00008498076,3.7816326e-7,0.006094722,0.9809571,0.005878784,0.0023445783,0.000025549527,0.0009416565],"study_design_scores_gemma":[0.0021694656,0.0003082854,0.00007710116,0.000077071425,0.00005853767,6.9532564e-7,0.007184411,0.98592573,0.0037037637,0.00038305562,0.000004126013,0.000107767286],"about_ca_topic_score_codex":0.000119952594,"about_ca_topic_score_gemma":0.00083265366,"teacher_disagreement_score":0.4726099,"about_ca_system_score_codex":0.00006058485,"about_ca_system_score_gemma":0.00017996023,"threshold_uncertainty_score":0.5420504},"labels":[],"label_agreement":null},{"id":"W4408768689","doi":"10.23952/asvao.7.2025.2.04","title":"($\\epsilon$-)efficiency in multicriteria fractional optimization","year":2025,"lang":"en","type":"article","venue":"Applied Set-Valued Analysis and Optimization","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Mathematical optimization; Applied mathematics","score_opus":0.007701793028205242,"score_gpt":0.2458815444683947,"score_spread":0.23817975144018944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408768689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014330081,0.00011612894,0.9920621,0.000060627914,0.00008049224,0.00026391435,0.000004920178,0.00020728452,0.0057715117],"genre_scores_gemma":[0.651629,0.00020015704,0.3475829,0.000121975856,0.00001507796,0.000060517134,0.00027369295,0.000022425242,0.00009426283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988288,0.000028041684,0.0004633458,0.00029392558,0.00016260025,0.00022326314],"domain_scores_gemma":[0.999569,0.00006543326,0.000056900848,0.00018014535,0.000065093445,0.000063447755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002363896,0.00019198951,0.0003234757,0.0007586586,0.0001167096,0.00013318985,0.000088565874,0.0001350157,0.00023599247],"category_scores_gemma":[0.00004716895,0.00019892369,0.00006981331,0.0022380236,0.00004240144,0.00016524189,0.000030917698,0.00012789696,0.000004145161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008614699,0.000053447886,0.00018753574,0.00004608497,0.00013418935,4.4775936e-7,0.00014033548,0.9934366,0.00005224175,0.004799442,0.000041053296,0.0011000282],"study_design_scores_gemma":[0.00048777997,0.0000053894387,0.00017948016,0.000016176757,0.0002647282,3.151446e-7,0.00015720996,0.99835175,0.000085685206,0.00019234765,0.000071930335,0.00018722085],"about_ca_topic_score_codex":0.000011677917,"about_ca_topic_score_gemma":0.000011627991,"teacher_disagreement_score":0.65019596,"about_ca_system_score_codex":0.00006917303,"about_ca_system_score_gemma":0.0000159371,"threshold_uncertainty_score":0.81118727},"labels":[],"label_agreement":null},{"id":"W4408768741","doi":"10.23952/asvao.7.2025.2.07","title":"Sequential approximate weak optimality conditions for robust multiobjective fractional optimization problems","year":2025,"lang":"en","type":"article","venue":"Applied Set-Valued Analysis and Optimization","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Mathematics; Applied mathematics; Multi-objective optimization; Computer science","score_opus":0.01676966989721251,"score_gpt":0.2641749552608463,"score_spread":0.24740528536363376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408768741","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013569318,0.000058343303,0.99319226,0.0000738662,0.00010751653,0.0010443358,0.000088021814,0.0004135613,0.0048863855],"genre_scores_gemma":[0.18754813,0.00014787307,0.8079495,0.00009947988,0.00006144411,0.00073869555,0.0031523395,0.000052104908,0.00025045843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984871,0.00003792406,0.00055501325,0.0004324853,0.00019434011,0.00029309356],"domain_scores_gemma":[0.99921286,0.00011566827,0.00013294698,0.00022134719,0.00021944675,0.000097728276],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032354105,0.00027710552,0.00043318336,0.0005195721,0.00040806344,0.00022656783,0.00010061676,0.00019723315,0.00018709249],"category_scores_gemma":[0.000058028294,0.00028991268,0.00017146616,0.0013886293,0.00008624978,0.0002776432,0.00003673031,0.00014305004,0.0000022215936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000202995,0.00007399312,0.000032942575,0.00015624716,0.00082201016,8.976423e-8,0.000116644784,0.9810001,0.00007926095,0.017132303,0.00015000222,0.000416107],"study_design_scores_gemma":[0.0008720951,0.000013361206,0.000033982924,0.000020369976,0.0012175858,6.0105316e-7,0.00024789837,0.99615383,0.0001667706,0.00089255616,0.00009947891,0.0002814886],"about_ca_topic_score_codex":0.0000109164375,"about_ca_topic_score_gemma":0.000009665367,"teacher_disagreement_score":0.18741243,"about_ca_system_score_codex":0.000113283655,"about_ca_system_score_gemma":0.000029157005,"threshold_uncertainty_score":0.9999553},"labels":[],"label_agreement":null},{"id":"W4409361355","doi":"10.1609/aaai.v39i27.35016","title":"Constraint Optimisation Approaches for Designing Group-Living Captive Breeding Programmes","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MacEwan University","funders":"","keywords":"Group (periodic table); Constraint (computer-aided design); Computer science; Engineering; Chemistry; Mechanical engineering","score_opus":0.10264776827526068,"score_gpt":0.28391618218217407,"score_spread":0.1812684139069134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409361355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015208258,0.00004620503,0.9538768,0.00056717196,0.00034876482,0.0015479131,0.0000048609377,0.00034132914,0.028058719],"genre_scores_gemma":[0.9480983,0.000012265583,0.05153054,0.00003230709,0.000033758173,0.00017699425,0.0000011603136,0.000019739919,0.00009493709],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877906,0.0000074906466,0.00047669644,0.00025294875,0.00017892414,0.0003049001],"domain_scores_gemma":[0.9991975,0.00024601808,0.00013252125,0.000096336094,0.0002765187,0.000051150364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000504815,0.00020366412,0.00024185295,0.00013188948,0.00015506617,0.00019847043,0.00037333302,0.00010051781,0.000022726423],"category_scores_gemma":[0.00064054696,0.00016837259,0.000113499314,0.00038825328,0.0002506851,0.00019155846,0.000069237365,0.00018805795,0.0000041849444],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020118741,0.00007626875,0.000035866196,0.0003517965,0.000043389646,3.4051382e-8,0.0012806795,0.0060450677,0.010218472,0.8171943,0.000051321953,0.16468273],"study_design_scores_gemma":[0.000036097183,0.00009617996,0.000011359794,0.00085472595,0.00004779221,0.0000010475405,0.0059298924,0.7518162,0.15756051,0.083363935,0.000062224855,0.0002200271],"about_ca_topic_score_codex":0.0000042769975,"about_ca_topic_score_gemma":0.0000029833188,"teacher_disagreement_score":0.93289006,"about_ca_system_score_codex":0.00007146335,"about_ca_system_score_gemma":0.000031086598,"threshold_uncertainty_score":0.6866035},"labels":[],"label_agreement":null},{"id":"W4409587611","doi":"10.1007/978-3-658-47462-1_13","title":"Quarter Management as a Method for Promoting Lively Quarter Development","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); History; Archaeology","score_opus":0.01307713450703968,"score_gpt":0.25565281295074566,"score_spread":0.24257567844370598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409587611","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.4999336e-8,0.000031747997,0.4995114,0.00006410996,0.0000962388,0.00066610187,0.0000014132513,0.0002939523,0.49933496],"genre_scores_gemma":[0.000021308713,0.000009302204,0.57265204,0.000118071,0.000028447987,0.00016760803,0.00002465095,0.000045998346,0.42693254],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988418,0.0000048019006,0.0004584247,0.00027806085,0.00015863068,0.0002582631],"domain_scores_gemma":[0.9995425,0.00008684831,0.00004903819,0.000189249,0.000056850822,0.000075560296],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021298698,0.00032119523,0.00032883542,0.00017258561,0.00006269338,0.000079836864,0.00013513428,0.00018749591,0.0006616466],"category_scores_gemma":[0.000008486404,0.00029829226,0.00012871104,0.000026129617,0.000008511472,0.000045541005,0.000047656853,0.00014249458,0.00021556526],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054453026,0.00002524646,5.4874963e-8,0.003575668,0.00057412894,0.0000066629345,0.0008888335,0.00021025277,0.0000037505913,0.60594493,0.024009783,0.3647552],"study_design_scores_gemma":[0.00039181262,0.000043876138,2.832869e-7,0.00075506256,0.00014812428,0.000003188268,0.00012710496,0.06783534,0.00016068753,0.020594988,0.9093478,0.00059177825],"about_ca_topic_score_codex":3.7188377e-7,"about_ca_topic_score_gemma":0.000002218459,"teacher_disagreement_score":0.88533795,"about_ca_system_score_codex":0.000086844724,"about_ca_system_score_gemma":0.00002015919,"threshold_uncertainty_score":0.9999469},"labels":[],"label_agreement":null},{"id":"W4409889871","doi":"10.1080/03155986.2025.2492739","title":"Investigation of a multi-objective fixed charge transportation problem with quantity dependent transportation cost and discount policy <i>via</i> metaheuristics","year":2025,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Metaheuristic; Transportation theory; Fixed charge; Mathematical optimization; Fixed cost; Computer science; Economics; Microeconomics; Mathematics; Chemistry","score_opus":0.03550530152760662,"score_gpt":0.30948186449868015,"score_spread":0.27397656297107353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409889871","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14977548,0.00027702254,0.8430759,0.0003114104,0.0001121998,0.0036429602,0.000493458,0.00013121446,0.0021804161],"genre_scores_gemma":[0.99356246,0.00010959345,0.0053871595,0.000035095054,0.000014418115,0.00027300793,0.0005193069,0.000007872073,0.000091064874],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986467,0.000037494596,0.0005925984,0.000093242044,0.00046281258,0.00016715347],"domain_scores_gemma":[0.9989291,0.00011401987,0.00007740918,0.000082166305,0.00071559695,0.00008170795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006811042,0.00011886453,0.00018340738,0.00039838636,0.0001772509,0.00020368955,0.00005464455,0.000083080515,0.0000052843693],"category_scores_gemma":[0.00006901299,0.00009858617,0.000017512315,0.00047307066,0.00011219493,0.0012888623,0.0000049576365,0.00015552995,0.0000036140218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021717146,0.000072296265,0.012922988,0.007514104,0.00031276516,0.000001411276,0.020469515,0.17351478,0.0019345165,0.7687215,0.00040854904,0.013910395],"study_design_scores_gemma":[0.0032971045,0.00019527733,0.028304422,0.00070023874,0.000067008485,0.000007298794,0.0042927708,0.9516576,0.004451842,0.00057415874,0.006000895,0.0004513753],"about_ca_topic_score_codex":0.00048547686,"about_ca_topic_score_gemma":0.00026679624,"teacher_disagreement_score":0.843787,"about_ca_system_score_codex":0.00007481065,"about_ca_system_score_gemma":0.00016512357,"threshold_uncertainty_score":0.40202272},"labels":[],"label_agreement":null},{"id":"W4410395635","doi":"10.1287/ijoc.2024.0847","title":"Solving Two-Stage Programs with Endogenous Uncertainty via Random Variable Transformation","year":2025,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Transformation (genetics); Stage (stratigraphy); Variable (mathematics); Mathematical optimization; Computer science; Mathematics; Random variable; Statistics","score_opus":0.013117761027357658,"score_gpt":0.22978674724366924,"score_spread":0.2166689862163116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410395635","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010518415,0.000041166473,0.9269679,0.000034646204,0.00027698753,0.0003212789,4.9760456e-7,0.00032893007,0.06151017],"genre_scores_gemma":[0.9534547,0.000011656051,0.046161212,0.00016220455,0.00007377871,0.000006014647,0.000008049585,0.000021895296,0.00010051929],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878395,0.000015082559,0.0005221932,0.0000809333,0.00022762631,0.0003701948],"domain_scores_gemma":[0.99947226,0.00011401612,0.00009836502,0.00010630298,0.00010766272,0.00010139127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053889136,0.00018099809,0.00024036241,0.00017174655,0.00031615084,0.00034503263,0.00014544393,0.000055277294,0.0000366153],"category_scores_gemma":[0.000032911797,0.00013041386,0.00006955555,0.00037851243,0.000023641996,0.0003693291,0.000015248652,0.0004243922,0.000010730838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042066975,0.000027163265,0.000018646677,0.00014338203,0.00006844292,0.0000068174304,0.00069303904,0.82638603,0.0000886415,0.004941614,0.000011981422,0.16757214],"study_design_scores_gemma":[0.0025937331,0.000100366095,0.0000025221807,0.0004701193,0.000024728191,0.00012152508,0.0002841651,0.9900495,0.00035618196,0.0005175332,0.0052976594,0.00018198128],"about_ca_topic_score_codex":0.000005494837,"about_ca_topic_score_gemma":0.0000023050395,"teacher_disagreement_score":0.94293624,"about_ca_system_score_codex":0.00013127041,"about_ca_system_score_gemma":0.000048140308,"threshold_uncertainty_score":0.53181225},"labels":[],"label_agreement":null},{"id":"W4411325846","doi":"10.18280/mmep.120533","title":"Enhancing Strategic Management Through Linear Programming: A Comparative Study Involving Doolittle’s and Simplex Methods","year":2025,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Simplex algorithm; Linear programming; Simplex; Computer science; Mathematical optimization; Mathematics; Combinatorics","score_opus":0.06705940661398704,"score_gpt":0.32169063291800426,"score_spread":0.25463122630401724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411325846","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031074474,0.00094759115,0.96305114,0.000021514463,0.00007063724,0.0008859283,7.055506e-7,0.0006752743,0.0032727367],"genre_scores_gemma":[0.46521285,0.000083758234,0.53443474,0.000007712377,0.000013357099,0.00014217557,0.0000019023144,0.000030493757,0.000073024494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984817,0.000035457226,0.0005656521,0.0003391718,0.00014978525,0.00042821615],"domain_scores_gemma":[0.9993376,0.00025035348,0.00003371368,0.00022106663,0.000038976545,0.00011825307],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005503453,0.00033699244,0.00050514884,0.00014164335,0.00012397957,0.00020005184,0.00011268869,0.00009602482,0.000007855279],"category_scores_gemma":[0.00001640075,0.0003077194,0.00004781826,0.00033015304,0.000047675476,0.00014520544,0.00010339071,0.00028254153,0.000004093711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032902446,0.00015522366,0.0000024523542,0.004144333,0.0002165495,0.0000035413213,0.004511308,0.7990503,0.00019398716,0.19031455,0.0000029813932,0.0014015097],"study_design_scores_gemma":[0.00035263775,0.0000461658,9.880047e-7,0.00052027503,0.000099077595,0.0000041983803,0.0025745656,0.932974,0.00021554361,0.06262181,0.00030885995,0.00028191484],"about_ca_topic_score_codex":0.0000038606827,"about_ca_topic_score_gemma":0.0000011073959,"teacher_disagreement_score":0.43413836,"about_ca_system_score_codex":0.000036597445,"about_ca_system_score_gemma":0.0000059766694,"threshold_uncertainty_score":0.9999375},"labels":[],"label_agreement":null},{"id":"W4412319399","doi":"","title":"Iterated local search and record-to-record travel applied to the fixed charge transportation problem","year":2011,"lang":"en","type":"article","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Fixed charge; Iterated function; Charge (physics); Iterated local search; Computer science; Geography; Mathematics; Local search (optimization); Physics; Algorithm; Chemical physics; Quantum mechanics","score_opus":0.024962579303539616,"score_gpt":0.215158200266429,"score_spread":0.1901956209628894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412319399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030365732,0.0000039839724,0.9529194,0.0001556117,0.000048696395,0.0006843943,0.000003891205,0.00022202102,0.015596273],"genre_scores_gemma":[0.90487015,0.000005027491,0.09424445,0.00020213536,0.00001832172,0.00009735909,0.000011694352,0.000026115318,0.0005247485],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994108,0.0000074582194,0.00018446743,0.0001269242,0.000084263454,0.00018609418],"domain_scores_gemma":[0.9997241,0.000016555148,0.00000625889,0.00009516053,0.000029954994,0.00012795127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012307856,0.00009799943,0.00010861252,0.000053263564,0.00004448904,0.00003225639,0.000070671376,0.000042061343,0.00053930905],"category_scores_gemma":[0.0000031934264,0.0000698026,0.000016885213,0.00021301224,0.000012631148,0.000046873076,0.0000052930745,0.00008108664,0.00016883992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001631535,0.00022376822,0.00029685022,0.0006346752,0.00013266296,0.000005752482,0.04306494,0.025469711,0.00825739,0.053113963,0.003807645,0.8648295],"study_design_scores_gemma":[0.0017972486,0.0004223946,0.005363273,0.0001388238,0.00008581939,0.0000048999736,0.0037867469,0.929549,0.03693113,0.001083306,0.01956857,0.001268793],"about_ca_topic_score_codex":0.000038937942,"about_ca_topic_score_gemma":0.0001357851,"teacher_disagreement_score":0.90407926,"about_ca_system_score_codex":0.000011442704,"about_ca_system_score_gemma":0.0000044356802,"threshold_uncertainty_score":0.5905056},"labels":[],"label_agreement":null},{"id":"W4412767618","doi":"10.23952/jano.7.2025.2.05","title":"Sequential approximate solutions for robust fractional optimization problems","year":2025,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Applied mathematics; Mathematics; Computer science","score_opus":0.018128478474513612,"score_gpt":0.23547929365608897,"score_spread":0.21735081518157534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412767618","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017158598,0.00012739298,0.9955806,0.00023774776,0.00025614948,0.0003105375,0.0000037239445,0.00008129965,0.0033853855],"genre_scores_gemma":[0.0628746,0.0002503341,0.936473,0.000109027904,0.0001247855,0.000046533074,0.00003893803,0.000029639164,0.000053132862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990827,0.000009906782,0.00047555784,0.00010506509,0.0001338218,0.00019296318],"domain_scores_gemma":[0.9994639,0.00008240694,0.000139872,0.000060994123,0.00017491936,0.0000778979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002355306,0.00012566209,0.0002287676,0.00017066809,0.00015667944,0.00008868959,0.000068306486,0.00009978304,0.00004912717],"category_scores_gemma":[0.000048123176,0.000115357034,0.00006949838,0.00025839722,0.000035491747,0.00021463008,0.000020358384,0.00013606713,5.935415e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039916846,0.000060830007,0.0000014181682,0.000120067554,0.00005728838,1.7178978e-7,0.000045871548,0.985398,0.00008033571,0.010979707,0.0004996502,0.0027167595],"study_design_scores_gemma":[0.00071533525,0.000037209273,0.0000016694737,0.000039560357,0.00007507449,0.0000065099994,0.000049452017,0.99581456,0.00007154107,0.0018961714,0.0011844862,0.000108419576],"about_ca_topic_score_codex":2.6976073e-7,"about_ca_topic_score_gemma":5.1476988e-8,"teacher_disagreement_score":0.06285744,"about_ca_system_score_codex":0.000057896395,"about_ca_system_score_gemma":0.0000329124,"threshold_uncertainty_score":0.47041234},"labels":[],"label_agreement":null},{"id":"W4413391696","doi":"10.1134/s199508022560623x","title":"A Comprehensive Study of Mellin’s Transformation on Diverse Fuzzy Numbers with Applications of Optimizing Production Planning","year":2025,"lang":"en","type":"article","venue":"Lobachevskii Journal of Mathematics","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Mathematics; Transformation (genetics); Fuzzy logic; Production (economics); Algebra over a field; Arithmetic; Production planning; Pure mathematics; Artificial intelligence; Computer science","score_opus":0.021695922100418267,"score_gpt":0.2717170929021405,"score_spread":0.25002117080172226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413391696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33544487,0.00012717512,0.6584521,0.000039330072,0.00010085149,0.0008001802,0.000002027873,0.000048903425,0.0049845716],"genre_scores_gemma":[0.90150744,0.000037775444,0.0983871,0.0000052921578,0.000016448657,0.000011932419,0.0000013570008,0.000014500734,0.000018142364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9987582,0.000022446198,0.00075296423,0.00006729052,0.00029442107,0.00010469482],"domain_scores_gemma":[0.9990111,0.0001149233,0.00036126276,0.00016597952,0.00030641747,0.00004029429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000191087,0.00013195608,0.0003735286,0.00024091468,0.000044555767,0.00001526599,0.0001374568,0.000048086848,0.00000550214],"category_scores_gemma":[0.000030692805,0.00010663835,0.00006570559,0.00039315643,0.000040393614,0.00015857468,0.000010810288,0.00017466307,0.0000011542995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055510904,0.0012450027,0.0000777916,0.002316932,0.00040742432,0.0000024463623,0.0185504,0.9674089,0.00064251054,0.005492065,0.00009420882,0.0037067847],"study_design_scores_gemma":[0.013936752,0.004481657,0.00045650746,0.012639133,0.0034637237,0.00030147453,0.59053284,0.30241698,0.05219428,0.016608637,0.0014511012,0.001516917],"about_ca_topic_score_codex":0.0000014162081,"about_ca_topic_score_gemma":0.0000011498198,"teacher_disagreement_score":0.6649919,"about_ca_system_score_codex":0.0000453157,"about_ca_system_score_gemma":0.0000211079,"threshold_uncertainty_score":0.43485856},"labels":[],"label_agreement":null},{"id":"W4413872449","doi":"10.5267/j.ijiec.2025.6.011","title":"Sugar beet transportation problem under growers’ equity regulations: Metaheuristic approach","year":2025,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Metaheuristic; Sugar beet; Equity (law); Sugar; Business; Economics; Chemistry; Mathematical optimization; Mathematics; Biochemistry; Agronomy; Biology","score_opus":0.03607868472909945,"score_gpt":0.28366096579137917,"score_spread":0.24758228106227972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413872449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042395066,0.00011241329,0.99145585,0.00043228493,0.001586105,0.00017538211,0.000015280482,0.00015271814,0.0018304512],"genre_scores_gemma":[0.9156819,0.000019296162,0.08385122,0.000037209174,0.00025755365,0.000011106171,0.00006199237,0.000023402785,0.000056295827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865067,0.000018853038,0.00072239223,0.00009820811,0.00036208346,0.00014776601],"domain_scores_gemma":[0.9991253,0.00018530908,0.00013049261,0.000078041645,0.00040359425,0.00007726307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028008549,0.00014668821,0.00022053487,0.00049217883,0.00004423938,0.00012537604,0.0002914713,0.00011051171,0.000025739644],"category_scores_gemma":[0.00015828229,0.00015090416,0.00012909158,0.0004031934,0.000025061623,0.0002857594,0.000017710106,0.00031945528,0.000002923791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008965556,0.000061699415,0.000024142473,0.000027477496,0.00030370094,0.0000034723048,0.00012038579,0.9498764,0.00012157486,0.043541502,0.0009346285,0.004976038],"study_design_scores_gemma":[0.0017576477,0.00003851484,0.00045575935,0.0003457517,0.0001798819,0.000028573251,0.00018097229,0.9837742,0.00028886317,0.008631342,0.004060591,0.00025790784],"about_ca_topic_score_codex":0.000003558316,"about_ca_topic_score_gemma":9.647829e-7,"teacher_disagreement_score":0.9114424,"about_ca_system_score_codex":0.00018027949,"about_ca_system_score_gemma":0.00008664916,"threshold_uncertainty_score":0.6153693},"labels":[],"label_agreement":null},{"id":"W4414748284","doi":"10.1038/s41598-025-17604-y","title":"An integrated TOPSIS and ARAS method multi-criteria decision-making approach for optimizing investment portfolios using goal programming and genetic algorithm model","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sharpe ratio; TOPSIS; Portfolio; Efficient frontier; Portfolio optimization; Asset allocation; Probabilistic logic; Project portfolio management; Investment strategy; Genetic algorithm","score_opus":0.025719695037178827,"score_gpt":0.3299792510054088,"score_spread":0.30425955596823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414748284","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016454957,0.00036999694,0.9813405,0.0000036263937,0.00062431215,0.00089959736,0.0000036933193,0.00022255484,0.00008073252],"genre_scores_gemma":[0.068829075,0.0000054200523,0.9309199,0.00002514102,0.000013749037,0.00009534124,0.000024715986,0.00003266859,0.000054030614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817,0.000029952555,0.00059626903,0.0006619975,0.00018760632,0.00035415887],"domain_scores_gemma":[0.9991597,0.000060283368,0.000101906546,0.00038306683,0.0001429871,0.00015207875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000947886,0.000227003,0.00029919075,0.0003148403,0.00032891086,0.00079059484,0.00008718165,0.00010512639,0.0000037736143],"category_scores_gemma":[0.0001362023,0.00021208356,0.00005600272,0.00045245714,0.000111537265,0.00026942926,0.00007357231,0.00010283418,6.9041405e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043485215,0.000097124415,0.000041732106,0.00023937762,0.000046013825,0.000025982506,0.0005916271,0.5859598,0.0032885019,0.00011655767,0.00006973578,0.40951917],"study_design_scores_gemma":[0.00019058542,0.000011935141,0.000008364671,0.00015473712,0.0000712646,0.000119324366,0.0003744562,0.9948448,0.00066291424,0.0030470146,0.00029822826,0.0002163638],"about_ca_topic_score_codex":0.0000043686155,"about_ca_topic_score_gemma":0.0000012249781,"teacher_disagreement_score":0.4093028,"about_ca_system_score_codex":0.00006824238,"about_ca_system_score_gemma":0.00006182084,"threshold_uncertainty_score":0.86485165},"labels":[],"label_agreement":null},{"id":"W4415046543","doi":"10.32920/30329818.v1","title":"Mathematics for Public and Occupational Health Professionals: 2nd Edition","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Metropolitan area; Occupational safety and health; Key (lock); Health professionals","score_opus":0.0610590105641526,"score_gpt":0.3461992853952491,"score_spread":0.2851402748310965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415046543","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000085814776,0.00024697167,0.99042755,0.002157135,0.00058416306,0.001076561,0.000120575394,0.00043410613,0.0048671137],"genre_scores_gemma":[0.010863569,0.00016138158,0.98352224,0.00071963377,0.00023616516,0.000838705,0.0009353425,0.00004076825,0.0026822179],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913657,0.0000122904985,0.00035626863,0.00016438759,0.00013275028,0.00019775401],"domain_scores_gemma":[0.99941134,0.00018664997,0.00006618453,0.00014415036,0.00010202,0.00008962106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003105555,0.00015611039,0.00024898228,0.00011907886,0.0000894675,0.0000919296,0.00008124761,0.00016292334,0.000118986594],"category_scores_gemma":[0.00013402541,0.00014052271,0.000052839496,0.00006308439,0.000017374861,0.00006623166,0.00013173596,0.00017482517,0.0000028795298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000071428203,0.000469837,0.000029478413,0.036155213,0.00024569823,2.6445565e-7,0.0011714775,0.011799684,0.000004475394,0.70160455,0.19774401,0.050768174],"study_design_scores_gemma":[0.00027358058,0.000015546804,0.000020393782,0.00062773156,0.000018785917,0.0000011841287,0.00022982697,0.9186092,0.000025343212,0.06501367,0.014918471,0.00024624143],"about_ca_topic_score_codex":0.0000012481995,"about_ca_topic_score_gemma":0.000005316227,"teacher_disagreement_score":0.90680957,"about_ca_system_score_codex":0.000061497296,"about_ca_system_score_gemma":0.0001359688,"threshold_uncertainty_score":0.573035},"labels":[],"label_agreement":null},{"id":"W4415046600","doi":"10.32920/30329818","title":"Mathematics for Public and Occupational Health Professionals: 2nd Edition","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Metropolitan area; Occupational safety and health; Key (lock); Health professionals","score_opus":0.0610590105641526,"score_gpt":0.3461992853952491,"score_spread":0.2851402748310965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415046600","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000085814776,0.00024697167,0.99042755,0.002157135,0.00058416306,0.001076561,0.000120575394,0.00043410613,0.0048671137],"genre_scores_gemma":[0.010863569,0.00016138158,0.98352224,0.00071963377,0.00023616516,0.000838705,0.0009353425,0.00004076825,0.0026822179],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913657,0.0000122904985,0.00035626863,0.00016438759,0.00013275028,0.00019775401],"domain_scores_gemma":[0.99941134,0.00018664997,0.00006618453,0.00014415036,0.00010202,0.00008962106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003105555,0.00015611039,0.00024898228,0.00011907886,0.0000894675,0.0000919296,0.00008124761,0.00016292334,0.000118986594],"category_scores_gemma":[0.00013402541,0.00014052271,0.000052839496,0.00006308439,0.000017374861,0.00006623166,0.00013173596,0.00017482517,0.0000028795298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000071428203,0.000469837,0.000029478413,0.036155213,0.00024569823,2.6445565e-7,0.0011714775,0.011799684,0.000004475394,0.70160455,0.19774401,0.050768174],"study_design_scores_gemma":[0.00027358058,0.000015546804,0.000020393782,0.00062773156,0.000018785917,0.0000011841287,0.00022982697,0.9186092,0.000025343212,0.06501367,0.014918471,0.00024624143],"about_ca_topic_score_codex":0.0000012481995,"about_ca_topic_score_gemma":0.000005316227,"teacher_disagreement_score":0.90680957,"about_ca_system_score_codex":0.000061497296,"about_ca_system_score_gemma":0.0001359688,"threshold_uncertainty_score":0.573035},"labels":[],"label_agreement":null},{"id":"W4415305391","doi":"10.1016/j.nlp.2025.100185","title":"OPT2CODE: A retrieval-augmented framework for solving linear programming problems","year":2025,"lang":"en","type":"article","venue":"Natural Language Processing Journal","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Executable; Solver; Code generation; Domain (mathematical analysis); Benchmark (surveying); Code (set theory); Linear programming; Integer programming","score_opus":0.00951167119287442,"score_gpt":0.2937268453066785,"score_spread":0.28421517411380404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415305391","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027776447,0.024647867,0.9699555,0.00036830347,0.00070353784,0.00048181962,0.0000019678812,0.0006302041,0.00043315662],"genre_scores_gemma":[0.48963574,0.00004080347,0.50942886,0.00013153927,0.00024473274,0.000021116424,0.000008776889,0.00004321531,0.00044522033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986677,0.000014225556,0.00044085484,0.00017671197,0.00022935227,0.0004711137],"domain_scores_gemma":[0.99932355,0.00012598177,0.00010798648,0.00011668216,0.0002193796,0.000106420506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042838845,0.00021348015,0.0002533871,0.00019308031,0.00035639372,0.00046000196,0.00022642786,0.00016834623,0.000025739873],"category_scores_gemma":[0.00079027977,0.00017797347,0.00012268066,0.0005479733,0.00003764365,0.0003153874,0.000035057503,0.0008261251,0.0000031737547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007517191,0.00009885621,0.000027544676,0.0028882811,0.00015764283,0.00002066295,0.003378899,0.009058055,0.0045876554,0.0019302386,0.00048288802,0.9772941],"study_design_scores_gemma":[0.0010416195,0.00005410292,0.000007850902,0.0028691536,0.000108515356,0.00008691769,0.0012959866,0.97955674,0.004723613,0.003818013,0.0060066218,0.00043089295],"about_ca_topic_score_codex":4.368078e-7,"about_ca_topic_score_gemma":7.9913406e-7,"teacher_disagreement_score":0.9768632,"about_ca_system_score_codex":0.000117926436,"about_ca_system_score_gemma":0.0000658723,"threshold_uncertainty_score":0.7257548},"labels":[],"label_agreement":null},{"id":"W4415471266","doi":"10.1080/03155986.2025.2567173","title":"Fair cost savings allocation in two-stage fixed-cost transportation problem","year":2025,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cost allocation; Production (economics); Quality (philosophy); Work (physics)","score_opus":0.03189441970223946,"score_gpt":0.34143389214367287,"score_spread":0.3095394724414334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415471266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06812525,0.0004462884,0.70716554,0.0015491187,0.0007355484,0.010676123,0.0002296778,0.0006560221,0.21041647],"genre_scores_gemma":[0.995848,0.00006522711,0.0018438892,0.00009609898,0.000022879723,0.00074675924,0.00045323698,0.000008364519,0.0009155585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985104,0.000035619007,0.00067531934,0.00009210565,0.0004417208,0.0002447989],"domain_scores_gemma":[0.99919236,0.00014599928,0.000037850572,0.00010868313,0.00044238425,0.000072704286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011492303,0.00011087222,0.00014216962,0.00053188973,0.00017685314,0.0005121746,0.00010024954,0.00007876211,0.000035443896],"category_scores_gemma":[0.00011913869,0.000106109896,0.000020780493,0.0006152263,0.00004598527,0.0019531131,0.000014192787,0.0002392941,0.00006240208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021648637,0.000022521823,0.0010723505,0.0010431651,0.000019231238,6.438944e-7,0.002444303,0.5142701,0.00013663835,0.4565626,0.001813912,0.022592818],"study_design_scores_gemma":[0.0010112424,0.000017334185,0.0018675069,0.0002616527,0.0000031051213,0.0000016460696,0.0021986917,0.8116731,0.0002419193,0.00020867818,0.18234849,0.00016662313],"about_ca_topic_score_codex":0.00020864078,"about_ca_topic_score_gemma":0.00016891935,"teacher_disagreement_score":0.92772275,"about_ca_system_score_codex":0.00016000956,"about_ca_system_score_gemma":0.00011077301,"threshold_uncertainty_score":0.49389115},"labels":[],"label_agreement":null},{"id":"W4416045513","doi":"10.11591/ijece.v15i6.pp5708-5716","title":"Computational modelling under uncertainty: statistical mean approach to optimize fuzzy multi-objective linear programming problem with trapezoidal numbers","year":2025,"lang":"","type":"article","venue":"International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Algoma University","funders":"","keywords":"Fuzzy logic; Robustness (evolution); Ranking (information retrieval); Linear programming; Fuzzy number; Software; Statistical model","score_opus":0.007173021181386305,"score_gpt":0.23749753314871916,"score_spread":0.23032451196733286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416045513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073267366,0.003012705,0.9869755,0.00045355683,0.001564982,0.0004447624,0.00002041692,0.000035730878,0.0001656438],"genre_scores_gemma":[0.64617014,0.0004035894,0.35272858,0.00011071146,0.00048300248,0.000008367403,0.000010704631,0.00005288191,0.00003202868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99532217,0.00011706522,0.002019019,0.00043270277,0.0014726982,0.00063633523],"domain_scores_gemma":[0.9949717,0.0005502262,0.0007870116,0.00011080818,0.0030718981,0.0005083699],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008789908,0.00059542945,0.0010124097,0.0012834938,0.00010634936,0.0007860503,0.0007543533,0.000224686,0.000005849912],"category_scores_gemma":[0.000074794916,0.00051453646,0.00026898467,0.0004995033,0.00009461083,0.00053031545,0.00016124168,0.0012758767,0.000001019908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051245093,0.00050786976,0.00005784868,0.000115963914,0.003668186,0.00011098013,0.0008910221,0.9218377,0.000044061326,0.063795425,0.00007706198,0.008381435],"study_design_scores_gemma":[0.003632627,0.0009841182,0.000051616404,0.0010623237,0.0002520835,0.0019141381,0.00028023575,0.9883936,0.00002424439,0.0007895231,0.0021407097,0.0004748005],"about_ca_topic_score_codex":0.000017814404,"about_ca_topic_score_gemma":0.0000023682069,"teacher_disagreement_score":0.6388434,"about_ca_system_score_codex":0.00082285434,"about_ca_system_score_gemma":0.0004986986,"threshold_uncertainty_score":0.99973065},"labels":[],"label_agreement":null},{"id":"W6912433229","doi":"10.5281/zenodo.3951375","title":"Optimized Capacity Management Drives Financial Clusters Approach to Linear Programming","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Linear programming; Variety (cybernetics); Cluster analysis; Financial management; Resource (disambiguation); Joint (building); Capital (architecture); Working capital","score_opus":0.04029277569668747,"score_gpt":0.2227012467252502,"score_spread":0.18240847102856275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912433229","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012559167,0.000014676721,0.87614685,0.00046728773,0.000060236947,0.00088880095,0.000021408207,0.0023158004,0.118829034],"genre_scores_gemma":[0.3646914,0.000054705546,0.6308615,0.00088472484,0.00030591057,9.579105e-7,0.000691112,0.002241638,0.00026801243],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998819,0.000070892165,0.00023384993,0.0002905351,0.0002465906,0.00033913518],"domain_scores_gemma":[0.9993245,0.000008341047,0.0000288244,0.00020186839,0.00012245946,0.00031399578],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022990929,0.00014807298,0.00015956309,0.00010942887,0.0006451547,0.00038458288,0.0005067589,0.00005032892,0.0006809874],"category_scores_gemma":[0.00024648738,0.00015962386,0.000052208416,0.0005743233,0.000055442648,0.00014979078,0.0004697981,0.00019522909,0.0017336493],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014180425,0.00042327907,5.5774166e-7,0.0016812403,0.00019054429,0.000019384841,0.011513967,0.34655866,0.0006854218,0.03332425,0.12530945,0.48015144],"study_design_scores_gemma":[0.00052866846,0.000086294196,0.0000070203637,0.000023333829,0.000017064911,0.000008801239,0.00036889155,0.20301312,0.000125556,0.000033289354,0.7955705,0.00021746806],"about_ca_topic_score_codex":6.388925e-7,"about_ca_topic_score_gemma":1.0369525e-8,"teacher_disagreement_score":0.670261,"about_ca_system_score_codex":0.00006687196,"about_ca_system_score_gemma":8.6923075e-7,"threshold_uncertainty_score":0.99904364},"labels":[],"label_agreement":null},{"id":"W6924753322","doi":"10.15468/dl.mgazbm","title":"Occurrence Download","year":2024,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Download; Matching (statistics); Range (aeronautics); Biodiversity; Data set","score_opus":0.011186326228513996,"score_gpt":0.20789230451682578,"score_spread":0.19670597828831177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6924753322","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011861847,0.000050277587,0.00012799719,0.000038995982,0.0008343499,0.00021244974,0.99761194,0.0005628731,0.0005492284],"genre_scores_gemma":[0.0000060932443,0.00009032443,0.000012584283,0.00009917386,0.0000013677945,0.0000041596777,0.9997862,2.5164374e-8,9.593633e-8],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990828,0.000011586916,0.00030710272,0.0001171486,0.00026507245,0.00021629827],"domain_scores_gemma":[0.9994745,0.000011318998,0.00004491732,0.00026503668,0.000078224686,0.000125991],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00011501057,0.00022877919,0.00019949021,0.0000770398,0.00007788342,0.00022123173,0.00024712336,0.00026182205,0.001536973],"category_scores_gemma":[0.000052617328,0.00022988458,0.00011029081,0.00027982975,0.00006569836,0.00051341,0.00012660255,0.00027311337,0.56647927],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026099806,0.000008872593,0.000004638124,0.0009155024,0.000030953226,0.0000015023811,0.000026962029,0.00051122275,9.844444e-9,3.2540183e-7,0.9966375,0.001859867],"study_design_scores_gemma":[0.0000897349,0.0000083708765,4.4479827e-7,0.0000064082183,0.00005246362,0.000003018064,0.00005293819,0.00003855916,0.0000012473342,0.000003722551,0.9995147,0.00022839471],"about_ca_topic_score_codex":0.000012484568,"about_ca_topic_score_gemma":0.0000021440785,"teacher_disagreement_score":0.5649423,"about_ca_system_score_codex":0.00019830588,"about_ca_system_score_gemma":0.000030138348,"threshold_uncertainty_score":0.99937576},"labels":[],"label_agreement":null},{"id":"W6944928001","doi":"10.21348/p.1970.0007","title":"PROGRESS REPORT FOR NORSAR PHASE 3 COVERING 3rd QUARTER 1970* STATUS PER 30 SEPTEMBER 1970","year":2023,"lang":"en","type":"article","venue":"NORSAR","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Phase (matter); Period (music); Work (physics)","score_opus":0.021376047137532606,"score_gpt":0.3021821305721034,"score_spread":0.2808060834345708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6944928001","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49383673,0.00048424085,0.45144942,0.0005505724,0.0033105006,0.0037145542,0.00018579872,0.0075823218,0.03888585],"genre_scores_gemma":[0.9406615,0.00003812201,0.051139884,0.00013127757,0.0005564022,0.00063248107,0.0006296505,0.0002726502,0.005938049],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99852586,0.000011647143,0.00040430189,0.00026311592,0.00022465356,0.00057040894],"domain_scores_gemma":[0.9993008,0.000076826305,0.000054736072,0.00030813852,0.00008088311,0.00017865941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022515417,0.00021261051,0.0002555172,0.000107378364,0.00009776651,0.00010290032,0.00010332199,0.000105223575,0.0004440287],"category_scores_gemma":[0.000070603084,0.00020568958,0.00012295113,0.0002508597,0.0000399019,0.00018134869,0.000035013356,0.00013158275,0.000723266],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032974058,0.0025084568,0.008054609,0.011440046,0.0020125092,0.0017831622,0.021595355,0.22589435,0.014191304,0.00644221,0.4987312,0.20701706],"study_design_scores_gemma":[0.0017698964,0.0001266313,0.00009993806,0.00007823836,0.000070454116,0.00003170464,0.0003041237,0.31834567,0.0011925289,0.0003629074,0.67707044,0.00054749876],"about_ca_topic_score_codex":0.0000030297183,"about_ca_topic_score_gemma":0.0000036448173,"teacher_disagreement_score":0.44682476,"about_ca_system_score_codex":0.00005587085,"about_ca_system_score_gemma":0.000020304793,"threshold_uncertainty_score":0.92963624},"labels":[],"label_agreement":null},{"id":"W6968941111","doi":"10.5281/zenodo.3264656","title":"Know Your Enemy: The Risk of Unauthorized Access in Smartphones by Insiders","year":2013,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nucleofection; TSG101; Gestational period; Hyporeflexia; Diafiltration; Proteogenomics; Dysgeusia; Tubulopathy; Articular cartilage damage","score_opus":0.026220971247054154,"score_gpt":0.24131211029173555,"score_spread":0.21509113904468138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6968941111","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25989133,0.00131933,0.27173156,0.002118113,0.0004864505,0.0039643804,0.00023956805,0.004399679,0.45584956],"genre_scores_gemma":[0.99704784,0.00042205895,0.0013403586,0.000037390728,0.000026813057,2.2269725e-7,0.00016090926,0.0006645014,0.00029989812],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990896,0.00015735353,0.00023750743,0.00013624382,0.00016988958,0.00020941561],"domain_scores_gemma":[0.99943733,0.000040253122,0.00005388548,0.00023129939,0.00016230241,0.00007492362],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00035910122,0.00009471795,0.00012428491,0.00011454156,0.00032631482,0.0003859725,0.00055744674,0.00004451923,0.004405084],"category_scores_gemma":[0.00044954792,0.00007669539,0.000032096217,0.00049166643,0.00008708452,0.0002680811,0.00030086056,0.00019080081,0.0008784103],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037900852,0.00044117114,0.0000786202,0.0006043665,0.00020022117,0.000002844384,0.0067008478,0.03203068,0.012188619,0.0063350424,0.47424188,0.4671378],"study_design_scores_gemma":[0.001155897,0.00008906966,0.0010777649,0.0000742561,0.000029604482,0.000014477351,0.0016054622,0.13178615,0.0026164353,0.0017197498,0.8594759,0.00035520853],"about_ca_topic_score_codex":0.000039367795,"about_ca_topic_score_gemma":3.443804e-7,"teacher_disagreement_score":0.7371565,"about_ca_system_score_codex":0.000042366446,"about_ca_system_score_gemma":0.0000011864092,"threshold_uncertainty_score":0.9998995},"labels":[],"label_agreement":null},{"id":"W6991091283","doi":"","title":"Examining the relationship between motivation and the physical activity behaviour of Canadian youth with physical disabilities","year":2016,"lang":"en","type":"article","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Physical activity; Action (physics); Qualitative research; Motor activity","score_opus":0.07339739516167143,"score_gpt":0.2422343964326275,"score_spread":0.16883700127095608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991091283","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9830782,0.0000012369262,0.013475566,0.0004205129,0.0000071318213,0.00015380645,0.000006873554,0.00004512947,0.0028115727],"genre_scores_gemma":[0.99964434,4.6727072e-7,0.00021073302,0.0000035471462,0.000035717934,0.000011223661,0.0000012454251,0.0000096636495,0.00008304402],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996624,0.000032716438,0.00006006005,0.0000622585,0.00008485236,0.00009771799],"domain_scores_gemma":[0.9980635,0.0017443283,0.000016638589,0.00011466106,0.000016957942,0.000043892076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010803643,0.00006487271,0.00010077961,0.00003265691,0.00007197562,0.000018512786,0.000048557486,0.000018677567,0.0000040673435],"category_scores_gemma":[0.00026116107,0.00002516406,0.000017191345,0.0001339141,0.00021907385,0.000108615204,0.000010044383,0.00006474567,0.000001377661],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025727648,0.00007607101,0.6892763,0.00009548931,0.00008009157,1.5244694e-7,0.039876,0.0012332925,0.000094221534,0.24689028,0.000032033247,0.022320358],"study_design_scores_gemma":[0.0013904264,0.0000843745,0.9585055,0.000112676295,0.00017432413,0.000001039311,0.011315496,0.018916143,0.0012310769,0.007988078,0.000016147396,0.00026472902],"about_ca_topic_score_codex":0.0011658823,"about_ca_topic_score_gemma":0.0006274864,"teacher_disagreement_score":0.2692292,"about_ca_system_score_codex":0.000019095367,"about_ca_system_score_gemma":0.00000931834,"threshold_uncertainty_score":0.17624731},"labels":[],"label_agreement":null},{"id":"W7006961595","doi":"","title":"What Happened to Jodi Henrickson? (BC)","year":2019,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cash; Homicide; Popular culture","score_opus":0.005378458706926196,"score_gpt":0.18200135132152157,"score_spread":0.17662289261459538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7006961595","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000032988178,0.00032385084,0.00005029532,0.00012245437,0.00085056166,0.00068598083,0.000021859025,0.0005989027,0.9973428],"genre_scores_gemma":[0.00010333303,0.00040189136,0.007475119,0.0002706614,0.0000919111,0.000022226683,0.00007237663,0.00020987628,0.9913526],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99869376,0.000019702959,0.0005183657,0.0001498188,0.0003160193,0.00030230638],"domain_scores_gemma":[0.9991902,0.000060652106,0.00014908393,0.00036757335,0.00007324866,0.0001592227],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000117808195,0.0003224757,0.00042635517,0.000105079474,0.000025577508,0.00015132985,0.00026893467,0.00032755945,0.22929612],"category_scores_gemma":[0.000059118425,0.00032730965,0.000123841,0.000012520049,0.000033336273,5.0062465e-7,0.00006717771,0.00018931749,0.06111819],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008859797,0.000017142977,1.0331955e-7,0.00094803364,0.00004768345,0.0000028188272,0.00014258253,0.011571663,1.9887108e-7,0.000074316435,0.9829736,0.004213042],"study_design_scores_gemma":[0.00027367592,0.00005481765,2.4464907e-7,0.00048400272,0.000029142753,0.000010706665,0.00015246129,0.00085371285,0.000008594558,0.000007517676,0.9977705,0.00035465407],"about_ca_topic_score_codex":0.0002639744,"about_ca_topic_score_gemma":0.00016393943,"teacher_disagreement_score":0.16817793,"about_ca_system_score_codex":0.00004435992,"about_ca_system_score_gemma":0.000010584882,"threshold_uncertainty_score":0.9999179},"labels":[],"label_agreement":null},{"id":"W7036884093","doi":"","title":"DeKalb News Update – October 2024 (Voting in the Nov. 5 General Election)","year":2024,"lang":"en","type":"other","venue":"Internet Archive (Internet Archive)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Voting; Event (particle physics); Quarter (Canadian coin); Block (permutation group theory); Channel (broadcasting); Commission","score_opus":0.01005876488084718,"score_gpt":0.23755008986810505,"score_spread":0.22749132498725788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036884093","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007826513,0.0008720154,0.11313355,0.00015580583,0.0016474512,0.00078062736,0.00008905164,0.0008003321,0.8824429],"genre_scores_gemma":[0.0059292093,0.00016133879,0.012691993,0.00048721195,0.0009562987,0.00020623708,0.00026245601,0.0009150161,0.9783902],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974364,0.00017634005,0.0006904825,0.0006569969,0.0003428865,0.0006968777],"domain_scores_gemma":[0.99911165,0.00016625928,0.00011572296,0.0004483315,0.000012655604,0.00014537582],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00021432318,0.00068068487,0.0005550071,0.0007286213,0.000017145445,0.00035774466,0.0008464726,0.00017964332,0.007133789],"category_scores_gemma":[0.00005247067,0.0005278747,0.00040105378,0.0002829268,0.00015231295,0.000079585676,0.00030612774,0.0013549109,0.006014361],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016419985,0.00005490186,0.000031357118,0.00044775516,0.0002844697,0.000102927166,0.0014702474,0.000577039,0.000027647533,0.012403899,0.9833001,0.0012832644],"study_design_scores_gemma":[0.00025741832,0.00004925606,0.000011909338,0.0010155934,0.00008214914,0.00007634012,0.00006135111,0.11477281,0.00003837672,0.0024958877,0.8806045,0.0005344001],"about_ca_topic_score_codex":0.0022093577,"about_ca_topic_score_gemma":0.021549188,"teacher_disagreement_score":0.11419577,"about_ca_system_score_codex":0.00006197133,"about_ca_system_score_gemma":0.000023480952,"threshold_uncertainty_score":0.9997173},"labels":[],"label_agreement":null},{"id":"W7084128316","doi":"10.7758/bwmi8829.7504","title":"Chapter 3. Fiscal Subordination Fallout: Prince George’s County’s Fiscal Year 2018 Budget Deliberations and Allocations","year":2025,"lang":"en","type":"book-chapter","venue":"Russell Sage Foundation eBooks","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fiscal year; Subordination (linguistics); Fiscal policy; Government (linguistics); Period (music)","score_opus":0.01053902369598495,"score_gpt":0.2199390761062182,"score_spread":0.20940005241023327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084128316","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000048487338,0.00012385995,0.23104024,0.00033578443,0.00045253025,0.0008443861,0.000081705424,0.0005891486,0.76648384],"genre_scores_gemma":[0.021213064,0.00046458727,0.015203456,0.00028295087,0.00029687188,0.00013675269,0.0010155094,0.00018801037,0.9611988],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983852,0.000016248223,0.00061084854,0.0004059371,0.00031409186,0.00026766822],"domain_scores_gemma":[0.9991126,0.00011235449,0.0001299427,0.00037438402,0.0001290336,0.00014170777],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019277941,0.00041825557,0.00034538022,0.00026788877,0.00026952004,0.00027803026,0.00016297567,0.00036891288,0.00061774085],"category_scores_gemma":[0.00003971589,0.00046533378,0.00010576858,0.000038571936,0.00013662287,0.00020556999,0.0000741376,0.0003772062,0.00023785101],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006168302,0.000019785251,0.000006635692,0.00024547172,0.000113149385,0.0000024296205,0.00028285777,0.00061739556,0.000081473365,0.9633996,0.0012044474,0.03402062],"study_design_scores_gemma":[0.0006520065,0.00004764189,0.00005410395,0.00041857452,0.0002753277,0.000010196183,0.000039350452,0.05226413,0.00012312588,0.029791294,0.9153904,0.0009338804],"about_ca_topic_score_codex":0.00000721668,"about_ca_topic_score_gemma":0.00009357054,"teacher_disagreement_score":0.9336083,"about_ca_system_score_codex":0.00015688426,"about_ca_system_score_gemma":0.00004941233,"threshold_uncertainty_score":0.9997798},"labels":[],"label_agreement":null},{"id":"W7130696075","doi":"10.47749/t/unicamp.2025.1507167","title":"Multi-objective production strategy optimization methodology under uncertainties","year":2025,"lang":"","type":"dissertation","venue":"","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Universidade Estadual de Campinas; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo; Energi Simulation","keywords":"Production (economics); Process (computing); Minification; Production planning; Work (physics)","score_opus":0.08312374647339024,"score_gpt":0.34062049195330757,"score_spread":0.2574967454799173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7130696075","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023170866,0.00076715794,0.9675031,0.00026602842,0.0038036772,0.0017377065,0.000008333505,0.00058922806,0.025093067],"genre_scores_gemma":[0.027819261,0.001352566,0.8612935,0.000069398935,0.00023316832,0.00033942034,0.0013914902,0.00016932724,0.10733184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99628365,0.00044190206,0.0012552333,0.0010150743,0.00033665684,0.0006674875],"domain_scores_gemma":[0.9975625,0.0005725778,0.00033705216,0.0005097748,0.0008625297,0.0001555267],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00067170995,0.000874885,0.0010124012,0.0007715468,0.00036624388,0.0002655766,0.00028999042,0.001120866,0.002217925],"category_scores_gemma":[0.0013209635,0.00091044273,0.0002876108,0.0011449945,0.00009652719,0.00047830495,0.000053727585,0.0008093403,0.00006665525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006585151,0.00021009296,0.0000027455224,0.001873427,0.0004949025,0.000001012847,0.0013966197,0.9656116,0.00048214226,0.018863577,0.00016746743,0.010830584],"study_design_scores_gemma":[0.0006044643,0.00009519328,0.00003426785,0.00047440676,0.00050538394,0.0000047185945,0.012976797,0.9772553,0.0034982485,0.0036006053,0.00007655651,0.0008740899],"about_ca_topic_score_codex":0.0001599123,"about_ca_topic_score_gemma":0.00038049495,"teacher_disagreement_score":0.10620957,"about_ca_system_score_codex":0.0003946591,"about_ca_system_score_gemma":0.00031636754,"threshold_uncertainty_score":0.99933463},"labels":[],"label_agreement":null},{"id":"W75241701","doi":"10.1023/a:1013138919445","title":"A Comprehensive Input Format for Stochastic Linear Programs","year":2001,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Theory of computation; Range (aeronautics); Computer science; Mathematical economics; Mathematical optimization; Mathematics; Applied mathematics; Algorithm; Engineering","score_opus":0.32938202019815765,"score_gpt":0.45775449056652306,"score_spread":0.1283724703683654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W75241701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061664037,0.0004497245,0.93158203,0.0014962781,0.00007392945,0.0018815316,0.00001526528,0.00021114346,0.0026260652],"genre_scores_gemma":[0.9616896,0.00023079575,0.03684344,0.000054812663,0.00007259574,0.0004085113,0.000065455235,0.000032249434,0.00060256745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991354,0.00002529488,0.00022979334,0.00008820749,0.00022222017,0.00029909017],"domain_scores_gemma":[0.9987675,0.00012269041,0.000006206401,0.00016011487,0.00085672026,0.00008677667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029343818,0.00007159,0.00012746129,0.0001583515,0.0001437937,0.00006491515,0.00012172388,0.000048239337,0.000044383658],"category_scores_gemma":[0.00016957526,0.000065328284,0.000050451294,0.0003857484,0.000063828746,0.00017188147,0.000030374706,0.00012565934,0.000035697238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028842227,0.00023880872,0.0000073452725,0.00036556585,0.00006796491,0.0000015233745,0.0010606304,0.93314976,0.00094238395,0.0119846305,0.0038371508,0.048315376],"study_design_scores_gemma":[0.00020637107,0.00015150655,0.00000442306,0.000041888154,0.000002567206,0.0000035823566,0.000282074,0.9796157,0.0009521035,0.00048507674,0.018176757,0.00007795243],"about_ca_topic_score_codex":0.00001085486,"about_ca_topic_score_gemma":0.000018316237,"teacher_disagreement_score":0.90002555,"about_ca_system_score_codex":0.000011193888,"about_ca_system_score_gemma":0.000027228734,"threshold_uncertainty_score":0.26640102},"labels":[],"label_agreement":null},{"id":"W847996784","doi":"10.5267/j.dsl.2015.5.004","title":"Vendor selection and order allocation using an integrated fuzzy mathematical programming model","year":2015,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Purchasing; Computer science; Fuzzy logic; Quality (philosophy); Selection (genetic algorithm); Operations research; Order (exchange); Supply chain; Context (archaeology); Vendor; Supply chain management; Service (business); Supplier relationship management; Production (economics); Delivery Performance; Operations management; Business; Process management; Marketing; Engineering; Economics; Microeconomics","score_opus":0.05476178526326572,"score_gpt":0.3092761112894087,"score_spread":0.254514326026143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W847996784","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34001082,0.000006698144,0.65948784,0.00007176002,0.00006144944,0.00012629402,2.3701001e-7,0.00016087858,0.00007402918],"genre_scores_gemma":[0.48886526,8.803839e-7,0.5109887,0.00010772459,0.000013368292,0.0000071973514,0.0000014183062,0.000012603151,0.000002838784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998804,0.0000143069,0.00023823688,0.00023419649,0.00044950537,0.0002597637],"domain_scores_gemma":[0.999382,0.000038865146,0.0000318559,0.00014631545,0.00016459834,0.00023631909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007752266,0.00011818822,0.00012377459,0.00021368869,0.00012727432,0.00029731385,0.00015328465,0.000044627566,0.0000059621416],"category_scores_gemma":[0.0003965173,0.000100759666,0.000014981087,0.0009600151,0.00013818245,0.0008313019,0.000034663713,0.000109332184,0.000013172291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057280618,0.000040696217,0.000023096014,0.000015222711,0.0000029884438,0.0000011619787,0.0007511692,0.8873615,0.01785834,0.0011594135,0.000073818795,0.09270685],"study_design_scores_gemma":[0.00017548144,0.000018242237,0.0000058479795,0.00002927064,0.000007233608,0.00002229907,0.00021835754,0.99619484,0.0007788208,0.0022654124,0.00015826483,0.00012593834],"about_ca_topic_score_codex":0.000002682116,"about_ca_topic_score_gemma":0.0000018016136,"teacher_disagreement_score":0.14885442,"about_ca_system_score_codex":0.00011791802,"about_ca_system_score_gemma":0.000047393227,"threshold_uncertainty_score":0.410886},"labels":[],"label_agreement":null},{"id":"W90079504","doi":"10.1080/03155986.2005.11732719","title":"Modeling and Analysis of Multicommodity Network Flows Via Goal Programming","year":2005,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multi-commodity flow problem; Flow network; Mathematical optimization; Linear programming; Lagrangian relaxation; Minimum-cost flow problem; Robustness (evolution); Computer science; Relaxation (psychology); Mathematics","score_opus":0.03883840468365876,"score_gpt":0.3158162269583731,"score_spread":0.27697782227471435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W90079504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1275988,0.0004137632,0.86805725,0.00008329534,0.00008129645,0.00078888057,0.000024220974,0.00010946022,0.002843032],"genre_scores_gemma":[0.97895956,0.00005119241,0.020725954,0.000016796423,0.00006123436,0.000064499116,0.00009636277,0.000005545863,0.000018871642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871755,0.000028836414,0.0005527348,0.00006285326,0.00042753114,0.00021047259],"domain_scores_gemma":[0.99925387,0.00011352713,0.000029350947,0.00009728887,0.0004057616,0.0001002133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011423848,0.00008496988,0.00019682701,0.00033660795,0.0001842094,0.0002947795,0.00006277962,0.000073498115,0.000016470332],"category_scores_gemma":[0.00009207543,0.00007482275,0.0000326075,0.0005240914,0.000040021114,0.0011418686,0.00004529043,0.00014379203,0.00000938794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046615055,0.000006047713,0.00014679722,0.0001316362,0.00009089313,4.450856e-8,0.00087450317,0.95836115,0.000008943419,0.008338906,0.000040309547,0.031996135],"study_design_scores_gemma":[0.00017876444,0.000014158378,0.00012998568,0.00002641267,0.000021831367,0.0000021227795,0.00028534682,0.9918094,0.000006915655,0.000015375363,0.0074321027,0.00007755165],"about_ca_topic_score_codex":0.00006223232,"about_ca_topic_score_gemma":0.000034053483,"teacher_disagreement_score":0.85136074,"about_ca_system_score_codex":0.00003890599,"about_ca_system_score_gemma":0.000021373535,"threshold_uncertainty_score":0.3051183},"labels":[],"label_agreement":null}]}