{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":1020,"total_is_capped":false,"direct_labels_cover":2,"predictions_cover":1020,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"4890092d1e6b","filters":{"topic":"Scheduling and Optimization Algorithms"}},"results":[{"id":"W3047863327","doi":"10.1016/j.ejor.2020.07.063","title":"Machine learning for combinatorial optimization: A methodological tour d'horizon","year":2021,"lang":"en","type":"article","venue":"Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":1336,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Heuristics; Artificial intelligence; Machine learning; Combinatorial optimization; Point (geometry); Task (project management); Optimization problem; Mathematical optimization; Online machine learning; Active learning (machine learning); Mathematics; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.02949669187414749,"gpt":0.2493383453831598,"spread":0.2198416535090123,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003067695,0.0003375835,0.0004693959,0.0001418515,0.0004899802,0.00007615983,0.0002917478,0.0001705451,0.0003051583],"category_scores_gemma":[0.0001743892,0.0003584914,0.000196173,0.0003905017,0.0001029037,0.0001611969,0.0002299505,0.0003537058,0.00003595761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001330657,"about_ca_system_score_gemma":0.0000750901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003006928,"about_ca_topic_score_gemma":0.000005123715,"domain_scores_codex":[0.9981409,0.0002312886,0.0003139474,0.0005162815,0.0002796865,0.0005179323],"domain_scores_gemma":[0.9987649,0.0004934104,0.00007732036,0.0002535092,0.0002448689,0.0001659616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001098237,0.0001100611,0.0008349341,0.00007494839,0.0002514927,0.0001453229,0.0002188536,0.9587358,0.0002597603,0.0374418,0.0008348928,0.0009822937],"study_design_scores_gemma":[0.003336165,0.0003158462,0.0006477962,0.00005691878,0.0002469883,0.0001251819,0.0005948626,0.9379454,0.001470105,0.0008575814,0.05354628,0.0008568343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009521885,0.0009179566,0.9379303,0.0005977904,0.002453966,0.0003926399,0.0001265329,0.0007712165,0.04728765],"genre_scores_gemma":[0.1974862,0.001674509,0.7704101,0.0002273398,0.0008976053,0.00007653923,0.001619492,0.0001741122,0.0274341],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1879643,"threshold_uncertainty_score":0.9998867,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2154429979","doi":"10.1016/j.ejor.2003.08.027","title":"Executing production schedules in the face of uncertainties: A review and some future directions","year":2003,"lang":"en","type":"review","venue":"European Journal of Operational Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":690,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Scheduling (production processes); Production (economics); Schedule; Operations research; Production schedule; Face (sociological concept); Work schedule; Work (physics); Management science; Operations management; Economics; Mathematics; Engineering; Microeconomics; Sociology","retraction":null,"screen_n_in":null,"score":{"opus":0.09072389035271536,"gpt":0.3721100444108805,"spread":0.2813861540581651,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006832136,0.0001559306,0.0005314515,0.0003986314,0.0001330426,0.00008559164,0.0002876374,0.00004233275,0.00002794267],"category_scores_gemma":[0.0008867825,0.0001007035,0.0001157178,0.000792205,0.00007898322,0.0001872755,0.00002652995,0.001110795,0.000007914515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007710333,"about_ca_system_score_gemma":0.0002091712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.449948e-7,"about_ca_topic_score_gemma":4.864888e-7,"domain_scores_codex":[0.9965271,0.001821009,0.0007924521,0.00013679,0.000558699,0.0001639637],"domain_scores_gemma":[0.9989097,0.0002246907,0.0001560196,0.000167113,0.0004912785,0.0000512491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006285604,0.0001161462,0.000004233638,0.03063002,0.000304676,0.00005995755,0.001517603,0.02045799,0.000003071806,0.001991309,0.005610241,0.9392985],"study_design_scores_gemma":[0.0001058942,0.00005756002,0.000008926327,0.01620415,0.0001074486,0.0003193846,0.0006564101,0.0003183643,0.000001300599,0.0000127611,0.9820771,0.0001307358],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0000223047,0.9980474,0.0002268551,0.0005193103,0.0002941191,0.0003379326,0.000006289124,0.000006895217,0.0005388986],"genre_scores_gemma":[0.00002669092,0.9945705,0.004595931,0.00002736821,0.0005860744,0.000008899541,0.00001041223,0.00003436501,0.000139767],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9764668,"threshold_uncertainty_score":0.4825914,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2030395122","doi":"10.1016/j.aei.2006.05.004","title":"Applications of agent-based systems in intelligent manufacturing: An updated review","year":2006,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":547,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary; National Research Council Canada","funders":"","keywords":"Manufacturing execution system; Negotiation; Supply chain; Multi-agent system; Computer science; Manufacturing engineering; Process management; Computer-integrated manufacturing; Scheduling (production processes); Engineering; Systems engineering; Engineering management; Business; Operations management; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.006032637507384548,"gpt":0.2183467126605937,"spread":0.2123140751532092,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001104023,0.0001509568,0.0002224263,0.0001848482,0.00001344193,0.00001548554,0.0001450707,0.00005414948,0.00001017817],"category_scores_gemma":[0.000005348994,0.0001628902,0.00003326377,0.0003052142,0.000008755392,0.000210341,0.000008766682,0.0001248577,0.00001179623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007358583,"about_ca_system_score_gemma":0.00001024306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007993865,"about_ca_topic_score_gemma":0.000001506517,"domain_scores_codex":[0.9989509,0.000004572493,0.0006688199,0.00005899221,0.000133507,0.0001831827],"domain_scores_gemma":[0.9995428,0.0000189387,0.00007361858,0.0002704661,0.00004297858,0.00005119054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[6.410152e-7,0.00001494584,0.000008993573,0.001256146,0.000005254199,3.558302e-7,0.00003076872,0.9938846,0.0000521306,0.0002143922,0.00002568694,0.004506086],"study_design_scores_gemma":[0.0001816866,0.000009942312,0.00004742782,0.000417932,0.000009612586,0.000002041269,0.00004268936,0.9793044,0.006205764,0.000005000191,0.01359957,0.0001739202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006272989,0.002382981,0.9899507,0.000004783713,0.0001641746,0.0004004023,0.00001326136,0.0003964813,0.0004142499],"genre_scores_gemma":[0.5358009,0.001189695,0.4617927,0.00006141493,0.00005836219,0.0002694402,0.0007159584,0.00008024407,0.00003130711],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5295279,"threshold_uncertainty_score":0.6642471,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1975784696","doi":"10.1016/j.dam.2007.02.003","title":"A survey of scheduling with controllable processing times","year":2007,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":419,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Scheduling (production processes); Computer science; Dynamic priority scheduling; Mathematical optimization; Rate-monotonic scheduling; Fair-share scheduling; Data processing; Distributed computing; Mathematics; Database; Schedule","retraction":null,"screen_n_in":null,"score":{"opus":0.01243576526964326,"gpt":0.2328712612258647,"spread":0.2204354959562214,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005507969,0.0001483799,0.0002745459,0.00007525737,0.00004371601,0.00003232075,0.0001088314,0.00006510986,0.00002476661],"category_scores_gemma":[0.00003681291,0.0001205539,0.00002047579,0.0003429758,0.00004454686,0.00004993504,0.00001271373,0.00009893119,0.00001133566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001679951,"about_ca_system_score_gemma":0.00002414512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004749389,"about_ca_topic_score_gemma":0.00001212438,"domain_scores_codex":[0.999129,0.000003533283,0.0003284595,0.0001098855,0.0002008953,0.0002282152],"domain_scores_gemma":[0.9994345,0.0001309042,0.00009165298,0.0001760524,0.0001045117,0.00006241031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001225775,0.0001554976,0.0006261894,0.001702144,0.0002646091,0.000006646688,0.003896515,0.9602208,0.01009374,0.00914966,0.00005456777,0.01370706],"study_design_scores_gemma":[0.0008190778,0.00002149839,0.0003048128,0.0001274279,0.00004151857,0.000003468848,0.000983908,0.9751574,0.02191588,0.0003411236,0.0000118165,0.0002720654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04961553,0.00022734,0.928381,0.000003964296,0.00002809034,0.0001908282,0.000007401092,0.0002372445,0.02130867],"genre_scores_gemma":[0.5726691,0.000004238421,0.4272251,0.000006556024,0.00001359122,0.000005706665,0.00001353365,0.00003361792,0.00002846935],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5230536,"threshold_uncertainty_score":0.4916045,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2518264398","doi":"10.1016/j.cor.2016.04.006","title":"Mixed Integer Programming models for job shop scheduling: A computational analysis","year":2016,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":227,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Computer science; Integer programming; Mathematical optimization; Job shop scheduling; Constraint programming; Scheduling (production processes); Thread (computing); Algorithm; Mathematics; Stochastic programming; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.09631262454021754,"gpt":0.3475213637295705,"spread":0.251208739189353,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000723783,0.0001485048,0.000211808,0.0007972874,0.0003874926,0.0003312967,0.000302719,0.00009061415,0.00004010692],"category_scores_gemma":[0.0001250638,0.0001200769,0.0001426622,0.001388751,0.0001008869,0.0003245107,0.00006962584,0.0001817374,0.0000403669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000149867,"about_ca_system_score_gemma":0.000100558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001014391,"about_ca_topic_score_gemma":0.00003668995,"domain_scores_codex":[0.9984047,0.00009321433,0.0003243129,0.0003318324,0.000413781,0.0004321886],"domain_scores_gemma":[0.9982091,0.0004668038,0.0000101366,0.0002740523,0.000880199,0.0001597537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006133778,0.00002984155,0.00005099462,0.00001240367,0.0002623694,9.689685e-7,0.0002539449,0.9694018,0.00006396375,0.006348775,0.0003582722,0.02321048],"study_design_scores_gemma":[0.0004904989,0.00003662791,0.00004857717,0.00003272866,0.00003484332,0.000001801701,0.0001168956,0.9981953,0.00011483,0.0004449724,0.0003134753,0.0001694757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01325295,0.0001190073,0.9846722,0.0008133823,0.0002434711,0.0004563302,0.0000236761,0.0002947579,0.0001241942],"genre_scores_gemma":[0.4467854,0.00001400285,0.5525792,0.00001782939,0.0001156574,0.0001851392,0.00006203312,0.00002586773,0.0002148157],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4335325,"threshold_uncertainty_score":0.4896596,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1903284337","doi":"10.1109/sffcs.1999.814574","title":"Approximation schemes for minimizing average weighted completion time with release dates","year":2003,"lang":"en","type":"article","venue":"","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":207,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Preemption; Approximation algorithm; Polynomial-time approximation scheme; Scheduling (production processes); Time complexity; Job shop scheduling; Constant (computer programming); Computer science; Running time; Processor scheduling; Execution time; Mathematical optimization; Mathematics; Binary logarithm; Combinatorics; Algorithm; Parallel computing; Schedule","retraction":null,"screen_n_in":null,"score":{"opus":0.009612584698921019,"gpt":0.1993969946474032,"spread":0.1897844099484821,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008699849,0.0001001556,0.00010094,0.00005857086,0.0000656616,0.00004155676,0.00003853347,0.00004273891,0.0002428942],"category_scores_gemma":[0.0000307569,0.00008575077,0.00002154795,0.0001314836,0.00001085907,0.0001251425,0.000002515538,0.00004469029,0.00003819805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002179594,"about_ca_system_score_gemma":0.000008631386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.924417e-7,"about_ca_topic_score_gemma":3.908286e-7,"domain_scores_codex":[0.9995306,0.00001076671,0.0001269552,0.0001163922,0.00008251379,0.0001327594],"domain_scores_gemma":[0.9997179,0.0000471078,0.00002102682,0.0001064258,0.00006343575,0.00004412583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009997428,0.000164705,0.0004350004,0.0003168598,0.000232098,0.000003728786,0.0005973761,0.946491,0.007699228,0.02919838,0.004189136,0.01057251],"study_design_scores_gemma":[0.0005992894,0.00002141371,0.00001059857,0.00001711044,0.00001102342,0.000003739787,0.00003536337,0.9738823,0.02127772,0.0001097108,0.003888775,0.0001429948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02092065,0.00004991945,0.9710521,0.00003231908,0.00005850641,0.0002090942,0.000005281227,0.0004636237,0.007208467],"genre_scores_gemma":[0.1722053,0.000008456133,0.8268715,0.00004478161,0.00003167326,0.00003761872,0.0001734295,0.00003169407,0.0005956515],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1512846,"threshold_uncertainty_score":0.3496815,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2612374285","doi":"10.1016/j.cie.2018.10.030","title":"Applications of learning curves in production and operations management: A systematic literature review","year":2018,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":201,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Forgetting; Learning curve; Production (economics); Categorization; Computer science; Systematic review; Knowledge management; Work (physics); Management science; Data science; Artificial intelligence; Engineering; Economics; Psychology; Political science","retraction":null,"screen_n_in":null,"score":{"opus":0.01135881319542197,"gpt":0.2172388100715448,"spread":0.2058799968761228,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002288824,0.0001057797,0.0002169316,0.0001845481,0.00003198479,0.00002832532,0.00008014008,0.00005559531,0.000002695004],"category_scores_gemma":[0.0000883953,0.000110553,0.00002188316,0.0007281151,0.00001391355,0.0001134297,0.00002335499,0.0001900004,0.000002121437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002595554,"about_ca_system_score_gemma":0.00000473348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.362159e-7,"about_ca_topic_score_gemma":5.862535e-7,"domain_scores_codex":[0.9993557,0.00002245163,0.0003038025,0.0001310439,0.00008397793,0.0001030347],"domain_scores_gemma":[0.9997261,0.00002471964,0.00002645938,0.0001345432,0.00005442622,0.00003373265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001071921,0.00001213523,0.00002840543,0.05630174,0.00005666724,0.000001718489,0.0002691748,0.9390151,0.00005506682,0.0002956596,0.0002188692,0.003744427],"study_design_scores_gemma":[0.0002104487,0.00001835979,0.00001810864,0.1361118,0.00005162372,0.00001607322,0.00003159598,0.862896,0.0001489265,0.000001424761,0.0003362119,0.0001594592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003339755,0.1226607,0.868003,0.0003013353,0.001614566,0.003107073,0.000005288964,0.0007157496,0.0002524923],"genre_scores_gemma":[0.6169533,0.1510353,0.2267397,0.0002193667,0.002785274,0.001468549,0.0001850721,0.0002127492,0.0004005929],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6412632,"threshold_uncertainty_score":0.450822,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4252209735","doi":"10.1007/978-0-387-74437-7_12","title":"Scheduling Algorithms","year":2008,"lang":"en","type":"book-chapter","venue":"","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":175,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.0192833074180686,"gpt":0.2119657228259414,"spread":0.1926824154078728,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00004493235,0.0003593532,0.0003139179,0.000176729,0.00006705229,0.00003339903,0.000167887,0.0004308555,0.001847134],"category_scores_gemma":[0.000007282865,0.0003729603,0.0001442458,0.00003165734,0.00004250235,0.00005931306,0.00002679023,0.0004501033,0.001475963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006713488,"about_ca_system_score_gemma":0.00002906896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001439028,"about_ca_topic_score_gemma":9.31632e-7,"domain_scores_codex":[0.9989881,0.000002057461,0.0003004928,0.0002469296,0.000238305,0.0002241009],"domain_scores_gemma":[0.9994326,0.0000283993,0.00003631496,0.0003063438,0.00007104473,0.0001253359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003669427,0.0000123589,0.000001971113,0.0001211098,0.0004319762,0.0001607076,0.000227071,0.8610425,0.000009023696,0.01981835,0.0282782,0.08989305],"study_design_scores_gemma":[0.000255989,0.00001566601,9.369138e-7,0.0001097083,0.00003720754,0.00007561599,0.000008670196,0.6944401,0.000123092,0.0005096369,0.3035933,0.0008301337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000002596689,0.002416983,0.1031806,0.00002197738,0.0009277258,0.00009830239,0.00001246017,0.001498827,0.8918405],"genre_scores_gemma":[0.00005532853,0.003675385,0.3224089,0.00009206362,0.0006185501,0.000005369975,0.00008030447,0.0001991666,0.6728649],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2753151,"threshold_uncertainty_score":0.9998722,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3188522200","doi":"10.24963/ijcai.2021/595","title":"Combinatorial Optimization and Reasoning with Graph Neural Networks","year":2021,"lang":"en","type":"article","venue":"Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":172,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Polytechnique Montréal","funders":"","keywords":"Computer science; Combinatorial optimization; Key (lock); Artificial intelligence; Artificial neural network; Graph; Theoretical computer science; Machine learning; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.007084019536007027,"gpt":0.1887620670911135,"spread":0.1816780475551064,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008311681,0.0002733908,0.0002808821,0.0001454436,0.0003489602,0.00008820658,0.0001529292,0.00009723471,0.00004955292],"category_scores_gemma":[0.00001242969,0.0002789537,0.00005652074,0.0004162508,0.000145395,0.0002202898,0.0001518446,0.0002451768,0.000005119508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006007667,"about_ca_system_score_gemma":0.00003451316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004674273,"about_ca_topic_score_gemma":0.00001015811,"domain_scores_codex":[0.9987349,0.00007314869,0.0001808852,0.0003928961,0.0002227354,0.0003954163],"domain_scores_gemma":[0.9993529,0.00008637049,0.00005186398,0.0002221621,0.0001377071,0.0001490531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004527944,0.00004029706,0.004417514,0.00002621146,0.000147967,0.0002136179,0.0002064969,0.9789525,0.00002115405,0.01548188,0.0001479444,0.0002990929],"study_design_scores_gemma":[0.001497613,0.00007119652,0.004152101,0.00005971366,0.0001293583,0.000173404,0.0004449718,0.9920523,0.0001173883,0.00008017182,0.0007664788,0.000455358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2189251,0.001322717,0.7303302,0.0004588504,0.001849344,0.0003298868,0.00005673433,0.0008050274,0.04592207],"genre_scores_gemma":[0.9375754,0.0008369788,0.05901939,0.0001228062,0.000216991,0.00001037272,0.0002997665,0.00007554943,0.001842755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7186503,"threshold_uncertainty_score":0.9999663,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2067884717","doi":"10.1016/j.cor.2005.07.004","title":"Scheduling and routing of automated guided vehicles: A hybrid approach","year":2005,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":161,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Scheduling (production processes); Mathematical optimization; Integer programming; Job shop scheduling; Routing (electronic design automation); Constraint programming; Distributed computing; Algorithm; Computer network; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.05043721699695757,"gpt":0.3291782384169027,"spread":0.2787410214199451,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006357919,0.00009741656,0.0001435648,0.0002703429,0.0002327686,0.0001562244,0.0001730919,0.00004711884,0.000009268195],"category_scores_gemma":[0.00009429474,0.0001011691,0.00002521391,0.000448765,0.00008610155,0.00018076,0.00008069667,0.0002461005,0.00001532861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005961765,"about_ca_system_score_gemma":0.00004955664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002032141,"about_ca_topic_score_gemma":0.000002242801,"domain_scores_codex":[0.998919,0.00008267311,0.0002836625,0.000189306,0.0002624297,0.0002629057],"domain_scores_gemma":[0.99933,0.00009233467,0.000008957865,0.0002062878,0.0002688313,0.00009358231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001168973,0.00002572172,0.00004160487,0.00002574275,0.000020911,8.218577e-7,0.0006153305,0.9896868,0.001174725,0.0003658395,0.0003818873,0.007659432],"study_design_scores_gemma":[0.0003080133,0.00001506694,0.00009446499,0.00003993394,0.000003152611,0.00001509772,0.0002010342,0.9952956,0.003846264,0.000003945515,0.00007889995,0.00009850886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4336124,0.0003835028,0.5641868,0.0001880046,0.0000763501,0.0002152748,0.000004415062,0.0005569484,0.0007763245],"genre_scores_gemma":[0.578446,0.00003413822,0.4213553,0.00001363444,0.00007672125,0.00001155558,0.00001609076,0.00001527709,0.00003123048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1448336,"threshold_uncertainty_score":0.4125556,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1571472016","doi":"10.1007/978-3-642-13520-0_23","title":"Automated Configuration of Mixed Integer Programming Solvers","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":152,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Parameterized complexity; Integer programming; Domain (mathematical analysis); Integer (computer science); Process (computing); Linear programming; Mathematical optimization; Algorithm; Programming language; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01002446137210096,"gpt":0.2252929834749474,"spread":0.2152685221028465,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002410792,0.0002235283,0.0002508688,0.0003669611,0.00005384805,0.00008905376,0.0003660241,0.0002967337,0.00003681211],"category_scores_gemma":[0.00005222802,0.000214431,0.00005307235,0.0002246014,0.0003798756,0.0001135161,0.00005044587,0.0005127047,0.00001079138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006795743,"about_ca_system_score_gemma":0.00009490189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004282711,"about_ca_topic_score_gemma":0.00001782577,"domain_scores_codex":[0.998851,0.000005363026,0.0003065313,0.0003084966,0.0003078182,0.0002208073],"domain_scores_gemma":[0.9992903,0.00009864939,0.00009326434,0.0002898595,0.0001680771,0.00005988404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001327484,0.000004629832,0.000005744395,0.00003682835,0.000008266822,0.000003726862,0.0002851593,0.7731276,0.0007049234,0.0002685787,0.000003576361,0.2255497],"study_design_scores_gemma":[0.0001169759,0.00002613631,0.00001193094,0.0001635301,0.000006626455,0.000006794016,2.724426e-7,0.9822432,0.01618765,0.0007714104,0.0002411671,0.0002243314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001707654,0.00008145173,0.9962364,0.00003117098,0.001919869,0.0001716587,0.000002721848,0.0005392713,0.0008466569],"genre_scores_gemma":[0.2036168,0.00001305936,0.7960145,0.00004627573,0.0001828294,0.000004714892,0.00002204158,0.00004047156,0.00005929252],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2253254,"threshold_uncertainty_score":0.8744243,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3205671243","doi":"10.1016/j.jmsy.2021.09.018","title":"Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance","year":2021,"lang":"en","type":"article","venue":"Journal of Manufacturing Systems","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":132,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Government of Canada","keywords":"Job shop; Scheduling (production processes); Job shop scheduling; Computer science; Industrial engineering; Production planning; Production (economics); Benchmark (surveying); Process (computing); Engineering; Operations research; Reliability engineering; Mathematical optimization; Schedule; Flow shop scheduling; Operations management","retraction":null,"screen_n_in":null,"score":{"opus":0.008529663143927913,"gpt":0.2191855303361605,"spread":0.2106558671922325,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004350519,0.0001772173,0.0003369564,0.0002343815,0.00006790079,0.0001573068,0.00005532692,0.00007357373,0.00000899294],"category_scores_gemma":[0.00007353297,0.0001430099,0.0000269038,0.0001633882,0.0000335469,0.0002666453,0.00000925227,0.0002838504,0.000001190856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000119451,"about_ca_system_score_gemma":0.00006834186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002508925,"about_ca_topic_score_gemma":0.00001229181,"domain_scores_codex":[0.9988863,0.00006480929,0.0004898879,0.0001770576,0.0001860868,0.0001958306],"domain_scores_gemma":[0.9993448,0.00007021802,0.0001969372,0.0001124738,0.0001865875,0.00008895326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000643008,0.00001273224,0.003324745,0.0002262259,0.00004709599,0.0001542346,0.0002952332,0.9900073,0.005376741,0.0000081265,0.00004062405,0.000442585],"study_design_scores_gemma":[0.002162008,0.0001371122,0.01061228,0.00394588,0.0000524449,0.001805365,0.001833901,0.9315815,0.04729963,0.00002825381,0.0001375932,0.0004040377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9584032,0.0007638055,0.03996591,0.0001404182,0.0004067216,0.0001252,0.000005108306,0.00007830957,0.0001113426],"genre_scores_gemma":[0.9696156,0.0001490125,0.02985855,0.000014163,0.0001300222,0.000007175385,0.00001169359,0.00003335369,0.0001804345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05842587,"threshold_uncertainty_score":0.5831774,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2046678043","doi":"10.1016/j.ejor.2005.02.077","title":"Integrated production and material handling scheduling using mathematical programming and constraint programming","year":2005,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":128,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Constraint programming; Scheduling (production processes); Job shop scheduling; Mathematical optimization; Resource constraints; Industrial engineering; Stochastic programming; Distributed computing; Mathematics; Engineering; Schedule","retraction":null,"screen_n_in":null,"score":{"opus":0.06525778728490876,"gpt":0.325279648839937,"spread":0.2600218615550283,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003342187,0.0001118323,0.0001506214,0.0002429863,0.0002472763,0.0004915108,0.00008459437,0.00002922674,0.00004411501],"category_scores_gemma":[0.0006420422,0.00009550195,0.00002386239,0.0001953544,0.0001674677,0.0003543212,0.00003958821,0.0004451465,0.000005400796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006878485,"about_ca_system_score_gemma":0.00007849489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.88453e-7,"about_ca_topic_score_gemma":5.391744e-7,"domain_scores_codex":[0.9985174,0.0002405379,0.0004542841,0.0001370949,0.0004163664,0.0002343005],"domain_scores_gemma":[0.999106,0.00007129917,0.00005181977,0.00006352145,0.000560439,0.0001468489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001301785,0.0001516888,0.0004273046,0.0002105308,0.0001766285,0.0001243496,0.003287734,0.5481498,0.05324852,0.001974967,0.00005469042,0.3920636],"study_design_scores_gemma":[0.0007114741,0.0001988757,0.0001584429,0.0007374121,0.00002584085,0.001685788,0.002099561,0.9834367,0.008718951,0.00004754118,0.001922045,0.0002573828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9018309,0.0004170494,0.09681023,0.0003610855,0.0001757532,0.0001817636,0.000001837559,0.00004465777,0.0001767293],"genre_scores_gemma":[0.5551499,0.00004200099,0.4442716,0.000004892559,0.0004908639,0.000001149748,0.000002372025,0.00002132617,0.0000159408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4352869,"threshold_uncertainty_score":0.473965,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2017773796","doi":"10.1023/b:apin.0000027769.48098.91","title":"Local Search Genetic Algorithms for the Job Shop Scheduling Problem","year":2004,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":128,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"Brock University","keywords":"Computer science; Crossover; Job shop scheduling; Tabu search; Genetic algorithm; Mathematical optimization; Operator (biology); Algorithm; Scheduling (production processes); Deadlock; Genetic operator; Local search (optimization); Schedule; Population-based incremental learning; Distributed computing; Artificial intelligence; Mathematics; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.02769975547881486,"gpt":0.2645209188918796,"spread":0.2368211634130648,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002363724,0.0001888575,0.0001419393,0.00006253339,0.0001926098,0.00008388711,0.0003905942,0.0001022474,0.00004423557],"category_scores_gemma":[0.00001442957,0.0001527865,0.0000642748,0.0003434089,0.000118605,0.00004530729,0.00003989501,0.0002601314,0.0001764181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008916895,"about_ca_system_score_gemma":0.00005056626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001696365,"about_ca_topic_score_gemma":0.000005646997,"domain_scores_codex":[0.9988217,0.000005618492,0.0002871066,0.0002610862,0.0002212169,0.0004032355],"domain_scores_gemma":[0.9993525,0.0001573682,0.00002037229,0.0002938755,0.00008273123,0.00009320098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005057482,0.00001010173,0.000003883472,0.00002765119,0.00002668414,8.301256e-7,0.0003702724,0.8721408,0.0001763608,0.006109416,0.00001107325,0.1211179],"study_design_scores_gemma":[0.0001561962,0.00002557672,0.00001841477,0.0000227101,0.00001794961,0.000008236947,0.0008966235,0.9608138,0.03452756,0.003006756,0.0002856108,0.0002205534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001612654,0.0007593217,0.99507,0.0001537871,0.0002516734,0.0005759026,0.000004211787,0.0003329855,0.001239507],"genre_scores_gemma":[0.4839574,0.000210815,0.515329,0.00008602851,0.0001423498,0.000185031,0.000004840964,0.00004461628,0.00003989955],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4823447,"threshold_uncertainty_score":0.6230451,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W27038116","doi":"10.1093/jnci/djw115","title":"Polyhedral Approaches to Machine Scheduling","year":2008,"lang":"en","type":"article","venue":"","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":119,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Polyhedron; Scheduling (production processes); Mathematical optimization; Polyhedral combinatorics; Mathematical proof; Travelling salesman problem; Computer science; Cutting-plane method; Mathematics; Algorithm; Combinatorics; Integer programming; Regular polygon; Convex optimization","retraction":null,"screen_n_in":null,"score":{"opus":0.07337646448938698,"gpt":0.2119761857776887,"spread":0.1385997212883018,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004622385,0.00009807781,0.0000930612,0.00008202044,0.00006726612,0.00001510756,0.00008892483,0.00004170365,0.000157685],"category_scores_gemma":[0.00001489564,0.00009154917,0.00003119847,0.0002114332,0.00001183008,0.00006242812,0.00001672493,0.0000854067,0.0002651241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001839539,"about_ca_system_score_gemma":0.000007340499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009246759,"about_ca_topic_score_gemma":0.000002552753,"domain_scores_codex":[0.9994969,0.00000514154,0.0001161024,0.0001119501,0.000096051,0.0001738952],"domain_scores_gemma":[0.9997231,0.00001217142,0.000005338976,0.0001323268,0.00001008651,0.0001169355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002016622,0.00001029433,0.0006667683,0.00000520747,0.00001229059,0.000004129716,0.000288132,0.9957371,0.00009007174,0.0006377365,0.0001853658,0.002360829],"study_design_scores_gemma":[0.000146885,0.000009977575,0.0004545612,0.000003620226,0.000002517413,0.00002490208,0.00005059586,0.9958233,0.002941719,0.00001608068,0.0003718254,0.0001540159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1551551,0.0002853062,0.8173701,0.0003534493,0.0003216325,0.00009948913,0.000002565953,0.001161856,0.02525051],"genre_scores_gemma":[0.5720232,0.00001080573,0.4270944,0.0001168601,0.00008042951,0.000006139547,0.000005356456,0.00001960672,0.0006431268],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4168681,"threshold_uncertainty_score":0.3733267,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2064816527","doi":"10.1016/j.disopt.2008.04.001","title":"The time-dependent traveling salesman problem and single machine scheduling problems with sequence dependent setup times","year":2008,"lang":"en","type":"article","venue":"Discrete Optimization","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":118,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Tardiness; Travelling salesman problem; Sequence (biology); Mathematical optimization; Single-machine scheduling; Scheduling (production processes); Computation; Branch and bound; Integer programming; Computer science; Job shop scheduling; Linear programming relaxation; Mathematics; Algorithm; Schedule","retraction":null,"screen_n_in":null,"score":{"opus":0.01172588068999795,"gpt":0.1993286561662797,"spread":0.1876027754762818,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002460116,0.0002848532,0.0002022007,0.00007219983,0.0005810566,0.0001919867,0.0001740354,0.0001044534,0.00004050029],"category_scores_gemma":[0.00002839473,0.0002117633,0.00003384281,0.0002644313,0.0001213549,0.000329369,0.00003874699,0.0002278657,0.00001726554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007847133,"about_ca_system_score_gemma":0.00003629341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001628321,"about_ca_topic_score_gemma":0.00001759303,"domain_scores_codex":[0.9985631,0.00005038154,0.0003517607,0.0003339689,0.000344485,0.0003563243],"domain_scores_gemma":[0.9993445,0.00006695664,0.00009761749,0.0002505655,0.0001164729,0.0001239485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001617408,0.00001536477,0.0002440248,0.00003061182,0.00004728239,0.000007513815,0.0005149206,0.9976181,0.0004549061,0.00006309851,0.000008551435,0.0009794261],"study_design_scores_gemma":[0.0005595281,0.00007166697,0.00001871623,0.00008681413,0.00003428416,0.0001285756,0.000116456,0.9975072,0.001081247,0.00004465852,0.00003159177,0.0003193356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01241708,0.001326781,0.9811237,0.0002417907,0.0001222928,0.0006192916,0.00002495681,0.000698143,0.003425999],"genre_scores_gemma":[0.5418719,0.001262103,0.4552725,0.0000438612,0.00009265327,0.0000771334,0.000242617,0.0001393104,0.0009978941],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5294548,"threshold_uncertainty_score":0.8635458,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2236520910","doi":"10.1016/j.tcs.2016.01.002","title":"Solving the Many to Many assignment problem by improving the Kuhn–Munkres algorithm with backtracking","year":2016,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":117,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; Nipissing University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Backtracking; Algorithm; Task (project management); Assignment problem; Computer science; Mathematics; Process (computing); Mathematical optimization","retraction":null,"screen_n_in":null,"score":{"opus":0.004772503037445953,"gpt":0.1974227877428128,"spread":0.1926502847053669,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001146408,0.0001915703,0.0001277113,0.00005607634,0.0005235586,0.0004084861,0.001039935,0.0000355646,0.00005597351],"category_scores_gemma":[0.00002973004,0.00008067008,0.00003244897,0.0004908479,0.001046032,0.0002223217,0.0002656836,0.0001762153,0.00005241494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009057255,"about_ca_system_score_gemma":0.00003319897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002901188,"about_ca_topic_score_gemma":3.667984e-7,"domain_scores_codex":[0.9982356,0.00005243523,0.0002089867,0.0003697264,0.0005742555,0.0005589706],"domain_scores_gemma":[0.9989377,0.0002902434,0.00003312782,0.0004777052,0.00008995363,0.000171248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000127639,0.00003255198,0.00002694111,0.00001400367,0.00002590861,0.000006613707,0.002080337,0.04967047,0.005756429,0.1518155,0.0003866134,0.7901719],"study_design_scores_gemma":[0.0001812754,0.0001169772,0.00007320192,0.0001063432,0.00000952186,0.00002009543,0.00009116574,0.9882178,0.009097137,0.001704863,0.0001559417,0.0002256849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00600379,0.00005864875,0.9903935,0.002006194,0.0002949407,0.0002659343,0.000003452922,0.0002558458,0.0007177095],"genre_scores_gemma":[0.6889623,0.000006720108,0.3104542,0.0003660833,0.0001393221,0.00002361377,2.96718e-7,0.00002166688,0.00002573816],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9385473,"threshold_uncertainty_score":0.4026843,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2065819816","doi":"10.1016/s0377-2217(01)00083-2","title":"Two-machine flow shops with limited machine availability","year":2002,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":116,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Flow shop scheduling; Computer science; Time horizon; Mathematical optimization; Heuristic; Job shop scheduling; Flow (mathematics); Upper and lower bounds; Constant (computer programming); Time complexity; Algorithm; Mathematics; Routing (electronic design automation)","retraction":null,"screen_n_in":null,"score":{"opus":0.05547564325294014,"gpt":0.2931843484889926,"spread":0.2377087052360525,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002746133,0.0001264402,0.0001561975,0.0002680853,0.000187778,0.0001734072,0.0003005231,0.00001781288,0.002365195],"category_scores_gemma":[0.000429565,0.00009432217,0.00005160866,0.0004651864,0.0000912256,0.0002560888,0.00003676204,0.0007947131,0.0003138169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008109382,"about_ca_system_score_gemma":0.00003843294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000205282,"about_ca_topic_score_gemma":0.000003878691,"domain_scores_codex":[0.9977409,0.0005416729,0.0004223877,0.0001347253,0.000910597,0.000249704],"domain_scores_gemma":[0.9984537,0.000194687,0.00003953719,0.0001925241,0.0009061655,0.0002133606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007037536,0.0001235672,0.0008856449,0.00001570908,0.00007825575,0.0002326995,0.0003894501,0.980944,0.0005040743,0.0001143744,0.004134525,0.01250733],"study_design_scores_gemma":[0.00120834,0.0002884802,0.001359186,0.00004419618,0.000006628663,0.0002032729,0.00004308864,0.991718,0.0003063354,0.000006310463,0.004689152,0.0001270236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3741129,0.01237292,0.348232,0.009591433,0.001474915,0.0008914322,0.0001537395,0.0004694909,0.2527012],"genre_scores_gemma":[0.8810828,0.000228117,0.1169129,0.00007981701,0.0004616133,0.000001443275,0.00001686481,0.00005568695,0.001160709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5069699,"threshold_uncertainty_score":0.9985468,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2255734301","doi":"10.1287/ijoc.2015.0666","title":"Decomposition Methods for the Parallel Machine Scheduling Problem with Setups","year":2016,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":113,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Job shop scheduling; Solver; Mathematical optimization; Computer science; Travelling salesman problem; Scheduling (production processes); Metaheuristic; Decomposition; Benders' decomposition; Integer programming; Branch and bound; Algorithm; Mathematics; Routing (electronic design automation)","retraction":null,"screen_n_in":null,"score":{"opus":0.01518101738347567,"gpt":0.3035211131566281,"spread":0.2883400957731524,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008726877,0.0001570182,0.0001493379,0.0000960383,0.0003813052,0.0001532692,0.0001860645,0.00005247885,0.00001407903],"category_scores_gemma":[0.00005482305,0.00007153482,0.00007878902,0.0001361903,0.00002285977,0.0002207655,0.00001822361,0.0002592879,0.000009819558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007730929,"about_ca_system_score_gemma":0.0000252601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.729581e-7,"about_ca_topic_score_gemma":5.135101e-7,"domain_scores_codex":[0.9991189,0.00002174331,0.0003487597,0.00008930316,0.000145055,0.0002762328],"domain_scores_gemma":[0.9989094,0.0006297139,0.0001301627,0.0001198949,0.0001207098,0.00009010296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003097714,0.000005128304,0.00006311683,0.00001010644,0.00006097266,9.697924e-7,0.0001139009,0.7552961,0.00008721799,0.0003039336,0.00002538555,0.2440023],"study_design_scores_gemma":[0.001100212,0.0001262181,0.00009613531,0.0002448953,0.00002000996,0.0002006834,0.00005259316,0.9948173,0.0007621802,0.0004572935,0.001957644,0.0001648631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004815283,0.0002211396,0.9932613,0.0005682622,0.0003284661,0.0001809594,0.000001940726,0.0001767165,0.0004458844],"genre_scores_gemma":[0.1201494,0.00005520972,0.8792222,0.0001739882,0.0003181353,0.000006269019,0.000002286773,0.0000318823,0.00004056899],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2438374,"threshold_uncertainty_score":0.293273,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1984200231","doi":"10.1080/00207543.2013.827806","title":"Mathematical modelling and a meta-heuristic for flexible job shop scheduling","year":2013,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":110,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"University of Windsor","funders":"","keywords":"Heuristics; Job shop scheduling; Benchmark (surveying); Mathematical optimization; Integer programming; Computer science; Simulated annealing; Heuristic; Scheduling (production processes); Algorithm; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1545567162866745,"gpt":0.3801708225125963,"spread":0.2256141062259218,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001253707,0.00008340181,0.0001749438,0.0004009169,0.00007195819,0.0001893573,0.0001982117,0.00004712819,0.0002247108],"category_scores_gemma":[0.0006376629,0.00006837991,0.00008037761,0.0001414181,0.00005501667,0.0003451603,0.00002775192,0.0003129886,0.00002639185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006421534,"about_ca_system_score_gemma":0.00003580345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003378832,"about_ca_topic_score_gemma":1.593934e-7,"domain_scores_codex":[0.9986904,0.00003816153,0.0003727665,0.000121051,0.0006064837,0.0001711882],"domain_scores_gemma":[0.9975744,0.0001894589,0.00005577557,0.0000853943,0.002001727,0.00009326309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002167998,0.00003317894,0.00001471602,0.0000503539,0.0006258479,0.000002289577,0.0002568792,0.9933156,0.0007050362,0.002103919,0.0008920679,0.001978461],"study_design_scores_gemma":[0.0002489771,0.00004019452,0.000009240997,0.0000281722,0.00008997781,0.0001250398,0.000324893,0.9767161,0.003676923,0.01830065,0.000361323,0.00007850646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06597225,0.0008355108,0.9281039,0.003492866,0.0008509078,0.0002611658,0.000002903285,0.00005759818,0.0004228743],"genre_scores_gemma":[0.5325727,0.0002529122,0.4649902,0.00001851743,0.0009888938,0.00005585745,0.000002261512,0.00003039493,0.00108819],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4666005,"threshold_uncertainty_score":0.2788452,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2013425394","doi":"10.1016/s0925-5273(03)00084-7","title":"Countering forgetting through training and deployment","year":2003,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":108,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Forgetting; Software deployment; Flexibility (engineering); Task (project management); Computer science; Context (archaeology); Similarity (geometry); Productivity; Artificial intelligence; Cognitive psychology; Psychology; Economics; Software engineering; Management","retraction":null,"screen_n_in":null,"score":{"opus":0.02265905760632428,"gpt":0.2391113325444366,"spread":0.2164522749381123,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002552336,0.00006321901,0.00009021681,0.00007697068,0.00002635293,0.00005689335,0.00006916458,0.00002343858,0.00001805263],"category_scores_gemma":[0.0001005473,0.00006813002,0.00003026823,0.00002450161,0.00001439282,0.0003890113,0.000006163847,0.00009284194,0.000001782177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009548523,"about_ca_system_score_gemma":0.00002121393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.417968e-7,"about_ca_topic_score_gemma":8.869558e-7,"domain_scores_codex":[0.9994949,0.000008556491,0.0002929465,0.00006904416,0.00006288847,0.00007172133],"domain_scores_gemma":[0.9996884,0.00001519205,0.0001103976,0.00004038949,0.0001159733,0.00002963335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009188036,0.00001702149,0.0006899053,0.000008142159,0.0002046459,0.000002885559,0.001967868,0.9699122,0.0005880503,0.002724332,0.0001752351,0.02370048],"study_design_scores_gemma":[0.005086479,0.0002661755,0.00128441,0.0004976782,0.0001545266,0.009307718,0.0142634,0.5221152,0.1267956,0.01966373,0.2990263,0.001538788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8781089,0.0005332626,0.1074605,0.0007087641,0.01147596,0.00005091595,0.000002124931,0.00005353416,0.001606134],"genre_scores_gemma":[0.9010873,0.0007776331,0.09723868,0.00006821437,0.0007648894,0.000001161146,9.651718e-7,0.00001800248,0.00004312748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.447797,"threshold_uncertainty_score":0.2778262,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W653365980","doi":"10.1007/978-3-540-33876-5","title":"Multiagent based Supply Chain Management","year":2006,"lang":"en","type":"book","venue":"Studies in computational intelligence","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Supply chain management; Computer science; Supply chain; Artificial intelligence; Business; Marketing","retraction":null,"screen_n_in":null,"score":{"opus":0.0390782186945678,"gpt":0.299767815151777,"spread":0.2606895964572092,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001696749,0.0003208426,0.000322781,0.0003465564,0.00006117368,0.00002468966,0.0002244073,0.0001163398,0.00006098977],"category_scores_gemma":[0.00001976923,0.0003535784,0.00008579926,0.0001914818,0.0001290967,0.00003233633,0.00006852511,0.0002755149,0.0001300114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005186324,"about_ca_system_score_gemma":0.00004366778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002652886,"about_ca_topic_score_gemma":0.00001225638,"domain_scores_codex":[0.998547,0.00002047763,0.0005107712,0.0003220254,0.000366545,0.0002331436],"domain_scores_gemma":[0.9992611,0.0003297396,0.00006370165,0.0001619332,0.0001477173,0.00003576544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000256644,0.00002047128,0.000009281944,0.0002848236,0.0001167984,0.00004984224,0.0001397616,0.9686215,1.472534e-8,0.00364813,0.01978324,0.007323573],"study_design_scores_gemma":[0.000114211,0.00001346095,0.00003806067,0.0004985185,0.00002226024,0.000001846351,0.0001085477,0.9773623,0.000009082631,0.01238898,0.009082727,0.0003599785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000006187347,0.005455348,0.8574927,0.00008205853,0.001619365,0.0004374366,0.0000565955,0.0003204218,0.1345299],"genre_scores_gemma":[0.008395934,0.001532259,0.7439309,0.0003956208,0.0005735414,0.0002762475,0.001097009,0.0001862243,0.2436122],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1135617,"threshold_uncertainty_score":0.9998916,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1680007418","doi":"10.1007/978-0-387-88843-9","title":"Operations Research and Cyber-Infrastructure","year":2009,"lang":"en","type":"book","venue":"","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":98,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Business","retraction":null,"screen_n_in":null,"score":{"opus":0.02136525928509491,"gpt":0.2763886287476179,"spread":0.255023369462523,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001008567,0.000136243,0.000134913,0.000177331,0.00009645112,0.0001168792,0.00009380221,0.0002814121,0.0004537505],"category_scores_gemma":[0.00001569961,0.0001270822,0.00001837458,0.00008124724,0.00004305749,0.0000466795,0.00001988326,0.000544447,0.00007866963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007781249,"about_ca_system_score_gemma":0.00007248209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002606064,"about_ca_topic_score_gemma":0.00001623405,"domain_scores_codex":[0.9993603,0.000008749425,0.0001252349,0.0001564757,0.0001863037,0.0001628958],"domain_scores_gemma":[0.9995863,0.00003197352,0.000003311247,0.0001803634,0.0001169183,0.00008117838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001020798,0.000005049333,0.000001335236,0.00004048753,0.00004144858,0.000007061301,0.0001567553,0.5688437,0.000007501526,0.01817223,0.395058,0.01766538],"study_design_scores_gemma":[0.0002676666,0.00006475626,0.00006024367,0.0001038197,0.00001660172,0.00002563759,0.00004489511,0.7339495,0.00006940557,0.008525428,0.2563767,0.0004953901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00002684832,0.0008751195,0.009894442,0.00009081893,0.0001507018,0.0001457274,0.000009653146,0.0003670058,0.9884397],"genre_scores_gemma":[0.00008675938,0.0005360916,0.08532246,0.00005658599,0.0003621988,0.00000588854,0.0001139835,0.00004291523,0.9134731],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1651058,"threshold_uncertainty_score":0.5182263,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1976972714","doi":"10.1016/s0278-6125(00)80011-4","title":"Scheduling of manufacturing systems under dual-resource constraints using genetic algorithms","year":2000,"lang":"en","type":"article","venue":"Journal of Manufacturing Systems","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":98,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Tardiness; Computer science; Staffing; Scheduling (production processes); Mathematical optimization; Job shop scheduling; Flow shop scheduling; Genetic algorithm scheduling; Dynamic priority scheduling; Fair-share scheduling; Schedule; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01795595434379066,"gpt":0.2304092839189099,"spread":0.2124533295751193,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007685092,0.0004356888,0.0008883426,0.0004741932,0.0001426734,0.0002221866,0.0003684271,0.0002669322,0.0001445563],"category_scores_gemma":[0.0000204775,0.0004089884,0.0002643307,0.0001438052,0.0001101073,0.0002864351,0.00003077778,0.0005782761,0.00002279528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000258939,"about_ca_system_score_gemma":0.0000711379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005209938,"about_ca_topic_score_gemma":4.793931e-7,"domain_scores_codex":[0.9965038,0.0001563091,0.001737386,0.0002638583,0.0007895545,0.0005490579],"domain_scores_gemma":[0.9983947,0.000170136,0.0006309057,0.0003807195,0.0001348532,0.0002886989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001794836,0.00002928795,0.00004706612,0.0004226086,0.0003011258,0.00008742265,0.0002442677,0.9936989,0.0003995384,0.000006649409,0.00003041588,0.004714762],"study_design_scores_gemma":[0.001909994,0.0001128467,0.0008929103,0.002190212,0.0002428495,0.003991973,0.002258496,0.9149606,0.07130989,0.00002264716,0.001307578,0.0008000623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8872411,0.003247875,0.106372,0.00001416502,0.002069278,0.0002583301,0.00001586934,0.0001441007,0.0006373071],"genre_scores_gemma":[0.9709851,0.0001700525,0.02749683,0.00001509788,0.001022265,0.000003639822,0.000003340031,0.0001136225,0.0001900907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08374397,"threshold_uncertainty_score":0.9998362,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2115919109","doi":"10.1109/tsmcc.2007.900670","title":"Toward Real-Time Distributed Intelligent Control: A Survey of Research Themes and Applications","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":94,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Real-time Control System; Distributed manufacturing; Control (management); Computer science; Key (lock); Scheduling (production processes); Intelligent control; Supply chain; Distributed computing; Process management; Systems engineering; Manufacturing engineering; Engineering; Operations management; Computer security; Artificial intelligence; Business","retraction":null,"screen_n_in":null,"score":{"opus":0.05148171698706466,"gpt":0.308105615226717,"spread":0.2566238982396523,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00129578,0.0001675293,0.0003575744,0.0001616569,0.0001951182,0.00006190634,0.00009648034,0.0001127725,0.0000112772],"category_scores_gemma":[0.000007197155,0.0001465272,0.00003944568,0.0004930404,0.0001906738,0.00003496448,0.000002572824,0.0001918223,0.00002131737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000258813,"about_ca_system_score_gemma":0.00001353101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001220728,"about_ca_topic_score_gemma":0.00004021534,"domain_scores_codex":[0.9986529,0.0001185511,0.0005798932,0.0002656436,0.0001664301,0.0002165866],"domain_scores_gemma":[0.9988747,0.0003507275,0.00007961709,0.0003150629,0.0002032357,0.0001766305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001686011,0.001482654,0.001878828,0.005764333,0.001070602,0.000004580863,0.003730909,0.08060139,0.00383312,0.01798538,0.002891419,0.8805882],"study_design_scores_gemma":[0.003961958,0.0006657254,0.01101744,0.002181359,0.0008064187,0.0002068117,0.003022623,0.334797,0.006305092,0.000579449,0.6339157,0.002540394],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002399318,0.01305103,0.9819478,0.00003536427,0.00006672156,0.001540596,0.0002627249,0.00009930853,0.0005971813],"genre_scores_gemma":[0.9135723,0.08298509,0.001895323,0.00001237721,0.00007548864,0.0009005056,0.00005993406,0.00003772715,0.0004612773],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9800524,"threshold_uncertainty_score":0.5975207,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2035133691","doi":"10.1287/opre.48.1.177.12451","title":"Minimizing Cycle Time in a Blocking Flowshop","year":2000,"lang":"en","type":"article","venue":"Operations Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Blocking (statistics); Heuristic; Computer science; Set (abstract data type); Mathematical optimization; Throughput; Algorithm; Mathematics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.02777353314887171,"gpt":0.3146552187995641,"spread":0.2868816856506924,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004563697,0.00006454957,0.00007829047,0.0002472667,0.0001733474,0.0001418294,0.0001262093,0.00005640668,0.003536139],"category_scores_gemma":[0.00006514691,0.00006921489,0.00001770615,0.0007345609,0.00002713514,0.0001404003,0.00001409354,0.0002768853,0.001433498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007049055,"about_ca_system_score_gemma":0.00003515955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005731617,"about_ca_topic_score_gemma":0.00008661429,"domain_scores_codex":[0.9991298,0.00006658645,0.0001553475,0.0001409495,0.0002247785,0.0002825156],"domain_scores_gemma":[0.9996285,0.00005910144,0.000001163063,0.0001831873,0.00006714367,0.00006091424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001808701,0.00001818952,0.00002365172,0.000003327287,0.000003691858,0.000005002477,0.0004764594,0.9825862,0.0008862735,0.00003119829,0.0002155613,0.01574864],"study_design_scores_gemma":[0.0001893726,0.000008157442,0.0001095167,0.00002272003,7.18609e-7,0.000003090739,0.00009153011,0.9975412,0.0007701663,0.00001219252,0.001172042,0.00007929794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9394111,0.0002662034,0.001672629,0.0004200056,0.00006730444,0.0002430097,0.000005528672,0.0002410226,0.05767319],"genre_scores_gemma":[0.9239958,0.0001767479,0.06540516,0.00003184823,0.0001587869,0.00009483416,0.00003082327,0.00004455862,0.01006145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06373253,"threshold_uncertainty_score":0.999344,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2159737632","doi":"10.1111/j.1937-5956.2005.tb00019.x","title":"Setup Time Reduction for Electronics Assembly: Combining Simple (SMED) and IT‐Based Methods","year":2005,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Sheridan College","funders":"Alfred P. Sloan Foundation; University of California, San Diego; National Science Foundation","keywords":"Key (lock); Computer science; Printed circuit board; Electronics; Reduction (mathematics); Simple (philosophy); Process (computing); Manufacturing engineering; Reliability engineering; Embedded system; Electrical engineering; Mathematics; Engineering; Computer security; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.01530818391747905,"gpt":0.2937214596672296,"spread":0.2784132757497505,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003560166,0.0001064964,0.00009548473,0.0001273142,0.0002584036,0.0001141952,0.0000329553,0.00003551713,0.00002796492],"category_scores_gemma":[0.0000336294,0.0001144728,0.00001821979,0.0001583774,0.00001839736,0.0002086662,0.00001312668,0.00006744575,0.000006765998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005283959,"about_ca_system_score_gemma":0.000006797847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001024697,"about_ca_topic_score_gemma":0.000004950487,"domain_scores_codex":[0.9993606,0.00003062566,0.0001732689,0.0002247952,0.00006679058,0.0001439332],"domain_scores_gemma":[0.9997396,0.00001300008,0.0000158231,0.0001302912,0.00005780219,0.00004350101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006662699,0.00004226502,0.000002887086,0.00005740368,0.00005270534,6.771423e-8,0.0002089691,0.8827218,0.001486595,0.001769114,0.003619369,0.1100321],"study_design_scores_gemma":[0.0003012006,0.00003118701,0.00001700628,0.00001037101,0.00004899573,0.000004457099,0.0001979432,0.9586123,0.00846695,0.00006539849,0.03210513,0.0001390276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01004114,0.0005794371,0.9811129,0.005677414,0.0004263767,0.00077057,0.00000389558,0.0003895505,0.0009987205],"genre_scores_gemma":[0.100107,0.0005207677,0.8962986,0.0001800476,0.0002808523,0.0002079065,0.0001034076,0.00003202919,0.002269366],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1098931,"threshold_uncertainty_score":0.4668067,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3039856838","doi":"10.1016/j.cie.2020.106605","title":"An efficient two-stage genetic algorithm for a flexible job-shop scheduling problem with sequence dependent attached/detached setup, machine release date and lag-time","year":2020,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Job shop scheduling; Sequence (biology); Selection (genetic algorithm); Genetic algorithm; Mathematical optimization; Algorithm; Set (abstract data type); Benchmark (surveying); Scheduling (production processes); Computer science; Mathematics; Schedule; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.02890173795538531,"gpt":0.2399162774381865,"spread":0.2110145394828012,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002452607,0.0004831484,0.000471608,0.0001686031,0.0001306057,0.0002565115,0.0003411378,0.0001931456,0.00001666116],"category_scores_gemma":[0.00004006548,0.0004989512,0.00007003141,0.000466784,0.00003655407,0.0002051006,0.00008678524,0.0005361824,0.000009938598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009072264,"about_ca_system_score_gemma":0.00007439964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001630071,"about_ca_topic_score_gemma":6.852212e-7,"domain_scores_codex":[0.9979944,0.00003450622,0.0004744779,0.0006179983,0.0002956316,0.0005830359],"domain_scores_gemma":[0.9988659,0.0001033081,0.00008184192,0.0003186623,0.00008043549,0.0005498296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003662116,0.00002640686,0.00005489293,0.00007566983,0.0001032398,0.00003344245,0.0002759915,0.9697606,0.001747993,0.00002357269,0.000007765396,0.02785383],"study_design_scores_gemma":[0.00326466,0.0002718412,0.00001240786,0.0001295954,0.0000767117,0.00003421532,0.00004195761,0.9921791,0.003207799,0.000001008627,0.0001781541,0.0006025537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1022803,0.0002782701,0.8951513,0.00010072,0.0003253859,0.0007274225,0.0001008846,0.001022496,0.00001323905],"genre_scores_gemma":[0.1881624,0.00001318863,0.8106654,0.00006921305,0.0007007592,0.00007086964,0.0001407885,0.000157954,0.0000194217],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.08588215,"threshold_uncertainty_score":0.9997462,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2147965468","doi":"10.1287/opre.1040.0144","title":"Optimal Cyclic Multi-Hoist Scheduling: A Mixed Integer Programming Approach","year":2004,"lang":"en","type":"article","venue":"Operations Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":88,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Hoist (device); Integer programming; Scheduling (production processes); Schedule; Computer science; Mathematical optimization; Engineering; Algorithm; Mathematics; Operating system; Structural engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.0718429271301883,"gpt":0.3443168042178998,"spread":0.2724738770877115,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006855113,0.0001603866,0.0001511957,0.0003509178,0.0005093106,0.0004577138,0.0002862647,0.0001347659,0.00005110572],"category_scores_gemma":[0.0002668785,0.0001567676,0.00005849207,0.001004239,0.0001331244,0.0002471207,0.00006760781,0.0006921621,0.000276928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002026434,"about_ca_system_score_gemma":0.0001363031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000984384,"about_ca_topic_score_gemma":0.00006574922,"domain_scores_codex":[0.9983901,0.00007677267,0.0002689973,0.0003072027,0.0004329036,0.0005239972],"domain_scores_gemma":[0.9990844,0.00003685396,0.000006376176,0.0003448763,0.0003411633,0.00018629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000301924,0.000152065,0.00002095672,0.00002105108,0.00002579074,0.000004271851,0.0007399783,0.9947305,0.0007217336,0.001114436,0.00002673305,0.002439441],"study_design_scores_gemma":[0.0007144619,0.00003373907,0.00004374226,0.00002759778,0.000004613366,0.00001350591,0.001301848,0.9943292,0.002628812,0.000006016457,0.0007054885,0.000190945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09134617,0.0003139299,0.9053591,0.0002395075,0.0001847806,0.0004954273,0.000005802206,0.0004586339,0.00159667],"genre_scores_gemma":[0.4166078,0.00003048325,0.582482,0.00000849899,0.0001079914,0.0002217812,0.00006376131,0.00004074511,0.0004370227],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3252616,"threshold_uncertainty_score":0.6392797,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4353070770","doi":"10.1287/ijoc.2023.1287","title":"Mixed-Integer Programming vs. Constraint Programming for Shop Scheduling Problems: New Results and Outlook","year":2023,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":85,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; University of Windsor","funders":"","keywords":"Integer programming; Computer science; Mathematical optimization; Constraint programming; Scheduling (production processes); Job shop scheduling; Flow shop scheduling; Schedule; Mathematics; Stochastic programming","retraction":null,"screen_n_in":null,"score":{"opus":0.03150559591981477,"gpt":0.2621582092926764,"spread":0.2306526133728616,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001299841,0.0002941797,0.0003225227,0.0003889075,0.0004150021,0.0007024606,0.0001947282,0.0001440863,0.000002954115],"category_scores_gemma":[0.0005015377,0.0002548849,0.0001349833,0.0004893953,0.00005030842,0.0003023512,0.00006022093,0.0006408284,0.00002342472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009601251,"about_ca_system_score_gemma":0.00009954073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002978381,"about_ca_topic_score_gemma":0.00000226193,"domain_scores_codex":[0.9978524,0.00001385554,0.0008818372,0.0002344137,0.000310343,0.0007070861],"domain_scores_gemma":[0.9988043,0.0003089007,0.0002224513,0.0001449545,0.0001802349,0.0003392053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002566787,0.000007802195,0.00009970344,0.000077089,0.00005489471,0.000009905413,0.001034808,0.5877948,0.00000820575,0.0001925575,0.000218824,0.4104757],"study_design_scores_gemma":[0.001809771,0.0002073102,0.0000551572,0.0007698959,0.00002045285,0.0001957862,0.00135495,0.9810142,0.0001533151,0.0001357468,0.01392612,0.0003572752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06957255,0.0002289644,0.9245561,0.0006496756,0.001940265,0.0008013586,0.000007382459,0.001695614,0.0005480249],"genre_scores_gemma":[0.3674698,0.00007587422,0.6308202,0.000141282,0.001164918,0.00001624491,0.00003889008,0.00009900545,0.0001737674],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4101184,"threshold_uncertainty_score":0.9999903,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2626429397","doi":"10.1016/j.ejor.2017.06.027","title":"Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times","year":2017,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":85,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tabu search; Job shop scheduling; Flow shop scheduling; Computer science; Permutation (music); Heuristic; Mathematical optimization; Scheduling (production processes); Sequence (biology); Algorithm; Mathematics; Schedule","retraction":null,"screen_n_in":null,"score":{"opus":0.05695077336828371,"gpt":0.3333236467879199,"spread":0.2763728734196362,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003030089,0.0001227235,0.0001238788,0.000134354,0.001120838,0.0008333744,0.0004956743,0.00002557515,0.00008569738],"category_scores_gemma":[0.0004735589,0.00008166946,0.00004953787,0.0001207406,0.0001107356,0.0005992808,0.00003924063,0.0003804614,0.0000142884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008807857,"about_ca_system_score_gemma":0.0001838199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002826957,"about_ca_topic_score_gemma":0.000004192916,"domain_scores_codex":[0.998256,0.0002164629,0.0003719192,0.0001432433,0.0007799006,0.0002324199],"domain_scores_gemma":[0.9976377,0.000285719,0.0001386294,0.0002193911,0.00162471,0.00009385502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002482815,0.00001399945,0.00001214415,0.00001117209,0.00005329076,0.00001416404,0.0004806652,0.94849,0.0008922798,0.00007005444,0.0002445805,0.04969288],"study_design_scores_gemma":[0.0007101251,0.0001999448,0.0001180887,0.00009613144,0.00001359638,0.00005987316,0.0003444672,0.9950266,0.003143655,0.00001583529,0.0001653836,0.0001062373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002232124,0.000178674,0.9944376,0.0008675049,0.0001797428,0.0003081007,0.00001887105,0.00002763577,0.001749754],"genre_scores_gemma":[0.282202,0.0001537026,0.7164046,0.00002434266,0.0005440366,0.00001908162,0.00002557691,0.00005252875,0.0005740497],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2799699,"threshold_uncertainty_score":0.8620697,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4321381430","doi":"10.1016/j.engappai.2023.105977","title":"Solving energy-efficient fuzzy hybrid flow-shop scheduling problem at a variable machine speed using an extended NSGA-II","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Fuzzy logic; Energy consumption; Scheduling (production processes); Sorting; Genetic algorithm; Population; Mathematical optimization; Algorithm; Artificial intelligence; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.02306533045524665,"gpt":0.2538835589898412,"spread":0.2308182285345945,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004570483,0.0003059884,0.0003004659,0.0004820035,0.0003891268,0.00008854178,0.0004077214,0.0001061291,0.00005765383],"category_scores_gemma":[0.00007403186,0.0003646027,0.00008705692,0.001687873,0.00005949108,0.0001394943,0.0001492561,0.0002230801,0.00005096674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001579827,"about_ca_system_score_gemma":0.00005044172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005190478,"about_ca_topic_score_gemma":0.000005801739,"domain_scores_codex":[0.9980074,0.00001901344,0.0007196375,0.0004374828,0.000310688,0.0005057604],"domain_scores_gemma":[0.9988407,0.0001152094,0.00009474524,0.0005820827,0.0001799184,0.0001873324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005388369,0.00008579066,0.00000264578,0.0000677373,0.00003346789,0.000001740278,0.0002941366,0.9457303,0.03860693,0.006354377,0.000007298792,0.008810138],"study_design_scores_gemma":[0.00003527556,0.00002190368,0.000002262005,0.00006485107,0.0000266328,0.00001043097,0.0001195881,0.9151607,0.0831596,0.0008355606,0.0002286607,0.0003345347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05384544,0.0002392176,0.9437153,0.00002169143,0.0002803396,0.000290587,0.00004580059,0.001361697,0.0001999026],"genre_scores_gemma":[0.5263634,0.0000383215,0.4730309,0.000006598952,0.0001888139,0.00008831071,0.0001178084,0.00009459578,0.00007127168],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4725179,"threshold_uncertainty_score":0.9998806,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1601067015","doi":"","title":"Slack-Based Techniques for Robust Schedules","year":2014,"lang":"en","type":"article","venue":"","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Tardiness; Computer science; Scheduling (production processes); Schedule; Real-time computing; Mathematical optimization; Job shop scheduling; Mathematics; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.01367933630756055,"gpt":0.220744325450681,"spread":0.2070649891431204,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001106555,0.00007413442,0.00007565277,0.00005245575,0.00003259563,0.00002667256,0.00006739518,0.00005651817,0.00006402521],"category_scores_gemma":[0.00004546348,0.00006788969,0.00003566494,0.00006641931,0.00001104128,0.00003637486,0.000003614233,0.00003981858,0.00001950344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001158585,"about_ca_system_score_gemma":0.000005422853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001395189,"about_ca_topic_score_gemma":0.000001893188,"domain_scores_codex":[0.9996568,0.00000516098,0.00009197343,0.00008128742,0.00004894944,0.000115853],"domain_scores_gemma":[0.9997375,0.00005593715,0.000008223406,0.0001192856,0.00004179019,0.00003724967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003447832,0.00001285457,0.00009063011,0.00005153315,0.000009880714,8.163897e-8,0.000009173299,0.9558631,0.0002295917,0.00704303,0.001593949,0.0350928],"study_design_scores_gemma":[0.0001525816,0.0000184735,0.00001289198,0.000009117242,0.000004067764,1.911592e-7,0.000007460616,0.9568932,0.03724861,0.0001870137,0.005364283,0.0001021174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00050239,0.00002285735,0.9906242,0.0001201908,0.0001229721,0.0001029752,0.000002105372,0.001403203,0.007099069],"genre_scores_gemma":[0.05742636,0.000003084892,0.9420176,0.0001760962,0.0001159509,0.0000462608,0.00001440945,0.00002629498,0.0001739307],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05692397,"threshold_uncertainty_score":0.2768461,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2027524005","doi":"10.1287/ijoc.1100.0388","title":"Combining Constraint Programming and Local Search for Job-Shop Scheduling","year":2010,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Sandia National Laboratories; U.S. Department of Energy","keywords":"Tabu search; Constraint programming; Computer science; Job shop scheduling; Mathematical optimization; Guided Local Search; Flow shop scheduling; Hybrid algorithm (constraint satisfaction); Job shop; Local search (optimization); Scheduling (production processes); Iterated local search; Algorithm; Mathematics; Constraint logic programming; Schedule","retraction":null,"screen_n_in":null,"score":{"opus":0.01661260934949381,"gpt":0.2663230316757701,"spread":0.2497104223262763,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008870304,0.0001683207,0.0001927549,0.0001807202,0.0003992002,0.0004015408,0.0001396465,0.0001156562,0.000009469171],"category_scores_gemma":[0.00008665665,0.0001487272,0.00006961469,0.0001416474,0.00008788185,0.0001894598,0.00003626307,0.001049836,0.000006007461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000372687,"about_ca_system_score_gemma":0.00005067622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001142703,"about_ca_topic_score_gemma":9.090926e-7,"domain_scores_codex":[0.9988297,0.000007858608,0.0004181201,0.0001169698,0.0002107821,0.000416535],"domain_scores_gemma":[0.9992033,0.0002413693,0.00007246103,0.00009316115,0.0001660161,0.0002236934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008709229,0.000007707475,0.0006645634,0.000035244,0.0000312057,0.000006169998,0.0005963654,0.5889105,0.0001745848,0.001442744,0.000005339371,0.4081169],"study_design_scores_gemma":[0.000874997,0.00009316657,0.0001526726,0.0001380126,0.000007708453,0.0003942858,0.00111281,0.99568,0.0008120388,0.00006469776,0.000467637,0.0002020086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4327304,0.00003412174,0.5656422,0.00005800257,0.0007861825,0.0001047399,8.217596e-7,0.0001525685,0.0004909699],"genre_scores_gemma":[0.6997358,0.000006364918,0.2998414,0.00006763838,0.0003154722,0.000001645599,0.000001860988,0.00002455038,0.000005272317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4079149,"threshold_uncertainty_score":0.606492,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2063635201","doi":"10.1016/j.cie.2009.01.010","title":"A genetic approach to two-phase optimization of dynamic supply chain scheduling","year":2009,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Procurement; Supply chain; Scheduling (production processes); Computer science; Tardiness; Build to order; Supply chain optimization; Mass customization; Job shop scheduling; Industrial engineering; Operations research; Personalization; Mathematical optimization; Supply chain management; Schedule; Engineering; Operations management; Production (economics); Business","retraction":null,"screen_n_in":null,"score":{"opus":0.01511577713601886,"gpt":0.2282072735914384,"spread":0.2130914964554195,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001502005,0.0002565224,0.0003194178,0.0003836006,0.00003772082,0.00005898116,0.0002514429,0.0001537981,0.000006743993],"category_scores_gemma":[0.00005995517,0.0003069419,0.00007910007,0.0007225767,0.00001024596,0.0001042005,0.00002765608,0.0002753913,0.000003739952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001074842,"about_ca_system_score_gemma":0.00002668109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003057884,"about_ca_topic_score_gemma":7.488613e-8,"domain_scores_codex":[0.9987533,0.00001606594,0.000441667,0.0002552684,0.0001958505,0.0003378429],"domain_scores_gemma":[0.9994125,0.00004058108,0.00004695514,0.000254061,0.00005584067,0.0001900813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001080886,0.00004832542,0.000004455248,0.00001526601,0.00002881653,0.000002375114,0.0001878853,0.9703077,0.0009767644,0.00006428174,0.00002461062,0.02832867],"study_design_scores_gemma":[0.001889116,0.00009395581,0.00003829306,0.0001011138,0.0000201147,0.00001036248,0.0000243905,0.9968578,0.0006318563,0.000002671342,0.00003266156,0.0002976308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07541293,0.0001361517,0.9226019,0.00003673443,0.0008954434,0.0003036921,0.000007272763,0.0005162589,0.00008956977],"genre_scores_gemma":[0.4978802,0.000007491959,0.5017861,0.00002202148,0.0002360855,0.000007817802,0.00002381494,0.00003276997,0.000003709563],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4224672,"threshold_uncertainty_score":0.9999382,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2086111473","doi":"10.1016/j.cor.2004.01.001","title":"Parallel machine selection and job scheduling to minimize machine cost and job tardiness","year":2004,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tardiness; Computer science; Tabu search; Mathematical optimization; Job shop scheduling; Scheduling (production processes); Selection (genetic algorithm); Due date; Heuristic; Algorithm; Schedule; Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03315502815199124,"gpt":0.3158891169154314,"spread":0.2827340887634402,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004528771,0.0001617401,0.0001795215,0.0003902176,0.000515301,0.0004078597,0.0001417835,0.00008148848,0.00002281353],"category_scores_gemma":[0.00009823641,0.0001641601,0.0000224464,0.0006967742,0.00005766083,0.0002063689,0.0001111383,0.0003847428,0.00003106741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001157501,"about_ca_system_score_gemma":0.00005604525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002501099,"about_ca_topic_score_gemma":0.0002738593,"domain_scores_codex":[0.9987723,0.00007247026,0.0002224982,0.0003135848,0.0002704589,0.0003486938],"domain_scores_gemma":[0.9992415,0.00008318367,0.000005974507,0.0001717047,0.0002418155,0.000255832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001238809,0.00001734325,0.000566305,0.00002181925,0.00002626823,0.000003830427,0.0004332156,0.9921819,0.0004987041,0.0005066359,0.00008533274,0.00564629],"study_design_scores_gemma":[0.0009508327,0.00005151242,0.002305488,0.00004860081,0.000006064849,0.00003596774,0.00007211778,0.9956868,0.000341967,0.0000409051,0.0002747668,0.0001849733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1520699,0.0007387109,0.8446209,0.001554993,0.0002122264,0.0004373789,0.00001166182,0.0001925205,0.0001616433],"genre_scores_gemma":[0.4556533,0.000264016,0.5435559,0.00008891943,0.0001517598,0.00007502662,0.00003447934,0.00003526841,0.0001413979],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3035833,"threshold_uncertainty_score":0.6694255,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2055063007","doi":"10.1016/j.ijpe.2007.02.031","title":"Meta-heuristics for scheduling a flowline manufacturing cell with sequence dependent family setup times","year":2007,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Wilfrid Laurier University; University of Manitoba","funders":"","keywords":"Job shop scheduling; Heuristics; Computer science; Mathematical optimization; Tabu search; Simulated annealing; Scheduling (production processes); Meta heuristic; Metaheuristic; Algorithm; Mathematics; Schedule","retraction":null,"screen_n_in":null,"score":{"opus":0.03375852091847666,"gpt":0.2480555141112968,"spread":0.2142969931928201,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000648347,0.0001392951,0.000207496,0.000235766,0.00004250692,0.00007602607,0.0002165797,0.00005042744,0.00002769693],"category_scores_gemma":[0.00004644437,0.0001253701,0.0001102164,0.00003580291,0.0000225379,0.0003249374,0.00001320286,0.000178917,0.000005056913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002042319,"about_ca_system_score_gemma":0.00005227492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001733039,"about_ca_topic_score_gemma":0.000004195011,"domain_scores_codex":[0.999016,0.000006976747,0.0005374731,0.0001404003,0.0001489238,0.0001501905],"domain_scores_gemma":[0.999127,0.00006306012,0.0002636961,0.00009787042,0.0003733923,0.00007498454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008608209,0.00002693585,0.00003656802,0.00001404124,0.0007494225,0.000007639755,0.00008859543,0.9952133,0.0009559221,0.00006434934,0.00005288414,0.002704205],"study_design_scores_gemma":[0.002063708,0.0001867338,0.0001585377,0.0000662372,0.0007680891,0.0008044207,0.0006892123,0.4638204,0.5236617,0.001102891,0.006099554,0.0005784407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3612905,0.000294991,0.6344991,0.0002676121,0.003260457,0.0001000683,0.00002083571,0.00004582972,0.0002206373],"genre_scores_gemma":[0.6188468,0.0002629988,0.379282,0.00006055527,0.001288082,0.00000344517,0.000013261,0.00003247993,0.0002102897],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5313929,"threshold_uncertainty_score":0.5112442,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2994826758","doi":"10.1016/j.jmsy.2019.11.010","title":"Mathematical modeling and a hybridized bacterial foraging optimization algorithm for the flexible job-shop scheduling problem with sequencing flexibility","year":2019,"lang":"en","type":"article","venue":"Journal of Manufacturing Systems","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología; Ontario Centres of Excellence","keywords":"Job shop scheduling; Tardiness; Simulated annealing; Mathematical optimization; Job shop; Scheduling (production processes); Integer programming; Computer science; Algorithm; Flow shop scheduling; Schedule; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01927759036749453,"gpt":0.2302902704082289,"spread":0.2110126800407343,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00131748,0.0002429945,0.0004665249,0.000148358,0.000159984,0.0003528275,0.0001940256,0.00009320954,0.00002510099],"category_scores_gemma":[0.00003862559,0.0001615609,0.00009901157,0.00008823645,0.00002367377,0.0004385504,0.00002960208,0.0002976792,0.000003421019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001816447,"about_ca_system_score_gemma":0.00007169925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005119865,"about_ca_topic_score_gemma":3.411909e-7,"domain_scores_codex":[0.9983543,0.0000473217,0.0007507656,0.0001990437,0.0003422507,0.0003063543],"domain_scores_gemma":[0.9989241,0.0002698781,0.0002667668,0.000225692,0.0002060492,0.0001074546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005821657,0.000007219247,0.00001161339,0.0004072348,0.0001334603,0.000002389601,0.0002820202,0.9968807,0.0001124069,0.00002305986,0.00000265154,0.002078997],"study_design_scores_gemma":[0.001395208,0.00009313574,0.000001773559,0.000620919,0.00009016039,0.0003186097,0.0009035866,0.9933625,0.002897326,0.00007997121,0.00002737949,0.0002094131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2286484,0.0003443871,0.7697375,0.00003485902,0.000492571,0.0005512485,0.000003917662,0.000101228,0.00008587143],"genre_scores_gemma":[0.4585297,0.00003794994,0.5408359,0.000007872381,0.0004187957,0.0000235824,0.000003386544,0.00006442406,0.00007842078],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2298812,"threshold_uncertainty_score":0.6588262,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1989829168","doi":"10.1007/s00170-008-1558-6","title":"A novel hybrid multi-objective shuffled frog-leaping algorithm for a bi-criteria permutation flow shop scheduling problem","year":2008,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Tardiness; Variable neighborhood search; Mathematical optimization; Flow shop scheduling; Job shop scheduling; Computer science; Permutation (music); Algorithm; Scheduling (production processes); Genetic algorithm; Mathematics; Metaheuristic; Schedule","retraction":null,"screen_n_in":null,"score":{"opus":0.01587845214281209,"gpt":0.2619729909600718,"spread":0.2460945388172597,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002591693,0.0002350087,0.000300337,0.0005962409,0.0001760838,0.00005140957,0.000712837,0.0001137363,0.00001578694],"category_scores_gemma":[0.0001754186,0.0001985412,0.0001510602,0.0001370281,0.0001051507,0.0003501616,0.00008163865,0.0004841926,0.000004904112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002582454,"about_ca_system_score_gemma":0.00006166024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002726716,"about_ca_topic_score_gemma":0.00000241993,"domain_scores_codex":[0.9985918,0.00001386294,0.0005895673,0.0002020989,0.000317992,0.0002847237],"domain_scores_gemma":[0.9988099,0.0001479964,0.0002992807,0.0001731723,0.0005147237,0.00005490227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000073618,0.00006737157,0.00001013923,0.00001359419,0.0003106299,0.00004094103,0.0004834451,0.8600153,0.01881295,0.00005081427,0.00002495859,0.1200963],"study_design_scores_gemma":[0.002182194,0.00007818898,0.00004894364,0.0001182268,0.00002781575,0.001681759,0.0004775551,0.7417046,0.2519735,0.001266909,0.0002381997,0.0002021462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2213806,0.0002774436,0.775998,0.000655745,0.00116226,0.0002271895,0.00002501923,0.0002548375,0.00001889351],"genre_scores_gemma":[0.2901536,0.0001725342,0.709234,0.00005858728,0.0002620279,0.00003486628,0.0000118441,0.00004379794,0.00002880042],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2331605,"threshold_uncertainty_score":0.8096275,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2018507331","doi":"10.1111/j.1937-5956.2002.tb00494.x","title":"PRACTICE‐FOCUSED RESEARCH ISSUES FOR SCHEDULING SYSTEMS*","year":2002,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Scheduling (production processes); Computer science; Operations research; Industrial engineering; Operations management; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.07076953537627277,"gpt":0.3274995812672346,"spread":0.2567300458909618,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006020058,0.00007973738,0.00007484751,0.0001973068,0.0004249067,0.0002686654,0.0000549593,0.00003187532,0.00003626716],"category_scores_gemma":[0.0002042364,0.0000807576,0.00001410889,0.0002862432,0.00002502582,0.0003098161,0.00001860482,0.00009548728,0.00005620444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004102074,"about_ca_system_score_gemma":0.000001965899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006423688,"about_ca_topic_score_gemma":0.000002998595,"domain_scores_codex":[0.9992436,0.00004406079,0.0001680665,0.0002141983,0.0001680204,0.0001621055],"domain_scores_gemma":[0.9995013,0.00002645611,0.000009520073,0.0001831116,0.0002364672,0.00004310117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003334304,0.000049265,0.0000042716,0.0001292264,0.00005700873,6.917708e-7,0.0005728818,0.9677343,0.00003989712,0.01270932,0.007063211,0.01163655],"study_design_scores_gemma":[0.0001771422,0.00002246204,0.000005820153,0.00003046213,0.00002087673,0.000004418501,0.001838597,0.9165511,0.0001815369,0.00002350624,0.08104164,0.0001024847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01105225,0.02529216,0.8804694,0.03571088,0.006565898,0.005617384,0.00001358981,0.001951101,0.0333273],"genre_scores_gemma":[0.243874,0.007088718,0.7235872,0.00009567144,0.001043043,0.0009387757,0.00002499824,0.00005799425,0.02328959],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2328218,"threshold_uncertainty_score":0.32932,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1999513701","doi":"10.1080/00207540110095222","title":"Hybrid genetic algorithm for the economic lot-scheduling problem","year":2002,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scheduling (production processes); Mathematical optimization; Computer science; Job shop scheduling; Genetic algorithm; Heuristic; Algorithm; Operations research; Mathematics; Schedule","retraction":null,"screen_n_in":null,"score":{"opus":0.05587131126522115,"gpt":0.3257764368061765,"spread":0.2699051255409553,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001191493,0.00008200797,0.00009805355,0.0003120049,0.00012327,0.0001526479,0.0004637349,0.00003456354,0.0002557417],"category_scores_gemma":[0.0001995594,0.00006453833,0.00008826789,0.00009401095,0.00006382732,0.0001993635,0.00002832676,0.0003911421,0.00006710516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002801883,"about_ca_system_score_gemma":0.00004777025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005353908,"about_ca_topic_score_gemma":8.973743e-7,"domain_scores_codex":[0.9987242,0.00004627124,0.0003867763,0.000130515,0.0005166512,0.0001955478],"domain_scores_gemma":[0.9985372,0.0001877002,0.00008447504,0.0001324015,0.0009963887,0.00006180406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000811197,0.00001937291,0.00001333758,0.000005950481,0.0001356592,0.000005031373,0.000144812,0.7330111,0.0001396118,0.00005286047,0.005828444,0.2606358],"study_design_scores_gemma":[0.0003449596,0.00005059658,0.00004680319,0.00002779395,0.000009878194,0.00029655,0.0001455748,0.9776635,0.003860315,0.0005378836,0.01694118,0.0000749805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02706764,0.003859148,0.9491102,0.008500122,0.01022997,0.0005396635,0.00002141126,0.00008913774,0.0005826842],"genre_scores_gemma":[0.2925784,0.003494348,0.6915818,0.00005977441,0.01033814,0.00006974614,0.000006084443,0.00007477598,0.001796945],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2655107,"threshold_uncertainty_score":0.2800192,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2171618037","doi":"10.1007/s00170-009-2388-x","title":"A parallel genetic algorithm for a flexible job-shop scheduling problem with sequence dependent setups","year":2009,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Job shop scheduling; Flow shop scheduling; Computer science; Mathematical optimization; Scheduling (production processes); Job shop; Fair-share scheduling; Sequence (biology); Minification; Algorithm; Mathematics; Routing (electronic design automation); Schedule","retraction":null,"screen_n_in":null,"score":{"opus":0.01108233046633449,"gpt":0.2520856453898344,"spread":0.2410033149234999,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001713601,0.0001881856,0.0002167963,0.0003429451,0.00008036165,0.00005886698,0.0008421867,0.0001010829,0.00000919889],"category_scores_gemma":[0.00003154899,0.0001359691,0.00007150903,0.0001176301,0.00005830458,0.0001918358,0.00003824271,0.0003912131,0.000003307852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001573467,"about_ca_system_score_gemma":0.00005006197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001116148,"about_ca_topic_score_gemma":0.000001931237,"domain_scores_codex":[0.9988245,0.000008541136,0.000415044,0.0001547012,0.0003336029,0.0002636389],"domain_scores_gemma":[0.9992067,0.00006034406,0.0002173333,0.0001908907,0.0002713861,0.00005335304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005381779,0.00001734762,0.00000872018,0.000004964568,0.000107743,0.00003495409,0.00005268583,0.8013642,0.001890852,0.0002343244,0.00001021024,0.1962202],"study_design_scores_gemma":[0.004218271,0.0008141198,0.0002092268,0.0003843081,0.0000977836,0.003985962,0.0006673911,0.5282966,0.4136225,0.04587613,0.001234081,0.0005936024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1610674,0.0004653548,0.8354161,0.002114629,0.0003650679,0.0002013723,0.000006890005,0.0002892302,0.00007391484],"genre_scores_gemma":[0.230162,0.0002353635,0.7692226,0.0001214538,0.0001529579,0.00001782346,0.000002508152,0.00002450134,0.00006074565],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4117317,"threshold_uncertainty_score":0.5544658,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2002455648","doi":"10.1016/s0305-0483(01)00048-2","title":"Heuristic algorithms for the two-machine flowshop with limited machine availability","year":2001,"lang":"en","type":"article","venue":"Omega","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Job shop scheduling; Computer science; Heuristic; Mathematical optimization; Constructive; Algorithm; Scheduling (production processes); Mathematics; Artificial intelligence; Schedule; Process (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.01634579333903639,"gpt":0.2402695771085099,"spread":0.2239237837694735,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002640909,0.0002020023,0.0001848816,0.000061699,0.0001669744,0.00006576355,0.0002045822,0.00005404569,0.0001558994],"category_scores_gemma":[0.0001111767,0.0001326361,0.00006326018,0.0003845957,0.00005342649,0.0000756862,0.00001938499,0.0001958036,0.00004455157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004742101,"about_ca_system_score_gemma":0.00001557768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000426055,"about_ca_topic_score_gemma":0.00007129846,"domain_scores_codex":[0.9990706,0.00002062607,0.0002137949,0.0002302947,0.0001713412,0.0002932796],"domain_scores_gemma":[0.9990483,0.0003071459,0.00002948013,0.0004293423,0.00009167846,0.0000940584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001041485,0.00008777485,0.002171343,0.00005053724,0.0001444793,0.0000123557,0.0002035262,0.9504374,0.00006142741,0.0001272374,0.0007638393,0.04583596],"study_design_scores_gemma":[0.001092002,0.00005904867,0.0006451265,0.00001219533,0.0000483443,0.00002806585,0.00002724091,0.9838952,0.0001150455,0.00005314553,0.01382355,0.0002010472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01611933,0.001819318,0.9764962,0.000675951,0.0006817782,0.0005776016,0.00008559234,0.0007975404,0.002746683],"genre_scores_gemma":[0.8387725,0.0002764334,0.1577042,0.0003040398,0.0004642374,0.0001843585,0.0001515263,0.000124111,0.002018553],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8226532,"threshold_uncertainty_score":0.5408741,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1556504363","doi":"10.3233/ica-2002-9306","title":"A reconfigurable concurrent function block model and its implementation in real-time Java","year":2002,"lang":"en","type":"article","venue":"Integrated Computer-Aided Engineering","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada; Athabasca University; University of Calgary","funders":"","keywords":"Java; Computer science; Block (permutation group theory); Function (biology); Programming language; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01472859796471018,"gpt":0.2166736728100674,"spread":0.2019450748453572,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001014235,0.0002345142,0.0002182854,0.0002734321,0.00003169763,0.00007657233,0.00008290415,0.00009944652,0.0001058718],"category_scores_gemma":[0.000008750915,0.0002597285,0.00003371518,0.00035656,0.000005360698,0.000213725,0.00001191399,0.0002346639,0.00004602771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001264952,"about_ca_system_score_gemma":0.000007960174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002453868,"about_ca_topic_score_gemma":0.000006476796,"domain_scores_codex":[0.9989913,0.00001428526,0.0003600903,0.0002326213,0.0001086553,0.000293029],"domain_scores_gemma":[0.9996596,0.0000393737,0.00003064518,0.000111004,0.00006334222,0.00009603189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001698813,0.00001223604,0.00001076431,0.00003087431,0.00002752297,0.000003404112,0.000236937,0.9769735,0.005365789,0.0001237025,0.0003251549,0.01688845],"study_design_scores_gemma":[0.0006146497,0.00004346332,0.00009338052,0.00009351868,0.00001240309,0.00001393656,0.0000318974,0.9965153,0.00218987,0.00001248383,0.0001157712,0.0002632702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5886675,0.0009506414,0.4061739,0.00005980545,0.001086946,0.0004065683,0.00002229354,0.001728642,0.0009037377],"genre_scores_gemma":[0.975573,0.000378374,0.02369311,0.00001925948,0.00009197098,0.00003898976,0.00004924871,0.00005866308,0.00009736827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3869055,"threshold_uncertainty_score":0.9999855,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2035028561","doi":"10.1080/07408170490257871","title":"Exact algorithms for the job sequencing and tool switching problem","year":2004,"lang":"en","type":"article","venue":"IIE Transactions","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Canada Research Chairs","keywords":"Computer science; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.01964753304419908,"gpt":0.2374291014801227,"spread":0.2177815684359236,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001152606,0.0001055563,0.00008468035,0.00004643983,0.0003092152,0.00007312495,0.00006424834,0.00005260341,0.00002072726],"category_scores_gemma":[0.000006257692,0.00008529514,0.00005224573,0.0001304675,0.0000176591,0.0001555589,0.000001306782,0.0001560573,0.00000478299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007492426,"about_ca_system_score_gemma":0.0000305423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004695871,"about_ca_topic_score_gemma":0.00003768174,"domain_scores_codex":[0.9994902,0.000004814538,0.0001437013,0.0001170911,0.00007156638,0.0001726122],"domain_scores_gemma":[0.9997092,0.00009264547,0.00001251757,0.0001143086,0.00002895433,0.00004235105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000233039,0.000004753043,0.000002534476,0.00002351066,0.00004068756,6.007513e-7,0.0008159367,0.960956,0.0006603062,0.0001434895,0.00000517396,0.03734472],"study_design_scores_gemma":[0.000836961,0.00002662041,0.0001001986,0.00004554202,0.00008391601,0.00004266478,0.0007283769,0.9932231,0.003159863,0.0009243352,0.0006018658,0.0002265988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008135054,0.0003148889,0.9899989,0.0004319248,0.0002870839,0.0002674156,0.000012246,0.0003406955,0.0002118179],"genre_scores_gemma":[0.6572462,0.0002007012,0.3420937,0.00006747956,0.0001139764,0.0001291527,0.000002978871,0.00004051513,0.0001053256],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6491111,"threshold_uncertainty_score":0.3478235,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2046059743","doi":"10.1016/j.cor.2005.02.047","title":"Multi-item dynamic production-distribution planning in process industries with divergent finishing stages","year":2005,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Bottleneck; Mathematical optimization; Computer science; Production (economics); Process (computing); Linear programming; Production planning; Piecewise linear function; Integer programming; Piecewise; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.05081746576595451,"gpt":0.3466062186514448,"spread":0.2957887528854903,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004072058,0.0001169558,0.0001090334,0.0002508385,0.0003527988,0.0002597283,0.000178238,0.00007352883,0.00001451576],"category_scores_gemma":[0.0001524009,0.0001124934,0.00001171529,0.0009686581,0.00006633751,0.000463697,0.0000403143,0.0005190828,0.00001661616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002655901,"about_ca_system_score_gemma":0.0001098406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000211424,"about_ca_topic_score_gemma":0.0001289638,"domain_scores_codex":[0.9988286,0.00007052978,0.0002091938,0.0002511076,0.0003266032,0.0003139807],"domain_scores_gemma":[0.9993846,0.00006136263,0.000009066517,0.000171236,0.0002998154,0.00007393236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000556094,0.0000491571,0.003185439,0.00002051926,0.00001313111,0.000004204623,0.001742131,0.9892331,0.00007402116,0.00001391149,0.0002625017,0.005396319],"study_design_scores_gemma":[0.0003174634,0.00002859504,0.005300047,0.0001231323,0.000002082535,0.000005670021,0.0009234867,0.9916814,0.001263786,0.000001094338,0.0002157467,0.0001375267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6861785,0.0002709028,0.3120102,0.0007277476,0.0001870091,0.0003665269,0.00001604484,0.0002141267,0.00002893765],"genre_scores_gemma":[0.934451,0.00003909411,0.06491095,0.00000950953,0.0001084683,0.00008603783,0.0002010131,0.00002102845,0.0001728669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2482725,"threshold_uncertainty_score":0.4587348,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4302378732","doi":"10.1007/978-0-387-09421-2","title":"Level Crossing Methods in Stochastic Models","year":2008,"lang":"en","type":"book","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Markov chain; Queue; Computer science; Queueing theory; Service (business); Operations research; Real-time computing; Mathematics; Computer network; Business; Machine learning; Marketing","retraction":null,"screen_n_in":null,"score":{"opus":0.1399341142666061,"gpt":0.456075938194698,"spread":0.316141823928092,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["sts"],"category_scores_codex":[0.01746306,0.0007272791,0.0005997528,0.01873173,0.003079364,0.005933336,0.007705259,0.0002815722,0.0006500212],"category_scores_gemma":[0.001314545,0.000811421,0.00013388,0.009909658,0.009297309,0.009458433,0.003747662,0.002544046,0.0002228745],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01014056,"about_ca_system_score_gemma":0.001671958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005880498,"about_ca_topic_score_gemma":0.004651588,"domain_scores_codex":[0.9840137,0.0004945928,0.001941387,0.002375837,0.008929238,0.002245214],"domain_scores_gemma":[0.9951392,0.0003107927,0.00007106743,0.001448084,0.002641155,0.0003896364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005386911,0.0001857234,0.00002543111,0.00005522274,0.00005791456,0.000178311,0.001388251,0.7572653,0.000137603,0.2361198,0.0008427243,0.003689774],"study_design_scores_gemma":[0.0009784448,0.00006805669,0.0009118412,0.0008162456,0.000007073925,0.00004108723,0.003239806,0.9681929,0.0001696594,0.01095182,0.01380941,0.0008136772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002461782,0.0004126871,0.15216,0.005073976,0.00606317,0.005029704,0.0002489824,0.0003776763,0.828172],"genre_scores_gemma":[0.0681185,0.007954803,0.4023429,0.0001461715,0.0005073379,0.003043189,0.0005744584,0.0002080363,0.5171046],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3110674,"threshold_uncertainty_score":0.9997571,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2100308419","doi":"10.1016/s0004-3702(00)00035-7","title":"Constraint-directed techniques for scheduling alternative activities","year":2000,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Scheduling (production processes); Mathematical optimization; Schedule; Constraint programming; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03535660262591858,"gpt":0.2865996582321427,"spread":0.2512430556062241,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001256921,0.000134028,0.0001325433,0.00007391552,0.00009780614,0.00007141638,0.0001308181,0.00007132056,0.0004469537],"category_scores_gemma":[0.00005764272,0.0001439866,0.00005669543,0.0001735497,0.00008980292,0.0001409369,0.000005936693,0.0001155419,0.00005629892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003372485,"about_ca_system_score_gemma":0.00001218622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001421552,"about_ca_topic_score_gemma":0.000009687492,"domain_scores_codex":[0.9992536,0.00001401334,0.0002426195,0.0001699262,0.00009391298,0.000225874],"domain_scores_gemma":[0.9995958,0.000143902,0.00002137019,0.0001222507,0.0000620103,0.00005469997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001970751,0.00002327024,0.000003695803,0.0000133006,0.00002855321,0.000001640469,0.0005545035,0.2518945,0.001770303,0.003300983,0.0000488995,0.7423406],"study_design_scores_gemma":[0.0000103419,0.00002219247,8.217268e-7,0.00002237424,0.000005621127,0.000002371114,0.0002974642,0.6385434,0.3558256,0.004577777,0.0005564491,0.0001355233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04319161,0.0001034846,0.950395,0.0001269298,0.0003570584,0.0002856867,0.00002534175,0.001632662,0.003882156],"genre_scores_gemma":[0.6871153,0.0001094548,0.3121665,0.00009519869,0.0002670305,0.00008032668,0.0000128791,0.0000327051,0.0001205736],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7422051,"threshold_uncertainty_score":0.5871605,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1972249265","doi":"10.1016/j.ejor.2015.01.003","title":"Single machine scheduling with two competing agents and equal job processing times","year":2015,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Time complexity; Computer science; Scheduling (production processes); Job shop scheduling; Single-machine scheduling; Computational complexity theory; Mathematical optimization; Set (abstract data type); Algorithm; Mathematics; Schedule","retraction":null,"screen_n_in":null,"score":{"opus":0.1016540663951278,"gpt":0.3421405488960593,"spread":0.2404864825009315,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003092704,0.00009603538,0.0001232228,0.0002090127,0.0001764282,0.000338597,0.0001554015,0.0000124345,0.00004328627],"category_scores_gemma":[0.0003530555,0.00007557228,0.00001681155,0.0002433261,0.00007538907,0.0003284469,0.00004739056,0.0004425344,0.0000232183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000621775,"about_ca_system_score_gemma":0.0001287498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001598307,"about_ca_topic_score_gemma":0.000001100292,"domain_scores_codex":[0.9983476,0.0003080363,0.0003029527,0.0001015997,0.0007386584,0.0002011266],"domain_scores_gemma":[0.9986687,0.00008949144,0.00004890404,0.00006341104,0.0009006996,0.0002287371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007177105,0.00004183739,0.001784692,0.00002626643,0.00004144469,0.0001711112,0.001393426,0.9814942,0.000999661,0.0001931769,0.0002809942,0.01350137],"study_design_scores_gemma":[0.001782362,0.0003603985,0.0007607002,0.0002544739,0.000008683724,0.0003676832,0.0008916309,0.993565,0.0006201764,0.00002759917,0.00120278,0.0001584685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7541349,0.002860553,0.2090489,0.0008938391,0.000222012,0.0001471499,0.000004798132,0.00008144247,0.03260646],"genre_scores_gemma":[0.8316604,0.00001861466,0.1677882,0.00003439964,0.0003251946,4.506359e-7,0.000004368651,0.00003472353,0.000133713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0775255,"threshold_uncertainty_score":0.3265099,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2087393302","doi":"10.1111/j.1467-8640.2005.00278.x","title":"APPLYING MACHINE LEARNING TO LOW-KNOWLEDGE CONTROL OF OPTIMIZATION ALGORITHMS","year":2005,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Irish Research Council; Science Foundation Ireland","keywords":"Computer science; Machine learning; Domain knowledge; Algorithm; Artificial intelligence; Control (management); Set (abstract data type); Optimization algorithm; Scheduling (production processes); Mathematical optimization; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01517091568407795,"gpt":0.2613674780904295,"spread":0.2461965624063516,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000162923,0.000134273,0.000162189,0.0001748874,0.0000691254,0.00002893953,0.0001521996,0.00005210752,0.0001496517],"category_scores_gemma":[0.0001075817,0.0001497509,0.00004648065,0.0003956691,0.00002555755,0.000109617,0.00002109666,0.0001429812,0.0001386694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005836579,"about_ca_system_score_gemma":0.00002381762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002766836,"about_ca_topic_score_gemma":0.000001329884,"domain_scores_codex":[0.9991186,0.00002680536,0.0003547259,0.0001642253,0.0001764323,0.0001591797],"domain_scores_gemma":[0.9992562,0.0001884615,0.00004796797,0.00008338904,0.0003301993,0.00009376949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004970265,0.00002647106,0.00003980549,0.00001963243,0.00001854272,3.604419e-7,0.0003627842,0.9251245,0.00003427809,0.0003475004,0.00001860897,0.0740026],"study_design_scores_gemma":[0.0001269584,0.0000240295,0.00002299655,0.00004125373,0.000007470315,0.000003841767,0.00005764913,0.9973548,0.00158558,0.00007974034,0.0005414417,0.0001542598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006209782,0.0005920987,0.9973856,0.00007010338,0.0001988979,0.0002437507,0.000008286212,0.0002371814,0.0006431633],"genre_scores_gemma":[0.5590617,0.00002103797,0.4405839,0.00008347938,0.00009775974,0.00003899682,0.00003429924,0.00002061954,0.00005814178],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5584407,"threshold_uncertainty_score":0.6106666,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}