{"meta":{"query_hash":"fbe72cf0b3f3","filters":{"topic":"Reliability and Maintenance Optimization"},"cohort_total":732,"direct_labels_cover":0,"predictions_cover":732,"exported":732,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/fbe72cf0b3f3","api":"https://metacan.xera.ac/api/v1/cohort?topic=Reliability+and+Maintenance+Optimization"},"results":[{"id":"W1002645676","doi":"10.1007/978-3-319-09507-3_36","title":"Selective Maintenance for Multi-state Systems Considering the Benefits of Repairing Multiple Components Simultaneously","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in mechanical engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability engineering; Genetic algorithm; Component (thermodynamics); Computer science; State (computer science); Reduction (mathematics); Cost reduction; Maintenance actions; Engineering; Algorithm; Mathematics; Machine learning","score_opus":0.018134629815727527,"score_gpt":0.20783029793173174,"score_spread":0.18969566811600422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1002645676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16560565,0.0032037138,0.8161699,0.0004938475,0.0001939051,0.000044016164,0.000094856565,0.00027086854,0.01392326],"genre_scores_gemma":[0.9770493,0.0007066241,0.01708772,0.000027401744,0.000095252624,0.000030706033,0.000048985952,0.00005723129,0.0048967833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984,0.00003472373,0.0000063799353,0.00003549139,0.000037202997,0.00004610635],"domain_scores_gemma":[0.9993888,0.00042052663,0.000056777622,0.000044516546,0.00006232057,0.000026979826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055235863,0.0008821662,0.0011450044,0.00046305184,0.0003842571,0.00087216304,0.0013475277,0.000876319,0.0028305047],"category_scores_gemma":[0.0012415567,0.00041093258,0.00083726185,0.00057703094,0.00058792444,0.0010964532,0.00078947016,0.00084398186,0.0001921333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012289225,0.000042907624,0.00039270992,0.00022311542,0.00007463117,0.00024439066,0.00007294943,0.9391008,0.005644581,0.024195155,0.0013480859,0.028537687],"study_design_scores_gemma":[0.0000045885386,0.000026275973,0.00019363707,0.0000050793014,0.000021203332,0.000042359192,0.000009159673,0.9933469,0.00024268868,0.005918463,0.00018625714,0.0000033541378],"about_ca_topic_score_codex":0.0027451406,"about_ca_topic_score_gemma":0.0033760525,"teacher_disagreement_score":0.0028305047,"about_ca_system_score_codex":0.00069805403,"about_ca_system_score_gemma":0.000518322,"threshold_uncertainty_score":0.009468973},"labels":[],"label_agreement":null},{"id":"W1043127509","doi":"10.1007/978-1-4615-4329-9_8","title":"A General Framework for Analyzing Maintenance Policies","year":2000,"lang":"en","type":"book-chapter","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Martingale (probability theory); Optimal stopping; Mathematical optimization; Computer science; Class (philosophy); Optimal maintenance; Stopping time; Operations research; Mathematics; Applied mathematics; Artificial intelligence; Statistics","score_opus":0.01120486430889851,"score_gpt":0.22244171939986057,"score_spread":0.21123685509096207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1043127509","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008788462,0.00071728654,0.98638165,0.00021165695,0.000055991644,0.0000410169,0.00015374922,0.0002950779,0.011264722],"genre_scores_gemma":[0.08244322,0.0045676245,0.8807117,0.00033049367,0.0004074587,0.00042609937,0.0006988572,0.00038013587,0.030034482],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999411,0.00013749319,0.000033842225,0.00014049599,0.00020669975,0.00007048773],"domain_scores_gemma":[0.99945337,0.00030773407,0.000035353878,0.00010902044,0.00007396684,0.000020467207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011471978,0.0017479772,0.0012705892,0.0013501009,0.00077015185,0.0024961007,0.0027165818,0.0015837649,0.009092196],"category_scores_gemma":[0.002351995,0.0009321043,0.0017566971,0.0022552,0.0012718303,0.0040453156,0.0010309721,0.002386024,0.0021796145],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012208147,0.000054406548,0.00017474803,0.00013853838,0.000042589825,0.000085800784,0.00007826859,0.10567626,0.0010961159,0.81605875,0.009856326,0.06672605],"study_design_scores_gemma":[0.0000074554655,0.000016791315,0.00014438131,0.00003882551,0.000028837017,0.00008376703,0.00002167382,0.25408727,0.0004800888,0.719649,0.02542554,0.000016396527],"about_ca_topic_score_codex":0.0053885146,"about_ca_topic_score_gemma":0.00542077,"teacher_disagreement_score":0.009092196,"about_ca_system_score_codex":0.0016080299,"about_ca_system_score_gemma":0.0014109892,"threshold_uncertainty_score":0.03041637},"labels":[],"label_agreement":null},{"id":"W1143538317","doi":"10.1016/j.ijpe.2015.07.034","title":"Joint optimal lot sizing and preventive maintenance policy for a production facility subject to condition monitoring","year":2015,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Ontario Centres of Excellence","keywords":"Computer science; Production (economics); Preventive maintenance; Statistic; Covariate; Operations research; Sizing; Process (computing); Markov chain; Reliability engineering; Operations management; Statistics; Mathematics; Engineering; Economics","score_opus":0.025762511836245564,"score_gpt":0.2663750514590995,"score_spread":0.24061253962285395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1143538317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38510394,0.0011076025,0.6025243,0.0014286467,0.00016392476,0.00042001455,0.0009073448,0.0013165377,0.0070277513],"genre_scores_gemma":[0.9807351,0.000132827,0.016870953,0.00003817396,0.00004457603,0.000083439896,0.00012949998,0.000030245616,0.0019352525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990914,0.00026021886,0.00004346048,0.00021836147,0.00015523564,0.00023139134],"domain_scores_gemma":[0.99664164,0.0018853162,0.00059593766,0.00016936183,0.000434148,0.00027360622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020291873,0.0014812154,0.0027359596,0.0012544248,0.00062987924,0.0017804556,0.0020599891,0.0023712723,0.0031541907],"category_scores_gemma":[0.003894908,0.0012601922,0.0008944852,0.001129924,0.0010365556,0.0014070194,0.00081912073,0.0011084604,0.00039643573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048162517,0.00011665923,0.00059824437,0.00011997309,0.000046229183,0.000093456256,0.00002523823,0.98496604,0.0031283842,0.001877486,0.0006766647,0.007869971],"study_design_scores_gemma":[0.00003477221,0.00010112571,0.0008393416,0.000005808415,0.000029248427,0.000021591382,0.0000125009,0.9970891,0.00056268775,0.0012217351,0.00007144116,0.000010695096],"about_ca_topic_score_codex":0.007501594,"about_ca_topic_score_gemma":0.00530281,"teacher_disagreement_score":0.007501594,"about_ca_system_score_codex":0.0016628529,"about_ca_system_score_gemma":0.002699507,"threshold_uncertainty_score":0.014915884},"labels":[],"label_agreement":null},{"id":"W125905195","doi":"10.1007/978-0-85729-215-5_8","title":"Filtering and M-ary Detection in a Minimal Repair Maintenance Model","year":2011,"lang":"en","type":"book-chapter","venue":"Springer series in reliability engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Saint Mary's University","funders":"","keywords":"Computer science; Reliability engineering; Engineering","score_opus":0.007870612237819541,"score_gpt":0.17057514274416052,"score_spread":0.16270453050634098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W125905195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0218437,0.0004504913,0.9737553,0.00038468343,0.000048183323,0.00001821053,0.0002077142,0.00025991307,0.0030317605],"genre_scores_gemma":[0.8080498,0.0009621966,0.17692915,0.00023324811,0.00030981144,0.00013244779,0.00067436055,0.00013792212,0.01257108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892586,0.0003313606,0.00006129554,0.00028286662,0.00024324933,0.00015530559],"domain_scores_gemma":[0.996411,0.0026894615,0.0002478519,0.00034261408,0.00023107471,0.00007802265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016994657,0.00085079466,0.0018575911,0.00094056106,0.0005505665,0.0018787071,0.0025282009,0.0023494062,0.0029219142],"category_scores_gemma":[0.006058923,0.0007139103,0.0014076144,0.0009975506,0.0012308687,0.002993556,0.0010361795,0.0015168085,0.0005291185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025692437,0.00007234669,0.0006395081,0.00014231318,0.00006467328,0.00015821784,0.000110174224,0.66510487,0.0022861147,0.2892219,0.0034632483,0.038479704],"study_design_scores_gemma":[0.000010291027,0.00001539529,0.00008591848,0.0000061538854,0.000010494219,0.000026521566,0.0000042616966,0.8895063,0.0002552548,0.10970117,0.00036919882,0.000009086512],"about_ca_topic_score_codex":0.0049624876,"about_ca_topic_score_gemma":0.0035262255,"teacher_disagreement_score":0.0049624876,"about_ca_system_score_codex":0.0012816077,"about_ca_system_score_gemma":0.0008552273,"threshold_uncertainty_score":0.009867191},"labels":[],"label_agreement":null},{"id":"W134874113","doi":"10.1007/978-1-4471-2924-0_10","title":"Modeling Risk in Discrete Multistate Repairable Systems","year":2012,"lang":"en","type":"book-chapter","venue":"Engineering asset management review","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Markov process; Component (thermodynamics); Reliability engineering; Markov chain; Range (aeronautics); State (computer science); Process (computing); Function (biology); Markov model; Computer science; Fault (geology); Mathematical optimization; Production (economics); Engineering; Mathematics; Algorithm; Statistics; Economics; Physics","score_opus":0.008784390898216072,"score_gpt":0.19798702147841193,"score_spread":0.18920263058019587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W134874113","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007981025,0.06943954,0.8855362,0.0023474083,0.00095276575,0.000027495751,0.00017458912,0.00033062635,0.033210382],"genre_scores_gemma":[0.52596027,0.19763032,0.17895243,0.0008408431,0.004236192,0.00025521874,0.00080447,0.00046587578,0.09085438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966097,0.00010599523,0.000017207933,0.000048054535,0.00014490884,0.000022859549],"domain_scores_gemma":[0.99941885,0.00042354406,0.00003900444,0.000043306805,0.000057605037,0.000017740242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007637321,0.0014675913,0.0011836997,0.000574847,0.0002268921,0.0014990146,0.0016057901,0.0011763726,0.0029232623],"category_scores_gemma":[0.0015021474,0.0006294679,0.0010363138,0.0010220804,0.00096263015,0.0019865686,0.0007384574,0.0024643815,0.0007103799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000146805405,0.000058235073,0.0002661533,0.00028403968,0.00007628212,0.00007322588,0.00006356844,0.6594961,0.0007056697,0.26481563,0.00940574,0.06474063],"study_design_scores_gemma":[0.0000066293924,0.00002226545,0.00023726666,0.0001281947,0.000021200889,0.000068387984,0.000018110577,0.61348224,0.00031429384,0.3682371,0.017440692,0.00002356287],"about_ca_topic_score_codex":0.0025761018,"about_ca_topic_score_gemma":0.0021446315,"teacher_disagreement_score":0.0029232623,"about_ca_system_score_codex":0.00093387946,"about_ca_system_score_gemma":0.0006822031,"threshold_uncertainty_score":0.009779274},"labels":[],"label_agreement":null},{"id":"W1442784167","doi":"10.3233/mas-2008-3208","title":"Estimating equations for repeated failure time measurements","year":2008,"lang":"en","type":"article","venue":"Model Assisted Statistics and Applications","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Agency of Canada","funders":"","keywords":"Statistics; Mathematics; Econometrics; Computer science","score_opus":0.037700643772676484,"score_gpt":0.24790033742209192,"score_spread":0.21019969364941543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1442784167","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008268232,0.0012373439,0.98569924,0.0005205975,0.00021591701,0.00044842545,0.0023823786,0.0006324422,0.0005954285],"genre_scores_gemma":[0.18947166,0.0054947836,0.76823485,0.0007531993,0.0007226939,0.0068665477,0.012391122,0.00028462335,0.015780563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9663866,0.021563182,0.002173051,0.006137397,0.0026711277,0.0010686155],"domain_scores_gemma":[0.858029,0.1181781,0.010384735,0.008420938,0.0046592797,0.00032802363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049225237,0.0032164336,0.0059920284,0.0044855624,0.000812763,0.0031116104,0.0059057083,0.0041061603,0.010190458],"category_scores_gemma":[0.12267509,0.0026393284,0.005505307,0.0054588527,0.0016392845,0.0034932804,0.0022771216,0.0068856133,0.0051025916],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006047934,0.0005094248,0.05940262,0.0021407777,0.007924159,0.00094670214,0.0012018236,0.3594989,0.0014054446,0.2328837,0.019566016,0.3139156],"study_design_scores_gemma":[0.0004203008,0.0006910069,0.023080055,0.0007643681,0.0017527054,0.00071302877,0.00025273146,0.7213352,0.0011971869,0.22285888,0.026614236,0.00032027706],"about_ca_topic_score_codex":0.019353537,"about_ca_topic_score_gemma":0.016100412,"teacher_disagreement_score":0.049225237,"about_ca_system_score_codex":0.0023366783,"about_ca_system_score_gemma":0.0033739835,"threshold_uncertainty_score":0.2603311},"labels":[],"label_agreement":null},{"id":"W1478349014","doi":"10.1016/j.ifacol.2015.06.126","title":"Reliability estimation of a production system subject to condition monitoring with two modes of failures","year":2015,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Reliability (semiconductor); Residual; Hidden Markov model; Computer science; Expectation–maximization algorithm; Reliability engineering; Conditional probability; Observable; Process (computing); Markov process; State (computer science); Maximum likelihood; Algorithm; Engineering; Mathematics; Statistics; Artificial intelligence","score_opus":0.011698497538025836,"score_gpt":0.2420603722577527,"score_spread":0.23036187471972686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1478349014","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16722132,0.00020737143,0.8316305,0.000112538255,0.000011957474,0.000020645692,0.00009844973,0.00024431554,0.00045286748],"genre_scores_gemma":[0.9821125,0.00009297131,0.017041452,0.000010093774,0.000011772244,0.00002641462,0.00011567962,0.000013425874,0.00057551055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966645,0.000111255256,0.000014402341,0.00009109793,0.00007420809,0.00004251536],"domain_scores_gemma":[0.9981856,0.0013670342,0.00020062315,0.00007992035,0.00013212765,0.000034772296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010014155,0.0006360327,0.00079736,0.00030665123,0.00016297337,0.00038230614,0.0005181306,0.000634458,0.0005421819],"category_scores_gemma":[0.0034704318,0.00034655692,0.0004651419,0.00024199185,0.00042202382,0.00052155496,0.0004205668,0.0005969762,0.00009870284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005464131,0.000013883944,0.0012811426,0.000026551967,0.000016375956,0.000042486612,0.000022716235,0.987721,0.0014813821,0.0006168827,0.00007831949,0.008644539],"study_design_scores_gemma":[0.0000019288677,0.000013226929,0.00060050684,0.000001315494,0.0000036030806,0.000007252343,0.0000022826148,0.998635,0.0003040549,0.00040565268,0.000022608752,0.0000024799178],"about_ca_topic_score_codex":0.0065812306,"about_ca_topic_score_gemma":0.0028141835,"teacher_disagreement_score":0.0065812306,"about_ca_system_score_codex":0.00039137428,"about_ca_system_score_gemma":0.0006703301,"threshold_uncertainty_score":0.013085842},"labels":[],"label_agreement":null},{"id":"W1479899315","doi":"10.1109/rams.2015.7105181","title":"Estimating and using direct operating cost as a design parameter","year":2015,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell Helicopter Textron (Canada)","funders":"","keywords":"Maintainability; Mean time between failures; Reliability engineering; Reliability (semiconductor); Metric (unit); Computer science; Process (computing); Maintenance engineering; Risk analysis (engineering); Operations research; Engineering; Operations management; Failure rate","score_opus":0.05139879635382896,"score_gpt":0.26466594918173025,"score_spread":0.2132671528279013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1479899315","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09623181,0.0010881394,0.8834409,0.00019988051,0.00007506665,0.00037612044,0.0006900854,0.0010310523,0.01686692],"genre_scores_gemma":[0.584632,0.0007214151,0.40841776,0.00007063179,0.000038890204,0.0004766185,0.0012000896,0.0003946472,0.0040479875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963936,0.00093819626,0.00021190652,0.00036051634,0.0019421865,0.00015354843],"domain_scores_gemma":[0.9904632,0.004961628,0.0011817672,0.001010122,0.0022742792,0.000109036584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035552785,0.0020952437,0.00087275624,0.004487264,0.00032617664,0.0030324876,0.0011955634,0.0008060011,0.002889743],"category_scores_gemma":[0.016235808,0.0007685386,0.0009884711,0.0019638771,0.00045957966,0.0023730882,0.0006494475,0.00077936234,0.0010073743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001481527,0.00013292803,0.021304261,0.0003746732,0.00022001842,0.00010942029,0.00010591228,0.63651794,0.009416117,0.009613622,0.0018079652,0.32024902],"study_design_scores_gemma":[0.00004049247,0.0006385259,0.01699633,0.00014142646,0.00025126635,0.0002705543,0.00019369068,0.9414568,0.015605752,0.007984947,0.016264716,0.00015559075],"about_ca_topic_score_codex":0.0051522367,"about_ca_topic_score_gemma":0.004466045,"teacher_disagreement_score":0.0051522367,"about_ca_system_score_codex":0.0019822856,"about_ca_system_score_gemma":0.001329355,"threshold_uncertainty_score":0.018802345},"labels":[],"label_agreement":null},{"id":"W1486476702","doi":"10.1002/9781118445112.stat04240","title":"Modules and Modular Decomposition","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Modular design; Decomposition; Reliability (semiconductor); Computer science; Reliability engineering; Modular decomposition; Theoretical computer science; Programming language; Engineering","score_opus":0.010368117830919428,"score_gpt":0.2495146902145423,"score_spread":0.23914657238362289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486476702","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019446526,0.0011409522,0.9460917,0.0004176239,0.00007795767,0.000050425595,0.00019855464,0.00031114178,0.03226509],"genre_scores_gemma":[0.55150515,0.0018974069,0.42589316,0.00040765904,0.0004317201,0.00020655865,0.0006083673,0.0004359472,0.018614084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987205,0.00047761473,0.00005875498,0.00026436904,0.0003583641,0.00012037266],"domain_scores_gemma":[0.9978969,0.00087742874,0.00027497398,0.00037764633,0.00047260232,0.000100339916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017868639,0.0006362976,0.00035117203,0.0017578873,0.00033225215,0.0013023863,0.0005210616,0.00036847667,0.0048516034],"category_scores_gemma":[0.004803669,0.0002617777,0.0005843204,0.00128224,0.0019114664,0.002024203,0.0012739665,0.001116897,0.0010426964],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016551014,0.000009379431,0.0004405717,0.000053805295,0.00001544788,0.000044562406,0.00010970775,0.008792576,0.0011337992,0.93961436,0.0033899012,0.046379276],"study_design_scores_gemma":[0.000005563212,0.000013661852,0.00051917805,0.00003561395,0.00000977234,0.00012331449,0.00002323647,0.03337733,0.0011737258,0.94795454,0.016755369,0.000008761005],"about_ca_topic_score_codex":0.0004973569,"about_ca_topic_score_gemma":0.00039320093,"teacher_disagreement_score":0.0048516034,"about_ca_system_score_codex":0.00071114936,"about_ca_system_score_gemma":0.00051051355,"threshold_uncertainty_score":0.016230226},"labels":[],"label_agreement":null},{"id":"W1491840929","doi":"10.1002/qre.1543","title":"Measurement Plan Optimization for Degradation Test Design based on the Bivariate Wiener Process","year":2013,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bivariate analysis; Test plan; Optimal design; Statistics; Design of experiments; Degradation (telecommunications); Computer science; Mathematics","score_opus":0.03524792124381177,"score_gpt":0.23859254936102187,"score_spread":0.2033446281172101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1491840929","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011621668,0.00006433688,0.98772323,0.00005055237,0.0000049999617,0.000066193854,0.000018249706,0.00007792718,0.00037277004],"genre_scores_gemma":[0.63275295,0.00014940047,0.36578608,0.000067788336,0.000020900827,0.00044219443,0.00010604643,0.000054645112,0.0006199937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99500424,0.0028340311,0.00020079402,0.0005752519,0.0010917024,0.000293908],"domain_scores_gemma":[0.987694,0.0086100055,0.0017853426,0.00049378636,0.0011693765,0.00024738672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006186686,0.0013061458,0.0013023,0.0012165618,0.00035463105,0.0009445426,0.0008570187,0.0007297147,0.0012937284],"category_scores_gemma":[0.0207383,0.0006520278,0.000619363,0.00065913325,0.0009945413,0.0014492562,0.0012845619,0.0011640876,0.00021393936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003074134,0.0001232976,0.0017199835,0.0001175489,0.00005530252,0.000059314425,0.00009146497,0.9086475,0.007058806,0.018994192,0.00028565162,0.06253954],"study_design_scores_gemma":[0.000047651603,0.00044316307,0.0008556015,0.000018077473,0.000024577508,0.000034482862,0.000021082911,0.98492014,0.003387623,0.009781402,0.0004447018,0.000021471258],"about_ca_topic_score_codex":0.0012575459,"about_ca_topic_score_gemma":0.0011398575,"teacher_disagreement_score":0.006186686,"about_ca_system_score_codex":0.001250662,"about_ca_system_score_gemma":0.001737299,"threshold_uncertainty_score":0.03271866},"labels":[],"label_agreement":null},{"id":"W1493032675","doi":"10.1109/rams.2006.1677445","title":"Availability optimization using spares modeling and the six sigma process","year":2006,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dow Chemical (Canada)","funders":"","keywords":"Spare part; Six Sigma; Reliability (semiconductor); Reliability engineering; Process (computing); Productivity; Computer science; Production (economics); Manufacturing engineering; Risk analysis (engineering); Industrial engineering; Operations research; Operations management; Engineering; Business","score_opus":0.008503405439051863,"score_gpt":0.20164350111153717,"score_spread":0.1931400956724853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493032675","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029709933,0.00029962018,0.96244687,0.00032342644,0.000023524937,0.000028020655,0.00003750916,0.00016784263,0.0069632325],"genre_scores_gemma":[0.8875363,0.0007705016,0.105388336,0.00008242521,0.000032852215,0.00010941154,0.000098715565,0.00007908573,0.005902319],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945825,0.0002650535,0.00001954864,0.00004637639,0.00015719634,0.000053576987],"domain_scores_gemma":[0.99927336,0.00047590298,0.00009703436,0.000043340657,0.000084549196,0.000025751226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010585886,0.0006529469,0.00063541863,0.00064471585,0.0003609868,0.0011145211,0.0007940646,0.000774272,0.0019311907],"category_scores_gemma":[0.0020003307,0.000496567,0.0009183314,0.0005398001,0.0006997475,0.0011328424,0.0007724602,0.0007457774,0.00028628032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012733216,0.00000834899,0.00012345228,0.000011858173,0.000008044674,0.00001504133,0.000027257274,0.9813384,0.00028076328,0.014860503,0.000074859745,0.0032387527],"study_design_scores_gemma":[0.00000343288,0.000012604693,0.000030279736,0.000005389107,0.0000034839381,0.0000060691987,0.000008407898,0.98872244,0.00023113035,0.010575796,0.00039719322,0.0000036861684],"about_ca_topic_score_codex":0.004744972,"about_ca_topic_score_gemma":0.0025158082,"teacher_disagreement_score":0.004744972,"about_ca_system_score_codex":0.00093479955,"about_ca_system_score_gemma":0.0010697121,"threshold_uncertainty_score":0.0094347},"labels":[],"label_agreement":null},{"id":"W1493227459","doi":"10.1109/rams.2015.7105176","title":"An improved d-MP search algorithm for multi-state networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Integer (computer science); Backtracking; Binary number; Algorithm; Path (computing); State (computer science); Computer science; Value (mathematics); State vector; Binary search algorithm; Mathematics; Search algorithm; Physics; Arithmetic","score_opus":0.03301198021849107,"score_gpt":0.2771858809276127,"score_spread":0.24417390070912165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493227459","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008701905,0.00029717587,0.9870704,0.000111053814,0.000031945336,0.00010334665,0.00013012762,0.0009009306,0.0026530807],"genre_scores_gemma":[0.17296676,0.00019998163,0.82129943,0.00012275077,0.000023734132,0.00030422566,0.00050528353,0.00017621544,0.0044016214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995555,0.00008616064,0.000039338724,0.00012246317,0.00014005514,0.00005650844],"domain_scores_gemma":[0.99919754,0.0003971325,0.00008430743,0.000091511014,0.00019396916,0.00003558133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005876597,0.0009827646,0.0009790036,0.0018692341,0.00073285593,0.0008331291,0.0018098112,0.0012458682,0.005213825],"category_scores_gemma":[0.0019446005,0.00052677904,0.0008110212,0.001523164,0.00049044326,0.0015268438,0.001090899,0.0010844872,0.0008923022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001885317,0.000090283706,0.0007553759,0.00022301407,0.000064340114,0.00012862385,0.00011172732,0.6698908,0.006107839,0.020045131,0.003546041,0.29884824],"study_design_scores_gemma":[0.00003022184,0.000031149146,0.00008697587,0.0000098483615,0.000010594497,0.000047030575,0.00000953116,0.9921456,0.0013360615,0.004467904,0.0018162319,0.00000888795],"about_ca_topic_score_codex":0.007981645,"about_ca_topic_score_gemma":0.0089275865,"teacher_disagreement_score":0.007981645,"about_ca_system_score_codex":0.001093907,"about_ca_system_score_gemma":0.0018115083,"threshold_uncertainty_score":0.017441988},"labels":[],"label_agreement":null},{"id":"W1494618744","doi":"10.5539/ijsp.v4n3p145","title":"On Periodic Maintenance of a Coherent System","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Series (stratigraphy); Applied mathematics; Detector; Algorithm; Reliability engineering; Computer science; Statistics; Mathematical optimization; Telecommunications; Engineering; Geology","score_opus":0.012626011338317648,"score_gpt":0.22905120810027274,"score_spread":0.2164251967619551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1494618744","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57042915,0.0018149501,0.41907078,0.00052292447,0.00009523426,0.000041225714,0.000094570314,0.000117212876,0.007814058],"genre_scores_gemma":[0.98965865,0.0003631157,0.008217439,0.000038828905,0.00006773632,0.000022750142,0.000046774465,0.000016622722,0.0015679718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996431,0.00009786771,0.0000113192455,0.000070791655,0.00010212137,0.00007479379],"domain_scores_gemma":[0.99905187,0.00041667765,0.00023024,0.00005855795,0.00017304771,0.00006966063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007253354,0.00042996832,0.00058094127,0.0006067962,0.00043186016,0.00047204495,0.00064750965,0.0005945717,0.0011077959],"category_scores_gemma":[0.0027114474,0.00020380944,0.00039538764,0.0005532396,0.000846237,0.00081855425,0.0004987231,0.00045080046,0.000105512445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022341582,0.000108664564,0.0051533724,0.00021482288,0.00010029608,0.0012695533,0.0002597144,0.8567497,0.0136165535,0.09786562,0.001811483,0.022626808],"study_design_scores_gemma":[0.000010458168,0.0000615557,0.00069249544,0.000005067339,0.000012948718,0.000089267385,0.00001919454,0.99258053,0.00033327355,0.006004227,0.00018493297,0.0000061103906],"about_ca_topic_score_codex":0.0034513006,"about_ca_topic_score_gemma":0.0014674802,"teacher_disagreement_score":0.0034513006,"about_ca_system_score_codex":0.0004547483,"about_ca_system_score_gemma":0.00041055708,"threshold_uncertainty_score":0.006862402},"labels":[],"label_agreement":null},{"id":"W1502743455","doi":"10.3968/j.pam.1925252820120402.zt301","title":"Statistical Analysis of MOBVE Distribution with TFR Model Under Step-Stress Accelerated Life Test","year":2012,"lang":"en","type":"article","venue":"Progress in applied mathematics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Monte Carlo method; Accelerated life testing; Maximum likelihood; Statistics; Stress (linguistics); Computer science; Mathematics; Econometrics; Weibull distribution","score_opus":0.022563579087153136,"score_gpt":0.26053233735346076,"score_spread":0.23796875826630762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1502743455","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5001495,0.00030204377,0.49762243,0.0001622024,0.0000164531,0.00004414603,0.00027057526,0.00035349876,0.0010791427],"genre_scores_gemma":[0.9877791,0.0000932839,0.011126219,0.000015284459,0.000011744755,0.00003156613,0.00024012105,0.00003766481,0.0006650219],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907756,0.00029893772,0.00003294595,0.00025220157,0.00023540929,0.00010278534],"domain_scores_gemma":[0.99126124,0.005631048,0.0012213656,0.0008515763,0.00089602516,0.00013868768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028500005,0.0004104819,0.00054930453,0.00088400726,0.00019733587,0.0004464209,0.0011869662,0.0007551915,0.0012578125],"category_scores_gemma":[0.0143172,0.00022126382,0.00058022083,0.00051450304,0.00088574283,0.0009592533,0.0005287557,0.0006251688,0.00018593461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004006808,0.00006667869,0.036361426,0.00021168569,0.0001718634,0.0008132344,0.00032377578,0.86413336,0.016221387,0.043079186,0.0009108722,0.037305813],"study_design_scores_gemma":[0.000008309225,0.00006867419,0.0092283,0.000011071631,0.000021509471,0.00016939014,0.000027578622,0.98223615,0.0020461308,0.0059446003,0.00021389856,0.000024387422],"about_ca_topic_score_codex":0.002004364,"about_ca_topic_score_gemma":0.0011463265,"teacher_disagreement_score":0.0028500005,"about_ca_system_score_codex":0.00041634977,"about_ca_system_score_gemma":0.00032461807,"threshold_uncertainty_score":0.015072405},"labels":[],"label_agreement":null},{"id":"W1503398761","doi":"10.1109/ceit.2015.7233173","title":"Modified block replacement with used items at scheduled periods and at failures","year":2015,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Block (permutation group theory); Cover (algebra); Computer science; Reliability engineering; Maintenance engineering; Operations research; Engineering; Mathematics; Mechanical engineering","score_opus":0.014183737861412441,"score_gpt":0.19857009631230393,"score_spread":0.1843863584508915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1503398761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1114669,0.0013716315,0.8765823,0.00034948412,0.0001427065,0.00016902719,0.00063956645,0.00039595185,0.008882418],"genre_scores_gemma":[0.91009027,0.0007199223,0.07406137,0.00007172889,0.0000621225,0.0002076073,0.00031854605,0.00008247486,0.01438584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989768,0.00031909562,0.00004394108,0.00020586807,0.00030338432,0.00015091478],"domain_scores_gemma":[0.99911326,0.00031335413,0.00019823496,0.00014906291,0.00015155679,0.00007463923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009526504,0.00093373854,0.000871533,0.000439705,0.00027379396,0.00080970227,0.0020410703,0.0009775697,0.0035482396],"category_scores_gemma":[0.00175308,0.00041938972,0.0006971635,0.0007387467,0.0005740446,0.0015085218,0.0005508006,0.000839928,0.000701418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006690717,0.00016408249,0.001375587,0.0002614405,0.00007716678,0.0004659299,0.00014408515,0.88806576,0.014791407,0.053722274,0.0017036847,0.038559474],"study_design_scores_gemma":[0.000049373062,0.00041102257,0.00075235293,0.00001402407,0.00004345944,0.00019061651,0.000019134897,0.9769056,0.0019128358,0.016100304,0.0035807213,0.000020550293],"about_ca_topic_score_codex":0.003052922,"about_ca_topic_score_gemma":0.0023923137,"teacher_disagreement_score":0.0035482396,"about_ca_system_score_codex":0.00074242044,"about_ca_system_score_gemma":0.0008979274,"threshold_uncertainty_score":0.011870027},"labels":[],"label_agreement":null},{"id":"W1506114007","doi":"10.1109/rams.2015.7105177","title":"Reliability analysis of multi-state systems with s-dependent components","year":2015,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Component (thermodynamics); Reliability (semiconductor); Reliability engineering; Computer science; Monte Carlo method; State (computer science); Degradation (telecommunications); Reliability theory; Function (biology); Engineering; Failure rate; Algorithm; Mathematics; Statistics","score_opus":0.025071687494329534,"score_gpt":0.22326725430627067,"score_spread":0.19819556681194114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1506114007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14667982,0.0008263198,0.8493013,0.00018947081,0.000022963353,0.000041769694,0.000055006265,0.00017049455,0.0027129068],"genre_scores_gemma":[0.98478484,0.00037804944,0.013912082,0.000015540569,0.000020801192,0.000039222345,0.00004194701,0.000020104546,0.00078748516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993773,0.00027399717,0.000027032209,0.00008559048,0.00016873892,0.000067392175],"domain_scores_gemma":[0.9982122,0.0012422572,0.00022569406,0.00008775139,0.0002055585,0.000026562548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016373714,0.00051738165,0.0006326103,0.0007777039,0.00030676907,0.00051659794,0.000506451,0.0004982012,0.00065809034],"category_scores_gemma":[0.0033240719,0.00026703128,0.0009939858,0.0004887953,0.0007677675,0.0005731674,0.00046959805,0.00041837693,0.00007316541],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026949601,0.000008086137,0.0009308925,0.00004022002,0.00003435558,0.0000731768,0.00004610588,0.98301184,0.0020876948,0.009493356,0.000081271624,0.0041660964],"study_design_scores_gemma":[0.0000010806073,0.000011843416,0.00031061252,0.0000022640402,0.000005782389,0.000011922096,0.000004715215,0.9974952,0.00020620615,0.0018919897,0.000055960798,0.000002526358],"about_ca_topic_score_codex":0.00419285,"about_ca_topic_score_gemma":0.0014044738,"teacher_disagreement_score":0.00419285,"about_ca_system_score_codex":0.00066366384,"about_ca_system_score_gemma":0.0006380782,"threshold_uncertainty_score":0.008659363},"labels":[],"label_agreement":null},{"id":"W1515296313","doi":"10.4271/2003-01-3287","title":"AAM/AIAM Fleet Test Program: Analysis and Comments","year":2003,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Test (biology)","score_opus":0.007600026511496848,"score_gpt":0.2321111792614568,"score_spread":0.22451115274995995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1515296313","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07673366,0.0011066662,0.07418672,0.041002516,0.012903521,0.011926538,0.15193534,0.021635396,0.6085696],"genre_scores_gemma":[0.16720617,0.000787306,0.036332585,0.009050036,0.004002499,0.004145723,0.0834942,0.004998534,0.68998295],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99185395,0.0009969891,0.00029465533,0.00026670838,0.0062407064,0.0003469353],"domain_scores_gemma":[0.95319664,0.0059698485,0.0020343869,0.0020897214,0.036050964,0.00065833784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008611732,0.0008179316,0.0003949593,0.0039364207,0.0013211413,0.0014184836,0.00234829,0.0016646468,0.06500554],"category_scores_gemma":[0.030665085,0.00039262392,0.0005355592,0.0023750474,0.00039421677,0.0010309431,0.00046902098,0.0010327947,0.040498655],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005190418,0.00023765801,0.0029358577,0.0001359319,0.000015749343,0.00026433973,0.00018630577,0.002324657,0.0056563485,0.001546609,0.93560445,0.050573044],"study_design_scores_gemma":[0.00014861494,0.000725415,0.024491087,0.00018932734,0.000036985915,0.00017304171,0.00044027966,0.009196805,0.0117913755,0.0008784566,0.951829,0.00009960194],"about_ca_topic_score_codex":0.032587536,"about_ca_topic_score_gemma":0.030071383,"teacher_disagreement_score":0.06500554,"about_ca_system_score_codex":0.0034886685,"about_ca_system_score_gemma":0.004025331,"threshold_uncertainty_score":0.21746522},"labels":[],"label_agreement":null},{"id":"W1518300632","doi":"","title":"Integrating Simulation and Optimization to Analyze Maintenance Policies Performances for Complex Systems","year":2005,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Flexibility (engineering); Computer science; Animation; Resource (disambiguation); Duration (music); Discrete event simulation; Distributed computing; Simulation; Industrial engineering; Engineering","score_opus":0.01605538092967723,"score_gpt":0.2429028139454465,"score_spread":0.22684743301576926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1518300632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04794611,0.00042103592,0.9489645,0.0001482375,0.00003093667,0.000043271757,0.000035450703,0.00038225832,0.0020281838],"genre_scores_gemma":[0.8111234,0.00078988646,0.18582341,0.000060566188,0.000045599627,0.00023148171,0.000109373,0.00012953977,0.0016867156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995347,0.0002599034,0.000020791033,0.000049597904,0.000102125196,0.000032949614],"domain_scores_gemma":[0.99852055,0.0012045007,0.000094291114,0.00009780182,0.000060984214,0.000021959866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009779922,0.0008725538,0.0007129218,0.0004990803,0.0001831595,0.0005313336,0.00050248636,0.00072800077,0.00082388485],"category_scores_gemma":[0.0024419809,0.0003407214,0.00057413924,0.00044243372,0.0006065738,0.00064999645,0.00048340508,0.00072303205,0.00012182187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001570256,0.000012862499,0.0002733522,0.000015020656,0.000018928695,0.000009842242,0.000012109963,0.99025613,0.00071650936,0.0045153056,0.00004788784,0.004106394],"study_design_scores_gemma":[0.000002400871,0.0000069066687,0.000043803913,0.0000011867301,0.000002534343,0.0000031812501,0.0000014431578,0.99810517,0.0003090704,0.0013662699,0.00015612654,0.0000018140449],"about_ca_topic_score_codex":0.0036867177,"about_ca_topic_score_gemma":0.0018110537,"teacher_disagreement_score":0.0036867177,"about_ca_system_score_codex":0.00055633456,"about_ca_system_score_gemma":0.00059693353,"threshold_uncertainty_score":0.007330537},"labels":[],"label_agreement":null},{"id":"W1520774566","doi":"10.1155/2015/274530","title":"A Decision Optimization Model for Leased Manufacturing Equipment with Warranty under Forecasting Production/Maintenance Problem","year":2015,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Warranty; Lease; Purchasing; Production (economics); Order (exchange); Operations research; Business; Computer science; Operations management; Engineering; Economics; Microeconomics; Finance","score_opus":0.03263666116965983,"score_gpt":0.21510007750250976,"score_spread":0.18246341633284993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1520774566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075353324,0.001697316,0.8973675,0.0017748848,0.0001655663,0.00020630463,0.0006889349,0.0002348504,0.022511428],"genre_scores_gemma":[0.9342728,0.0012310421,0.044050995,0.0001854187,0.00010153918,0.0004141777,0.00042744412,0.00006221888,0.019254211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986998,0.00044510487,0.000057141067,0.0003081232,0.00018662223,0.0003031189],"domain_scores_gemma":[0.9982603,0.0010817349,0.00025800438,0.00004119512,0.00022300646,0.00013578964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022859732,0.0015512162,0.002566,0.0008852033,0.00078130537,0.0028998223,0.0026339216,0.0037823622,0.0063580554],"category_scores_gemma":[0.0031358327,0.001020216,0.0014956966,0.0012600519,0.0011745672,0.0015966023,0.0012403842,0.0027263684,0.00054490234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051506733,0.00003905529,0.0002773234,0.000073366646,0.000027699973,0.00016435537,0.000052464246,0.97036576,0.0005179235,0.0255129,0.0005571315,0.0023605803],"study_design_scores_gemma":[0.000010636117,0.0000151340055,0.00007738999,0.0000063648677,0.00000993881,0.000012740647,0.00001429988,0.9963701,0.00005547072,0.003195231,0.00022604367,0.000006736258],"about_ca_topic_score_codex":0.015126636,"about_ca_topic_score_gemma":0.0069499337,"teacher_disagreement_score":0.015126636,"about_ca_system_score_codex":0.002799766,"about_ca_system_score_gemma":0.0022445866,"threshold_uncertainty_score":0.030077219},"labels":[],"label_agreement":null},{"id":"W1528964841","doi":"10.1108/jqme-11-2013-0074","title":"A nearly optimal inspection policy for a two-component series system","year":2015,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Reliability engineering; Context (archaeology); Component (thermodynamics); Sequence (biology); Series (stratigraphy); Condition-based maintenance; Function (biology); Engineering; Preventive maintenance; Set (abstract data type); Total cost; Computer science; Mathematical optimization; Mathematics","score_opus":0.019920589644512533,"score_gpt":0.27051747991665154,"score_spread":0.250596890272139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1528964841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26534536,0.0003306806,0.7297705,0.00020708686,0.000032339198,0.00013154482,0.00008433429,0.0003622083,0.0037360482],"genre_scores_gemma":[0.96729106,0.00008899415,0.03139181,0.000024453911,0.000011082046,0.00003866701,0.000046037214,0.000022084432,0.0010858759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948514,0.0001240672,0.000025401703,0.00013334151,0.0001302688,0.00010187024],"domain_scores_gemma":[0.9988857,0.00046680597,0.0003018531,0.00007177288,0.00018893376,0.000084860214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008615296,0.0007431152,0.0008311523,0.0007646938,0.0003908424,0.000804071,0.00055897626,0.0007494547,0.0014914364],"category_scores_gemma":[0.0022860647,0.00042654484,0.00050568086,0.0003689646,0.00062058197,0.0006187691,0.0004386787,0.0006396896,0.000177029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001666991,0.00008413423,0.0010622999,0.000086917935,0.000022118475,0.00011251181,0.000048067934,0.9698359,0.010230142,0.0036629066,0.0002896864,0.014398662],"study_design_scores_gemma":[0.000008793018,0.00011864408,0.00046329084,0.0000055191376,0.00001114971,0.000039756975,0.000014958841,0.996405,0.0011315683,0.0016577143,0.00013846593,0.0000051678185],"about_ca_topic_score_codex":0.0041457573,"about_ca_topic_score_gemma":0.0028054134,"teacher_disagreement_score":0.0041457573,"about_ca_system_score_codex":0.0013089585,"about_ca_system_score_gemma":0.0012848373,"threshold_uncertainty_score":0.009497225},"labels":[],"label_agreement":null},{"id":"W1539705619","doi":"10.1002/qre.1395","title":"Optimizing the Periodic Inspection Interval for a 1‐out‐of‐2 Cold Standby System Using the Delay‐Time Concept","year":2012,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Downtime; Interval (graph theory); Reliability engineering; Component (thermodynamics); Process (computing); Inspection time; Point (geometry); Renewal theory; Epoch (astronomy); Computer science; Reliability (semiconductor); Engineering; Mathematics; Statistics; Power (physics); Physics","score_opus":0.019184885988400045,"score_gpt":0.2589261004403154,"score_spread":0.23974121445191535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539705619","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6490671,0.00038985413,0.34732217,0.00013835238,0.0000252898,0.00007721943,0.000047717054,0.00027975242,0.0026525722],"genre_scores_gemma":[0.9919011,0.000028598992,0.007710051,0.000005768954,0.0000028453867,0.000014649973,0.000011725009,0.000009885008,0.00031536195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967897,0.00007741734,0.000010460604,0.000070029906,0.00007655883,0.00008662974],"domain_scores_gemma":[0.9991654,0.00040172556,0.00021426988,0.000036437534,0.000098622295,0.000083539446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077561935,0.0006523327,0.00066853414,0.00048575946,0.00034883997,0.0006100112,0.0007795005,0.00045964928,0.0012513568],"category_scores_gemma":[0.0017381279,0.000360157,0.00032224754,0.00026241416,0.00031433484,0.00048730915,0.00036466878,0.00037770093,0.00009176119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000246593,0.000075159005,0.0011520897,0.000064261345,0.000020110781,0.00009294149,0.000040495084,0.9767199,0.009097876,0.0012448877,0.00015217472,0.011093369],"study_design_scores_gemma":[0.000012509489,0.00015560513,0.000813492,0.0000031192944,0.00001240543,0.000021789014,0.000017563752,0.9974355,0.0010809947,0.00037991273,0.00006113806,0.0000060015004],"about_ca_topic_score_codex":0.0044295914,"about_ca_topic_score_gemma":0.0027642616,"teacher_disagreement_score":0.0044295914,"about_ca_system_score_codex":0.00075181597,"about_ca_system_score_gemma":0.00086212176,"threshold_uncertainty_score":0.0088076},"labels":[],"label_agreement":null},{"id":"W1542400653","doi":"","title":"Planification de la maintenance d'un parc de turbines-alternateurs par programmation mathématique","year":2010,"lang":"fr","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science; Humanities; Art","score_opus":0.00428886714361444,"score_gpt":0.20856404242758536,"score_spread":0.20427517528397093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1542400653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1305859,0.0002184916,0.85868853,0.00031777186,0.00005045817,0.000109675944,0.00018296004,0.0009129304,0.008933304],"genre_scores_gemma":[0.8878096,0.00013676389,0.10627591,0.00004028462,0.000013706309,0.0001914949,0.0001253687,0.00008716096,0.0053197364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998042,0.000053063297,0.000009004244,0.000054971682,0.000045403074,0.00003326223],"domain_scores_gemma":[0.9994485,0.00034389095,0.00005398462,0.00003305267,0.00009358258,0.000027041868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005216298,0.0006109,0.000509147,0.00034738306,0.00044396648,0.0008764872,0.0006123462,0.00083464594,0.004523493],"category_scores_gemma":[0.0012447082,0.00028080682,0.000538326,0.00024366901,0.0004893144,0.0005419207,0.0005324944,0.0007249943,0.00036919015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003960365,0.000014268943,0.0005045426,0.000029303084,0.0000061708715,0.000026768308,0.000026543283,0.98603916,0.0016254213,0.001659885,0.00014973512,0.009878684],"study_design_scores_gemma":[0.000006289934,0.000024049268,0.0001956019,0.0000040356786,0.0000040085556,0.0000071495815,0.000012086805,0.99814653,0.0005122112,0.00066661194,0.00041891765,0.0000025603279],"about_ca_topic_score_codex":0.016761411,"about_ca_topic_score_gemma":0.00983642,"teacher_disagreement_score":0.016761411,"about_ca_system_score_codex":0.00086484186,"about_ca_system_score_gemma":0.0010361514,"threshold_uncertainty_score":0.0333277},"labels":[],"label_agreement":null},{"id":"W1545719165","doi":"10.1016/j.ifacol.2015.06.410","title":"An Optimal Maintenance Policy for a Two-unit Production System Using a Proportional Hazards Model","year":2015,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Unit (ring theory); Production (economics); Covariate; Markov decision process; Discrete time and continuous time; Mathematical optimization; Gamma process; Computer science; Markov process; Mathematics; Operations research; Econometrics; Statistics; Economics","score_opus":0.033381067985993926,"score_gpt":0.29479066627080786,"score_spread":0.2614095982848139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1545719165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.120168835,0.00042576398,0.8754998,0.00064843876,0.00004494119,0.00011798123,0.00016177741,0.00021102407,0.002721503],"genre_scores_gemma":[0.95958704,0.00026488904,0.03549546,0.000053275868,0.000029068033,0.00015617488,0.00012234996,0.0000326663,0.004259154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999271,0.00027154293,0.00002479613,0.00016216548,0.00013047087,0.00014009273],"domain_scores_gemma":[0.9985806,0.000998738,0.00017183032,0.000045788678,0.00012028809,0.000082879225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020099345,0.0008813349,0.0015760618,0.000661689,0.00052361144,0.001211899,0.0016555972,0.0013022076,0.0034057556],"category_scores_gemma":[0.003259996,0.0007190045,0.00088673976,0.0006761083,0.0009431819,0.0009781786,0.0010796856,0.0012356815,0.00022054347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037972255,0.000018913648,0.00026236183,0.00002133423,0.000013332005,0.000047758454,0.000020112248,0.9937775,0.00029473557,0.002896545,0.000118621036,0.0024907659],"study_design_scores_gemma":[0.000012753883,0.000018923964,0.00008630495,0.0000017504701,0.000006068656,0.00000813074,0.0000047053204,0.99819785,0.00006160441,0.0015399387,0.00005864817,0.0000032240132],"about_ca_topic_score_codex":0.012993128,"about_ca_topic_score_gemma":0.0070280596,"teacher_disagreement_score":0.012993128,"about_ca_system_score_codex":0.0015324821,"about_ca_system_score_gemma":0.0023091007,"threshold_uncertainty_score":0.025834978},"labels":[],"label_agreement":null},{"id":"W1553032772","doi":"10.1109/isuma.1995.527660","title":"Reliability assessment of systems operating in variable conditions","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Failure rate; Variable (mathematics); Computer science; Random variable; Load sharing; Engineering; Distributed computing; Mathematics; Statistics","score_opus":0.010154448537704502,"score_gpt":0.2296144996592627,"score_spread":0.2194600511215582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1553032772","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7603347,0.00089377625,0.23512802,0.00013076529,0.000026773154,0.000036788908,0.00012212471,0.00027373098,0.0030534382],"genre_scores_gemma":[0.99543977,0.00013298685,0.0040950356,0.0000033821177,0.000010899082,0.000010913675,0.000040571114,0.000009517115,0.0002569618],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999343,0.00026279254,0.000024389867,0.0000785358,0.00022716138,0.000064067084],"domain_scores_gemma":[0.9982054,0.0010463486,0.00029696754,0.00016880744,0.00022295125,0.000059394242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000867346,0.00044719432,0.00039848516,0.00061337743,0.00027744815,0.00048335444,0.0005540816,0.0005699188,0.0007178717],"category_scores_gemma":[0.0038606136,0.00018767874,0.00030600495,0.0004150326,0.00056310237,0.0006741728,0.0005149394,0.0004160215,0.00015912252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049251807,0.00006704754,0.010445189,0.00012853395,0.00009058063,0.00026353443,0.0001601866,0.9072832,0.029459734,0.005774513,0.0004194252,0.045415513],"study_design_scores_gemma":[0.000018230567,0.00046171882,0.009535361,0.000013156029,0.00004380641,0.00017522914,0.00008120123,0.9702172,0.011105145,0.0073875645,0.0009256348,0.000035615038],"about_ca_topic_score_codex":0.0011985325,"about_ca_topic_score_gemma":0.00069243775,"teacher_disagreement_score":0.0011985325,"about_ca_system_score_codex":0.00035425546,"about_ca_system_score_gemma":0.00027712403,"threshold_uncertainty_score":0.0045870543},"labels":[],"label_agreement":null},{"id":"W1556551376","doi":"10.1109/ceit.2015.7233136","title":"A maintenance optimization model for a second hand stochastically deteriorating system under different operating environments","year":2015,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Dalhousie University","funders":"","keywords":"Upgrade; Preventive maintenance; Process (computing); Maintenance engineering; Computer science; Reliability engineering; Optimal maintenance; Mathematical optimization; Simulation; Operations research; Engineering; Operating system; Mathematics","score_opus":0.019530419817724406,"score_gpt":0.20912865586951623,"score_spread":0.18959823605179182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1556551376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23677035,0.0017176872,0.7442874,0.0013366828,0.00010910649,0.00015643696,0.0008037003,0.0003460074,0.014472629],"genre_scores_gemma":[0.9696101,0.00063287164,0.015471029,0.00008497268,0.00004507239,0.0001608201,0.00025732035,0.00004638302,0.013691412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992305,0.000216461,0.000035707842,0.00021357305,0.00012529021,0.00017840357],"domain_scores_gemma":[0.99875665,0.00062232726,0.00032047895,0.00004574978,0.0001695048,0.000085171916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001576329,0.0013140754,0.0016783982,0.0008507156,0.00067420147,0.0017804743,0.0019422254,0.0027662867,0.0033486246],"category_scores_gemma":[0.002179317,0.0008068648,0.0011379382,0.00095405657,0.0010883829,0.0011750768,0.0008976386,0.0015878357,0.00040232812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035830497,0.000021904689,0.0002866395,0.000034482935,0.000016934622,0.000120843775,0.000027881199,0.99326646,0.0008552228,0.003910148,0.0001939065,0.0012297328],"study_design_scores_gemma":[0.000009119451,0.000031078613,0.00029476476,0.0000036686795,0.0000135908695,0.000023972558,0.000010359108,0.99837023,0.000104525956,0.0010009507,0.00013005054,0.0000076071483],"about_ca_topic_score_codex":0.018142248,"about_ca_topic_score_gemma":0.009293459,"teacher_disagreement_score":0.018142248,"about_ca_system_score_codex":0.0019223978,"about_ca_system_score_gemma":0.0015003027,"threshold_uncertainty_score":0.036073327},"labels":[],"label_agreement":null},{"id":"W1560517505","doi":"10.1108/02656711211224875","title":"Product support improvement by considering system operating environment","year":2012,"lang":"en","type":"article","venue":"International Journal of Quality & Reliability Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Spare part; Maintainability; Reliability engineering; Product (mathematics); Reliability (semiconductor); Computer science; Engineering; Manufacturing engineering; Operations management","score_opus":0.011653305889892652,"score_gpt":0.24952612990628945,"score_spread":0.23787282401639678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1560517505","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.790163,0.0006251761,0.19907577,0.00012257273,0.000028019193,0.00010223497,0.000115596216,0.00086651294,0.008901089],"genre_scores_gemma":[0.97701466,0.00008940188,0.02225445,0.000008749865,0.0000059854465,0.00001895742,0.00006859342,0.000038223236,0.00050095085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9994978,0.00014446037,0.000023859307,0.00008638768,0.0001932581,0.000054333344],"domain_scores_gemma":[0.998389,0.00071049365,0.0003755919,0.000118894386,0.00033399972,0.0000720212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061842013,0.0005736254,0.0002788893,0.0006724855,0.00021172944,0.0008998788,0.00040380034,0.0002243248,0.0015686712],"category_scores_gemma":[0.0031218138,0.00017667472,0.00024225687,0.00044075854,0.0001929407,0.00079002744,0.000520888,0.00025290402,0.00021247711],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048478984,0.0003807773,0.058430634,0.0005839531,0.0000865006,0.00047534786,0.0005066636,0.52420914,0.06575456,0.0027391177,0.00091463054,0.3454339],"study_design_scores_gemma":[0.000041753872,0.0011075409,0.049965486,0.000068465204,0.0001671165,0.0002942242,0.0005211244,0.91240096,0.027031984,0.0027185846,0.0056356713,0.000047136837],"about_ca_topic_score_codex":0.0012234336,"about_ca_topic_score_gemma":0.0012367707,"teacher_disagreement_score":0.0015686712,"about_ca_system_score_codex":0.00028991007,"about_ca_system_score_gemma":0.0005803696,"threshold_uncertainty_score":0.005247712},"labels":[],"label_agreement":null},{"id":"W1568215525","doi":"","title":"Causes and effects of cascading failures in aircraft systems","year":2007,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Computer science; Aeronautics; Engineering","score_opus":0.003922496294360728,"score_gpt":0.19609898870449852,"score_spread":0.1921764924101378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1568215525","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99154186,0.0010490195,0.0049796076,0.00021629066,0.000021200114,0.000025433079,0.00022483271,0.000107322514,0.0018344949],"genre_scores_gemma":[0.999233,0.00020374455,0.00033403924,0.0000053361755,0.000012786569,0.0000040229584,0.000038247545,0.0000059550803,0.00016297387],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939907,0.00013502201,0.000046535217,0.000105291525,0.00020151555,0.000112545604],"domain_scores_gemma":[0.98977566,0.005075166,0.0028943,0.0004638597,0.0012229296,0.0005680667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000933622,0.00055277994,0.0005206289,0.0024470203,0.0007554913,0.00094034785,0.00064255047,0.0006475167,0.0016058604],"category_scores_gemma":[0.0061702114,0.0004946926,0.00067148835,0.0014129989,0.00078680966,0.00066094333,0.0007401299,0.00075781706,0.000112205526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063195906,0.00035238118,0.7084195,0.00032617946,0.00054494705,0.0029752941,0.0009947486,0.22831863,0.0055525573,0.0071564065,0.002423017,0.0423043],"study_design_scores_gemma":[0.000054536777,0.00022270842,0.8430076,0.0000567334,0.0004435329,0.0011518047,0.0014617749,0.13166915,0.0013124957,0.019268475,0.0012713814,0.00007973476],"about_ca_topic_score_codex":0.011795821,"about_ca_topic_score_gemma":0.018411951,"teacher_disagreement_score":0.011795821,"about_ca_system_score_codex":0.0008970633,"about_ca_system_score_gemma":0.00055162515,"threshold_uncertainty_score":0.023454309},"labels":[],"label_agreement":null},{"id":"W1576476555","doi":"","title":"Evolving Optimally Reliable Networks by Adding an Edge","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Terminal (telecommunication); Reliability (semiconductor); Enhanced Data Rates for GSM Evolution; Computer science; Reliability theory; Upper and lower bounds; Mathematical optimization; Mathematics; Computer network; Statistics; Failure rate; Telecommunications; Power (physics)","score_opus":0.007449443564022774,"score_gpt":0.180964436258202,"score_spread":0.17351499269417922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1576476555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3392489,0.0003720927,0.6434582,0.0005639669,0.00008340207,0.00014574613,0.000112348396,0.0006835372,0.015331813],"genre_scores_gemma":[0.7490825,0.0003037417,0.24595812,0.00014912435,0.000041960368,0.00016384665,0.00011919154,0.00006860174,0.004112997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997427,0.00008135363,0.000014139043,0.00005724636,0.00004944329,0.00005509704],"domain_scores_gemma":[0.9990916,0.0004729376,0.00015314647,0.00011085945,0.000101474354,0.0000699632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005629537,0.00061153347,0.0005777276,0.0005602108,0.0003522154,0.0006493256,0.0010371676,0.0011679644,0.0030736628],"category_scores_gemma":[0.0033350615,0.00037992437,0.00035151013,0.00039289033,0.0004944189,0.0014116942,0.0014904646,0.0005986234,0.00042454616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017839287,0.00010412286,0.0013961908,0.00008130267,0.000037385515,0.00018875262,0.00016645419,0.8809195,0.009766106,0.020369262,0.0013174079,0.08547517],"study_design_scores_gemma":[0.000029641293,0.00011260049,0.00015706898,0.000011458665,0.000025315658,0.000059857186,0.000046113153,0.9818111,0.0019568042,0.013231948,0.0025507754,0.0000073765846],"about_ca_topic_score_codex":0.0006338442,"about_ca_topic_score_gemma":0.00075472525,"teacher_disagreement_score":0.0030736628,"about_ca_system_score_codex":0.00041045906,"about_ca_system_score_gemma":0.00033975448,"threshold_uncertainty_score":0.010282397},"labels":[],"label_agreement":null},{"id":"W1581192214","doi":"10.1002/9780470382844.ch13","title":"System Test Execution","year":2008,"lang":"en","type":"other","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Test (biology); Computer science; Product (mathematics); Preparedness; Tracking (education); Software engineering; Reliability engineering; Engineering; Psychology; Management; Mathematics","score_opus":0.003782537973981304,"score_gpt":0.16038670504186647,"score_spread":0.15660416706788516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1581192214","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059010904,0.0011841877,0.49687645,0.00090020534,0.00044567263,0.0012154174,0.007907805,0.062046014,0.3704134],"genre_scores_gemma":[0.45560113,0.001689536,0.23202714,0.000807134,0.00017605477,0.00076900865,0.021702675,0.009759498,0.2774678],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983664,0.00025325493,0.00007976356,0.00020678208,0.00095249194,0.00014133597],"domain_scores_gemma":[0.99800533,0.0007671798,0.00010792758,0.00052844896,0.00052542676,0.000065761225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010560965,0.0011990966,0.00042752648,0.0013756342,0.00035202308,0.001860271,0.0011176559,0.00040691975,0.048718818],"category_scores_gemma":[0.0046044947,0.00036721583,0.00056182966,0.0007592754,0.00030437874,0.001814404,0.0008650215,0.00077031745,0.01598687],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038668397,0.00032452977,0.008959828,0.0005607374,0.0000746789,0.0003667433,0.00039967,0.038987108,0.024027864,0.040766794,0.069289885,0.81585544],"study_design_scores_gemma":[0.000103894265,0.00065693504,0.01614785,0.0005387764,0.00016877765,0.0011146679,0.00040680164,0.27686948,0.14341322,0.043593418,0.5168541,0.00013216391],"about_ca_topic_score_codex":0.0035563093,"about_ca_topic_score_gemma":0.0029855417,"teacher_disagreement_score":0.048718818,"about_ca_system_score_codex":0.000709236,"about_ca_system_score_gemma":0.0012408545,"threshold_uncertainty_score":0.16298068},"labels":[],"label_agreement":null},{"id":"W1585500081","doi":"10.1007/978-3-540-37368-1_6","title":"Optimal Redundancy Allocation of Multi-State Systems with Genetic Algorithms","year":2006,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Redundancy (engineering); Genetic algorithm; State (computer science); Algorithm; Mathematical optimization; Mathematics; Machine learning","score_opus":0.04181044156938194,"score_gpt":0.2826040294484932,"score_spread":0.2407935878791113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1585500081","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03056408,0.0011253627,0.9588692,0.00019328683,0.000060564802,0.000044147597,0.000024686788,0.0002914814,0.008827196],"genre_scores_gemma":[0.7078759,0.00076444016,0.28710273,0.00007298831,0.00007538806,0.00015306997,0.000057176527,0.000119629665,0.0037787105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997726,0.00008756346,0.000009374932,0.00003058313,0.00007200479,0.000027851145],"domain_scores_gemma":[0.9997309,0.00017123506,0.00002605189,0.000027886605,0.000036148467,0.000007716185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060335675,0.0007554153,0.00092700514,0.0005938949,0.0003066492,0.00077451253,0.0008761808,0.00081107987,0.0015198339],"category_scores_gemma":[0.001440969,0.0005337226,0.00060435047,0.0008372754,0.0007280986,0.0008952206,0.00058421347,0.0007714384,0.00021463649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025616755,0.0000131781035,0.00003632849,0.000022674805,0.000016260932,0.0000102301465,0.000021497497,0.9665396,0.0007709559,0.007515696,0.00029336903,0.024734484],"study_design_scores_gemma":[0.000009763577,0.000013791454,0.000027736707,0.0000053142153,0.0000059895942,0.0000049828395,0.000002782574,0.9913338,0.00024992027,0.008118267,0.00022474244,0.0000029739394],"about_ca_topic_score_codex":0.0022351153,"about_ca_topic_score_gemma":0.0022625679,"teacher_disagreement_score":0.0022351153,"about_ca_system_score_codex":0.00071052206,"about_ca_system_score_gemma":0.0006022927,"threshold_uncertainty_score":0.0051552057},"labels":[],"label_agreement":null},{"id":"W1589446810","doi":"10.1002/9781118445112.stat00203","title":"Optimal Sample Size Allocation for Accelerated Degradation Test Based on Wiener Process","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Reliability (semiconductor); Degradation (telecommunications); Wiener process; Reliability engineering; Computer science; Accelerated life testing; Product (mathematics); Process (computing); Variance (accounting); Statistics; Mathematics; Weibull distribution; Engineering; Power (physics)","score_opus":0.02599973752213671,"score_gpt":0.2831793939733867,"score_spread":0.25717965645125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1589446810","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053029567,0.00023701673,0.94500667,0.00017772758,0.00002371661,0.00022630536,0.000057787474,0.0001565783,0.0010846442],"genre_scores_gemma":[0.6246893,0.00023920054,0.3718695,0.00014090912,0.000060297156,0.000827628,0.0002684451,0.00007710645,0.0018275969],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944154,0.0039915214,0.00017558204,0.00054316455,0.0005992009,0.00027515244],"domain_scores_gemma":[0.97341985,0.0235232,0.0007797449,0.0005871068,0.001405595,0.00028444032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010174492,0.0006832347,0.0017627454,0.0011571611,0.0003931867,0.00078975386,0.0010732786,0.0010026562,0.0023899637],"category_scores_gemma":[0.032127194,0.00048441853,0.00063459005,0.0005843727,0.0011715613,0.0011513957,0.00126364,0.001185806,0.00029059825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018088632,0.00038455002,0.0043347036,0.0003172963,0.0001342358,0.000195254,0.00016887613,0.7944204,0.010007536,0.05070199,0.0016995758,0.13582669],"study_design_scores_gemma":[0.00007402529,0.00020748023,0.0010146932,0.00001838214,0.000021388014,0.00002470572,0.000020189947,0.9870046,0.0022743922,0.009056087,0.00026890245,0.000015132996],"about_ca_topic_score_codex":0.001554341,"about_ca_topic_score_gemma":0.0010152608,"teacher_disagreement_score":0.010174492,"about_ca_system_score_codex":0.00091978896,"about_ca_system_score_gemma":0.0018217978,"threshold_uncertainty_score":0.05380851},"labels":[],"label_agreement":null},{"id":"W1593751081","doi":"10.21236/ada462925","title":"Reliability Information Analysis Center 1st Quarter 2007, Technical Area Task (TAT) Report","year":2007,"lang":"en","type":"report","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Center (category theory); Quarter (Canadian coin); Reliability (semiconductor); Information center; Task (project management); Computer science; Reliability engineering; Engineering; Geography; Psychology; Systems engineering; Archaeology; Physics; Chemistry; Mathematics education","score_opus":0.009135753874792795,"score_gpt":0.23973721871537415,"score_spread":0.23060146484058136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1593751081","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04052697,0.0055606817,0.11913375,0.010579905,0.008996362,0.005910112,0.23790975,0.021126725,0.5502559],"genre_scores_gemma":[0.07504434,0.007231641,0.059175804,0.0010056746,0.001778623,0.0022786672,0.24470492,0.005118681,0.6036616],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958467,0.0006052647,0.00020048107,0.00026114268,0.0028617799,0.00022473274],"domain_scores_gemma":[0.9879232,0.0013795675,0.00044222464,0.0009849487,0.009056375,0.00021374802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006120692,0.0010652859,0.0011540387,0.003782222,0.0011423515,0.002941544,0.0016038785,0.0010932366,0.08122294],"category_scores_gemma":[0.010469057,0.00077095925,0.0004677345,0.003507701,0.00034215514,0.0016745524,0.00053814123,0.0014126867,0.068136305],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000172249,0.00014028286,0.0010859102,0.00013431945,0.000019967354,0.000030417732,0.000041032683,0.0014287321,0.0020209122,0.0018271827,0.9501561,0.0429429],"study_design_scores_gemma":[0.00019662856,0.0007097377,0.017316366,0.00022948957,0.00012442518,0.00025370004,0.00022814753,0.018421242,0.02922559,0.0025355336,0.93066895,0.00009022794],"about_ca_topic_score_codex":0.012730497,"about_ca_topic_score_gemma":0.017007656,"teacher_disagreement_score":0.08122294,"about_ca_system_score_codex":0.0020250932,"about_ca_system_score_gemma":0.0063245,"threshold_uncertainty_score":0.2717178},"labels":[],"label_agreement":null},{"id":"W1599115091","doi":"10.1002/9781118985960.meh206","title":"Reliability in the Mechanical Design Process","year":2015,"lang":"en","type":"other","venue":"Mechanical Engineers' Handbook","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Weibull distribution; Reliability (semiconductor); Reliability engineering; Failure rate; Computer science; Process (computing); Hazard; Engineering; Statistics; Mathematics","score_opus":0.012525251743708072,"score_gpt":0.21953990329423784,"score_spread":0.20701465155052975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1599115091","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022533795,0.0419365,0.7856544,0.0039242445,0.00051846605,0.00015890355,0.00022053518,0.000524126,0.14452909],"genre_scores_gemma":[0.6003003,0.035700027,0.26409173,0.00080505246,0.0007067038,0.00046466087,0.00035878993,0.00033401942,0.09723874],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99701214,0.0012413169,0.000113211274,0.0003116918,0.0012303361,0.00009125063],"domain_scores_gemma":[0.9966988,0.0019049939,0.00025393112,0.00029803294,0.00078712933,0.000057093188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031633452,0.0006298381,0.00040352537,0.0013097342,0.0005083232,0.0023062013,0.0006900892,0.0008791978,0.0059434436],"category_scores_gemma":[0.0062369662,0.0005101171,0.0004550252,0.0013738654,0.0014012827,0.0016011655,0.0011351482,0.0011369041,0.0020246943],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040928528,0.00005565218,0.0014355199,0.0006488924,0.000038339607,0.00020152761,0.00067049655,0.085706346,0.004105891,0.55272526,0.013056321,0.34131482],"study_design_scores_gemma":[0.000025157779,0.00018249298,0.0027108374,0.0009795682,0.000046808975,0.00043998822,0.00037300456,0.14433658,0.0047743632,0.5789207,0.26713818,0.00007235149],"about_ca_topic_score_codex":0.0015104164,"about_ca_topic_score_gemma":0.0011533112,"teacher_disagreement_score":0.0059434436,"about_ca_system_score_codex":0.0012634346,"about_ca_system_score_gemma":0.0012231056,"threshold_uncertainty_score":0.019882798},"labels":[],"label_agreement":null},{"id":"W1601712886","doi":"10.1109/tr.2015.2430491","title":"Ordering Heuristics for Reliability Evaluation of Multistate Networks","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":130,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Reliability (semiconductor); Disjoint sets; Heuristic; Computer science; Path (computing); Terminal (telecommunication); Mathematical optimization; Reliability engineering; Algorithm; Mathematics; Artificial intelligence; Discrete mathematics; Engineering","score_opus":0.03342313046835089,"score_gpt":0.2706196956357848,"score_spread":0.23719656516743393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1601712886","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014689366,0.00037947434,0.9822221,0.000052264404,0.000024289911,0.0000859185,0.00008120912,0.0003716491,0.0020936867],"genre_scores_gemma":[0.34906292,0.000558122,0.6480523,0.00006228192,0.000037521015,0.00019040047,0.0003622119,0.00023574477,0.0014384366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985317,0.0006130692,0.00008246354,0.00017604137,0.00045899517,0.00013769514],"domain_scores_gemma":[0.9961456,0.0025916216,0.00028287456,0.00021099945,0.000672947,0.00009589078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019216182,0.0012484584,0.00086658064,0.0022467768,0.0006117824,0.0009703314,0.0011236918,0.00054987817,0.0019491034],"category_scores_gemma":[0.0058056302,0.0005891178,0.0007748307,0.0016308615,0.0006592969,0.0016696348,0.0006898019,0.00093476305,0.00029119232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009904116,0.00004387847,0.0007337351,0.00017599089,0.000033103268,0.00007174384,0.00010935039,0.8821346,0.0034331453,0.018042138,0.001343016,0.093780175],"study_design_scores_gemma":[0.000011471598,0.00003926039,0.00014885436,0.000019822675,0.000014534258,0.00002317322,0.000026811971,0.98657155,0.0022919546,0.009946249,0.00089445076,0.000011853853],"about_ca_topic_score_codex":0.0056189382,"about_ca_topic_score_gemma":0.0067429645,"teacher_disagreement_score":0.0056189382,"about_ca_system_score_codex":0.0017630694,"about_ca_system_score_gemma":0.0015858707,"threshold_uncertainty_score":0.012792051},"labels":[],"label_agreement":null},{"id":"W1602065646","doi":"10.1002/9780470986424.ch6","title":"Activity Management through Bernoulli Scheduling","year":2008,"lang":"en","type":"other","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Reliability (semiconductor); Bernoulli's principle; Event (particle physics); Scheduling (production processes); Operations management; Engineering","score_opus":0.009084955206505364,"score_gpt":0.2055407621805167,"score_spread":0.19645580697401133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1602065646","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013147858,0.00046670026,0.9732108,0.00040208412,0.00021308142,0.00013999832,0.000098930606,0.0005523018,0.011768182],"genre_scores_gemma":[0.69058764,0.0013764721,0.27814707,0.00036072737,0.00040266768,0.00039592938,0.00032336364,0.00025652896,0.028149657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800164,0.0006096912,0.00011420644,0.0004044651,0.00062446186,0.00024556607],"domain_scores_gemma":[0.99694484,0.0017322946,0.00026652488,0.00039915377,0.0004075523,0.00024963947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028707394,0.0007982521,0.0011097611,0.000938656,0.0010579993,0.002461797,0.0019700392,0.0006269353,0.006069543],"category_scores_gemma":[0.0070962543,0.00065077184,0.0007339746,0.001552303,0.0009960576,0.0021874886,0.0012867489,0.001515699,0.0012398165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019309037,0.00018427044,0.0013682947,0.00007298069,0.000062352534,0.00005805713,0.00014345029,0.67674273,0.0029088524,0.22028072,0.004569651,0.09341549],"study_design_scores_gemma":[0.000017028855,0.000025771133,0.00018558987,0.000007793754,0.000013456655,0.000027773127,0.000013440085,0.9516177,0.0005078835,0.045265503,0.0023074069,0.000010699012],"about_ca_topic_score_codex":0.004260417,"about_ca_topic_score_gemma":0.004399198,"teacher_disagreement_score":0.006069543,"about_ca_system_score_codex":0.0018590294,"about_ca_system_score_gemma":0.00218788,"threshold_uncertainty_score":0.02030462},"labels":[],"label_agreement":null},{"id":"W1603749820","doi":"10.1002/qre.1466","title":"Condition‐based Maintenance Optimization Using Neural Network‐based Health Condition Prediction","year":2012,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Prognostics; Benchmark (surveying); Artificial neural network; Condition-based maintenance; Set (abstract data type); Key (lock); Computer science; Reliability engineering; Data mining; Engineering; Machine learning; Artificial intelligence","score_opus":0.020490239845338716,"score_gpt":0.27601655462931374,"score_spread":0.25552631478397503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603749820","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16619904,0.0008721798,0.8272188,0.0003302979,0.00005014971,0.00010188779,0.0001721223,0.000753213,0.004302481],"genre_scores_gemma":[0.96598977,0.00014479608,0.032521468,0.000042926236,0.000016415488,0.000076067714,0.00013526528,0.000023141558,0.001050137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997235,0.000071542454,0.000018465616,0.00006943026,0.00008485237,0.0000321744],"domain_scores_gemma":[0.9993988,0.0003458574,0.00011041234,0.000028430046,0.00009598336,0.00002035846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007976369,0.000733432,0.00070148683,0.0006155478,0.00025112453,0.0005392051,0.0005969785,0.0006920393,0.0009892362],"category_scores_gemma":[0.0019707915,0.0003339206,0.00040368043,0.0003551209,0.00032285854,0.0006769548,0.00041834082,0.0005088404,0.000111378744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002197649,0.000019913286,0.000479269,0.000012940028,0.00001106088,0.0000098648825,0.000004855339,0.9878352,0.00048482645,0.00023737643,0.00011462978,0.01076798],"study_design_scores_gemma":[0.0000020486566,0.0000055320697,0.00014019075,0.0000011179112,0.0000018585331,0.0000015800296,7.474344e-7,0.99957436,0.000103622355,0.0001465573,0.000021298589,0.000001054529],"about_ca_topic_score_codex":0.009357932,"about_ca_topic_score_gemma":0.0070121842,"teacher_disagreement_score":0.009357932,"about_ca_system_score_codex":0.0009995727,"about_ca_system_score_gemma":0.00078458706,"threshold_uncertainty_score":0.018606901},"labels":[],"label_agreement":null},{"id":"W1622685496","doi":"10.1109/icc.1991.162445","title":"Performance of the Selfhealing Network protocol with random individual link failure times","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Energy","funders":"Advanced Technology Research Council","keywords":"Protocol (science); Asynchronous communication; Computer science; Disjoint sets; Computer network; Link (geometry); Spare part; Distributed computing; Mathematics; Discrete mathematics; Engineering","score_opus":0.005935746138107527,"score_gpt":0.17199173031429188,"score_spread":0.16605598417618436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1622685496","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9748752,0.00037440626,0.017127361,0.0002998963,0.00008907986,0.00007217712,0.00017104304,0.0012520873,0.0057387776],"genre_scores_gemma":[0.99725986,0.00006194216,0.0018112455,0.000022504015,0.0000073762108,0.000020392219,0.000108213935,0.000041023544,0.00066745875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979715,0.00046807196,0.00011887514,0.00027038588,0.0007319849,0.00043922654],"domain_scores_gemma":[0.9863665,0.008863309,0.0010709724,0.0013057208,0.0017572741,0.0006362513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003516424,0.00065534434,0.00074743474,0.0008043055,0.00071255054,0.0009928799,0.00090467685,0.00073709205,0.0026567033],"category_scores_gemma":[0.013116925,0.00021577901,0.00022131846,0.0006951324,0.0014436769,0.0015747682,0.00085947895,0.00077067787,0.00030174598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0052764057,0.00061125593,0.007644523,0.00027652466,0.00023237795,0.0004040205,0.00045791568,0.87410086,0.044907723,0.013017047,0.003963024,0.049108353],"study_design_scores_gemma":[0.00012978274,0.0009884217,0.0013442183,0.0000122986785,0.00003925471,0.00011831794,0.00008429232,0.9745462,0.01988589,0.0022600507,0.0005615034,0.000029898576],"about_ca_topic_score_codex":0.0039421683,"about_ca_topic_score_gemma":0.0016366839,"teacher_disagreement_score":0.0039421683,"about_ca_system_score_codex":0.0011663418,"about_ca_system_score_gemma":0.0013313397,"threshold_uncertainty_score":0.018596828},"labels":[],"label_agreement":null},{"id":"W1637181560","doi":"10.1063/1.2937611","title":"Optimal inspection period and replacement policy for CBM with imperfect information using PHM","year":2008,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Condition-based maintenance; Reliability engineering; Computer science; Hidden Markov model; Optimal maintenance; Degradation (telecommunications); State (computer science); Maintenance engineering; Markov process; Engineering; Statistics; Algorithm; Mathematics; Artificial intelligence","score_opus":0.012749020461936638,"score_gpt":0.21883107859608514,"score_spread":0.2060820581341485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1637181560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07955958,0.0006532914,0.91439426,0.0005808084,0.00004046786,0.00010332943,0.0002498848,0.00026433825,0.0041540186],"genre_scores_gemma":[0.93623716,0.00046009512,0.056726538,0.00006451043,0.00003876176,0.00023230234,0.00018141312,0.00006440599,0.0059947744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990735,0.00029187417,0.00003151754,0.000238052,0.00018432182,0.00018067552],"domain_scores_gemma":[0.9978637,0.0014695569,0.00034710896,0.00009461251,0.00014251268,0.00008255674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024121902,0.0009205907,0.0022075379,0.00096990855,0.00041178096,0.0012125087,0.0016943322,0.0014733248,0.0028622537],"category_scores_gemma":[0.0061791088,0.000878586,0.00080937444,0.0007582078,0.0010498741,0.0012008934,0.00097536674,0.0012122425,0.0002703562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006994604,0.00002994014,0.0005167835,0.00005407672,0.000018850444,0.000057002344,0.000031486346,0.98208916,0.00073390827,0.010805865,0.00031583745,0.0052771494],"study_design_scores_gemma":[0.000012195184,0.000027663526,0.0002769107,0.0000070634587,0.000009248862,0.000014180483,0.00000800239,0.99459684,0.00013320411,0.0047710757,0.00013789025,0.0000057789207],"about_ca_topic_score_codex":0.008098321,"about_ca_topic_score_gemma":0.0043598935,"teacher_disagreement_score":0.008098321,"about_ca_system_score_codex":0.0020999967,"about_ca_system_score_gemma":0.0019728774,"threshold_uncertainty_score":0.016102374},"labels":[],"label_agreement":null},{"id":"W1641855109","doi":"10.1109/iecon.1996.571027","title":"An inspection strategy for randomly failing systems subjected to random shocks","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Reliability engineering; Computer science; Censoring (clinical trials); Electric power system; Process (computing); ALARM; Transient (computer programming); Critical system; Stochastic process; Automotive industry; Real-time computing; Engineering; Power (physics); Mathematics; Statistics; Electrical engineering","score_opus":0.016660522854230767,"score_gpt":0.2223959065983925,"score_spread":0.20573538374416173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1641855109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20068601,0.00017392471,0.7962635,0.00014324747,0.000014782768,0.00007058352,0.000026218793,0.00043656462,0.002185265],"genre_scores_gemma":[0.9545476,0.00005807268,0.04443856,0.000024437448,0.000006111151,0.000032597174,0.000019863033,0.000021471673,0.00085131405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997273,0.00010190387,0.000013961128,0.000044182925,0.00007229616,0.00004028829],"domain_scores_gemma":[0.9990068,0.00048432362,0.00017873108,0.000073561605,0.0002022781,0.000054194283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005453273,0.00030295897,0.0003234408,0.0004044954,0.00018503529,0.00031987473,0.00047926916,0.0003854364,0.0007855061],"category_scores_gemma":[0.002490598,0.00019771988,0.00016484335,0.00023375038,0.0003406675,0.00034737808,0.0002624827,0.00026733289,0.00012152671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031881756,0.00009275121,0.0020864378,0.0000860972,0.000028431241,0.00031291865,0.00017169435,0.8738269,0.026298188,0.009844939,0.00086908415,0.086063825],"study_design_scores_gemma":[0.00001755405,0.00018240124,0.0004935104,0.0000043836812,0.000010536332,0.000058188405,0.000016690601,0.9943474,0.002765779,0.0018297305,0.00026810967,0.000005698933],"about_ca_topic_score_codex":0.0010620068,"about_ca_topic_score_gemma":0.0008611295,"teacher_disagreement_score":0.0010620068,"about_ca_system_score_codex":0.00033635713,"about_ca_system_score_gemma":0.00034249094,"threshold_uncertainty_score":0.0028839707},"labels":[],"label_agreement":null},{"id":"W164657745","doi":"10.1007/1-4020-4891-2_62","title":"A Comparison of Probabilistic Models of Deterioration for Life Cycle Management of Structures","year":2007,"lang":"en","type":"book-chapter","venue":"Solid mechanics and its applications","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Probabilistic logic; Reliability (semiconductor); Reliability engineering; Random variable; Computer science; Stochastic modelling; Product life-cycle management; Process (computing); Stochastic process; Exposition (narrative); Preventive maintenance; Gamma process; Variable (mathematics); Statistical model; Risk analysis (engineering); Engineering; Econometrics; Mathematics; Statistics; Artificial intelligence; Business","score_opus":0.036627340870914675,"score_gpt":0.28666689449342014,"score_spread":0.25003955362250546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W164657745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029921904,0.004207488,0.94374365,0.00075718464,0.00019081403,0.00007698122,0.0003221876,0.00035984075,0.020419883],"genre_scores_gemma":[0.7712626,0.007250452,0.20308188,0.00033431864,0.00034102274,0.00030627643,0.00069340516,0.00049816514,0.016231908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988919,0.0004986063,0.00004746303,0.00009725231,0.00039447562,0.00007030709],"domain_scores_gemma":[0.996567,0.0026625837,0.00015806843,0.00022741915,0.000327736,0.00005703797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028283831,0.00085749244,0.0014406953,0.001048679,0.00042430963,0.0015880209,0.0028510604,0.0017561058,0.00540317],"category_scores_gemma":[0.007059145,0.00055650115,0.0015184663,0.0015725407,0.00048980623,0.002184803,0.000777927,0.0013145705,0.0006049351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004702099,0.000043016873,0.00023499467,0.000061175815,0.000029478546,0.000015300593,0.000041042564,0.93493074,0.00024785445,0.038925845,0.0011370186,0.024286475],"study_design_scores_gemma":[0.0000039537526,0.000027102637,0.00020682378,0.00001134177,0.00001125602,0.000013679431,0.0000091239435,0.982274,0.00008257616,0.016426764,0.0009264961,0.0000067607752],"about_ca_topic_score_codex":0.005274625,"about_ca_topic_score_gemma":0.005796236,"teacher_disagreement_score":0.00540317,"about_ca_system_score_codex":0.0019951654,"about_ca_system_score_gemma":0.0010711424,"threshold_uncertainty_score":0.018075347},"labels":[],"label_agreement":null},{"id":"W1676976962","doi":"10.1063/1.1291337","title":"On the independence of multiple inspections and the resulting probability of detection","year":2000,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Independence (probability theory); Reliability (semiconductor); Reliability engineering; Statistical power; Interval (graph theory); Measure (data warehouse); Computer science; Point of delivery; Sensitivity (control systems); Power (physics); Statistics; Data mining; Mathematics; Engineering; Electronic engineering","score_opus":0.012372522298359875,"score_gpt":0.19378799402073882,"score_spread":0.18141547172237893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1676976962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033427067,0.0008972956,0.95884305,0.0004020876,0.00009490349,0.00006677868,0.00014880847,0.00023764335,0.0058823754],"genre_scores_gemma":[0.82315964,0.0024797774,0.16809437,0.00050154445,0.00039468383,0.00028356555,0.0005507995,0.00034688745,0.0041888272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98597705,0.0034346594,0.00065540156,0.0024066493,0.006970319,0.0005559335],"domain_scores_gemma":[0.8061491,0.17144203,0.008132802,0.008541933,0.005292143,0.0004419369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014686388,0.0013983692,0.0019966243,0.0019341579,0.0006734164,0.0021106175,0.002494254,0.0016785293,0.0026776346],"category_scores_gemma":[0.09111325,0.0010012668,0.0011439627,0.0018637469,0.0051807035,0.0043179896,0.002447365,0.0037477682,0.0010237786],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009867504,0.00024955312,0.022181662,0.0008216648,0.00039994123,0.0014783882,0.00052457117,0.5951327,0.019519914,0.1729379,0.0024436263,0.18332334],"study_design_scores_gemma":[0.00006671361,0.0005017096,0.017089235,0.00018660151,0.0001590432,0.0024433776,0.00008897181,0.8334521,0.018277032,0.12347985,0.0040642107,0.00019117244],"about_ca_topic_score_codex":0.0013659052,"about_ca_topic_score_gemma":0.0010816982,"teacher_disagreement_score":0.014686388,"about_ca_system_score_codex":0.0011597566,"about_ca_system_score_gemma":0.0010330422,"threshold_uncertainty_score":0.07766998},"labels":[],"label_agreement":null},{"id":"W1681165416","doi":"10.1002/9780470061572.eqr105","title":"Group Maintenance Policies","year":2007,"lang":"en","type":"other","venue":"Encyclopedia of Statistics in Quality and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Generalization; Feature (linguistics); State (computer science); Computer science; Group (periodic table); Mathematics; Algorithm","score_opus":0.01066407379303127,"score_gpt":0.2700272141882417,"score_spread":0.25936314039521047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1681165416","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.102429174,0.0010028112,0.87092745,0.0009078517,0.00039106733,0.0002990578,0.00033067752,0.0020075217,0.02170438],"genre_scores_gemma":[0.8631154,0.00035408582,0.12580414,0.00022472854,0.0002164221,0.0002443466,0.00033969505,0.0002006512,0.009500527],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974964,0.0007863871,0.00014994312,0.0004962257,0.00075246545,0.00031857807],"domain_scores_gemma":[0.993733,0.0022291422,0.00079151616,0.002138749,0.0007490713,0.00035846423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002948096,0.0006290313,0.00078736426,0.0008318433,0.00076136785,0.0013126688,0.0023824447,0.0009996926,0.0071285055],"category_scores_gemma":[0.007516185,0.00026255404,0.00047556954,0.0007814584,0.0009028027,0.0022757114,0.0013235185,0.0010786225,0.0014332838],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009588285,0.0005867571,0.0069305315,0.00042919756,0.00016959808,0.0006236782,0.0005751404,0.32459983,0.013112032,0.25689572,0.036160428,0.35895824],"study_design_scores_gemma":[0.0001870319,0.00044613695,0.0016318823,0.00007311441,0.000083679595,0.00071681075,0.00012738386,0.7619203,0.00993607,0.18451883,0.040308114,0.000050691644],"about_ca_topic_score_codex":0.00076895143,"about_ca_topic_score_gemma":0.0006965532,"teacher_disagreement_score":0.0071285055,"about_ca_system_score_codex":0.0010346816,"about_ca_system_score_gemma":0.0008843173,"threshold_uncertainty_score":0.023847222},"labels":[],"label_agreement":null},{"id":"W1725113592","doi":"","title":"Optimal replacement policies for two-component parallel system with stochastic dependence","year":2013,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université Laval","funders":"","keywords":"Component (thermodynamics); Probability density function; Random variable; Function (biology); Domino effect; Stochastic modelling; Constant (computer programming); Stochastic process; Computer science; State (computer science); Mathematical optimization; Mathematics; Statistics; Algorithm","score_opus":0.0065789481323337545,"score_gpt":0.20230319663712615,"score_spread":0.1957242485047924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1725113592","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3483409,0.0012354789,0.64662653,0.00039312875,0.000052665295,0.00008576855,0.000090344874,0.000252538,0.002922548],"genre_scores_gemma":[0.9861935,0.0002460155,0.012042439,0.00002230818,0.000014195516,0.000030443987,0.000033425273,0.000017660877,0.0013999988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995565,0.00012356498,0.000024807285,0.00008317871,0.00009994934,0.00011205629],"domain_scores_gemma":[0.9987437,0.0005772305,0.00032574823,0.0000742997,0.00018038315,0.00009863239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011413259,0.0006999943,0.00088716694,0.000585502,0.00034966986,0.00059254054,0.00075883203,0.00064427784,0.001059428],"category_scores_gemma":[0.0024290185,0.00043700935,0.00041921667,0.00038947834,0.0005625777,0.0007135958,0.0005125231,0.0005397615,0.00014037512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070527654,0.000032399264,0.00053812686,0.00003396923,0.000015967618,0.00007363585,0.000023183922,0.98870313,0.0018998788,0.0027249116,0.00016741622,0.0057168934],"study_design_scores_gemma":[0.0000072646853,0.000038637885,0.00025551373,0.0000023957462,0.000007917836,0.00002377632,0.00000716472,0.9975527,0.00037099962,0.0016302622,0.00009961379,0.0000037413088],"about_ca_topic_score_codex":0.0035536638,"about_ca_topic_score_gemma":0.002276073,"teacher_disagreement_score":0.0035536638,"about_ca_system_score_codex":0.0009791721,"about_ca_system_score_gemma":0.0008515112,"threshold_uncertainty_score":0.007104397},"labels":[],"label_agreement":null},{"id":"W1746451909","doi":"10.1109/etfa.1995.496788","title":"Replacement strategy for non self announcing failure equipment","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Reliability engineering; Computer science; Inspection time; Sequence (biology); Function (biology); Engineering","score_opus":0.01555060174381663,"score_gpt":0.21023077961548226,"score_spread":0.19468017787166564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1746451909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5941865,0.0004383414,0.40170833,0.00015648098,0.000029985466,0.00010876222,0.000051896877,0.0006018918,0.0027178603],"genre_scores_gemma":[0.97539026,0.00004017533,0.02374114,0.000021072372,0.0000048926768,0.000022149548,0.000033366738,0.00001848886,0.00072846754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994073,0.00020859788,0.00003409449,0.00009957178,0.00017168303,0.00007883809],"domain_scores_gemma":[0.99831057,0.0007857327,0.00036457833,0.00024954486,0.00022404476,0.00006550371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083776185,0.00045846036,0.000533688,0.00060367654,0.00024222843,0.00044447213,0.00088626414,0.0005203521,0.0009916773],"category_scores_gemma":[0.0030562778,0.0002408398,0.00031503118,0.00024166997,0.00044993818,0.000494649,0.00023701007,0.00026006738,0.00017090653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051062275,0.00037168936,0.006517056,0.0002168446,0.00006609774,0.00070812204,0.00030872328,0.7641457,0.032745197,0.014086386,0.0010817453,0.17924179],"study_design_scores_gemma":[0.000048265792,0.00054645125,0.0019604254,0.00001159252,0.000042974283,0.00019815846,0.000037519385,0.9838583,0.007360824,0.0051717036,0.00074800703,0.00001567789],"about_ca_topic_score_codex":0.0012873705,"about_ca_topic_score_gemma":0.0014035472,"teacher_disagreement_score":0.0012873705,"about_ca_system_score_codex":0.00051077886,"about_ca_system_score_gemma":0.0005130818,"threshold_uncertainty_score":0.0044305325},"labels":[],"label_agreement":null},{"id":"W1749194589","doi":"10.4271/2003-01-2984","title":"Maintenance Action Based on the Time Dependent Failure Rate for Safety–Critical Components","year":2003,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Reliability engineering; Action (physics); Failure rate; Computer science; Component (thermodynamics); Engineering","score_opus":0.01134950563166622,"score_gpt":0.22735925453173036,"score_spread":0.21600974890006414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1749194589","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35538277,0.0029324915,0.61595035,0.00033979968,0.00036528683,0.00054095406,0.0013785056,0.0055433093,0.017566541],"genre_scores_gemma":[0.94229764,0.000439001,0.05131499,0.000059536036,0.000060797418,0.00013136338,0.0008603395,0.0001375532,0.004698746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804604,0.0002633604,0.000096956595,0.0003818359,0.0011170033,0.00009479256],"domain_scores_gemma":[0.9955853,0.0019726616,0.0007730916,0.0006251618,0.0009599294,0.00008377401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017750047,0.00071885105,0.00054500514,0.0018739571,0.00027232565,0.00070517167,0.0014420123,0.00067194924,0.0034664315],"category_scores_gemma":[0.008701054,0.00018962771,0.00063053635,0.00062990293,0.00031602266,0.0007382083,0.000300729,0.0005608963,0.0013538225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014471209,0.00053369155,0.065311,0.000768055,0.00033129196,0.0004672393,0.00049027137,0.24338423,0.11903433,0.0138561595,0.009064406,0.54531217],"study_design_scores_gemma":[0.00006490236,0.0011973828,0.07055034,0.00011075338,0.00025881614,0.0010545151,0.00011123989,0.8645545,0.04877636,0.0042495383,0.008914013,0.00015775418],"about_ca_topic_score_codex":0.002679489,"about_ca_topic_score_gemma":0.0023737396,"teacher_disagreement_score":0.0034664315,"about_ca_system_score_codex":0.00075138296,"about_ca_system_score_gemma":0.0004681038,"threshold_uncertainty_score":0.011596382},"labels":[],"label_agreement":null},{"id":"W1859387410","doi":"10.1109/pcicon.1996.564882","title":"Elements of a power systems risk analysis and reliability study","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Dynamics (Canada)","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Upgrade; Computer science; Power station; Power (physics); Failure rate; Risk analysis (engineering); Engineering; Electrical engineering; Business","score_opus":0.004936086834455799,"score_gpt":0.19032826068333975,"score_spread":0.18539217384888396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1859387410","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041638777,0.0010019486,0.88264585,0.0027556715,0.000067498375,0.00024935667,0.0001743043,0.00032027438,0.07114621],"genre_scores_gemma":[0.7028389,0.0022274142,0.2791093,0.00039150406,0.00032095445,0.000394712,0.00020291805,0.00018024839,0.014334039],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840814,0.00041934007,0.00007332048,0.00012637126,0.00089974154,0.00007302565],"domain_scores_gemma":[0.9971354,0.0018622064,0.0001825582,0.0003101161,0.00042195863,0.00008767638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016712929,0.00079409976,0.0005355666,0.0017143486,0.0009108016,0.0026474143,0.0012149441,0.0012734152,0.0039495653],"category_scores_gemma":[0.0041711316,0.0008243172,0.0007274595,0.0009580585,0.00205158,0.0034244708,0.0015239858,0.002576989,0.0008311662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041041614,0.00016259977,0.0030247476,0.000121401245,0.00007891379,0.00055888714,0.00046534836,0.22504245,0.004420069,0.7206616,0.0019479721,0.043474965],"study_design_scores_gemma":[0.00001895259,0.0001516221,0.0018158898,0.000105650004,0.000048169433,0.00036803735,0.00035226712,0.27918845,0.0024104882,0.6926187,0.022884132,0.000037647504],"about_ca_topic_score_codex":0.0022846283,"about_ca_topic_score_gemma":0.0008625556,"teacher_disagreement_score":0.0039495653,"about_ca_system_score_codex":0.0007942689,"about_ca_system_score_gemma":0.0012080531,"threshold_uncertainty_score":0.013212562},"labels":[],"label_agreement":null},{"id":"W1867625458","doi":"10.1016/j.ifacol.2015.06.127","title":"Production Planning and Opportunistic Preventive Maintenance for Unreliable One-Machine Two-Products Manufacturing Systems","year":2015,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Preventive maintenance; Robustness (evolution); Reliability engineering; Economic shortage; Synchronization (alternating current); Computer science; Production planning; Discrete event simulation; Production (economics); Control (management); Operations research; Industrial engineering; Variance (accounting); Production control; Proactive maintenance; Engineering; Risk analysis (engineering); Simulation; Artificial intelligence","score_opus":0.0386810661580436,"score_gpt":0.2529649696475627,"score_spread":0.21428390348951912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1867625458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23974049,0.00037277836,0.7572825,0.000108604756,0.000023050698,0.0000800799,0.000041039362,0.00013176278,0.0022196907],"genre_scores_gemma":[0.9886785,0.0000642751,0.010954459,0.0000047481635,0.0000042635797,0.00002882631,0.000011154871,0.0000041488065,0.00024958246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999469,0.00018986691,0.000022517996,0.00008700479,0.00014334134,0.00008827232],"domain_scores_gemma":[0.99871695,0.00072391285,0.0003385335,0.00007807562,0.000100603735,0.00004198702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000702062,0.0006168836,0.0005410755,0.00032992556,0.0002906667,0.00055390445,0.00059752073,0.00037754685,0.00058290834],"category_scores_gemma":[0.0020064914,0.0002623261,0.00029534995,0.00030239826,0.000461513,0.00037964666,0.00048517896,0.0004300169,0.000054757365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075675795,0.00004098123,0.00065625395,0.000060916966,0.000012547955,0.00008893264,0.00003518802,0.98312634,0.0032548376,0.0017832114,0.000067397756,0.010797664],"study_design_scores_gemma":[0.000010605776,0.00010844148,0.0006024449,0.000003597915,0.0000084049625,0.000024352574,0.00001198043,0.99721473,0.000979612,0.0008912462,0.0001404404,0.0000042441006],"about_ca_topic_score_codex":0.0024910388,"about_ca_topic_score_gemma":0.0015289905,"teacher_disagreement_score":0.0024910388,"about_ca_system_score_codex":0.0003973231,"about_ca_system_score_gemma":0.0007786827,"threshold_uncertainty_score":0.0049530864},"labels":[],"label_agreement":null},{"id":"W1877672870","doi":"10.3968/j.ans.1715787020090201.006","title":"Reliability Analysis of a Repairable C (2, 3; G ) System with Repair Priority and one is＂as good as new＂","year":2010,"lang":"en","type":"article","venue":"Advances in natural science/Advances in natural sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Component (thermodynamics); Reliability (semiconductor); Markov process; Laplace transform; Reliability engineering; Key (lock); Process (computing); Markov chain; Markov model; Variable (mathematics); Computer science; Engineering; Mathematical optimization; Mathematics; Statistics","score_opus":0.003076702232479231,"score_gpt":0.24728957351871905,"score_spread":0.2442128712862398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1877672870","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6033103,0.00043931784,0.3916928,0.0006141346,0.000034493733,0.00005523259,0.00013022168,0.000195111,0.0035283924],"genre_scores_gemma":[0.9919734,0.00011193162,0.006546992,0.000032970504,0.00002060579,0.00002105264,0.00006432358,0.000020752474,0.0012080668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993592,0.00019339574,0.00001870216,0.00014580355,0.0001281011,0.00015482982],"domain_scores_gemma":[0.9963548,0.0021674503,0.00057997036,0.00015155808,0.00057375414,0.00017240338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018624158,0.00057651114,0.00067448,0.0008059595,0.00033616798,0.00056809,0.0009122025,0.00066028634,0.00189473],"category_scores_gemma":[0.00502155,0.00019854867,0.00059037976,0.00046942712,0.0014596184,0.00097283174,0.00063238933,0.0006125628,0.00021755873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014079294,0.000029555795,0.0031520713,0.000085195614,0.00005494529,0.00039707642,0.00018766081,0.91684777,0.0075581046,0.063563436,0.00069860433,0.0072848084],"study_design_scores_gemma":[0.0000069325697,0.000036475245,0.0008102882,0.0000032785765,0.000014238269,0.000057470203,0.000026972859,0.9855826,0.00043969235,0.012892268,0.00011986286,0.000009856683],"about_ca_topic_score_codex":0.005439244,"about_ca_topic_score_gemma":0.0017697377,"teacher_disagreement_score":0.005439244,"about_ca_system_score_codex":0.0012309373,"about_ca_system_score_gemma":0.00093223754,"threshold_uncertainty_score":0.010815203},"labels":[],"label_agreement":null},{"id":"W1884531310","doi":"10.1109/rams.1995.513217","title":"Rationalizing scheduled-maintenance requirements using reliability centered maintenance-a Canadian Air Force perspective","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Reliability engineering; Task (project management); Reliability (semiconductor); Failure mode and effects analysis; Preventive maintenance; Aircraft maintenance; Aviation; Perspective (graphical); Computer science; Service (business); Maintenance engineering; Predictive maintenance; Risk analysis (engineering); Engineering; Systems engineering; Aeronautics","score_opus":0.027791245786430215,"score_gpt":0.23631242201661987,"score_spread":0.20852117623018965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1884531310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3936342,0.0022095535,0.5183094,0.009556803,0.00009447693,0.0009949361,0.0025679737,0.0012407613,0.071391955],"genre_scores_gemma":[0.8660954,0.000665971,0.12867829,0.00018483942,0.000032892993,0.00009521271,0.0007899756,0.00005907417,0.0033982976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954847,0.000665369,0.00016034918,0.00037468897,0.0027599277,0.00055504765],"domain_scores_gemma":[0.9944055,0.001524371,0.00039873476,0.00030072258,0.0032003312,0.00017032489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040607606,0.00094861904,0.00052249915,0.00371147,0.0015806238,0.002535127,0.0025182122,0.0006879918,0.0020207807],"category_scores_gemma":[0.012751262,0.00045717668,0.00054325786,0.0014055155,0.0012202947,0.0013985331,0.0005760819,0.00064425974,0.00015804196],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001921459,0.00021141292,0.03817405,0.000330253,0.00015961038,0.0004962103,0.0014273605,0.6203272,0.014305473,0.10142736,0.011221409,0.21172749],"study_design_scores_gemma":[0.0000730532,0.0002464323,0.045645315,0.00012486592,0.00013651987,0.00016617533,0.001340313,0.8900242,0.0074582947,0.028825399,0.02578566,0.00017380217],"about_ca_topic_score_codex":0.8703816,"about_ca_topic_score_gemma":0.90440094,"teacher_disagreement_score":0.1296184,"about_ca_system_score_codex":0.017820923,"about_ca_system_score_gemma":0.030916514,"threshold_uncertainty_score":0.26076347},"labels":[],"label_agreement":null},{"id":"W189508827","doi":"","title":"Traffic Signal Warrant Handbook","year":2007,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Warrant; SIGNAL (programming language); Matrix (chemical analysis); Component (thermodynamics); Computer science; Operations research; Engineering; Business","score_opus":0.004527309524095631,"score_gpt":0.17958974054852408,"score_spread":0.17506243102442845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W189508827","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035143828,0.005574448,0.052239083,0.0039704097,0.0018775789,0.0011913059,0.018581493,0.008401464,0.90464985],"genre_scores_gemma":[0.0193878,0.0071487273,0.056239523,0.001024845,0.00026412756,0.00045693983,0.019282287,0.0019665028,0.8942291],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99617743,0.00015834923,0.00015168045,0.00013748596,0.0031944318,0.00018064881],"domain_scores_gemma":[0.99459213,0.0003753676,0.00012277707,0.0002469172,0.004517879,0.00014485365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016743155,0.00090221793,0.0005099188,0.0052938014,0.002752437,0.0026067249,0.002957484,0.0014168317,0.18101361],"category_scores_gemma":[0.0071962606,0.0006606879,0.00041872426,0.004593495,0.0005534236,0.0023582915,0.0010420012,0.0013211173,0.0659619],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002696125,0.0000521122,0.00029106432,0.00032020526,0.0000031502632,0.000121846315,0.00023425778,0.0017464143,0.0011699661,0.025030859,0.75757766,0.21342556],"study_design_scores_gemma":[0.00000394739,0.000012845406,0.0004955854,0.00009650488,0.0000027376666,0.000099904115,0.00011073987,0.00067615084,0.00030961062,0.0012154621,0.9969599,0.000016667622],"about_ca_topic_score_codex":0.39497304,"about_ca_topic_score_gemma":0.55529404,"teacher_disagreement_score":0.39497304,"about_ca_system_score_codex":0.00727936,"about_ca_system_score_gemma":0.01846825,"threshold_uncertainty_score":0.78534806},"labels":[],"label_agreement":null},{"id":"W1907805485","doi":"10.1109/rams.2002.981625","title":"Optimizing condition based maintenance decisions","year":2003,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Condition-based maintenance; Key (lock); Computer science; Risk analysis (engineering); Process (computing); Condition monitoring; Control (management); Maintenance engineering; Artificial neural network; Engineering; Reliability engineering; Artificial intelligence; Business; Computer security","score_opus":0.01000914573518447,"score_gpt":0.21298175134787592,"score_spread":0.20297260561269145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1907805485","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22973005,0.0011685992,0.746044,0.0012377205,0.00010110253,0.00030601304,0.00032016626,0.00090826006,0.020184163],"genre_scores_gemma":[0.96568,0.00020864773,0.031424284,0.00008822988,0.00003680604,0.000077264325,0.000115163595,0.000041966963,0.0023275537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992895,0.00020189531,0.000030487357,0.00016727063,0.00018611815,0.0001247137],"domain_scores_gemma":[0.9988562,0.0007001152,0.0001647234,0.00005567302,0.00017266223,0.00005074968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011206309,0.00092435384,0.0009094202,0.0006351649,0.00029150647,0.0010537946,0.0007151834,0.0014747131,0.0037770171],"category_scores_gemma":[0.004578652,0.00034942594,0.0002660415,0.00037998086,0.00039387456,0.0013464502,0.00049041,0.00069860846,0.00041067705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021960364,0.00012792325,0.0010822958,0.000080318445,0.000036418784,0.00008546609,0.000056366513,0.91614133,0.0045925006,0.010502539,0.0015876798,0.065487474],"study_design_scores_gemma":[0.00002663525,0.00013636319,0.00059331243,0.000010663681,0.000018019082,0.00002478016,0.000016982853,0.99034494,0.001512157,0.006716997,0.00059048214,0.000008530928],"about_ca_topic_score_codex":0.0021273738,"about_ca_topic_score_gemma":0.0017254854,"teacher_disagreement_score":0.0037770171,"about_ca_system_score_codex":0.00083740434,"about_ca_system_score_gemma":0.0008475944,"threshold_uncertainty_score":0.01263541},"labels":[],"label_agreement":null},{"id":"W1926032255","doi":"10.1109/ectc.1993.346847","title":"Field reliability enhancement of electronic modules using ESS","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nortel (Canada)","funders":"","keywords":"Reliability (semiconductor); Original equipment manufacturer; Reliability engineering; Field (mathematics); Computer science; Engineering; Mathematics; Operating system; Physics","score_opus":0.009046208638640469,"score_gpt":0.20011287445383688,"score_spread":0.1910666658151964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1926032255","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9044128,0.0004475432,0.087714314,0.00008414935,0.000028347546,0.000057374145,0.000069437156,0.000669448,0.0065164804],"genre_scores_gemma":[0.9920936,0.00008992166,0.006530147,0.000013737744,0.00001047894,0.000011030694,0.000046674864,0.000020271898,0.0011841384],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986863,0.00003840397,0.00000805849,0.000017218303,0.00005090403,0.000016782542],"domain_scores_gemma":[0.99946254,0.00014362628,0.000062963445,0.00010004474,0.00020644031,0.000024366045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040224913,0.0002913556,0.00031941332,0.00058668654,0.00016952338,0.00022566752,0.000227516,0.00020881141,0.0016666568],"category_scores_gemma":[0.00087540894,0.00011103876,0.00023411395,0.0003084401,0.00017473334,0.00043523542,0.00034766848,0.00013827953,0.00026698012],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089341524,0.0002391748,0.0122460555,0.00030416553,0.000058585927,0.0003892123,0.00041400924,0.09603001,0.63161975,0.0021311187,0.0012083907,0.25446603],"study_design_scores_gemma":[0.00009991208,0.0047988244,0.04321595,0.00007308797,0.00013032509,0.00070328516,0.00025109033,0.3235928,0.6128351,0.0034919784,0.01076191,0.000045647168],"about_ca_topic_score_codex":0.00027030642,"about_ca_topic_score_gemma":0.00031818546,"teacher_disagreement_score":0.0016666568,"about_ca_system_score_codex":0.00016602421,"about_ca_system_score_gemma":0.00010113948,"threshold_uncertainty_score":0.0055755377},"labels":[],"label_agreement":null},{"id":"W1928184781","doi":"10.1109/icsmc.1996.561483","title":"Reliability evaluation of a furnace system using the k-out-of-n and the consecutive-k-out-of-n reliability models","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Reliability engineering; Computer science; Engineering; Thermodynamics; Physics","score_opus":0.052724917941680736,"score_gpt":0.2597416736207007,"score_spread":0.20701675567901998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1928184781","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3599461,0.00023815274,0.6349091,0.00012437056,0.000034579894,0.0000877165,0.00015176157,0.00037291873,0.004135352],"genre_scores_gemma":[0.97762644,0.000116420626,0.021057945,0.000010284293,0.00001026971,0.00003953261,0.000092822156,0.00002904407,0.0010172294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988464,0.0005400288,0.00004778829,0.00012426716,0.00035657594,0.00008489217],"domain_scores_gemma":[0.9977767,0.0013155058,0.00027844508,0.00022481337,0.00035098428,0.000053536143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018488494,0.00093027484,0.0006707221,0.00060121185,0.00039762395,0.00055830536,0.00088786375,0.0006244099,0.0010977047],"category_scores_gemma":[0.0060417433,0.00034417643,0.0013549448,0.00039589524,0.00044793927,0.0010757743,0.00042615546,0.000581039,0.000210449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105627296,0.000023178578,0.00091843255,0.000020860729,0.000024804123,0.000028250905,0.000032994776,0.989224,0.0016772726,0.0013591559,0.00010303796,0.0064823893],"study_design_scores_gemma":[0.0000031814664,0.00006312261,0.00035653778,0.0000018470726,0.000008115107,0.000015199555,0.0000048001384,0.99835205,0.00055894227,0.0005481497,0.00008194959,0.0000061322617],"about_ca_topic_score_codex":0.0068801115,"about_ca_topic_score_gemma":0.006702848,"teacher_disagreement_score":0.0068801115,"about_ca_system_score_codex":0.00089418696,"about_ca_system_score_gemma":0.0008675514,"threshold_uncertainty_score":0.0136801},"labels":[],"label_agreement":null},{"id":"W1964000286","doi":"10.1002/nav.10122","title":"Preservation of stochastic orders for random minima and maxima, with applications","year":2003,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Lanzhou University","keywords":"Maxima and minima; Maxima; Order (exchange); Regular polygon; Mathematics; Reliability (semiconductor); Stochastic ordering; Statistical physics; Combinatorics; Mathematical optimization; Applied mathematics; Mathematical analysis; Geometry; Physics; Economics","score_opus":0.05179451081522327,"score_gpt":0.31450936148794056,"score_spread":0.26271485067271727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964000286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12085265,0.0011519464,0.8445019,0.0010912319,0.00016169166,0.00006857695,0.00033407396,0.00029658046,0.031541396],"genre_scores_gemma":[0.9029692,0.0015323048,0.07862621,0.00022866575,0.00050807366,0.00010749962,0.0003048567,0.00022333869,0.015499842],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982256,0.0005234334,0.00014220859,0.0004155435,0.00048453207,0.00020866621],"domain_scores_gemma":[0.99055415,0.0045644855,0.0017037346,0.0013193412,0.0012235144,0.00063488167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039218175,0.0007665189,0.0007965113,0.0017140759,0.001081229,0.0027822147,0.0010221204,0.0005320251,0.0037193843],"category_scores_gemma":[0.016056154,0.000402378,0.0013930484,0.0016685225,0.0037150807,0.0041712327,0.0025973914,0.0025946035,0.00046094783],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008794829,0.000025436802,0.00058579684,0.000053217827,0.000023558045,0.00018363487,0.00022321874,0.022755558,0.003164418,0.9520296,0.0008531381,0.020014497],"study_design_scores_gemma":[0.000019246394,0.00009784142,0.00070093764,0.000026970565,0.00002622284,0.00025593254,0.00011285809,0.11172873,0.00420311,0.87906307,0.0037356878,0.000029374685],"about_ca_topic_score_codex":0.0018262994,"about_ca_topic_score_gemma":0.0013709364,"teacher_disagreement_score":0.0039218175,"about_ca_system_score_codex":0.0014907179,"about_ca_system_score_gemma":0.0012038104,"threshold_uncertainty_score":0.020740807},"labels":[],"label_agreement":null},{"id":"W1965406319","doi":"10.1057/palgrave.jors.2601450","title":"An optimal maintenance policy for skipping imminent preventive maintenance for systems experiencing random failures","year":2003,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Preventive maintenance; Planner; Operations research; Computer science; Operations management; Mean time between failures; Weibull distribution; Weighting; Reliability engineering; Economics; Failure rate; Mathematics; Statistics; Engineering","score_opus":0.024042599302717745,"score_gpt":0.3338758463127009,"score_spread":0.30983324700998316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965406319","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44858605,0.0006097473,0.54616344,0.000545693,0.00005252907,0.00014563564,0.00008156473,0.0003526524,0.0034627044],"genre_scores_gemma":[0.987708,0.00006408592,0.011680818,0.00002835925,0.000008002863,0.00001870343,0.0000127792,0.000009777309,0.00046944176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992293,0.00027840058,0.0000426395,0.00013891536,0.00014928066,0.00016144983],"domain_scores_gemma":[0.9962603,0.002202594,0.0007654186,0.0001461454,0.00038125558,0.00024430276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002182842,0.0005062121,0.0006842962,0.00070192956,0.0003063628,0.0008033293,0.0009788652,0.000692063,0.0011718208],"category_scores_gemma":[0.005724791,0.0003306567,0.00025212544,0.00027823143,0.0007333789,0.0008581669,0.00041569644,0.0005630876,0.00013406403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019857427,0.0001202589,0.0017161866,0.000059733047,0.000025686799,0.000093767296,0.00005504914,0.9714329,0.003778058,0.009193079,0.0004316826,0.012895045],"study_design_scores_gemma":[0.000018366178,0.00010261911,0.00041773575,0.0000064800392,0.000010851993,0.00001946116,0.000019524294,0.9963314,0.0007087157,0.0022199035,0.0001378261,0.0000071822506],"about_ca_topic_score_codex":0.0033308798,"about_ca_topic_score_gemma":0.0022068152,"teacher_disagreement_score":0.0033308798,"about_ca_system_score_codex":0.0011975738,"about_ca_system_score_gemma":0.0011977262,"threshold_uncertainty_score":0.011544108},"labels":[],"label_agreement":null},{"id":"W1967297224","doi":"10.1080/00207543.2012.671587","title":"A variable neighbourhood search for integrated production and preventive maintenance planning in multi-state systems","year":2012,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preventive maintenance; Time horizon; Heuristics; Sizing; Corrective maintenance; Operations research; Production (economics); Production planning; Reliability engineering; Engineering; Mathematical optimization; Computer science; Operations management; Mathematics; Economics","score_opus":0.06636746926750205,"score_gpt":0.3601704604147111,"score_spread":0.2938029911472091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967297224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034026816,0.00058763416,0.9625664,0.00012397478,0.00004904557,0.000061351944,0.000058899775,0.0002016522,0.00232422],"genre_scores_gemma":[0.6132712,0.00034527024,0.3826138,0.000081326616,0.00005184363,0.00027993615,0.0002293977,0.00008973048,0.0030375642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994824,0.00023864208,0.000021087413,0.000087446,0.00012145793,0.000049034472],"domain_scores_gemma":[0.9992078,0.00056635396,0.00006271359,0.000038205675,0.00008294702,0.000042092346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009991247,0.00047124465,0.0010256999,0.0008059538,0.00039564833,0.0005501948,0.001146124,0.0010366228,0.0017254994],"category_scores_gemma":[0.0023660476,0.00043547613,0.00079109904,0.0009587699,0.0006506854,0.0008419317,0.00082966784,0.0006625011,0.00019848715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006965059,0.00003342041,0.00039255826,0.00005773851,0.000036530288,0.000046402074,0.00005005938,0.9622138,0.00094323256,0.0061551374,0.0004607139,0.029540703],"study_design_scores_gemma":[0.000021725842,0.00004073207,0.00010751477,0.000006777422,0.000007503053,0.00001548922,0.0000065540103,0.9965318,0.00021839711,0.0024915868,0.0005470235,0.0000049845157],"about_ca_topic_score_codex":0.004598855,"about_ca_topic_score_gemma":0.003561573,"teacher_disagreement_score":0.004598855,"about_ca_system_score_codex":0.00082050887,"about_ca_system_score_gemma":0.0008713466,"threshold_uncertainty_score":0.009144187},"labels":[],"label_agreement":null},{"id":"W1967385216","doi":"10.3166/jds.12.47-65","title":"Spare Parts Identification and Provisioning Models","year":2003,"lang":"en","type":"article","venue":"Journal of Decision System","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Inventory management; Spare part; Humanities; Operations research; Computer science; Mathematics; Philosophy; Operations management; Engineering","score_opus":0.01195917935334307,"score_gpt":0.21225629749442165,"score_spread":0.2002971181410786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967385216","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035039853,0.00086427666,0.9431392,0.0005225798,0.00007771704,0.00006962194,0.00046801442,0.00047734988,0.019341353],"genre_scores_gemma":[0.8464582,0.0012612013,0.11520343,0.000126605,0.00008884655,0.00022669183,0.0007044221,0.00010258262,0.035828102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994809,0.00013569661,0.00002583877,0.0001094989,0.00017866367,0.00006940203],"domain_scores_gemma":[0.99940884,0.00033493256,0.00008414821,0.000054367487,0.000093691124,0.000024084737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053696946,0.0006685102,0.00066749164,0.00092066225,0.00044000417,0.0013221797,0.0012554743,0.0012461274,0.0072362297],"category_scores_gemma":[0.0014606209,0.00048066847,0.00085768895,0.001004157,0.0006186403,0.0012901452,0.0008174735,0.000903733,0.0011641402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017164522,0.000010180579,0.00021310493,0.00003606573,0.000006336669,0.00005127988,0.000028507056,0.97757494,0.00062838383,0.014710334,0.00041509225,0.006308589],"study_design_scores_gemma":[0.0000022921124,0.000007972154,0.00009547377,0.000009645093,0.000003785634,0.000025517998,0.000009752218,0.9923482,0.00031771068,0.0061324877,0.0010432325,0.0000039415754],"about_ca_topic_score_codex":0.005929467,"about_ca_topic_score_gemma":0.0039702253,"teacher_disagreement_score":0.0072362297,"about_ca_system_score_codex":0.0010507551,"about_ca_system_score_gemma":0.0010235524,"threshold_uncertainty_score":0.024207652},"labels":[],"label_agreement":null},{"id":"W1968862017","doi":"10.1108/13552510510601339","title":"Artificial neural networks for reliability maximization under budget and weight constraints","year":2005,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Reliability (semiconductor); Artificial neural network; Mathematical optimization; Maximization; Computer science; Integer programming; Computation; Linear programming; Hopfield network; Power (physics); Artificial intelligence; Mathematics; Algorithm","score_opus":0.013196477595588486,"score_gpt":0.24687446208759473,"score_spread":0.23367798449200625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968862017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025208537,0.000877101,0.96879756,0.00038422266,0.000034937322,0.000029478537,0.000036297555,0.00013992519,0.0044920114],"genre_scores_gemma":[0.80902845,0.001336526,0.18265201,0.00013273182,0.00010738079,0.00019195839,0.00010080145,0.00006633038,0.0063839117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996352,0.00016391832,0.00001776367,0.000047454025,0.00010078788,0.00003486323],"domain_scores_gemma":[0.9991868,0.0005706964,0.00008132414,0.000025872365,0.000118260046,0.00001693337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011511779,0.00047504852,0.0005159407,0.00042920437,0.00020712159,0.0005326074,0.0006434421,0.0008612087,0.0014190888],"category_scores_gemma":[0.0034021307,0.0003076568,0.00029687586,0.0006662139,0.00050355174,0.00102797,0.0004928018,0.0008521026,0.00014939372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023004646,0.0000134798765,0.00014247416,0.000045650657,0.000014765633,0.000018254492,0.000017891773,0.9647919,0.0007740953,0.014174902,0.00038821768,0.019595321],"study_design_scores_gemma":[0.0000030030337,0.000005254804,0.000027115366,0.0000035194569,0.0000024191297,0.0000031184825,0.0000018640588,0.9955577,0.00016229099,0.0040887264,0.00014366467,0.0000013353372],"about_ca_topic_score_codex":0.0029280277,"about_ca_topic_score_gemma":0.0030874265,"teacher_disagreement_score":0.0029280277,"about_ca_system_score_codex":0.0009365924,"about_ca_system_score_gemma":0.00073442026,"threshold_uncertainty_score":0.0067955256},"labels":[],"label_agreement":null},{"id":"W1968975376","doi":"10.1007/s10845-013-0766-6","title":"Multi-objective modeling for preventive maintenance scheduling in a multiple production line","year":2013,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"University of Tehran","keywords":"Downtime; Preventive maintenance; Reliability engineering; Maintainability; Production line; Scheduling (production processes); Spare part; Production (economics); Reliability (semiconductor); Engineering; Predictive maintenance; Computer science; Operations management","score_opus":0.022404862603634865,"score_gpt":0.23774503959239643,"score_spread":0.21534017698876157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968975376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17479345,0.0008946857,0.8129006,0.0005569061,0.00013341963,0.00015811899,0.00046569452,0.00031737017,0.00977976],"genre_scores_gemma":[0.9554491,0.00039380652,0.03658545,0.00005880784,0.000045201126,0.0001755189,0.00019366015,0.00007611853,0.0070223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936515,0.00024903944,0.00002948457,0.00010168079,0.0001332988,0.00012137816],"domain_scores_gemma":[0.99865603,0.00081640284,0.00022131967,0.00004130746,0.00016936978,0.000095518226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016894444,0.001366622,0.0019001412,0.0010787213,0.00076724414,0.0019294114,0.0022635702,0.0020024222,0.0033554237],"category_scores_gemma":[0.0024537318,0.0013259168,0.0014938625,0.001146901,0.0006885582,0.001177892,0.0008747906,0.0014215357,0.00032944308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009342981,0.000008581347,0.000052087507,0.0000074231593,0.000007808445,0.000014117789,0.0000046537016,0.99901414,0.00007963725,0.00033328007,0.000025966836,0.00044292753],"study_design_scores_gemma":[0.0000019077888,0.00000556606,0.000024733834,8.055614e-7,0.0000026778423,0.0000011630524,0.0000015084354,0.99982315,0.000018013596,0.000102656835,0.000016759306,0.0000010650451],"about_ca_topic_score_codex":0.028957296,"about_ca_topic_score_gemma":0.016307823,"teacher_disagreement_score":0.028957296,"about_ca_system_score_codex":0.0016844182,"about_ca_system_score_gemma":0.0014355567,"threshold_uncertainty_score":0.05757749},"labels":[],"label_agreement":null},{"id":"W1969004393","doi":"10.1016/j.renene.2012.02.030","title":"Opportunistic maintenance for wind farms considering multi-level imperfect maintenance thresholds","year":2012,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":190,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Corrective maintenance; Maintenance actions; Preventive maintenance; Wind power; Proactive maintenance; Imperfect; Reliability engineering; Turbine; Predictive maintenance; Optimal maintenance; Risk analysis (engineering); Component (thermodynamics); Planned maintenance; Computer science; Engineering; Business","score_opus":0.03896343840959492,"score_gpt":0.2386543358455003,"score_spread":0.1996908974359054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969004393","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69497186,0.00062735484,0.29660082,0.00044301595,0.00007480908,0.000081545644,0.0002887933,0.00026315247,0.0066486057],"genre_scores_gemma":[0.997267,0.000032904372,0.0022393386,0.0000068747713,0.000006562324,0.000008998936,0.00002080526,0.000008084307,0.00040953496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995957,0.00012293429,0.00002138059,0.00006700492,0.00006711261,0.00012601184],"domain_scores_gemma":[0.9975924,0.0016523404,0.0003318284,0.00014020916,0.00018550993,0.000097711374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010679002,0.00053148664,0.0010269206,0.00037272295,0.00038786844,0.00082959794,0.0010927296,0.0008797638,0.0015626663],"category_scores_gemma":[0.0036638184,0.0005866921,0.00042857413,0.0006829434,0.00041289433,0.001036377,0.00045569363,0.00043320472,0.00010178219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001853626,0.00004384149,0.0013019079,0.000038744256,0.00003591167,0.00015186981,0.00001646607,0.98678,0.0006752123,0.0029251836,0.00035753215,0.007487863],"study_design_scores_gemma":[0.000009456486,0.00003163238,0.0007319961,0.0000025848774,0.000013029133,0.000029625744,0.000010697304,0.99726653,0.00011466856,0.0017420911,0.00004442167,0.0000031996171],"about_ca_topic_score_codex":0.0035800508,"about_ca_topic_score_gemma":0.006583176,"teacher_disagreement_score":0.0035800508,"about_ca_system_score_codex":0.0007453738,"about_ca_system_score_gemma":0.0006938633,"threshold_uncertainty_score":0.0071184635},"labels":[],"label_agreement":null},{"id":"W1969660353","doi":"10.1109/iciea.2008.4582470","title":"A replacement policy of deteriorating production systems subject to imperfect repairs","year":2008,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Imperfect; Production (economics); Computer science; Markov decision process; Time horizon; Operations research; Production planning; Mathematical optimization; Decision support system; Markov process; Decision process; Reliability engineering; Engineering; Mathematics; Economics; Artificial intelligence; Microeconomics; Statistics","score_opus":0.009760935123675372,"score_gpt":0.21467104928727937,"score_spread":0.204910114163604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969660353","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46658534,0.0016034892,0.52603275,0.0005612922,0.00008634552,0.00016660496,0.00027470186,0.00032773957,0.0043617417],"genre_scores_gemma":[0.98508877,0.00030174715,0.012908157,0.000031083684,0.000017947768,0.000038862054,0.00006774884,0.00002116469,0.001524671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984151,0.0006801048,0.00009224903,0.0002760424,0.0002199631,0.00031656327],"domain_scores_gemma":[0.9958189,0.0023811879,0.00085684547,0.0002163683,0.00047259542,0.00025413532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003711498,0.0011567352,0.0016577605,0.0007151391,0.0006327394,0.0011234398,0.0013147471,0.0011355776,0.0017796024],"category_scores_gemma":[0.006364539,0.0007060855,0.0007158666,0.0007024368,0.0012945093,0.0009295917,0.0006072246,0.0010690846,0.00019534882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012658515,0.00003255983,0.00066499534,0.00008656908,0.000020412646,0.00023637088,0.000071767325,0.9855135,0.0026569595,0.0067662066,0.00020121907,0.003622928],"study_design_scores_gemma":[0.000030471514,0.00018472299,0.00080470054,0.0000129121645,0.000031354706,0.00007554846,0.000029817751,0.9936893,0.00091485854,0.003855888,0.00035485925,0.000015575695],"about_ca_topic_score_codex":0.006874067,"about_ca_topic_score_gemma":0.0023286063,"teacher_disagreement_score":0.006874067,"about_ca_system_score_codex":0.001293498,"about_ca_system_score_gemma":0.001217767,"threshold_uncertainty_score":0.019628465},"labels":[],"label_agreement":null},{"id":"W1973585648","doi":"10.1016/j.jlp.2003.08.011","title":"Risk-based maintenance (RBM): a quantitative approach for maintenance/inspection scheduling and planning","year":2003,"lang":"en","type":"article","venue":"Journal of Loss Prevention in the Process Industries","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":405,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Risk analysis (engineering); HVAC; Reliability engineering; Preventive maintenance; Profitability index; Engineering; Scheduling (production processes); Risk management; Asset management; Risk assessment; Reliability (semiconductor); Operations management; Computer science; Air conditioning; Business","score_opus":0.027293532777046468,"score_gpt":0.28942910957244833,"score_spread":0.26213557679540184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973585648","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026423328,0.0003067442,0.99600005,0.00010278319,0.000021567017,0.00002697042,0.000037843423,0.00011503533,0.0007466523],"genre_scores_gemma":[0.48007026,0.0007903513,0.51639205,0.00012797411,0.00020396749,0.00031211175,0.00015014547,0.00017243972,0.0017806215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969681,0.0014552915,0.00012761913,0.00028222072,0.0010093289,0.00015740638],"domain_scores_gemma":[0.99559563,0.003059525,0.0005313781,0.00031694287,0.0003990681,0.00009749739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005494465,0.0018046347,0.002139177,0.0024626814,0.0004785387,0.001794423,0.002699883,0.0012314862,0.0020076165],"category_scores_gemma":[0.009644231,0.0011863727,0.001573483,0.0018013676,0.0012443052,0.0024399953,0.0012203814,0.0016400417,0.00023204014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028891496,0.000043114804,0.00038505174,0.000103720195,0.000075555115,0.000026259486,0.00004829343,0.91150016,0.0008322132,0.048986286,0.00052806776,0.03744246],"study_design_scores_gemma":[0.000005020677,0.000027426126,0.00011748503,0.000010098248,0.000017361366,0.000016061476,0.0000059301046,0.9777265,0.00020553789,0.02154306,0.00031601358,0.000009435917],"about_ca_topic_score_codex":0.0039198287,"about_ca_topic_score_gemma":0.003523469,"teacher_disagreement_score":0.005494465,"about_ca_system_score_codex":0.0019358044,"about_ca_system_score_gemma":0.0019494328,"threshold_uncertainty_score":0.02905786},"labels":[],"label_agreement":null},{"id":"W1975205469","doi":"10.1080/00207543.2015.1005250","title":"Extended preventive replacement policy for a two-unit system subject to damage shocks","year":2015,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Shock (circulatory); Unit (ring theory); Poisson process; Homogeneous; Poisson distribution; Mathematics; Statistics; Combinatorics; Medicine","score_opus":0.07630365741572998,"score_gpt":0.41025284882555496,"score_spread":0.333949191409825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975205469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3154327,0.0018207374,0.66605973,0.0015810047,0.000236423,0.0003295846,0.0005894912,0.0006973122,0.013253026],"genre_scores_gemma":[0.97849715,0.00033722832,0.012606921,0.000086755776,0.00004222461,0.00016067509,0.00011052603,0.000046286914,0.008112219],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988881,0.0003088733,0.000059455837,0.00019623712,0.00018863194,0.00035874863],"domain_scores_gemma":[0.99771714,0.001001567,0.0005032777,0.000104909224,0.00034849157,0.00032467904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001927407,0.001500369,0.0023957437,0.0009810308,0.00089561,0.0018805491,0.0026240866,0.0028721238,0.007014698],"category_scores_gemma":[0.0033452224,0.0010560342,0.0011925396,0.00079407066,0.0014798167,0.0011709137,0.0019040792,0.0018279812,0.00059344823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009178367,0.000035439996,0.00038259703,0.00006054999,0.000026273852,0.00020138915,0.000039455943,0.99350923,0.0009477675,0.0028849714,0.00021857292,0.001601952],"study_design_scores_gemma":[0.000021783995,0.000052011772,0.0002215556,0.000006904672,0.000016289572,0.000025535926,0.000016765181,0.9981516,0.00011826039,0.0012380063,0.00012276968,0.000008467885],"about_ca_topic_score_codex":0.01824356,"about_ca_topic_score_gemma":0.007811323,"teacher_disagreement_score":0.01824356,"about_ca_system_score_codex":0.0021233966,"about_ca_system_score_gemma":0.0020788983,"threshold_uncertainty_score":0.03627473},"labels":[],"label_agreement":null},{"id":"W1975631038","doi":"10.1239/aap/999187904","title":"Optimal repair/replacement policy for a general repair model","year":2001,"lang":"en","type":"article","venue":"Advances in Applied Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Preventive maintenance; Limit (mathematics); Optimal maintenance; Reliability engineering; Function (biology); Average cost; Computer science; Mathematical optimization; Mathematics; Engineering; Economics","score_opus":0.010738226307536203,"score_gpt":0.2555185162644116,"score_spread":0.24478028995687537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975631038","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15651017,0.001871945,0.8301089,0.0011988288,0.00012932197,0.0001034999,0.00033281228,0.00044052594,0.009304015],"genre_scores_gemma":[0.94596225,0.0008116012,0.044190444,0.00013899132,0.00011431229,0.000112877664,0.00021776132,0.0000785197,0.008373198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990609,0.00027291154,0.00003710373,0.00024618892,0.00016575382,0.00021701517],"domain_scores_gemma":[0.99849236,0.0008290273,0.00029037485,0.00012630707,0.00015718586,0.00010457404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017792993,0.0009647852,0.0020398535,0.00083528605,0.0004831271,0.0011888988,0.0018858677,0.0023882168,0.0034215737],"category_scores_gemma":[0.004878884,0.0006791399,0.0009654817,0.00075759605,0.0012556955,0.0018756185,0.00079211744,0.0015104365,0.0004219693],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044503962,0.000038729115,0.0001986166,0.000051365856,0.00001717013,0.00009377458,0.000027281838,0.9699855,0.0006678919,0.024694538,0.00068006665,0.0035005948],"study_design_scores_gemma":[0.000015234509,0.000021651402,0.00011615147,0.0000046924306,0.0000140169905,0.00003504513,0.000011004464,0.9874887,0.0001209076,0.011854824,0.0003113501,0.000006317958],"about_ca_topic_score_codex":0.0066551766,"about_ca_topic_score_gemma":0.0045295455,"teacher_disagreement_score":0.0066551766,"about_ca_system_score_codex":0.0017737977,"about_ca_system_score_gemma":0.0015524529,"threshold_uncertainty_score":0.013232887},"labels":[],"label_agreement":null},{"id":"W1975782246","doi":"10.1016/j.jairtraman.2014.09.006","title":"Efficient aircraft spare parts inventory management under demand uncertainty","year":2014,"lang":"en","type":"article","venue":"Journal of Air Transport Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Spare part; Mean time between failures; Operations research; Reorder point; Economic shortage; Computer science; Total cost; Point (geometry); Economic order quantity; Order (exchange); Reduction (mathematics); Reliability engineering; Mathematical optimization; Linear programming; Cost reduction; Operations management; Failure rate; Engineering; Mathematics; Economics; Supply chain; Algorithm","score_opus":0.00586787784124496,"score_gpt":0.1888684523639723,"score_spread":0.18300057452272733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975782246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3897777,0.0008094306,0.6027732,0.00048126723,0.000093948714,0.00010428671,0.00038558184,0.00027282198,0.0053017554],"genre_scores_gemma":[0.9870046,0.00010253282,0.011863718,0.000022709175,0.000020349387,0.00002013,0.000078905774,0.0000220288,0.0008650873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994592,0.00015415368,0.000033720695,0.00007936483,0.0001342209,0.0001392762],"domain_scores_gemma":[0.9986481,0.0009050009,0.00017180476,0.0000771248,0.00013709336,0.000060797687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011917618,0.0007417215,0.0016208598,0.00067659555,0.00043698394,0.0016234808,0.0010933543,0.0010002485,0.00116078],"category_scores_gemma":[0.0024000485,0.0010435322,0.00058118196,0.0011040084,0.00048287224,0.0016173659,0.0007205537,0.00078341377,0.00014777885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057948513,0.000019048675,0.00019986548,0.000018641373,0.000018211022,0.000031590487,0.000011677472,0.9939389,0.0006500558,0.0009130524,0.00013230284,0.0040086666],"study_design_scores_gemma":[0.0000028574598,0.000013982327,0.00012141183,0.0000013582626,0.0000046987443,0.0000055414916,0.000006433548,0.9989305,0.00016290342,0.0007168007,0.000031742653,0.0000016806698],"about_ca_topic_score_codex":0.0042203707,"about_ca_topic_score_gemma":0.003389089,"teacher_disagreement_score":0.0042203707,"about_ca_system_score_codex":0.00095376256,"about_ca_system_score_gemma":0.0011400562,"threshold_uncertainty_score":0.008391619},"labels":[],"label_agreement":null},{"id":"W1975910296","doi":"10.1109/tr.2012.2206270","title":"A Block Replacement Policy for Systems Subject to Non-homogeneous Pure Birth Shocks","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Homogeneous; Failure rate; Block (permutation group theory); Shock (circulatory); Reliability engineering; Interval (graph theory); Computer science; Mathematics; Engineering; Medicine","score_opus":0.008717334743095984,"score_gpt":0.23438627268758203,"score_spread":0.22566893794448603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975910296","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25816676,0.0012162256,0.7361971,0.00035260583,0.00006090542,0.00012237456,0.00014089001,0.000234114,0.0035090973],"genre_scores_gemma":[0.97944516,0.0009300357,0.013541402,0.000049564806,0.000030875497,0.000053905216,0.00007939779,0.000049033628,0.005820651],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993142,0.00024939276,0.000026982005,0.00009561436,0.00011962526,0.0001942458],"domain_scores_gemma":[0.9982449,0.0009542279,0.00043537546,0.00007695959,0.00012250664,0.00016606129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014669584,0.0008707113,0.001196461,0.0005595341,0.0003573169,0.001033559,0.0013679523,0.0009262114,0.0031653468],"category_scores_gemma":[0.0034190267,0.0005337582,0.0006498896,0.00041970293,0.0009818325,0.0012576118,0.0010027417,0.00083284295,0.0003641968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003101486,0.00005619567,0.0007322391,0.00011732607,0.00003707346,0.00017636431,0.00007479437,0.95947814,0.0057271337,0.024781533,0.000611727,0.007897434],"study_design_scores_gemma":[0.00002553441,0.0001603162,0.00044207973,0.000007642379,0.000023832214,0.00004568579,0.000026389214,0.99278945,0.00087735924,0.0052392557,0.00035086522,0.000011629701],"about_ca_topic_score_codex":0.0045326017,"about_ca_topic_score_gemma":0.0024095445,"teacher_disagreement_score":0.0045326017,"about_ca_system_score_codex":0.0015249229,"about_ca_system_score_gemma":0.0011677113,"threshold_uncertainty_score":0.011064172},"labels":[],"label_agreement":null},{"id":"W1976638601","doi":"10.1063/1.4913184","title":"Reliability block diagrams based analysis: A survey","year":2015,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Icon; Citation; Computer science; Information retrieval; Download; World Wide Web; Block (permutation group theory); Reliability (semiconductor); Programming language","score_opus":0.028783218417512507,"score_gpt":0.23269977223594132,"score_spread":0.20391655381842883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976638601","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17309673,0.41060188,0.28332266,0.009223419,0.00053099374,0.0007339435,0.008191815,0.0027919018,0.11150674],"genre_scores_gemma":[0.60019755,0.28319672,0.09603469,0.0015948013,0.00052294566,0.00065380085,0.0081489375,0.0009463973,0.008704261],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9925092,0.001975219,0.0005280496,0.00076378917,0.004063119,0.00016067203],"domain_scores_gemma":[0.91827357,0.06478969,0.0035396316,0.002601008,0.010298597,0.00049748237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007290785,0.00071352604,0.00076046184,0.012214803,0.00042667027,0.0017510776,0.0016346836,0.00072091946,0.0072275884],"category_scores_gemma":[0.042502962,0.00056852336,0.00093845703,0.009966989,0.000586983,0.0034607714,0.00082835805,0.0008968376,0.0024430763],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008509472,0.00012581222,0.026874326,0.0032748342,0.000168782,0.00007673932,0.00084561395,0.0046137227,0.0011631707,0.00768479,0.02140237,0.9336848],"study_design_scores_gemma":[0.000057128967,0.0007291188,0.14043911,0.011170003,0.0007007559,0.0025528562,0.0056059137,0.04443235,0.009305974,0.056326535,0.7284712,0.00020910504],"about_ca_topic_score_codex":0.0016189567,"about_ca_topic_score_gemma":0.0017147461,"teacher_disagreement_score":0.012214803,"about_ca_system_score_codex":0.001098648,"about_ca_system_score_gemma":0.00142987,"threshold_uncertainty_score":0.038557768},"labels":[],"label_agreement":null},{"id":"W1977450474","doi":"10.1111/poms.12178","title":"Flexible‐Duration Extended Warranties with Dynamic Reliability Learning","year":2013,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Duration (music); Reliability (semiconductor); Computer science; Economics; Physics","score_opus":0.0033353434860202125,"score_gpt":0.18390389554747227,"score_spread":0.18056855206145206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977450474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32032827,0.00082826137,0.6673697,0.001223581,0.00006984608,0.00012942121,0.00030474277,0.0003898058,0.009356356],"genre_scores_gemma":[0.98670375,0.00021721883,0.008422331,0.00004753673,0.00004804917,0.000039698203,0.00007428663,0.000021566997,0.0044255853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988337,0.00028624907,0.000054186647,0.00025592945,0.00024654504,0.00032337],"domain_scores_gemma":[0.9940882,0.0036463959,0.0012729211,0.00030726014,0.00035792746,0.00032733573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017580244,0.00095495774,0.0015159776,0.0006375162,0.00044300093,0.0012266361,0.0019383508,0.0017557322,0.004350378],"category_scores_gemma":[0.009654018,0.0007035241,0.00085435377,0.00080974854,0.0013621866,0.0030767324,0.0011125162,0.0017330033,0.0004006386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020139526,0.00012404953,0.0013526133,0.00006126215,0.000029981162,0.00026323972,0.000069346584,0.9469043,0.0015407128,0.030812275,0.00078862184,0.017852308],"study_design_scores_gemma":[0.000018218463,0.0000851357,0.00049759605,0.0000051614643,0.0000104641185,0.00004830887,0.000016906835,0.985129,0.00021300222,0.013733978,0.00023051622,0.000011741485],"about_ca_topic_score_codex":0.005165971,"about_ca_topic_score_gemma":0.0039027096,"teacher_disagreement_score":0.005165971,"about_ca_system_score_codex":0.0017600633,"about_ca_system_score_gemma":0.0009261602,"threshold_uncertainty_score":0.014553428},"labels":[],"label_agreement":null},{"id":"W1977733940","doi":"10.1016/j.cirpj.2010.06.004","title":"A periodicity metric for assessing maintenance strategies","year":2010,"lang":"en","type":"article","venue":"CIRP journal of manufacturing science and technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Metric (unit); Reset (finance); Computer science; Function (biology); Reliability engineering; Domain (mathematical analysis); Mathematical optimization; Engineering; Mathematics; Operations management","score_opus":0.0060018091268388895,"score_gpt":0.23334477185404576,"score_spread":0.22734296272720686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977733940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31230205,0.00090424204,0.6780507,0.000117185904,0.000077600664,0.0002059352,0.001833465,0.0011383091,0.0053705117],"genre_scores_gemma":[0.8449062,0.00017553118,0.15286337,0.000023853885,0.000060705068,0.000115640236,0.0012863703,0.0000688101,0.0004994894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978551,0.00047794005,0.0003339902,0.00028932656,0.0009101803,0.00013330775],"domain_scores_gemma":[0.98979783,0.004585833,0.0020415948,0.0014377298,0.0015378426,0.00059911294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034512528,0.0008457848,0.0010594634,0.0055261664,0.0004631255,0.0011942288,0.0008678429,0.0008147772,0.0012533141],"category_scores_gemma":[0.012248092,0.00026731938,0.0006345317,0.0026055546,0.00041219316,0.0017933112,0.00071732426,0.0005206986,0.00038517304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009521582,0.0005160897,0.085915044,0.0004085022,0.00035812674,0.00019190417,0.00017864657,0.3635459,0.032965068,0.01606605,0.0034905444,0.49541193],"study_design_scores_gemma":[0.00003357516,0.0013950561,0.030769622,0.00004231397,0.000118182186,0.00046201987,0.000106990825,0.947814,0.0074541736,0.009923181,0.0018103211,0.00007058422],"about_ca_topic_score_codex":0.0013232897,"about_ca_topic_score_gemma":0.0011421976,"teacher_disagreement_score":0.0055261664,"about_ca_system_score_codex":0.00078912725,"about_ca_system_score_gemma":0.00078696077,"threshold_uncertainty_score":0.018252194},"labels":[],"label_agreement":null},{"id":"W1978269173","doi":"10.1007/s00170-013-4730-6","title":"A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem","year":2013,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Particle swarm optimization; Redundancy (engineering); Mathematical optimization; Reliability (semiconductor); Computer science; Computation; Nonlinear system; Genetic algorithm; Reliability engineering; Engineering; Mathematics; Algorithm","score_opus":0.004808010910748596,"score_gpt":0.20890820665426182,"score_spread":0.20410019574351324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978269173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026562622,0.0010101368,0.9610006,0.00022467862,0.00022841517,0.000086185595,0.000049215465,0.00031332835,0.010524768],"genre_scores_gemma":[0.5830646,0.0006508125,0.40706787,0.00025970422,0.00017880712,0.0003090556,0.00015254329,0.000101156365,0.008215481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997247,0.00009438673,0.000012476223,0.00003619557,0.00009808631,0.000034130306],"domain_scores_gemma":[0.9997658,0.00011197815,0.000016544287,0.000016145048,0.0000725572,0.000016883776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047247755,0.00073504593,0.0009609107,0.0006611695,0.00035627844,0.0006877361,0.0010689787,0.001254337,0.0020923673],"category_scores_gemma":[0.0008196368,0.00044088336,0.0008421459,0.0007745037,0.00027474607,0.00041355749,0.0005648168,0.0006723214,0.00035672841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044784774,0.00006642252,0.0002676945,0.0000732352,0.000095969684,0.00007354141,0.00001878104,0.9455395,0.0028232182,0.0027848862,0.0010621889,0.047149714],"study_design_scores_gemma":[0.000008524198,0.000026870674,0.000067115885,0.0000032437406,0.0000089732,0.000015532567,0.0000026623084,0.9990069,0.00016072727,0.00038622765,0.00031061165,0.0000025901168],"about_ca_topic_score_codex":0.004447043,"about_ca_topic_score_gemma":0.0041947677,"teacher_disagreement_score":0.004447043,"about_ca_system_score_codex":0.0003148567,"about_ca_system_score_gemma":0.00074152637,"threshold_uncertainty_score":0.008842289},"labels":[],"label_agreement":null},{"id":"W1978542154","doi":"10.1109/ptc.2005.4524593","title":"A probabilistic approach to life cycle management","year":2005,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kinectrics (Canada)","funders":"Uniwersytet Łódzki","keywords":"Probabilistic logic; Computer science; Monte Carlo method; Continuation; Reliability engineering; Product life-cycle management; Component (thermodynamics); Simple (philosophy); Process (computing); Production (economics); Operations research; Exponential function; Work (physics); Mathematical optimization; Industrial engineering; Engineering; Mathematics; Artificial intelligence; Statistics; Economics","score_opus":0.006375206120143131,"score_gpt":0.1835159908283109,"score_spread":0.17714078470816777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978542154","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000954567,0.0011172026,0.98674774,0.00057849486,0.00006821377,0.000049886752,0.00011686869,0.00011648936,0.010250439],"genre_scores_gemma":[0.32795843,0.0105145965,0.6337278,0.0007338372,0.0011443386,0.0009398273,0.0006167789,0.00040772118,0.023956759],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997416,0.00096835854,0.00010858342,0.00038598108,0.00096167065,0.00015935831],"domain_scores_gemma":[0.99635196,0.0023320415,0.00032493434,0.0004052135,0.00047231116,0.00011349303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002634736,0.0013553979,0.0009111484,0.0026500858,0.0010854619,0.003184578,0.00270163,0.0012414067,0.009237575],"category_scores_gemma":[0.008317564,0.00081516325,0.0016237436,0.0030277572,0.0016700902,0.0034810284,0.0014922773,0.0023184866,0.0018266296],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013410726,0.000044389413,0.00043394085,0.0001340365,0.000068955094,0.00006667664,0.000107060085,0.27754807,0.00033309238,0.667707,0.0025727216,0.050970566],"study_design_scores_gemma":[0.000006639662,0.000042772364,0.0002095964,0.000050698294,0.000027466545,0.00011112679,0.000031235268,0.318322,0.00023748865,0.6529383,0.027993482,0.000029097484],"about_ca_topic_score_codex":0.0035911177,"about_ca_topic_score_gemma":0.0034741191,"teacher_disagreement_score":0.009237575,"about_ca_system_score_codex":0.0026227678,"about_ca_system_score_gemma":0.0019709913,"threshold_uncertainty_score":0.030902803},"labels":[],"label_agreement":null},{"id":"W1978626708","doi":"10.1108/jqme-10-2012-0035","title":"Optimal lockout/tagout, preventive maintenance, human error and production policies of manufacturing systems with passive redundancy","year":2014,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Preventive maintenance; Reliability engineering; Corrective maintenance; Maintenance actions; Redundancy (engineering); Engineering; Markov chain; Production (economics); Operations research; Computer science","score_opus":0.009276134657986743,"score_gpt":0.24030392307087436,"score_spread":0.23102778841288762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978626708","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2403553,0.0013814383,0.7507758,0.0005460241,0.000047989466,0.00021033164,0.00022967074,0.00020015816,0.006253261],"genre_scores_gemma":[0.98554796,0.0003220565,0.011920593,0.000022955692,0.0000143948455,0.000087640634,0.000055670225,0.000017662855,0.002011038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990963,0.0003724024,0.000029448962,0.00016453375,0.00015451299,0.00018288982],"domain_scores_gemma":[0.9965252,0.0023165208,0.0007273999,0.00006945437,0.00023733637,0.00012401362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019027096,0.0012708808,0.0011487139,0.0007544776,0.00043821495,0.0011301769,0.00091209915,0.0010945273,0.0021670826],"category_scores_gemma":[0.0049162456,0.0008081563,0.00081981765,0.0004528942,0.0011567,0.0008959182,0.0007195614,0.0009007341,0.00017624834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065433014,0.000023744531,0.0005330621,0.000057901925,0.000017110522,0.00007892381,0.000033717828,0.994406,0.00065604475,0.0020283614,0.00008405837,0.0020156787],"study_design_scores_gemma":[0.0000109387,0.00007746901,0.0005631991,0.00001288245,0.00002035556,0.000018265215,0.000022408803,0.9969139,0.00043667242,0.0018062125,0.00011055031,0.0000071205823],"about_ca_topic_score_codex":0.0108026415,"about_ca_topic_score_gemma":0.0054994253,"teacher_disagreement_score":0.0108026415,"about_ca_system_score_codex":0.001638108,"about_ca_system_score_gemma":0.0017944633,"threshold_uncertainty_score":0.021479487},"labels":[],"label_agreement":null},{"id":"W1978758487","doi":"10.1109/rams.2013.6517680","title":"Condition-based replacement policy for a device using interval-censored inspection data","year":2013,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Censoring (clinical trials); Computer science; Reliability engineering; Reliability (semiconductor); Process (computing); Robustness (evolution); Interval (graph theory); Degradation (telecommunications); Engineering","score_opus":0.0350998579202284,"score_gpt":0.29565582150163444,"score_spread":0.26055596358140604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978758487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12401315,0.00041791546,0.8714839,0.00048756163,0.00004433666,0.00018520319,0.0001625107,0.0008271897,0.002378199],"genre_scores_gemma":[0.971377,0.000094627954,0.02705349,0.000047468475,0.000016290034,0.000074121635,0.00008218189,0.00001993861,0.0012348505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983713,0.00042356804,0.00012240479,0.00040834647,0.00049367343,0.00018076303],"domain_scores_gemma":[0.99415886,0.0033863697,0.0010613282,0.0005047738,0.0006750164,0.00021361507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028935075,0.00067371747,0.0010238391,0.0008420953,0.0003452611,0.0010103051,0.0014987714,0.0011900244,0.0015602041],"category_scores_gemma":[0.008799556,0.00029652056,0.00045999995,0.00048432645,0.00080179167,0.001356263,0.00054040214,0.00095666165,0.00027585975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005383576,0.0003486143,0.004031901,0.00017088723,0.000041738807,0.00022054742,0.00017088007,0.89178693,0.012745489,0.014546583,0.0015615727,0.073836565],"study_design_scores_gemma":[0.00001820619,0.00014864413,0.0010744528,0.000012722498,0.000016628512,0.00005666927,0.000018413635,0.9926864,0.0023712968,0.0032490902,0.0003318713,0.000015658925],"about_ca_topic_score_codex":0.0033435298,"about_ca_topic_score_gemma":0.0025064049,"teacher_disagreement_score":0.0033435298,"about_ca_system_score_codex":0.0010551785,"about_ca_system_score_gemma":0.0010647705,"threshold_uncertainty_score":0.015302539},"labels":[],"label_agreement":null},{"id":"W1978950682","doi":"10.5539/mas.v1n4p55","title":"Reliability Analysis of an n-unit Standby Repairable System with K Repair Facilities","year":2007,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reliability engineering; Redundancy (engineering); Reliability (semiconductor); Computer science; Engineering; Physics; Power (physics)","score_opus":0.00829707442138933,"score_gpt":0.20687988168560625,"score_spread":0.19858280726421693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978950682","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6181652,0.0017218998,0.37378955,0.0003807862,0.000021823555,0.000041895444,0.00019270343,0.00020442283,0.005481781],"genre_scores_gemma":[0.9951806,0.0002055355,0.0035786165,0.000009342023,0.000011202027,0.000014790033,0.000046117962,0.0000113285405,0.00094253424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976665,0.000065371736,0.000011540249,0.000048268794,0.00006402497,0.000044200086],"domain_scores_gemma":[0.9991228,0.0004495023,0.00018902081,0.000035402973,0.00017039775,0.000032899716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054559455,0.00044672485,0.00046076797,0.0004665055,0.00025250815,0.00034222312,0.00050102524,0.00039948415,0.0012056889],"category_scores_gemma":[0.0016648094,0.00020877297,0.00045007977,0.0003348725,0.0005499835,0.0005920006,0.00029276233,0.00026691039,0.00015849939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012080167,0.000013483374,0.002183382,0.00009015345,0.000042668078,0.0002642908,0.000105734915,0.9726927,0.010836004,0.008097585,0.00030221697,0.005250967],"study_design_scores_gemma":[0.0000034655875,0.000032101998,0.0011335261,0.0000044406984,0.000011388045,0.000056129455,0.000018498571,0.99643457,0.00062409404,0.0015712131,0.00010482454,0.0000057738944],"about_ca_topic_score_codex":0.005047818,"about_ca_topic_score_gemma":0.0016265002,"teacher_disagreement_score":0.005047818,"about_ca_system_score_codex":0.00063630217,"about_ca_system_score_gemma":0.00046738036,"threshold_uncertainty_score":0.010036886},"labels":[],"label_agreement":null},{"id":"W1980688912","doi":"10.3166/jds.12.11-20","title":"Optional Preventive Replacement Policy for Two-Component System","year":2003,"lang":"en","type":"article","venue":"Journal of Decision System","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Component (thermodynamics); Preventive maintenance; Computer science; Failure rate; Series (stratigraphy); Mathematical optimization; Operations research; Reliability engineering; Mathematics; Engineering","score_opus":0.008889218284537806,"score_gpt":0.2562579880692828,"score_spread":0.24736876978474498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980688912","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25631762,0.0008696009,0.73765075,0.0003764665,0.00009367551,0.00009170751,0.00006868666,0.0005341885,0.0039972947],"genre_scores_gemma":[0.98275983,0.000112237554,0.016134858,0.000019089457,0.00001904705,0.000026252033,0.00003161591,0.000012755016,0.00088433403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921334,0.00024186207,0.00005313344,0.00013560776,0.00021969454,0.00013635993],"domain_scores_gemma":[0.9987696,0.00047824896,0.00026014392,0.00016170154,0.00023846644,0.00009178326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010882781,0.0005420442,0.00058170344,0.00063935923,0.00036256967,0.0005348272,0.0013444524,0.00064233615,0.0012606526],"category_scores_gemma":[0.002134434,0.00029428524,0.00038993344,0.0003875393,0.0005746172,0.0006462076,0.00040462014,0.00052353385,0.00017417675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003272009,0.0001615078,0.0023093033,0.00023620151,0.000048253736,0.00044608643,0.00013004823,0.914763,0.013188567,0.021001762,0.001116952,0.04627118],"study_design_scores_gemma":[0.00003649789,0.00017937929,0.00083685096,0.000009704063,0.000029038805,0.00017679592,0.000012639186,0.9904897,0.0024188992,0.004952554,0.00084472046,0.00001322011],"about_ca_topic_score_codex":0.0018268158,"about_ca_topic_score_gemma":0.0015178502,"teacher_disagreement_score":0.0018268158,"about_ca_system_score_codex":0.00081204,"about_ca_system_score_gemma":0.0007881645,"threshold_uncertainty_score":0.0058918},"labels":[],"label_agreement":null},{"id":"W1980707820","doi":"10.1145/2815317.2815336","title":"Reliability Evaluation of Imperfect K-Terminal Stochastic Networks using Polygon-to Chain and Series-parallel Reductions","year":2015,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Reliability (semiconductor); Imperfect; Series (stratigraphy); Wireless sensor network; Reduction (mathematics); Wireless ad hoc network; Algorithm; Wireless; Theoretical computer science; Mathematics; Computer network; Telecommunications","score_opus":0.029070205388320915,"score_gpt":0.26851376815690503,"score_spread":0.2394435627685841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980707820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043030836,0.000197649,0.9532196,0.0001260106,0.000015944697,0.00002798427,0.00004820731,0.000065451924,0.0032683404],"genre_scores_gemma":[0.9311346,0.0004623461,0.066165194,0.000025731852,0.00003015706,0.000062195264,0.00008502333,0.00004345757,0.0019913013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945074,0.00020874346,0.000022848857,0.000070624104,0.00018919124,0.00005786119],"domain_scores_gemma":[0.998654,0.00091121654,0.0001752993,0.000078934194,0.00014263588,0.000037918795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012764588,0.0006792681,0.00088217674,0.00085453247,0.00029276594,0.00078665523,0.0007868298,0.0005154172,0.001091492],"category_scores_gemma":[0.0036961562,0.0003303388,0.0009318086,0.00065971684,0.0012694353,0.001272244,0.0006143343,0.00071366515,0.000117922355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012176365,0.0000052265614,0.00016474284,0.000017459744,0.000006929942,0.000027279319,0.000016238459,0.9776642,0.00037379083,0.019430742,0.00008945618,0.002191655],"study_design_scores_gemma":[8.901306e-7,0.000008487383,0.000038601054,0.0000019673707,0.0000026817347,0.000008338166,0.0000055647865,0.9940408,0.00016671991,0.0056402474,0.000083760744,0.000001992854],"about_ca_topic_score_codex":0.003324728,"about_ca_topic_score_gemma":0.0019912575,"teacher_disagreement_score":0.003324728,"about_ca_system_score_codex":0.0013924324,"about_ca_system_score_gemma":0.0007263768,"threshold_uncertainty_score":0.010102928},"labels":[],"label_agreement":null},{"id":"W1981300442","doi":"10.1057/jors.2010.173","title":"Optimal inspection intervals for safety systems with partial inspections","year":2010,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"IEC 61508; Warranty; Reliability engineering; Safety instrumented system; Reliability (semiconductor); Computer science; Downtime; Functional safety; Purchasing; Risk analysis (engineering); System safety; Work (physics); Operations research; Operations management; Work in process; Engineering; Business","score_opus":0.025236050826913263,"score_gpt":0.309776259610299,"score_spread":0.2845402087833857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981300442","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18150534,0.000611419,0.8130321,0.0002572961,0.000025610572,0.00008720455,0.00015922872,0.00036973058,0.003952091],"genre_scores_gemma":[0.9714807,0.00020619013,0.026635682,0.000026248805,0.000012799174,0.00007036163,0.00009728712,0.00005593223,0.0014145875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917173,0.00025205125,0.000033441924,0.0001654688,0.0001736367,0.00020358522],"domain_scores_gemma":[0.997029,0.0017795084,0.0005681931,0.0001338357,0.00031693515,0.00017239472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013421554,0.00097165705,0.0012568568,0.0011023234,0.00036200118,0.0011690658,0.0008185371,0.0009114232,0.0018513262],"category_scores_gemma":[0.0053655272,0.0008004397,0.001137196,0.00047461785,0.0008153738,0.0008018608,0.0009656155,0.0010649515,0.00018214574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087915025,0.00002828479,0.00040089668,0.000055272456,0.00001662241,0.000025768482,0.000047479774,0.98813874,0.0017282118,0.0039963876,0.00015807705,0.0053163795],"study_design_scores_gemma":[0.0000061262367,0.000063012914,0.00021873871,0.000009327811,0.0000108114755,0.000010448495,0.000015030345,0.9962,0.00042988354,0.0029174106,0.00011397364,0.0000051660013],"about_ca_topic_score_codex":0.0066737086,"about_ca_topic_score_gemma":0.0032739036,"teacher_disagreement_score":0.0066737086,"about_ca_system_score_codex":0.0014542,"about_ca_system_score_gemma":0.0015257493,"threshold_uncertainty_score":0.013269722},"labels":[],"label_agreement":null},{"id":"W1981530540","doi":"10.1002/qre.913","title":"Maintenance contract assessment for aging systems","year":2008,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Failure rate; Reliability engineering; Piecewise; Order (exchange); Function (biology); Time horizon; Computer science; Process (computing); Mathematical optimization; Markov process; Service (business); Total cost; Markov decision process; Operations research; Engineering; Mathematics; Economics; Statistics; Microeconomics","score_opus":0.01670853669979372,"score_gpt":0.2703999733059956,"score_spread":0.25369143660620186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981530540","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5241328,0.0014720076,0.46574527,0.00043538323,0.000047042,0.00016004004,0.00026697505,0.00023727032,0.0075031845],"genre_scores_gemma":[0.98276895,0.00017125609,0.015769804,0.0000149239295,0.000010562021,0.000033595403,0.00010306844,0.00002312932,0.0011048202],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9978537,0.0011055465,0.00007038957,0.00014287213,0.0006605978,0.00016684594],"domain_scores_gemma":[0.993193,0.004295989,0.0008078921,0.0002915511,0.001103769,0.0003076913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062970677,0.00060328346,0.00090775336,0.001411043,0.00033159018,0.0010286375,0.0011399058,0.0008915144,0.002427049],"category_scores_gemma":[0.018179026,0.00024723972,0.0004421089,0.00076664885,0.00060765876,0.001222766,0.0008997027,0.0007456679,0.00013219038],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012717882,0.00003891659,0.002549011,0.00005564717,0.000016340997,0.000100772755,0.000047073223,0.95825386,0.0007009347,0.016286211,0.0004443509,0.02137967],"study_design_scores_gemma":[0.0000043300483,0.000038993967,0.000600328,0.000006189213,0.000004993594,0.00002399061,0.000011870404,0.9950512,0.0003183823,0.0036643308,0.00026962068,0.0000058281003],"about_ca_topic_score_codex":0.0056212624,"about_ca_topic_score_gemma":0.0019305465,"teacher_disagreement_score":0.0062970677,"about_ca_system_score_codex":0.0022930675,"about_ca_system_score_gemma":0.0016205704,"threshold_uncertainty_score":0.033302486},"labels":[],"label_agreement":null},{"id":"W1982132068","doi":"10.1016/j.ress.2007.03.023","title":"System repairs: When to perform and what to do?","year":2007,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Reliability engineering; Reduction (mathematics); Set (abstract data type); Process (computing); Interval (graph theory); Computer science; State (computer science); Scale (ratio); Function (biology); Engineering; Mathematics; Algorithm","score_opus":0.0030337705995530117,"score_gpt":0.1783615453205497,"score_spread":0.1753277747209967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982132068","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16891278,0.06874065,0.106184244,0.5750077,0.009148312,0.0005778637,0.0019513664,0.002424413,0.06705263],"genre_scores_gemma":[0.90500224,0.02104203,0.04937815,0.008070004,0.003664152,0.00021136455,0.00048189508,0.00059722876,0.01155287],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99294937,0.0036017308,0.00041877304,0.0004698967,0.0016047754,0.00095552613],"domain_scores_gemma":[0.97065365,0.012151708,0.004327199,0.0012745627,0.007754039,0.0038388025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009918838,0.0011014579,0.0014056969,0.0017961783,0.0017339274,0.005792816,0.002166633,0.004537508,0.011211634],"category_scores_gemma":[0.04401626,0.0005673498,0.00071822724,0.0010552162,0.003263034,0.008118209,0.001055488,0.0033807263,0.005841531],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056032545,0.0011794792,0.046168435,0.0011951883,0.0003411343,0.0003198895,0.0011810752,0.0062876074,0.002114749,0.027108163,0.13047257,0.78307134],"study_design_scores_gemma":[0.0006870502,0.002368494,0.17968574,0.0092837205,0.0014612352,0.004212471,0.050070215,0.08774876,0.015068149,0.38512677,0.2633,0.0009873813],"about_ca_topic_score_codex":0.010882297,"about_ca_topic_score_gemma":0.025433246,"teacher_disagreement_score":0.011211634,"about_ca_system_score_codex":0.00258858,"about_ca_system_score_gemma":0.007848656,"threshold_uncertainty_score":0.05245644},"labels":[],"label_agreement":null},{"id":"W1983371987","doi":"10.1080/15732470601012154","title":"The influence of temporal uncertainty of deterioration on life-cycle management of structures","year":2008,"lang":"en","type":"article","venue":"Structure and Infrastructure Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":187,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University Network of Excellence in Nuclear Engineering","keywords":"Probabilistic logic; Reliability (semiconductor); Reliability engineering; Gamma process; Stochastic modelling; Process (computing); Random variable; Preventive maintenance; Stochastic process; Computer science; Product life-cycle management; Conceptual model; Risk analysis (engineering); Engineering; Econometrics; Mathematics; Statistics; Artificial intelligence; Business","score_opus":0.0024464329574657375,"score_gpt":0.17312567763192616,"score_spread":0.1706792446744604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983371987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3554574,0.003943067,0.62872016,0.0022919034,0.00011023578,0.000032750006,0.0005514728,0.00011443091,0.008778451],"genre_scores_gemma":[0.99424595,0.0009620262,0.0041221264,0.000045713306,0.000048755996,0.00001308462,0.00006228296,0.000015058784,0.00048500492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852633,0.000549995,0.00006738342,0.00024122992,0.00042922757,0.00018576454],"domain_scores_gemma":[0.9869016,0.009674596,0.0021746664,0.0004433175,0.00062868267,0.00017710231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037497035,0.0006058649,0.0005983502,0.0005796697,0.00041519525,0.001468698,0.00092702813,0.00095914863,0.00082993077],"category_scores_gemma":[0.020689314,0.00041671173,0.00056085666,0.00072129705,0.0010535797,0.0027167548,0.0010143501,0.00083933276,0.00009383435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009166501,0.000023571221,0.0060220305,0.00007280666,0.00005942278,0.00018487814,0.00012220119,0.9278863,0.0010686808,0.05059221,0.00025242023,0.013623821],"study_design_scores_gemma":[0.000009970255,0.00010569822,0.0063763065,0.000031738236,0.00005838908,0.00011778193,0.000076462566,0.91411674,0.0010745752,0.077121295,0.00087162084,0.000039440565],"about_ca_topic_score_codex":0.003964917,"about_ca_topic_score_gemma":0.0036917569,"teacher_disagreement_score":0.003964917,"about_ca_system_score_codex":0.0013424725,"about_ca_system_score_gemma":0.000905742,"threshold_uncertainty_score":0.019830525},"labels":[],"label_agreement":null},{"id":"W1983569116","doi":"10.1016/j.ress.2008.03.002","title":"Modelling and optimizing sequential imperfect preventive maintenance","year":2008,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Imperfect; Preventive maintenance; Reliability engineering; Scheduling (production processes); Sensitivity (control systems); Computer science; Operations research; Engineering; Mathematical optimization; Operations management; Mathematics","score_opus":0.0069353273789129755,"score_gpt":0.17712367424968573,"score_spread":0.17018834687077275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983569116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38067916,0.0009783657,0.6022415,0.00049306214,0.00013969268,0.00008606955,0.0002998997,0.00046388083,0.014618387],"genre_scores_gemma":[0.9805464,0.00015558314,0.015214759,0.000016561526,0.000015777941,0.00004109895,0.000069432004,0.00004216947,0.0038982297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996822,0.00009007856,0.000015919051,0.00006635558,0.00007771851,0.00006776247],"domain_scores_gemma":[0.9988686,0.00073112897,0.00018274877,0.00006979805,0.00010816154,0.000039640458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000692816,0.00079790264,0.0012052613,0.00051442435,0.00029532882,0.0009252099,0.0012870964,0.0011480139,0.0023987736],"category_scores_gemma":[0.002965653,0.0009144146,0.0006638008,0.00061928475,0.0006868401,0.0010818014,0.0005017199,0.0006746343,0.00018483514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012141604,0.0000065328113,0.000059177768,0.000007744828,0.0000037937048,0.000008204482,0.0000033915799,0.9980648,0.00010913582,0.00083446194,0.000032375912,0.0008582605],"study_design_scores_gemma":[0.000002650369,0.0000062826944,0.000048994323,7.096859e-7,0.0000033377644,0.0000023715788,0.0000013157255,0.999228,0.00006263954,0.00060460984,0.00003817323,9.984743e-7],"about_ca_topic_score_codex":0.013312479,"about_ca_topic_score_gemma":0.009520188,"teacher_disagreement_score":0.013312479,"about_ca_system_score_codex":0.0011308565,"about_ca_system_score_gemma":0.0011746773,"threshold_uncertainty_score":0.026470006},"labels":[],"label_agreement":null},{"id":"W1985043293","doi":"10.1016/j.cor.2009.04.011","title":"An efficient heuristic for reliability design optimization problems","year":2009,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Computer science; Tabu search; Redundancy (engineering); Disjoint sets; Genetic algorithm; Heuristic; Mathematics","score_opus":0.04840033153782702,"score_gpt":0.32876582777591684,"score_spread":0.2803654962380898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985043293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012618885,0.000988793,0.9731371,0.0002546738,0.00024727228,0.00019134948,0.00014488182,0.0007283216,0.011688691],"genre_scores_gemma":[0.13762714,0.0005465241,0.85646677,0.0002658246,0.00012824083,0.00054423173,0.00025843698,0.00023800974,0.0039248546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910754,0.00038154022,0.000035139594,0.00007672734,0.0002806797,0.00011826938],"domain_scores_gemma":[0.99828714,0.001194609,0.00009905069,0.00014759663,0.00020527675,0.000066397704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018212118,0.0016318852,0.0018107487,0.0020832762,0.0008209656,0.0012347056,0.0017448333,0.0021993325,0.0058764233],"category_scores_gemma":[0.004218507,0.0010796773,0.001352559,0.0020989827,0.0008463866,0.0012359181,0.0012893694,0.0016551836,0.0010254146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016959285,0.00016224757,0.00019212696,0.0001688893,0.000050739178,0.000093387105,0.0000475641,0.8469134,0.0015917871,0.015383033,0.0043067164,0.13092045],"study_design_scores_gemma":[0.000079019956,0.000053219726,0.00006103336,0.000022972905,0.000025033616,0.000028109007,0.000010537671,0.9884008,0.00036055132,0.008972398,0.0019774092,0.000008997134],"about_ca_topic_score_codex":0.0031988064,"about_ca_topic_score_gemma":0.004511602,"teacher_disagreement_score":0.0058764233,"about_ca_system_score_codex":0.0011484037,"about_ca_system_score_gemma":0.0018748571,"threshold_uncertainty_score":0.019658566},"labels":[],"label_agreement":null},{"id":"W1987056569","doi":"10.1504/ijseam.2014.063882","title":"Selective maintenance considering two types of failure modes","year":2014,"lang":"en","type":"article","venue":"International Journal of Strategic Engineering Asset Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability engineering; Computer science; Failure rate; Preventive maintenance; Maintenance actions; Proactive maintenance; Optimal maintenance; Planned maintenance; Hazard; Engineering","score_opus":0.008462638889081024,"score_gpt":0.2167799590998934,"score_spread":0.20831732021081237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987056569","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74160343,0.00049443165,0.25330466,0.00018320022,0.000030548865,0.00006292009,0.000092446244,0.00012285648,0.004105498],"genre_scores_gemma":[0.99230367,0.00006547564,0.0068721157,0.000008543057,0.000007795445,0.000013581138,0.000021472268,0.0000033922001,0.000703908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941826,0.00009405548,0.000028565075,0.0001010206,0.00017944032,0.00017872064],"domain_scores_gemma":[0.99820626,0.00095141865,0.00029472337,0.00023026527,0.00022878425,0.000088433626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010857396,0.00058259035,0.00059837213,0.000543928,0.00038582168,0.00056859216,0.00095732126,0.00077684096,0.0011323732],"category_scores_gemma":[0.0025899517,0.00021283234,0.0006730722,0.0003712964,0.0006056426,0.00084715674,0.00071689615,0.0004275974,0.00007408938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005639512,0.00013454036,0.014839966,0.00021267233,0.00012877857,0.0016332457,0.0003021214,0.86503047,0.029314056,0.022894194,0.00062698754,0.06431898],"study_design_scores_gemma":[0.000039626746,0.00042784485,0.008045025,0.000021478836,0.0001058342,0.00066562515,0.00014772856,0.96683407,0.0067916946,0.016030226,0.0008567545,0.000034163517],"about_ca_topic_score_codex":0.0038214328,"about_ca_topic_score_gemma":0.003718342,"teacher_disagreement_score":0.0038214328,"about_ca_system_score_codex":0.00042520286,"about_ca_system_score_gemma":0.00063767453,"threshold_uncertainty_score":0.0075984},"labels":[],"label_agreement":null},{"id":"W1987140338","doi":"10.1115/1.1870043","title":"On Stability in Nonsequential MIMO QFT Designs","year":2004,"lang":"en","type":"article","venue":"Journal of Dynamic Systems Measurement and Control","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"MIMO; Quantitative feedback theory; Control theory (sociology); Stability (learning theory); Salient; Mathematics; Stability conditions; Closed loop; Computer science; Robust control; Control engineering; Control system; Engineering; Channel (broadcasting); Control (management); Telecommunications","score_opus":0.017042588857085136,"score_gpt":0.1982761832847642,"score_spread":0.18123359442767906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987140338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018874146,0.00023839777,0.9776999,0.00007630063,0.00002835157,0.000019410632,0.000010806343,0.00004850583,0.0030042224],"genre_scores_gemma":[0.87744856,0.0007535508,0.11921396,0.00011788794,0.00013273243,0.00010436735,0.00003576208,0.000030023133,0.0021632689],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988702,0.00038424594,0.00007554816,0.00013347327,0.0004727302,0.00006385632],"domain_scores_gemma":[0.9965777,0.0023547586,0.00033661092,0.00025168282,0.00045040384,0.000028936058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020904029,0.0005601192,0.0003178353,0.00038611513,0.0003300045,0.0006053209,0.0003943906,0.0006059007,0.0021010502],"category_scores_gemma":[0.006722041,0.00021839631,0.0003742738,0.00022057796,0.00097399415,0.0010767842,0.0007268029,0.0004978369,0.00031507865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027509072,0.000058682177,0.0009238252,0.00037421862,0.000040423438,0.00039195153,0.0004330873,0.46352425,0.049312796,0.37975633,0.00060532516,0.104303986],"study_design_scores_gemma":[0.00004096216,0.00054695865,0.00045903132,0.000059887323,0.00001986798,0.00020535785,0.00004269461,0.8852463,0.019925244,0.08987667,0.0035459267,0.00003112634],"about_ca_topic_score_codex":0.00046495526,"about_ca_topic_score_gemma":0.0003091086,"teacher_disagreement_score":0.0021010502,"about_ca_system_score_codex":0.00038264156,"about_ca_system_score_gemma":0.0003723128,"threshold_uncertainty_score":0.011055291},"labels":[],"label_agreement":null},{"id":"W1988682375","doi":"10.1109/icqr2mse.2012.6246259","title":"Selective maintenance for binary systems using age-based imperfect repair model","year":2012,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Imperfect; Component (thermodynamics); Reliability engineering; Computer science; Maintenance engineering; Reduction (mathematics); Preventive maintenance; Engineering; Mathematics; Physics","score_opus":0.020417001837184822,"score_gpt":0.23655823496220052,"score_spread":0.2161412331250157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988682375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10024855,0.0018524765,0.88632894,0.0005875626,0.00010993699,0.000067195186,0.0004764927,0.00031555127,0.01001329],"genre_scores_gemma":[0.972192,0.0008926793,0.017542623,0.000057080302,0.000060226004,0.0000765675,0.00018231501,0.000037334354,0.008959139],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945694,0.000097139506,0.00003384294,0.0001230784,0.00015592374,0.0001329965],"domain_scores_gemma":[0.99884653,0.00051028753,0.00031703338,0.00008168316,0.00017645497,0.000067914196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080470875,0.00083648553,0.0011690782,0.0010134812,0.0004533871,0.0009295333,0.0017069937,0.0010921521,0.0031932858],"category_scores_gemma":[0.0022804465,0.00041829894,0.0007395025,0.00079760957,0.0007568305,0.0014721825,0.00071286544,0.00084433024,0.00040889357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072407885,0.000031787346,0.0007110324,0.00011328989,0.000022895128,0.00013050671,0.00008648199,0.9546411,0.0022431484,0.0320567,0.00077699777,0.009113619],"study_design_scores_gemma":[0.0000060745483,0.000028303679,0.00029096793,0.0000058456258,0.000013697198,0.000044648317,0.0000097650955,0.99069303,0.00024352003,0.00827912,0.0003781916,0.0000068822947],"about_ca_topic_score_codex":0.0068886755,"about_ca_topic_score_gemma":0.0044126743,"teacher_disagreement_score":0.0068886755,"about_ca_system_score_codex":0.0012991811,"about_ca_system_score_gemma":0.0006096631,"threshold_uncertainty_score":0.013697207},"labels":[],"label_agreement":null},{"id":"W1989455494","doi":"10.1057/palgrave.jors.2602471","title":"A finite horizon model for repairable systems with repair restrictions","year":2007,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Component (thermodynamics); Operations research; Time horizon; Computer science; Purchasing; Function (biology); Reliability engineering; Engineering; Mathematical optimization; Operations management; Mathematics","score_opus":0.045504795346182736,"score_gpt":0.31273260420822396,"score_spread":0.2672278088620412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989455494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18399242,0.0011663948,0.7945525,0.0020137846,0.00016093285,0.00018958889,0.0019018386,0.00040734909,0.015615267],"genre_scores_gemma":[0.9654427,0.00047596986,0.021786077,0.00012730091,0.000072566465,0.0002633074,0.00055145164,0.000038403457,0.01124223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980446,0.0008252892,0.0000745154,0.00037022395,0.00032972347,0.00035570507],"domain_scores_gemma":[0.9948014,0.0037452986,0.0007060998,0.000160405,0.00027042272,0.0003164153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034070069,0.0011279925,0.0022105144,0.001004455,0.00068508706,0.0027124367,0.0029370065,0.0026003579,0.009410772],"category_scores_gemma":[0.0064168535,0.0010763699,0.0010815411,0.0010899019,0.0018479977,0.0026976115,0.0010300684,0.0031927456,0.0005774502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000478447,0.000041234365,0.00018096947,0.000029607747,0.000023149603,0.00008677331,0.000030880372,0.9699412,0.00027104007,0.028087221,0.00023448489,0.0010257498],"study_design_scores_gemma":[0.000025090327,0.000025731813,0.00011176623,0.000007213283,0.000009477866,0.000012944236,0.000012218665,0.9885212,0.00007951546,0.010881399,0.0003025151,0.000010991477],"about_ca_topic_score_codex":0.020281339,"about_ca_topic_score_gemma":0.011409117,"teacher_disagreement_score":0.020281339,"about_ca_system_score_codex":0.0040544844,"about_ca_system_score_gemma":0.0020414246,"threshold_uncertainty_score":0.040326595},"labels":[],"label_agreement":null},{"id":"W1989464134","doi":"10.1109/tr.2014.2336391","title":"Best Constant-Stress Accelerated Life-Test Plans With Multiple Stress Factors for One-Shot Device Testing Under a Weibull Distribution","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Weibull distribution; Estimator; Shape parameter; Reliability (semiconductor); Scale parameter; Constant (computer programming); Accelerated life testing; Statistics; Delta method; Test plan; Sensitivity (control systems); Mathematics; Stress (linguistics); Variance (accounting); Computer science; Engineering; Power (physics); Electronic engineering","score_opus":0.054182913214770076,"score_gpt":0.25193587561119224,"score_spread":0.19775296239642215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989464134","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18907985,0.0006337747,0.80576885,0.0001679946,0.000014547895,0.00027412377,0.00014677994,0.0007199972,0.0031942038],"genre_scores_gemma":[0.68706626,0.0001518761,0.31131747,0.00004817293,0.000008965172,0.00026749496,0.00018386124,0.0000836679,0.00087228615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936825,0.00026402558,0.000024208104,0.00010478402,0.0001670831,0.0000717786],"domain_scores_gemma":[0.997074,0.0018871526,0.00037586133,0.00018008347,0.00035616834,0.00012667339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014976826,0.0008242091,0.0006488069,0.0010113998,0.00033525744,0.0006228271,0.0008244971,0.0005186655,0.0013849009],"category_scores_gemma":[0.0064262054,0.0004387213,0.000444513,0.00037538528,0.0005611072,0.0007188637,0.00047345815,0.0005281229,0.00020321025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036229505,0.00014381693,0.0025111772,0.000107803506,0.00003583295,0.00009949308,0.00007168521,0.8909909,0.012718323,0.0053652,0.00071282557,0.08688065],"study_design_scores_gemma":[0.000044271615,0.00056679145,0.0018674845,0.000025626281,0.00003389522,0.00006758145,0.000052430692,0.98341894,0.0067975265,0.006570017,0.00053391093,0.000021507338],"about_ca_topic_score_codex":0.0019415193,"about_ca_topic_score_gemma":0.003090616,"teacher_disagreement_score":0.0019415193,"about_ca_system_score_codex":0.00087778474,"about_ca_system_score_gemma":0.0017098263,"threshold_uncertainty_score":0.007920623},"labels":[],"label_agreement":null},{"id":"W1989497202","doi":"10.1080/0740817x.2012.706734","title":"Learning and forgetting effects on maintenance outsourcing","year":2013,"lang":"en","type":"article","venue":"IIE Transactions","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MacEwan University","funders":"","keywords":"Forgetting; Outsourcing; Business; Computer science; Operations management; Process management; Industrial organization; Engineering; Marketing; Psychology; Cognitive psychology","score_opus":0.002355930984310226,"score_gpt":0.1722965969871512,"score_spread":0.16994066600284097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989497202","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8809832,0.00128399,0.10899998,0.0006299958,0.00006297327,0.000105413725,0.00006100249,0.00011984774,0.007753509],"genre_scores_gemma":[0.9962967,0.00028507443,0.0026408294,0.00004575249,0.000028025608,0.000012921413,0.0000103865295,0.000008199357,0.0006721042],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99804187,0.00072939816,0.00011040089,0.000254554,0.0003618739,0.0005018542],"domain_scores_gemma":[0.9306981,0.05565346,0.008218239,0.0019148556,0.0023205623,0.001194785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048654703,0.0009390957,0.0008723721,0.0005367911,0.0005704744,0.0011971953,0.0012507836,0.0013177376,0.0031801928],"category_scores_gemma":[0.041714873,0.0003880027,0.00083919405,0.00037063792,0.0024059047,0.0028323357,0.0011728895,0.0017575165,0.00016061074],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010499632,0.0007085441,0.017099457,0.00036670163,0.00014126826,0.0008473743,0.00046509973,0.8855101,0.0048042247,0.025070371,0.00037809653,0.06355887],"study_design_scores_gemma":[0.00016628084,0.001779153,0.012880757,0.00009552823,0.000291429,0.00036175275,0.00044237674,0.9399566,0.00860844,0.03442012,0.00090404256,0.00009345452],"about_ca_topic_score_codex":0.0033582794,"about_ca_topic_score_gemma":0.0025196297,"teacher_disagreement_score":0.0048654703,"about_ca_system_score_codex":0.0018265261,"about_ca_system_score_gemma":0.0011345856,"threshold_uncertainty_score":0.025731385},"labels":[],"label_agreement":null},{"id":"W1989618028","doi":"10.1016/j.renene.2010.10.028","title":"Condition based maintenance optimization for wind power generation systems under continuous monitoring","year":2010,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":357,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Turbine; Wind power; Prognostics; Condition monitoring; Reliability engineering; Condition-based maintenance; Maintenance actions; Engineering; Maintenance engineering; Interval (graph theory); Computer science; Mechanical engineering","score_opus":0.007728710704583281,"score_gpt":0.20307393731428824,"score_spread":0.19534522660970496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989618028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4093872,0.0010611935,0.5831575,0.00047402698,0.00007483377,0.0001574666,0.00040285737,0.0004777471,0.0048072306],"genre_scores_gemma":[0.98639864,0.000108005384,0.012358648,0.000016189819,0.00002257982,0.00004938466,0.0001165029,0.00003235879,0.0008977398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996879,0.00012372428,0.000017446348,0.000055412977,0.000074629774,0.00004098471],"domain_scores_gemma":[0.9985114,0.0010720438,0.0001817506,0.000047189544,0.00014621101,0.000041436444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010478052,0.0010445039,0.0013664634,0.0007427729,0.0002878063,0.000844894,0.000708681,0.0010794459,0.0015442638],"category_scores_gemma":[0.0030286626,0.0007793709,0.00055062264,0.00053043774,0.0004749978,0.0011692708,0.0003711262,0.0006571972,0.00015073117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000109707136,0.000037794245,0.0003532771,0.000030159426,0.000028262828,0.00002662715,0.000011210528,0.9922565,0.00064542756,0.00047727337,0.00021408733,0.0058095795],"study_design_scores_gemma":[0.0000085028905,0.000025973452,0.00025960366,0.0000012415477,0.0000056644867,0.0000044544036,0.000002171166,0.9992601,0.00009642995,0.0003168384,0.000017291888,0.0000017230411],"about_ca_topic_score_codex":0.0047857007,"about_ca_topic_score_gemma":0.003745221,"teacher_disagreement_score":0.0047857007,"about_ca_system_score_codex":0.00062677637,"about_ca_system_score_gemma":0.00054268725,"threshold_uncertainty_score":0.009515643},"labels":[],"label_agreement":null},{"id":"W1990311848","doi":"10.1016/j.orl.2008.09.004","title":"Evaluating a warm standby system with components having proportional hazard rates","year":2008,"lang":"en","type":"article","venue":"Operations Research Letters","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Component (thermodynamics); Statistics; Proportional hazards model; Hazard ratio; Hazard; Computer science; Reliability engineering; Mathematics; Control theory (sociology); Engineering; Physics; Artificial intelligence; Biology; Control (management)","score_opus":0.10389035107292438,"score_gpt":0.3509936052648227,"score_spread":0.24710325419189832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990311848","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90444916,0.0001949216,0.09369361,0.00009907714,0.000029596731,0.00009013732,0.000052616208,0.00022204896,0.0011687252],"genre_scores_gemma":[0.99190253,0.00002077491,0.0072753592,0.000012265558,0.00000805452,0.000023195546,0.000026012389,0.0000144306,0.000717393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991856,0.000365987,0.00002752591,0.00008308872,0.00019018762,0.00014751083],"domain_scores_gemma":[0.9965844,0.002472704,0.00023893638,0.00018694188,0.00030845797,0.00020860763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00300557,0.0011206105,0.0012470898,0.00074963144,0.0005948291,0.0011293988,0.0010266047,0.0010418658,0.002117227],"category_scores_gemma":[0.0055313366,0.00047295634,0.00058415445,0.00031118878,0.00079210906,0.0012062782,0.0007936247,0.0007560516,0.00015772837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001222719,0.00009500674,0.0014368475,0.000064216576,0.00005262636,0.0000741712,0.000030305222,0.9761177,0.008950104,0.0007333725,0.00014483591,0.011078147],"study_design_scores_gemma":[0.000049874427,0.0009318823,0.0013816318,0.000003782535,0.00006047273,0.00001819678,0.000024918798,0.99288803,0.0038991831,0.00067272777,0.00005823114,0.000010974718],"about_ca_topic_score_codex":0.003708352,"about_ca_topic_score_gemma":0.002984148,"teacher_disagreement_score":0.003708352,"about_ca_system_score_codex":0.0010868479,"about_ca_system_score_gemma":0.0010896153,"threshold_uncertainty_score":0.015895128},"labels":[],"label_agreement":null},{"id":"W1990617766","doi":"10.1016/j.ress.2004.01.008","title":"A combined approach to solve the redundancy optimization problem for multi-state systems under repair policies","year":2004,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Redundancy (engineering); Mathematical optimization; Computer science; Petri net; Reliability engineering; Heuristic; Function (biology); Stochastic Petri net; Distributed computing; Engineering; Mathematics","score_opus":0.012193488736966462,"score_gpt":0.20923047441596554,"score_spread":0.19703698567899908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990617766","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005507142,0.00029790564,0.9907568,0.00012715551,0.00007091173,0.00004143446,0.000039546503,0.00012723217,0.0030319018],"genre_scores_gemma":[0.3376571,0.00064986356,0.6489727,0.0002390167,0.00031863514,0.00056062837,0.000246267,0.00022126839,0.011134636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992341,0.00021806612,0.000038854785,0.00010202148,0.00032401553,0.00008299802],"domain_scores_gemma":[0.9988839,0.0006456967,0.00007444419,0.00011195291,0.00022913943,0.00005491668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012321216,0.0011736467,0.0016326024,0.00096591975,0.00047235886,0.0013204544,0.0014833898,0.0017606625,0.005385569],"category_scores_gemma":[0.003041867,0.00085633266,0.00128336,0.00093690166,0.00051170326,0.0020721436,0.0016747629,0.0015964183,0.0006991942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001121097,0.00012995345,0.00026832902,0.00020374167,0.00016024339,0.00008133497,0.00005340272,0.891366,0.0026076843,0.024943689,0.0023282124,0.07774537],"study_design_scores_gemma":[0.0000131233055,0.00003362114,0.00004289949,0.0000056376575,0.00001525779,0.000011329343,0.0000044437866,0.994072,0.00028483936,0.004959211,0.00055336684,0.0000042704714],"about_ca_topic_score_codex":0.001757464,"about_ca_topic_score_gemma":0.003033637,"teacher_disagreement_score":0.005385569,"about_ca_system_score_codex":0.00051757455,"about_ca_system_score_gemma":0.0011734108,"threshold_uncertainty_score":0.018016577},"labels":[],"label_agreement":null},{"id":"W1991651351","doi":"10.1108/13552510410526839","title":"Maintenance management – an AHP application for centralization/decentralization","year":2004,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Analytic hierarchy process; Decentralization; Process (computing); Process management; Organizational structure; Management science; Engineering; Operations research; Risk analysis (engineering); Computer science; Operations management; Business; Management; Economics","score_opus":0.010508687688687719,"score_gpt":0.2572392669741839,"score_spread":0.24673057928549622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991651351","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038892247,0.000054874556,0.99265844,0.00010823382,0.000028780758,0.0003418673,0.00013370677,0.0012651806,0.0015197096],"genre_scores_gemma":[0.0785977,0.00007129298,0.9194502,0.00003785302,0.000020412463,0.0006691537,0.00020742745,0.00015195919,0.0007940666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99765503,0.001055932,0.00020894488,0.00021392884,0.0007394936,0.00012656166],"domain_scores_gemma":[0.9975871,0.0013880873,0.00018416415,0.00017535532,0.000589096,0.00007607661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068982923,0.0011095252,0.0012462385,0.0023428684,0.0016182577,0.0017180805,0.0016048874,0.00097426336,0.005741442],"category_scores_gemma":[0.007214544,0.00087227486,0.0013123795,0.002433135,0.00061388017,0.0012196649,0.0018738009,0.0017653982,0.0008397299],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003231115,0.00032386673,0.0015507238,0.0007127056,0.00034390876,0.00026862157,0.0012334618,0.5220001,0.0055493875,0.0379063,0.0070525263,0.42273527],"study_design_scores_gemma":[0.00008491274,0.000053335898,0.00023970813,0.000061743056,0.00003843693,0.0000378996,0.00014559389,0.97656745,0.0014470387,0.016408809,0.0048918016,0.000023235578],"about_ca_topic_score_codex":0.0053679147,"about_ca_topic_score_gemma":0.004571915,"teacher_disagreement_score":0.0068982923,"about_ca_system_score_codex":0.001349399,"about_ca_system_score_gemma":0.0021952572,"threshold_uncertainty_score":0.036482155},"labels":[],"label_agreement":null},{"id":"W1991973200","doi":"10.1109/syscon.2014.6819244","title":"Detailed cannibalization decision making for maintenance systems in the military context","year":2014,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Cannibalization; Spare part; Context (archaeology); Computer science; Reliability (semiconductor); Operations research; Reliability engineering; Engineering; Operations management; Economics","score_opus":0.007845967655589871,"score_gpt":0.21866421911712464,"score_spread":0.21081825146153477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991973200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22332428,0.00063057576,0.7719926,0.00042443498,0.000051368726,0.00014244755,0.00015942463,0.00018413295,0.0030906966],"genre_scores_gemma":[0.9640856,0.000296267,0.033535358,0.00004367801,0.000024448114,0.000060215843,0.00009392311,0.00002233835,0.0018381565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985128,0.00047706158,0.000050979776,0.00031000384,0.00027402083,0.00037510507],"domain_scores_gemma":[0.99646914,0.0023233083,0.0005992588,0.00012773261,0.00025298318,0.00022755474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003238017,0.0012936057,0.0022070536,0.0008475981,0.00077885156,0.0018165419,0.0013359167,0.0018985431,0.0019580128],"category_scores_gemma":[0.004090882,0.0013243406,0.0014544597,0.0008866485,0.001211078,0.0017982844,0.0010730339,0.001613651,0.00014172278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004317171,0.000018529958,0.0002070387,0.000023513723,0.000013560246,0.000038758037,0.000021858668,0.993741,0.00047809564,0.0027875055,0.00008245553,0.002544528],"study_design_scores_gemma":[0.0000073420238,0.000060680628,0.00021333198,0.0000047153785,0.000010306684,0.0000138458345,0.000011792107,0.99682707,0.00021212772,0.0025048172,0.00012566366,0.000008267134],"about_ca_topic_score_codex":0.012030112,"about_ca_topic_score_gemma":0.013719306,"teacher_disagreement_score":0.012030112,"about_ca_system_score_codex":0.0023715466,"about_ca_system_score_gemma":0.0029568358,"threshold_uncertainty_score":0.023920178},"labels":[],"label_agreement":null},{"id":"W1992188767","doi":"10.1111/j.1751-5823.2007.00015_13.x","title":"Reliability, Life Testing and the Prediction of Service Lives: For Engineers and Scientists by Sam C. Saunders","year":2007,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Citation; Reliability (semiconductor); Library science; Service (business); Operations research; Computer science; Statistics; Sociology; Mathematics; Actuarial science; Economics; Business; Physics; Marketing","score_opus":0.01982295008400146,"score_gpt":0.27179304080000904,"score_spread":0.25197009071600757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992188767","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001101044,0.90942895,0.029610122,0.044449456,0.012300764,0.000022446273,0.00023098898,0.00017548063,0.0026807485],"genre_scores_gemma":[0.02270409,0.8854545,0.018755306,0.012037631,0.03818768,0.00013180818,0.0005087232,0.00039691367,0.021823345],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99789655,0.00088237686,0.00014837024,0.00038325944,0.0006199327,0.00006952409],"domain_scores_gemma":[0.98733425,0.0086564915,0.0007879228,0.00043360764,0.0023165937,0.00047119157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058922726,0.0034652983,0.0027533143,0.0040208604,0.0007398038,0.0024957922,0.0011060823,0.00272057,0.0042516827],"category_scores_gemma":[0.021894155,0.0018244948,0.0011732589,0.0057297004,0.0033953923,0.00619678,0.0023688935,0.009860237,0.0035635468],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012852518,0.00009423139,0.0018765554,0.0011617945,0.00019300448,0.00011946561,0.0002025567,0.008165461,0.00071758195,0.031416744,0.7315495,0.22437455],"study_design_scores_gemma":[0.00003988502,0.00030188865,0.0047735665,0.0021744005,0.00018719718,0.0011314915,0.0002781822,0.013650866,0.0010091411,0.15139402,0.8248441,0.00021523528],"about_ca_topic_score_codex":0.0054265982,"about_ca_topic_score_gemma":0.0053769653,"teacher_disagreement_score":0.0058922726,"about_ca_system_score_codex":0.001392109,"about_ca_system_score_gemma":0.0015275911,"threshold_uncertainty_score":0.031161726},"labels":[],"label_agreement":null},{"id":"W1992536516","doi":"10.4236/am.2014.511164","title":"Availability Equivalence Factors of a General Repairable Parallel-Series System","year":2014,"lang":"en","type":"article","venue":"Applied Mathematics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Equivalence (formal languages); Series (stratigraphy); Computer science; Series and parallel circuits; Exponential distribution; Function (biology); Applied mathematics; Reliability engineering; Mathematics; Engineering; Discrete mathematics; Statistics; Geology","score_opus":0.00961388467032662,"score_gpt":0.18922653443396648,"score_spread":0.17961264976363986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992536516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31634048,0.0010214475,0.65953773,0.00016035441,0.000045071363,0.00006475153,0.0001853701,0.00024650045,0.02239823],"genre_scores_gemma":[0.99043006,0.00024215796,0.007670566,0.000008949661,0.00002295507,0.000021393374,0.000061413964,0.000017815168,0.0015247106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999627,0.00007892134,0.000023253759,0.000068208814,0.00014916116,0.000053390002],"domain_scores_gemma":[0.99911267,0.00040281957,0.00019098962,0.00006720837,0.00019119226,0.00003504359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006281093,0.00042993922,0.00045806155,0.00083359797,0.0002587642,0.0004760612,0.0003898864,0.0002637405,0.0028601806],"category_scores_gemma":[0.0023325223,0.000096761825,0.0004006113,0.00050764065,0.0005363528,0.0009005316,0.00047983002,0.00033779902,0.00020434221],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019813444,0.00005731545,0.0017266182,0.0001920381,0.000036327903,0.0005302555,0.00015406993,0.81981844,0.015590603,0.11918996,0.0010555275,0.04145058],"study_design_scores_gemma":[0.000011483859,0.00007123528,0.0013870342,0.0000077849645,0.00001935564,0.00022330672,0.000042829808,0.95036787,0.0018135326,0.044795316,0.0012445169,0.000015736097],"about_ca_topic_score_codex":0.0013259477,"about_ca_topic_score_gemma":0.00045222577,"teacher_disagreement_score":0.0028601806,"about_ca_system_score_codex":0.00038810493,"about_ca_system_score_gemma":0.00024459307,"threshold_uncertainty_score":0.009568274},"labels":[],"label_agreement":null},{"id":"W1992923086","doi":"10.1002/qre.859","title":"Optimizing the performance of a repairable system under a maintenance and repair contract","year":2007,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Reliability engineering; Context (archaeology); Horizon; Computer science; Time horizon; Meaning (existential); Operations research; Risk analysis (engineering); Engineering; Business; Mathematical optimization; Mathematics","score_opus":0.008801323296122262,"score_gpt":0.22705927726403297,"score_spread":0.2182579539679107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992923086","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5712089,0.0008526174,0.42136347,0.0005215667,0.000048144106,0.000078410616,0.00015698651,0.00024436574,0.005525596],"genre_scores_gemma":[0.9926307,0.00008872707,0.0062712766,0.000011511581,0.0000071081668,0.000016633843,0.000029906845,0.000011043551,0.0009330158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902165,0.00045640432,0.000039312836,0.00014590034,0.00019837942,0.00013837128],"domain_scores_gemma":[0.99786216,0.0012863936,0.00036874218,0.00014053815,0.00017273387,0.00016945251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002248335,0.00072804734,0.0010124573,0.00040886938,0.00028540092,0.0011483419,0.00082043145,0.0011342219,0.001678062],"category_scores_gemma":[0.004611003,0.0002682129,0.00038875168,0.0003788737,0.00078318577,0.0010928811,0.00062461046,0.0007050615,0.0001884455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001157638,0.000040003542,0.000355787,0.000041114246,0.000023441835,0.000055750625,0.000022827311,0.98693836,0.0026828402,0.0040374994,0.00012041802,0.005566223],"study_design_scores_gemma":[0.000013210066,0.00017070584,0.0002885048,0.0000063800007,0.000014097289,0.00001822902,0.000012698473,0.9957709,0.0009309896,0.002646974,0.000119838835,0.000007452691],"about_ca_topic_score_codex":0.0036060985,"about_ca_topic_score_gemma":0.0012089786,"teacher_disagreement_score":0.0036060985,"about_ca_system_score_codex":0.0010837684,"about_ca_system_score_gemma":0.0010353294,"threshold_uncertainty_score":0.011890471},"labels":[],"label_agreement":null},{"id":"W1995074584","doi":"10.1007/s10845-008-0192-3","title":"Inspection strategy for availability improvement","year":2008,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Shock (circulatory); Sequence (biology); State (computer science); Reliability engineering; Computer science; Mathematical optimization; Engineering; Algorithm; Mathematics","score_opus":0.02494567256911193,"score_gpt":0.23349349596783936,"score_spread":0.20854782339872743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995074584","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16447008,0.0005558718,0.79975927,0.0008488548,0.00012789588,0.00018639397,0.00009800255,0.0015294139,0.032424208],"genre_scores_gemma":[0.9277655,0.00012259239,0.0655473,0.0001071722,0.000027719503,0.00004303324,0.000050781717,0.00004411874,0.0062918123],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963224,0.000084974265,0.00001689078,0.0000773414,0.00011302334,0.000075515934],"domain_scores_gemma":[0.9988944,0.00033249668,0.00009543794,0.00016373734,0.00043843797,0.000075601376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070393254,0.00049089274,0.00054733374,0.0009917279,0.00052247103,0.00074126216,0.00086218544,0.0006656265,0.0057315617],"category_scores_gemma":[0.002260101,0.00022481491,0.00042640904,0.00048535637,0.0002886758,0.00094242336,0.0006124364,0.0006341935,0.0006132381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011414259,0.0010648645,0.007056878,0.00034192248,0.000115511524,0.000984142,0.000490085,0.17273737,0.11361435,0.07662014,0.008314087,0.6175192],"study_design_scores_gemma":[0.000057455232,0.0007779281,0.0042911232,0.0000330847,0.000082082675,0.0005260859,0.000155766,0.94629264,0.018608538,0.025809644,0.0033348002,0.00003072402],"about_ca_topic_score_codex":0.0015934327,"about_ca_topic_score_gemma":0.0019614934,"teacher_disagreement_score":0.0057315617,"about_ca_system_score_codex":0.0005350125,"about_ca_system_score_gemma":0.0009098016,"threshold_uncertainty_score":0.01917398},"labels":[],"label_agreement":null},{"id":"W1995373625","doi":"10.1080/00949655.2012.658805","title":"Bivariate degradation analysis of products based on Wiener processes and copulas","year":2012,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":146,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Bivariate analysis; Copula (linguistics); Mathematics; Joint probability distribution; Markov chain Monte Carlo; Markov chain; Dependency (UML); Bayesian probability; Applied mathematics; Mathematical optimization; Algorithm; Computer science; Econometrics; Statistics; Artificial intelligence","score_opus":0.015873342043485058,"score_gpt":0.2693712940123766,"score_spread":0.2534979519688915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995373625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01705151,0.0005249085,0.9808842,0.00006720135,0.000013797444,0.000015518817,0.000059341986,0.00006515404,0.001318426],"genre_scores_gemma":[0.8923062,0.0031873626,0.098533414,0.00006499234,0.00010088324,0.00013675717,0.00033696205,0.00016009377,0.005173465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99927694,0.00030666383,0.00003891082,0.00013851558,0.00016300779,0.00007598791],"domain_scores_gemma":[0.99805945,0.0012054011,0.00029144363,0.000107222455,0.00027417473,0.00006228552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023657717,0.0011905851,0.0010561106,0.0015618287,0.00035505067,0.001378264,0.00083792466,0.0006829733,0.0013479263],"category_scores_gemma":[0.0059825303,0.00062104705,0.0015417638,0.001497009,0.0009184084,0.0018535956,0.00093373755,0.0011636318,0.00030986665],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025727242,0.000017417256,0.001192701,0.000068412584,0.0000879046,0.00015559686,0.00010375478,0.9007911,0.0018485056,0.08494646,0.0004890043,0.010273484],"study_design_scores_gemma":[0.0000013888259,0.000008217852,0.00031282727,0.0000043314412,0.000011772002,0.000019043571,0.0000073960214,0.98931646,0.00013595993,0.00998562,0.00018962157,0.0000074416766],"about_ca_topic_score_codex":0.004087528,"about_ca_topic_score_gemma":0.0020177315,"teacher_disagreement_score":0.004087528,"about_ca_system_score_codex":0.0007850126,"about_ca_system_score_gemma":0.0006319989,"threshold_uncertainty_score":0.012511551},"labels":[],"label_agreement":null},{"id":"W1996703228","doi":"10.1049/ip-gtd:20000603","title":"Application of Monte Carlo simulation to optimal maintenance scheduling in a parallel-redundant system","year":2000,"lang":"en","type":"article","venue":"IEE Proceedings - Generation Transmission and Distribution","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Weibull distribution; Monte Carlo method; Exponential distribution; Interval (graph theory); Computer science; Reliability engineering; Component (thermodynamics); Exponential function; Applied mathematics; Mathematical optimization; Mathematics; Statistics; Engineering; Physics","score_opus":0.006940003043254317,"score_gpt":0.2125713582268016,"score_spread":0.20563135518354728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996703228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36472526,0.00052062265,0.62116605,0.00036182205,0.00006461272,0.00014318974,0.00012892515,0.00059023453,0.012299188],"genre_scores_gemma":[0.95781684,0.0001395502,0.040954195,0.000034447807,0.0000120828545,0.00009546094,0.00005687923,0.000032576823,0.00085790316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956876,0.00024567713,0.000016840973,0.00003181423,0.00009140997,0.0000455567],"domain_scores_gemma":[0.9971558,0.0022594477,0.00016856578,0.00013301033,0.00022664864,0.000056535366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001324584,0.00039174905,0.00073514954,0.0006386504,0.00045713285,0.0005977221,0.00053732144,0.00082457095,0.0012160138],"category_scores_gemma":[0.0043250327,0.0005163156,0.00041617858,0.0006300387,0.00047498825,0.00036765754,0.00032815474,0.0005486839,0.00011705946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016943182,0.000009484317,0.00017512731,0.0000044776352,0.0000050028148,0.000010973236,0.000005180731,0.99769515,0.00011367725,0.0008375598,0.000027952254,0.0010985614],"study_design_scores_gemma":[0.0000046415516,0.0000057562784,0.000041250878,0.000001310054,0.0000018107232,0.0000036679407,0.0000016446731,0.99944943,0.0000909615,0.00034107227,0.000056654546,0.0000017281832],"about_ca_topic_score_codex":0.014921523,"about_ca_topic_score_gemma":0.0073915995,"teacher_disagreement_score":0.014921523,"about_ca_system_score_codex":0.0010699487,"about_ca_system_score_gemma":0.0011144382,"threshold_uncertainty_score":0.029669344},"labels":[],"label_agreement":null},{"id":"W1997179609","doi":"10.1016/j.cie.2004.05.024","title":"Stohastic optimal production control problem with corrective maintenance","year":2004,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; École de Technologie Supérieure","funders":"","keywords":"Corrective maintenance; Production (economics); Time horizon; Failure rate; Constant (computer programming); Point (geometry); Control (management); Optimal maintenance; Mathematical optimization; Holding cost; Reliability engineering; Production planning; Exponential function; Operations research; Inventory control; Optimal control; Computer science; Preventive maintenance; Engineering; Mathematics; Economics","score_opus":0.006563401087699064,"score_gpt":0.1605064311682621,"score_spread":0.15394303008056304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997179609","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11896127,0.00075006805,0.8478406,0.0016332013,0.0002708095,0.00022789229,0.00044706033,0.0002913925,0.029577693],"genre_scores_gemma":[0.9463473,0.0002664362,0.038403776,0.00013883325,0.00013697888,0.00015508955,0.00017141149,0.00006595727,0.014314246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995474,0.00015338021,0.000018504688,0.00010975282,0.00010131125,0.00006966162],"domain_scores_gemma":[0.998976,0.00061891024,0.00013005824,0.00005456772,0.00016346689,0.000057044857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012289428,0.00096758956,0.0014333015,0.0007656669,0.0004879297,0.001641,0.0009384405,0.0022325432,0.005563268],"category_scores_gemma":[0.0030622869,0.0006444759,0.00044195045,0.0007491847,0.0010598813,0.0012141967,0.0010808144,0.0014009352,0.00037109028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003635561,0.00011663638,0.0002789702,0.00021085305,0.0000444348,0.00018121835,0.00005979185,0.9547808,0.0030400231,0.015944995,0.0016775527,0.023301143],"study_design_scores_gemma":[0.000056170495,0.000099628545,0.000167249,0.000007950086,0.000010622576,0.00002445084,0.000010574596,0.99131095,0.00066435983,0.007200806,0.00044124652,0.0000060275393],"about_ca_topic_score_codex":0.0028936423,"about_ca_topic_score_gemma":0.0015791699,"teacher_disagreement_score":0.005563268,"about_ca_system_score_codex":0.00069648057,"about_ca_system_score_gemma":0.0011306383,"threshold_uncertainty_score":0.018610954},"labels":[],"label_agreement":null},{"id":"W1997879906","doi":"10.5539/mas.v2n6p163","title":"An N-Component Series Repairable System with Repairman Doing Other Work and Priority in Repair","year":2008,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reliability engineering; Reliability (semiconductor); Exponential distribution; Laplace transform; Computer science; Idle; Interval (graph theory); Component (thermodynamics); Work (physics); Variable (mathematics); Series (stratigraphy); Service (business); Statistics; Mathematics; Engineering","score_opus":0.006313460011945238,"score_gpt":0.18243917651221897,"score_spread":0.17612571650027373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997879906","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5483512,0.00030612614,0.4466053,0.00023325134,0.000060490634,0.00006328045,0.00010763279,0.00024687088,0.004025831],"genre_scores_gemma":[0.9793688,0.00009570155,0.018042179,0.000014642645,0.00002621845,0.000023252906,0.000041063337,0.000009825377,0.0023783352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995183,0.00012471441,0.000033546716,0.0001768878,0.000088690154,0.000057906065],"domain_scores_gemma":[0.9990393,0.0004129603,0.00022076807,0.00009602535,0.00015931838,0.00007154519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076688454,0.0005127256,0.00086740387,0.00036129338,0.00051574677,0.00054530386,0.0007449066,0.00052018545,0.0019371584],"category_scores_gemma":[0.0020333477,0.0002233776,0.00053017033,0.0005164234,0.00061946484,0.0009250274,0.00042269178,0.00040542113,0.00021162209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005007133,0.00009830242,0.0048149573,0.00022561771,0.00007822217,0.0012695518,0.00024963007,0.92507815,0.019463353,0.013490803,0.0005599501,0.0341707],"study_design_scores_gemma":[0.00004653124,0.00033941353,0.0022176132,0.000009943818,0.00007545149,0.00041775958,0.000108288914,0.9820425,0.0048933965,0.00867729,0.0011405625,0.000031303895],"about_ca_topic_score_codex":0.0026538305,"about_ca_topic_score_gemma":0.0021340614,"teacher_disagreement_score":0.0026538305,"about_ca_system_score_codex":0.0005244834,"about_ca_system_score_gemma":0.0004935676,"threshold_uncertainty_score":0.006480396},"labels":[],"label_agreement":null},{"id":"W1998284002","doi":"10.1007/s10985-009-9138-0","title":"Proportional hazards and threshold regression: their theoretical and practical connections","year":2009,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Institute for Occupational Safety and Health; Natural Sciences and Engineering Research Council of Canada; Centers for Disease Control and Prevention","keywords":"Regression; Regression analysis; Context (archaeology); Proportional hazards model; Statistics; Scale (ratio); Hazard; Econometrics; Linear regression; Regression diagnostic; Mathematics; Computer science; Polynomial regression; Chemistry; Geography","score_opus":0.012999702560322672,"score_gpt":0.272799463851323,"score_spread":0.2597997612910003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998284002","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076322737,0.0064387457,0.976661,0.0052119507,0.00026360012,0.00004890782,0.00017393536,0.0001361804,0.003433459],"genre_scores_gemma":[0.57128626,0.022510162,0.38558164,0.0026532838,0.0051536052,0.00075845583,0.00039808577,0.0003009687,0.011357553],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9930346,0.004711024,0.0002693772,0.0006262204,0.0011284195,0.0002304114],"domain_scores_gemma":[0.87355036,0.116627,0.0034660583,0.0036364668,0.0022238542,0.00049629225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021894615,0.0013925391,0.0018750026,0.003085116,0.0007956059,0.004000419,0.0032881144,0.0027384027,0.0046260455],"category_scores_gemma":[0.12956782,0.0012907861,0.0014026158,0.004809447,0.0067532435,0.0067298072,0.0034620268,0.0061071464,0.000731859],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047142756,0.000037753896,0.0019719538,0.00012489867,0.00004410953,0.000113536444,0.0002880047,0.021723224,0.00011713444,0.9452678,0.0016412907,0.02862316],"study_design_scores_gemma":[0.000019188443,0.000028697861,0.0005125174,0.000030938038,0.000018050509,0.0002032141,0.00005518485,0.07746427,0.00010178012,0.91921234,0.0023315705,0.000022160872],"about_ca_topic_score_codex":0.003024821,"about_ca_topic_score_gemma":0.0015029967,"teacher_disagreement_score":0.021894615,"about_ca_system_score_codex":0.0017887623,"about_ca_system_score_gemma":0.0022777228,"threshold_uncertainty_score":0.1157912},"labels":[],"label_agreement":null},{"id":"W1999655097","doi":"10.1115/1.4000897","title":"The Impact of Probabilistic Modeling in Life-Cycle Management of Nuclear Piping Systems","year":2010,"lang":"en","type":"article","venue":"Journal of Engineering for Gas Turbines and Power","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Hydro-Québec; University of Waterloo","funders":"","keywords":"Piping; Probabilistic logic; Service life; Coolant; Nuclear engineering; Nuclear reactor; Engineering; Product life-cycle management; Reliability engineering; Environmental science; Structural engineering; Computer science; Mechanical engineering","score_opus":0.0059580458622151816,"score_gpt":0.20684958217453484,"score_spread":0.20089153631231965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999655097","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5716736,0.0010716318,0.41588274,0.0010062805,0.000056062272,0.00017281063,0.00093798165,0.00058725954,0.008611595],"genre_scores_gemma":[0.9873886,0.00022691498,0.011241998,0.000031517564,0.000017684775,0.000059723985,0.00019356552,0.000026243404,0.00081374525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980071,0.0009774356,0.000089324014,0.0002658891,0.00043685784,0.00022336788],"domain_scores_gemma":[0.98954415,0.008269672,0.0010951577,0.00028946705,0.0006569445,0.00014459714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037689928,0.0011469502,0.0010506265,0.0009231024,0.0006578674,0.0016902471,0.0017500259,0.0015788497,0.0010636689],"category_scores_gemma":[0.010860997,0.0011236619,0.0009904073,0.0009071239,0.00079340505,0.0018369202,0.0006583604,0.0010747203,0.00019734848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014012405,0.000006538079,0.00042699205,0.000004421321,0.000005114511,0.000008782196,0.0000075412895,0.9981603,0.000052784362,0.00059839274,0.000019894553,0.0006952489],"study_design_scores_gemma":[0.0000017294501,0.000011370792,0.00021010816,0.0000014947443,0.000004047485,0.0000049170567,0.0000035042897,0.999141,0.00006166927,0.00051559386,0.00004125451,0.0000032456953],"about_ca_topic_score_codex":0.031918827,"about_ca_topic_score_gemma":0.01854586,"teacher_disagreement_score":0.031918827,"about_ca_system_score_codex":0.002466732,"about_ca_system_score_gemma":0.0014084653,"threshold_uncertainty_score":0.06346607},"labels":[],"label_agreement":null},{"id":"W2000625936","doi":"10.1108/13552510510616478","title":"Fuzzy set‐valued and grey filtering statistical inferences on a system operating data","year":2005,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Schedule; Reliability engineering; Fuzzy logic; Probabilistic logic; Computer science; Set (abstract data type); Fuzzy set; Failure rate; Function (biology); Operations research; Production (economics); Product (mathematics); Industrial engineering; Engineering; Artificial intelligence; Mathematics","score_opus":0.040525353149743,"score_gpt":0.2963117320154809,"score_spread":0.2557863788657379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000625936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25953674,0.00031421456,0.7341248,0.0006490962,0.000063640524,0.00008082081,0.0007133824,0.00036134088,0.0041559692],"genre_scores_gemma":[0.96838254,0.00023752071,0.030128941,0.000057750185,0.00004024955,0.00004430078,0.00039105315,0.00001269534,0.0007048868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984439,0.00057609106,0.000110173736,0.00032967678,0.00044970584,0.000090495516],"domain_scores_gemma":[0.99226135,0.0060337614,0.0006501245,0.00044361324,0.0005369584,0.00007424827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003816089,0.00042940123,0.000663469,0.0020078525,0.00038401486,0.0015757049,0.00061633653,0.00071999186,0.0016797467],"category_scores_gemma":[0.017857851,0.0002674869,0.0008870148,0.001417682,0.00074698403,0.0019072926,0.00044965866,0.0009530435,0.00029507637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027060768,0.00010277634,0.019956747,0.00023220897,0.0002538267,0.00039232327,0.0006196236,0.78716195,0.0054786014,0.04618612,0.0016460862,0.13769908],"study_design_scores_gemma":[0.0000061969813,0.00004945758,0.0074395095,0.000028527027,0.0000314085,0.00003559973,0.000071476796,0.9715146,0.0010633257,0.01914956,0.0005838776,0.000026403903],"about_ca_topic_score_codex":0.009295181,"about_ca_topic_score_gemma":0.0051072445,"teacher_disagreement_score":0.009295181,"about_ca_system_score_codex":0.0015857205,"about_ca_system_score_gemma":0.00080179016,"threshold_uncertainty_score":0.020181596},"labels":[],"label_agreement":null},{"id":"W2000726943","doi":"10.1007/s00291-012-0294-3","title":"Availability maximization under partial observations","year":2012,"lang":"en","type":"article","venue":"OR Spectrum","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Unobservable; Mathematical optimization; Computer science; Maximization; Control limits; Markov decision process; Observable; Optimal control; Markov process; Mathematics; Process (computing); Econometrics; Statistics; Control chart","score_opus":0.029221920826680358,"score_gpt":0.2253394337205343,"score_spread":0.19611751289385396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000726943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10693261,0.0007993758,0.8809071,0.0016525476,0.00009813796,0.00006344233,0.0010001365,0.00033941574,0.008207257],"genre_scores_gemma":[0.95132565,0.00066373806,0.040587734,0.00018396095,0.00033164755,0.000114167975,0.0005888758,0.00013857051,0.006065692],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864024,0.00062013976,0.000047629343,0.00030001331,0.00024020894,0.00015176165],"domain_scores_gemma":[0.98944086,0.008837088,0.0007163227,0.00041022128,0.0003985526,0.00019704588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002168119,0.0013649396,0.0020049927,0.0007536626,0.0004125094,0.0017793128,0.0012820923,0.001429045,0.0033473645],"category_scores_gemma":[0.011987465,0.0011977113,0.00091566925,0.0014129444,0.0015283143,0.0028107492,0.0015018936,0.0015622252,0.00046899397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047304382,0.0000621737,0.0009609023,0.0002763042,0.00013774907,0.00028396715,0.000116825664,0.9229359,0.0028308504,0.05054003,0.002819346,0.018562896],"study_design_scores_gemma":[0.000022612516,0.000030861636,0.0004524065,0.000015869402,0.000019532434,0.00005710972,0.000017158098,0.9536383,0.0005660747,0.044872202,0.00029511823,0.000012698875],"about_ca_topic_score_codex":0.0020296287,"about_ca_topic_score_gemma":0.0013144673,"teacher_disagreement_score":0.0033473645,"about_ca_system_score_codex":0.00092353957,"about_ca_system_score_gemma":0.0008613803,"threshold_uncertainty_score":0.011466265},"labels":[],"label_agreement":null},{"id":"W2001308389","doi":"10.1016/j.ress.2011.03.018","title":"Trend analysis of the power law process using Expectation–Maximization algorithm for data censored by inspection intervals","year":2011,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"U.S. Food and Drug Administration","keywords":"Censoring (clinical trials); Expectation–maximization algorithm; Maximization; Algorithm; Computer science; Statistics; Failure rate; Process (computing); Mathematics; Maximum likelihood; Mathematical optimization","score_opus":0.015893062003255274,"score_gpt":0.22640764006577727,"score_spread":0.210514578062522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001308389","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019149832,0.00008710423,0.98006254,0.000058305268,0.000009254983,0.000024031435,0.00007849918,0.00028340926,0.00024702735],"genre_scores_gemma":[0.5700477,0.00044341883,0.42338687,0.00005889578,0.00005782045,0.00025579747,0.0011713356,0.00040775593,0.0041704657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990994,0.00036620712,0.00006193924,0.00022658982,0.00016661859,0.000079250654],"domain_scores_gemma":[0.9925355,0.0056644045,0.00050234405,0.00046029282,0.0007546099,0.000082833474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005260755,0.000604017,0.0011273292,0.0016345761,0.00033659805,0.0009132439,0.0014384408,0.00078042696,0.0024023775],"category_scores_gemma":[0.016320994,0.0005249635,0.0012218859,0.0019225852,0.000523241,0.001792597,0.0006686925,0.0014734075,0.0005457345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041541358,0.00017366414,0.009645069,0.00030555794,0.0003101581,0.00022545896,0.00032613444,0.73003554,0.006842503,0.054653436,0.0021585096,0.19490847],"study_design_scores_gemma":[0.00000488687,0.000013907186,0.0006537488,0.000004409403,0.000008923513,0.000018449347,0.0000065694403,0.99528354,0.0003471575,0.0034650194,0.0001872369,0.0000061354917],"about_ca_topic_score_codex":0.0037597371,"about_ca_topic_score_gemma":0.002620946,"teacher_disagreement_score":0.005260755,"about_ca_system_score_codex":0.00054248294,"about_ca_system_score_gemma":0.0009850675,"threshold_uncertainty_score":0.027821839},"labels":[],"label_agreement":null},{"id":"W2002736705","doi":"10.1016/j.apnum.2006.11.010","title":"Simultaneous control of production, preventive and corrective maintenance rates of a failure-prone manufacturing system","year":2007,"lang":"en","type":"article","venue":"Applied Numerical Mathematics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Corrective maintenance; Preventive maintenance; Production (economics); Reliability engineering; Productivity; Failure rate; Control (management); Production planning; Computer science; Operations research; Operations management; Engineering; Economics","score_opus":0.003525842608239134,"score_gpt":0.19487398254865626,"score_spread":0.19134813994041713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002736705","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6440611,0.0002811331,0.35315496,0.00027876152,0.000049914815,0.000032122425,0.000040725146,0.0003303281,0.0017710393],"genre_scores_gemma":[0.99606115,0.00003820326,0.0036002179,0.0000070067035,0.000011396074,0.000009471891,0.0000062286113,0.000006819387,0.00025944493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997509,0.000067387286,0.000014900667,0.000059006277,0.00006733865,0.00004046977],"domain_scores_gemma":[0.99767846,0.0012526823,0.000565461,0.00013040366,0.00025849757,0.00011461696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085852866,0.0004851882,0.00037676858,0.00034352558,0.0002448084,0.00064961775,0.0006485271,0.00046069722,0.0005263464],"category_scores_gemma":[0.003965465,0.0002549255,0.00023658856,0.00019737633,0.0003876629,0.00036607566,0.00035216217,0.00042648567,0.000071540475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008156651,0.00012038119,0.002254122,0.00012660195,0.000059860213,0.00014960865,0.0001337049,0.86725205,0.08683539,0.006200051,0.00029043164,0.035762154],"study_design_scores_gemma":[0.000021286252,0.00013195895,0.0010201506,0.0000032173127,0.000027059968,0.000029474702,0.000006664501,0.98933333,0.00821976,0.0010977908,0.00010042231,0.000008858143],"about_ca_topic_score_codex":0.0011097838,"about_ca_topic_score_gemma":0.0006167317,"teacher_disagreement_score":0.0011097838,"about_ca_system_score_codex":0.00030727222,"about_ca_system_score_gemma":0.00045121665,"threshold_uncertainty_score":0.004540384},"labels":[],"label_agreement":null},{"id":"W2002779342","doi":"10.1108/13552510710829498","title":"Availability analysis of a generalized maintainable three‐state device parallel system with human error and common‐cause failures","year":2007,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Redundancy (engineering); Reliability engineering; Common cause failure; Human error; Computer science; Reliability (semiconductor); Failure mode and effects analysis; State (computer science); Markov chain; Engineering; Common cause and special cause; Distributed computing; Algorithm; Operations management","score_opus":0.01860619051724609,"score_gpt":0.27286155454051225,"score_spread":0.25425536402326615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002779342","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72882587,0.00018765882,0.26734057,0.00012507057,0.000021507874,0.00005585032,0.00014481111,0.00030247774,0.0029960398],"genre_scores_gemma":[0.99744093,0.00003528841,0.0019858324,0.0000044875737,0.0000030417016,0.000014855071,0.000023036964,0.0000055287974,0.00048694154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996886,0.00006643578,0.000015056896,0.000090153924,0.00009022497,0.000049554757],"domain_scores_gemma":[0.998831,0.00052741007,0.0002865375,0.00010827429,0.00020902227,0.00003784415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005379492,0.00037843725,0.00048737373,0.00037667237,0.00035999375,0.0005381472,0.0005592414,0.0003531072,0.0015992449],"category_scores_gemma":[0.0014473695,0.00019415445,0.000554066,0.00026831805,0.00058037514,0.0005467489,0.00049960695,0.0003508541,0.00012763364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013817419,0.000025848367,0.0023463303,0.000061610306,0.00004479946,0.00028881803,0.000102434286,0.97841805,0.009366955,0.002987635,0.00013411677,0.0060852272],"study_design_scores_gemma":[0.0000043279047,0.000051395167,0.0013913203,0.000002078935,0.000013909494,0.000039833565,0.000023271457,0.99677867,0.0007592617,0.0008402366,0.00009141598,0.0000043802406],"about_ca_topic_score_codex":0.008334348,"about_ca_topic_score_gemma":0.0035548953,"teacher_disagreement_score":0.008334348,"about_ca_system_score_codex":0.0007747687,"about_ca_system_score_gemma":0.0006023418,"threshold_uncertainty_score":0.016571641},"labels":[],"label_agreement":null},{"id":"W2004573436","doi":"10.1198/004017007000000137","title":"On the Reliability of the Self-Dual<i>k</i>-Out-of-<i>n</i>Systems","year":2008,"lang":"en","type":"article","venue":"Technometrics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"European Regional Development Fund; Royal Canadian Geographical Society","keywords":"Reliability (semiconductor); Dual (grammatical number); Class (philosophy); Reliability engineering; Computer science; Mathematics; Physics; Engineering; Artificial intelligence","score_opus":0.014124828719877858,"score_gpt":0.19137679039307506,"score_spread":0.1772519616731972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004573436","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86844337,0.0011625738,0.122040115,0.00062320026,0.00005421306,0.0000149778225,0.0000790015,0.00012470913,0.0074576954],"genre_scores_gemma":[0.9963832,0.0002809606,0.002722604,0.000031924337,0.000033489938,0.000011907976,0.000034112254,0.000021211426,0.0004805663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99943095,0.00022407407,0.00002256172,0.00009491794,0.00012352297,0.000103933475],"domain_scores_gemma":[0.99343514,0.0038336217,0.0010966246,0.0006069796,0.0006987172,0.00032896493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022936545,0.00048528047,0.00070722937,0.0009537098,0.0004137953,0.000726545,0.0008565235,0.0005732286,0.0009276399],"category_scores_gemma":[0.011705723,0.00028194158,0.00031148116,0.0004310772,0.002332489,0.0012209627,0.00095524715,0.0008515218,0.00015652229],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045455128,0.000114132745,0.0074639805,0.00024095285,0.000079589096,0.00060140574,0.00039646306,0.670407,0.024480661,0.2814064,0.0015132352,0.012841652],"study_design_scores_gemma":[0.000017810016,0.000075035685,0.0030311984,0.00003250144,0.000017430611,0.00025681604,0.00006498779,0.92963684,0.0018926022,0.06455403,0.00039201064,0.000028650873],"about_ca_topic_score_codex":0.0007964457,"about_ca_topic_score_gemma":0.00033168535,"teacher_disagreement_score":0.0022936545,"about_ca_system_score_codex":0.00074002804,"about_ca_system_score_gemma":0.00040783623,"threshold_uncertainty_score":0.012130201},"labels":[],"label_agreement":null},{"id":"W2005006676","doi":"10.1007/s10845-014-0926-3","title":"Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan–Meier estimation","year":2014,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Prognostics; Reliability engineering; Reliability (semiconductor); Thresholding; Data mining; Hazard; Proportional hazards model; Estimation; Computer science; Engineering; Statistics; Artificial intelligence; Mathematics","score_opus":0.09751947791469256,"score_gpt":0.3055849809544451,"score_spread":0.20806550303975252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005006676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1717649,0.0007074242,0.82497257,0.00016731945,0.00004276053,0.000070319635,0.0006431126,0.00062204135,0.001009616],"genre_scores_gemma":[0.9593276,0.0002741842,0.039366983,0.000019973944,0.000032297135,0.00006502063,0.00051144126,0.000016027398,0.00038655402],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954456,0.00014442774,0.00006649695,0.00008485839,0.0001091671,0.000050545284],"domain_scores_gemma":[0.994945,0.0033955467,0.00069285074,0.0002868417,0.00060749816,0.00007221885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026431673,0.0006569085,0.000719996,0.0020527695,0.00027370002,0.0007248413,0.0005625463,0.00050347834,0.0017350807],"category_scores_gemma":[0.008789255,0.00024087541,0.0007174412,0.00088448205,0.0002971174,0.0014464787,0.00050864014,0.000564544,0.0003030364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005312013,0.00020059997,0.11582844,0.0003700353,0.00033539356,0.00030009454,0.00023088355,0.60139614,0.004654083,0.012681067,0.0020522424,0.26141977],"study_design_scores_gemma":[0.000012302997,0.00015268553,0.007406424,0.000024478497,0.000063699874,0.00017379031,0.00003398765,0.98462635,0.0011539496,0.005926797,0.0004053954,0.000020148196],"about_ca_topic_score_codex":0.0016624429,"about_ca_topic_score_gemma":0.0011457098,"teacher_disagreement_score":0.0026431673,"about_ca_system_score_codex":0.0003882722,"about_ca_system_score_gemma":0.00068724767,"threshold_uncertainty_score":0.0139786005},"labels":[],"label_agreement":null},{"id":"W2005308630","doi":"10.1007/s10985-010-9157-x","title":"Models and estimation for systems with recurrent events and usage processes","year":2010,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Warranty; Computer science; Estimation; Maximum likelihood; Random effects model; Data mining; Econometrics; Machine learning; Statistics; Mathematics; Engineering","score_opus":0.014889459524127912,"score_gpt":0.24284715732593704,"score_spread":0.22795769780180913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005308630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03682827,0.0011526638,0.96011794,0.0004716048,0.000048609552,0.000043386022,0.0004172429,0.00036136803,0.00055891415],"genre_scores_gemma":[0.88854486,0.0024696172,0.09708961,0.00013280755,0.00024747322,0.00045640997,0.002282716,0.0002563805,0.008520188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981937,0.00067717384,0.00015190527,0.0005114249,0.0002427442,0.00022300008],"domain_scores_gemma":[0.9864283,0.011370064,0.001043742,0.00050217024,0.00054232497,0.00011341673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005623483,0.0018926383,0.003687641,0.0013682154,0.00061131903,0.0023439517,0.00315396,0.0025761067,0.00217867],"category_scores_gemma":[0.018828752,0.0020988411,0.002126618,0.0018345542,0.0015739085,0.0035460955,0.0015584737,0.0029063316,0.00050228334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045479588,0.00002352929,0.00065210095,0.000055869303,0.00006981825,0.000034879224,0.000056653946,0.9785752,0.00021008342,0.011073844,0.0003830516,0.008819325],"study_design_scores_gemma":[0.0000033688814,0.0000049311852,0.00013778779,0.0000031600362,0.00000962955,0.0000054589905,0.0000043006908,0.993473,0.000049440147,0.0062230923,0.000080291844,0.0000055382825],"about_ca_topic_score_codex":0.018253457,"about_ca_topic_score_gemma":0.015568761,"teacher_disagreement_score":0.018253457,"about_ca_system_score_codex":0.0015518079,"about_ca_system_score_gemma":0.0014744567,"threshold_uncertainty_score":0.0362944},"labels":[],"label_agreement":null},{"id":"W2005362891","doi":"10.1142/s0218539300000213","title":"GENERAL SEQUENTIAL IMPERFECT PREVENTIVE MAINTENANCE MODELS","year":2000,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":176,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Alberta; University of New Brunswick","funders":"University of Hong Kong; University of Windsor; City University of Hong Kong","keywords":"Weibull distribution; Imperfect; Preventive maintenance; Schedule; Hazard ratio; Hazard; Statistics; Reliability engineering; Reduction (mathematics); Computer science; Mathematics; Econometrics; Engineering; Confidence interval; Biology","score_opus":0.010416701035903224,"score_gpt":0.24879311241115604,"score_spread":0.2383764113752528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005362891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054120935,0.001046769,0.9209729,0.0005587226,0.00020975189,0.00012481681,0.0011920471,0.0006224661,0.021151608],"genre_scores_gemma":[0.8924771,0.0014488036,0.06391245,0.00021353048,0.0002342518,0.00028065965,0.0008572114,0.00012755717,0.04044834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99910456,0.00016220621,0.000053328265,0.00023315387,0.00027983828,0.00016677493],"domain_scores_gemma":[0.99866736,0.0004833547,0.00036618108,0.00017380576,0.0002079417,0.000101313504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012184039,0.0013756212,0.0012865143,0.00083421025,0.00038995148,0.001186523,0.0035176391,0.0013437631,0.008195108],"category_scores_gemma":[0.0026292296,0.000766004,0.0010669561,0.0009510081,0.0009466201,0.0021002865,0.0009622873,0.0013296151,0.0010613904],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009658456,0.000053673903,0.0005956696,0.00009554903,0.000035638102,0.00012958964,0.00006116911,0.9441948,0.00095840887,0.041321088,0.0015545174,0.010903341],"study_design_scores_gemma":[0.00002441664,0.00005108144,0.0002576022,0.000008913133,0.000027639802,0.000066384535,0.000010022539,0.97787094,0.00023119825,0.019166311,0.0022726757,0.000012735772],"about_ca_topic_score_codex":0.0069955466,"about_ca_topic_score_gemma":0.0047566714,"teacher_disagreement_score":0.008195108,"about_ca_system_score_codex":0.0010588206,"about_ca_system_score_gemma":0.0010019309,"threshold_uncertainty_score":0.027415335},"labels":[],"label_agreement":null},{"id":"W2007073703","doi":"10.1007/s10479-011-0885-4","title":"Workforce-constrained maintenance scheduling for military aircraft fleet: a case study","year":2011,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":90,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Schedule; Theory of computation; Integer programming; Operations research; Computer science; Scheduling (production processes); Aircraft maintenance; Job shop scheduling; Constraint (computer-aided design); Workforce; Constraint programming; Fleet management; Mathematical optimization; Engineering; Aeronautics; Operations management; Stochastic programming; Mathematics; Algorithm","score_opus":0.22534984035422112,"score_gpt":0.399505614178517,"score_spread":0.17415577382429587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007073703","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98186755,0.00023260833,0.012106096,0.00035214543,0.00003302478,0.00009917847,0.0003723897,0.000090670634,0.0048463703],"genre_scores_gemma":[0.99042237,0.0001145078,0.0073213805,0.0000179977,0.000013421865,0.000038304242,0.00017611384,0.000022958964,0.0018728975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9995215,0.00018105519,0.000016205113,0.00007109086,0.0000741597,0.00013603346],"domain_scores_gemma":[0.99673295,0.002444209,0.00023146326,0.00015615682,0.0001867901,0.0002484709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012137856,0.0008374964,0.00058911444,0.0008221826,0.00092861307,0.0007885942,0.0015203732,0.002289136,0.003262597],"category_scores_gemma":[0.0031307077,0.00041728537,0.000785371,0.0010067855,0.00054846174,0.0006441201,0.00046482697,0.0008032297,0.0002474857],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008205892,0.001096027,0.008673612,0.00024943755,0.00010555175,0.00457381,0.00031939062,0.9520833,0.0040243245,0.0036055415,0.0029996792,0.021448757],"study_design_scores_gemma":[0.00022426568,0.0008711213,0.0077074715,0.000023998675,0.000074875425,0.0011342043,0.0007453882,0.9824578,0.002278596,0.002565305,0.0018757427,0.000041196345],"about_ca_topic_score_codex":0.015757397,"about_ca_topic_score_gemma":0.01913416,"teacher_disagreement_score":0.015757397,"about_ca_system_score_codex":0.0012584111,"about_ca_system_score_gemma":0.00094806333,"threshold_uncertainty_score":0.0313313},"labels":[],"label_agreement":null},{"id":"W2008385561","doi":"10.1007/s13198-014-0249-y","title":"Reliability for multiple units adopting sequential imperfect maintenance policies","year":2014,"lang":"en","type":"article","venue":"International Journal of Systems Assurance Engineering and Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"King Fahd University of Petroleum and Minerals","keywords":"Preventive maintenance; Imperfect; Reliability engineering; Reliability (semiconductor); Unit (ring theory); Process (computing); Computer science; Engineering","score_opus":0.007634982855205097,"score_gpt":0.20878445733754344,"score_spread":0.20114947448233836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008385561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8872152,0.00056842004,0.107047476,0.0005648309,0.000066992085,0.000039906707,0.00021908621,0.00025830968,0.0040198267],"genre_scores_gemma":[0.99678504,0.000059191632,0.0024978844,0.000010898076,0.000013381678,0.000009734116,0.000032028383,0.000014560862,0.0005772748],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930227,0.0002888305,0.000035830755,0.00009203316,0.00011752308,0.00016338653],"domain_scores_gemma":[0.99176776,0.005690866,0.0010307033,0.00047450373,0.0008710849,0.00016497874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003135572,0.0007453858,0.0009698638,0.0007071138,0.00032129776,0.0008153112,0.0011442608,0.00083585293,0.0021656188],"category_scores_gemma":[0.0085339965,0.00047012034,0.000643169,0.0007470816,0.0009742336,0.0010220269,0.00053740415,0.00079856056,0.00017405256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029899337,0.000025354688,0.0008012591,0.000034039775,0.000020136344,0.00006578073,0.000021646742,0.9911827,0.0009976907,0.0034227069,0.0002016447,0.00292805],"study_design_scores_gemma":[0.000011380472,0.0000615243,0.0006577279,0.0000039658207,0.000017412163,0.000020302226,0.000015732077,0.9971831,0.0002436324,0.00174873,0.00003131154,0.0000053391263],"about_ca_topic_score_codex":0.0065242792,"about_ca_topic_score_gemma":0.003484035,"teacher_disagreement_score":0.0065242792,"about_ca_system_score_codex":0.0014458515,"about_ca_system_score_gemma":0.0009038635,"threshold_uncertainty_score":0.016582727},"labels":[],"label_agreement":null},{"id":"W2008486416","doi":"10.1016/j.ress.2006.10.018","title":"Health state evaluation of an item: A general framework and graphical representation","year":2007,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; University of Toronto","funders":"","keywords":"Covariate; Representation (politics); Residual; State (computer science); Computer science; Decision model; Data mining; Statistics; Reliability engineering; Mathematics; Algorithm; Machine learning; Engineering","score_opus":0.00898866214553608,"score_gpt":0.2625086924261163,"score_spread":0.25352003028058023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008486416","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01142974,0.000231154,0.9792206,0.00023558027,0.000034763474,0.00020972776,0.0013210705,0.002059101,0.005258249],"genre_scores_gemma":[0.40984967,0.00049422757,0.57693326,0.00015622332,0.00010183985,0.0007030824,0.0021515328,0.00034129972,0.009268921],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989975,0.00030642256,0.000119299846,0.00027187227,0.0002138389,0.00009095471],"domain_scores_gemma":[0.9978015,0.0012142315,0.0002027571,0.00025143594,0.00045048798,0.00007956061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001825108,0.00091914454,0.00070668163,0.0026646017,0.00042546843,0.0025224737,0.001287121,0.0011043277,0.0098506985],"category_scores_gemma":[0.004818651,0.00032224966,0.0011648934,0.0014294916,0.0011175659,0.0027834824,0.0008959131,0.0006033729,0.001386244],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001015841,0.0003627745,0.019535929,0.0013590292,0.00038714675,0.0010552997,0.0017286697,0.12779504,0.037984587,0.40725476,0.015254381,0.3862667],"study_design_scores_gemma":[0.00008063657,0.00054573896,0.009876785,0.00026620968,0.00038867455,0.0010165173,0.00030254273,0.6400914,0.022683494,0.29522017,0.029342094,0.00018571285],"about_ca_topic_score_codex":0.0036234064,"about_ca_topic_score_gemma":0.002341725,"teacher_disagreement_score":0.0098506985,"about_ca_system_score_codex":0.00058268505,"about_ca_system_score_gemma":0.00067756575,"threshold_uncertainty_score":0.032953918},"labels":[],"label_agreement":null},{"id":"W2009733660","doi":"10.1109/rams.2010.5447985","title":"Developing effective spare parts estimations results in improved system availability","year":2010,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Spare part; Reliability engineering; Reliability (semiconductor); Production (economics); Weibull distribution; Computer science; Parametric statistics; Operating cost; Engineering; Operations management; Statistics; Mathematics","score_opus":0.0071816675498001335,"score_gpt":0.2159402898103943,"score_spread":0.20875862226059416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009733660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09450995,0.00024526144,0.90283126,0.00012537377,0.00001480901,0.000024124254,0.000088734494,0.0007114416,0.0014490553],"genre_scores_gemma":[0.8897572,0.00017483192,0.109091915,0.000027398224,0.00001959778,0.000032285585,0.00022302242,0.00008383241,0.00058991456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984232,0.0006145721,0.00009238466,0.0002644222,0.0004991427,0.00010622402],"domain_scores_gemma":[0.9926238,0.0046226815,0.0011688661,0.0008326149,0.00068074785,0.00007132139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022662815,0.0007837853,0.0007629494,0.0013548544,0.00018940402,0.0010351327,0.00073773117,0.00052206207,0.0013736358],"category_scores_gemma":[0.01101449,0.00040442054,0.0005845513,0.0006020336,0.0003855838,0.0016142796,0.0007422094,0.00070685946,0.0005556629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011851162,0.0000750736,0.011095318,0.00015066825,0.00008628451,0.00009878728,0.00016925391,0.85064846,0.017405558,0.0036925254,0.00036046532,0.11609899],"study_design_scores_gemma":[0.0000067604556,0.00008643328,0.004688835,0.000022779403,0.000028510338,0.00008696104,0.00005580638,0.9785009,0.011851191,0.0039646537,0.00068775617,0.000019502437],"about_ca_topic_score_codex":0.0016720576,"about_ca_topic_score_gemma":0.0013556166,"teacher_disagreement_score":0.0022662815,"about_ca_system_score_codex":0.0004383186,"about_ca_system_score_gemma":0.0010358385,"threshold_uncertainty_score":0.011985421},"labels":[],"label_agreement":null},{"id":"W2013010657","doi":"10.1016/j.ress.2003.11.005","title":"Lifetime replacement policy in discrete time for a single unit system","year":2004,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Unit (ring theory); Reliability engineering; Computer science; Discrete time and continuous time; Distribution (mathematics); Engineering; Mathematics; Statistics","score_opus":0.004397546725402216,"score_gpt":0.19398380133243998,"score_spread":0.18958625460703776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013010657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4681411,0.0013873981,0.51784956,0.002486864,0.00021241284,0.00016549719,0.000463947,0.000621505,0.0086717345],"genre_scores_gemma":[0.98246014,0.00023632674,0.013902972,0.00006669348,0.000047255744,0.000039830422,0.00007852748,0.000033675064,0.0031346865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991147,0.00031238305,0.000050205967,0.0001346555,0.00017267662,0.00021530467],"domain_scores_gemma":[0.993135,0.004911184,0.0006489827,0.00028169816,0.00057009916,0.00045301777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00292799,0.0007045198,0.0014383813,0.0008154681,0.00047210092,0.0014007965,0.0019751084,0.0017670358,0.003611405],"category_scores_gemma":[0.0070465887,0.0006707365,0.0004457461,0.0007383638,0.0013456547,0.0014751843,0.0007616048,0.0011927913,0.00033373566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004672571,0.00008368994,0.0005157512,0.00010477588,0.000028215538,0.00009972719,0.00004969566,0.9768013,0.0012783488,0.013477375,0.0008013959,0.00629247],"study_design_scores_gemma":[0.00002578334,0.000050407627,0.00015199374,0.000004829421,0.000010280356,0.000015742442,0.000010228165,0.99656856,0.00016608277,0.0029035832,0.00008673617,0.000005789338],"about_ca_topic_score_codex":0.0049369615,"about_ca_topic_score_gemma":0.0028381448,"teacher_disagreement_score":0.0049369615,"about_ca_system_score_codex":0.0017166465,"about_ca_system_score_gemma":0.0012836502,"threshold_uncertainty_score":0.0154848695},"labels":[],"label_agreement":null},{"id":"W2013164640","doi":"10.1007/s10589-006-6449-x","title":"Optimizing Preventive Maintenance Models","year":2006,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematical optimization; Preventive maintenance; Spurious relationship; Minification; Scheduling (production processes); Hazard; Failure rate; Mathematics; Computer science; Function (biology); Reliability engineering; Statistics; Engineering","score_opus":0.0055769508890640114,"score_gpt":0.19736079289617944,"score_spread":0.19178384200711543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013164640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.113525525,0.0015888804,0.8543527,0.0012933408,0.00020258302,0.0000927812,0.0006040766,0.00076856685,0.027571578],"genre_scores_gemma":[0.92768896,0.0005996082,0.057697643,0.00011919123,0.00008917328,0.00016027436,0.00042064287,0.00015438694,0.013070214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970007,0.00009564007,0.000009498312,0.000073598116,0.00007572804,0.000045478857],"domain_scores_gemma":[0.99924755,0.00046195192,0.00008918956,0.00008652615,0.00008069009,0.00003415604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064147264,0.0008421635,0.0011455291,0.00064696616,0.0003145312,0.0010359348,0.0013138796,0.0015680298,0.0040064477],"category_scores_gemma":[0.0028499314,0.0007231864,0.0007995482,0.00067702064,0.0005362212,0.0010464008,0.0005573632,0.000947848,0.0004163643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013389631,0.00001498191,0.000090158384,0.000014566736,0.000008049819,0.000010132068,0.0000055250825,0.9877281,0.00012182804,0.0074531776,0.00047039785,0.004069644],"study_design_scores_gemma":[0.0000039019415,0.000005503889,0.00003782073,0.0000022977738,0.0000047252097,0.0000038682765,0.0000015580795,0.9951485,0.000072096445,0.0045085773,0.0002097691,0.0000013988048],"about_ca_topic_score_codex":0.004306784,"about_ca_topic_score_gemma":0.003829928,"teacher_disagreement_score":0.004306784,"about_ca_system_score_codex":0.0011109884,"about_ca_system_score_gemma":0.001090443,"threshold_uncertainty_score":0.013402879},"labels":[],"label_agreement":null},{"id":"W2013946748","doi":"10.1007/978-1-84628-814-2_30","title":"Opportunistic Electrode Replacement in a Robotic Spot Welding System","year":2008,"lang":"en","type":"book-chapter","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Spot welding; Welding; Robot; Electrode; Compensation (psychology); Engineering; Mechanical engineering; Reliability engineering; Simulation; Computer science; Artificial intelligence","score_opus":0.014248628731364,"score_gpt":0.18530364486634804,"score_spread":0.17105501613498403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013946748","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08555169,0.0022107542,0.86702836,0.00025563533,0.00017266379,0.000048933252,0.000075829514,0.0007161112,0.04393987],"genre_scores_gemma":[0.82505435,0.0011445751,0.12921196,0.000077540964,0.00010127959,0.000040070663,0.000092483315,0.00011629448,0.044161454],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984145,0.000029440418,0.0000075283024,0.000031849504,0.00006996238,0.000019708594],"domain_scores_gemma":[0.9998915,0.00005062411,0.000011177387,0.000024681469,0.000015766507,0.0000061660967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020859968,0.00031892527,0.00039806392,0.00015165094,0.0002961739,0.00045282405,0.0008707651,0.00046065115,0.0027995876],"category_scores_gemma":[0.00035132974,0.00020336607,0.00023058233,0.00037793018,0.0002684327,0.0006293515,0.00033494722,0.00036490615,0.00039889905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003834673,0.00013211642,0.0013682966,0.00029402776,0.000062933446,0.0009551226,0.00021506874,0.30373707,0.07638125,0.047549553,0.006710312,0.56221074],"study_design_scores_gemma":[0.00003120221,0.00034517926,0.0019300663,0.000023056224,0.00006889957,0.0021042218,0.00009670332,0.909661,0.024957972,0.03637731,0.024363022,0.000041327577],"about_ca_topic_score_codex":0.0007409066,"about_ca_topic_score_gemma":0.0018425207,"teacher_disagreement_score":0.0027995876,"about_ca_system_score_codex":0.00023249544,"about_ca_system_score_gemma":0.00027098999,"threshold_uncertainty_score":0.009365618},"labels":[],"label_agreement":null},{"id":"W2013992075","doi":"10.1080/08982110108918688","title":"AN INTEGRATED ECONOMIC DESIGN MODEL FOR QUALITY CONTROL, REPLACEMENT, AND MAINTENANCE","year":2001,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Quality (philosophy); Reliability engineering; Control (management); Manufacturing engineering; Business; Operations management; Engineering; Computer science","score_opus":0.030416631782877613,"score_gpt":0.27546791641363366,"score_spread":0.24505128463075604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013992075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025977273,0.00040640897,0.9470951,0.0008304029,0.0001057934,0.00042961098,0.00043349483,0.00027377697,0.024448022],"genre_scores_gemma":[0.83263296,0.0008960545,0.11987631,0.00024231517,0.00011826357,0.0016186382,0.00042314958,0.00015289638,0.04403943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774915,0.0007968624,0.000059847323,0.00039410495,0.0005495784,0.00045043472],"domain_scores_gemma":[0.99716526,0.0018867196,0.00029424898,0.0001035835,0.00041494932,0.00013524054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004509065,0.0019137594,0.0022302512,0.0013564457,0.00080498407,0.0028506957,0.0036267515,0.002845847,0.013816595],"category_scores_gemma":[0.008003634,0.0018757228,0.0013685967,0.0012611425,0.0016808488,0.002529346,0.0015451206,0.002125146,0.0011624132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050499515,0.00004290291,0.00014073752,0.00004106576,0.000019029303,0.000026675378,0.000023040922,0.95338374,0.00037005846,0.04028381,0.0005470974,0.0050713513],"study_design_scores_gemma":[0.00003667959,0.000037932452,0.00013528786,0.0000070425413,0.000020799704,0.000008810963,0.000009280211,0.9897968,0.000119741984,0.009144665,0.0006730459,0.000009958823],"about_ca_topic_score_codex":0.013437418,"about_ca_topic_score_gemma":0.011764752,"teacher_disagreement_score":0.013816595,"about_ca_system_score_codex":0.0053590597,"about_ca_system_score_gemma":0.0040424294,"threshold_uncertainty_score":0.046221137},"labels":[],"label_agreement":null},{"id":"W2017352962","doi":"10.1016/j.ress.2015.03.005","title":"A stochastic alternating renewal process model for unavailability analysis of standby safety equipment","year":2015,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University Network of Excellence in Nuclear Engineering","keywords":"Unavailability; Reliability engineering; Process (computing); Computer science; Engineering; Operating system","score_opus":0.014165815184390595,"score_gpt":0.23424978902486213,"score_spread":0.22008397384047154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017352962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04836097,0.0008305322,0.9461402,0.0003451568,0.0000852821,0.000050486502,0.0001991869,0.00020326066,0.003784795],"genre_scores_gemma":[0.9608484,0.00086891046,0.025460782,0.00009723589,0.000107104526,0.00014089208,0.00034611436,0.00009061436,0.012039997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990446,0.0003507653,0.000048624664,0.0001721625,0.00021625073,0.00016762386],"domain_scores_gemma":[0.9971494,0.0018713602,0.00030467546,0.00012905744,0.00041614225,0.00012940535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022693002,0.0012013024,0.0023633442,0.0011944557,0.0005245929,0.0017651435,0.0030706516,0.0020241202,0.003266232],"category_scores_gemma":[0.005782151,0.0010035273,0.0016601052,0.0010940691,0.0012629204,0.0020761944,0.0010404196,0.002095866,0.00038111513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007041658,0.000054333497,0.00053247716,0.00008646795,0.00006347723,0.00014791632,0.000055804594,0.9431493,0.001872469,0.047575347,0.00060867605,0.005783258],"study_design_scores_gemma":[0.0000028876693,0.000008186408,0.000080304155,0.0000023611733,0.000010098949,0.00000885345,0.0000024100725,0.9968316,0.00005289065,0.0029156837,0.00008014327,0.0000045258003],"about_ca_topic_score_codex":0.011920227,"about_ca_topic_score_gemma":0.0068542412,"teacher_disagreement_score":0.011920227,"about_ca_system_score_codex":0.0014269869,"about_ca_system_score_gemma":0.001597749,"threshold_uncertainty_score":0.023701668},"labels":[],"label_agreement":null},{"id":"W2018424172","doi":"10.1109/pmaps.2006.360274","title":"Reliability functions and optimal decisions using condition data for EDF primary pumps","year":2006,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Shutdown; Reliability (semiconductor); Reliability engineering; Moment (physics); Nuclear power plant; Nuclear power; Computer science; Condition monitoring; Process (computing); Function (biology); Preventive maintenance; Reliability theory; Power (physics); Engineering; Nuclear engineering; Failure rate; Electrical engineering","score_opus":0.02407762675532467,"score_gpt":0.24813433579868643,"score_spread":0.22405670904336178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018424172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13288386,0.0004132061,0.86386937,0.00028364375,0.000020782885,0.000072411254,0.00023727547,0.00033178556,0.0018876422],"genre_scores_gemma":[0.92527366,0.0003336302,0.07241418,0.000025350293,0.000021895958,0.000094881056,0.00026933555,0.000051760726,0.001515228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998502,0.00074406975,0.000054842,0.00024971677,0.00026771723,0.00018162535],"domain_scores_gemma":[0.98840207,0.009536108,0.00076690095,0.00028019023,0.000878866,0.00013586224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005211454,0.0011799493,0.0014381435,0.0016195963,0.0003889919,0.0014937053,0.0010346037,0.0012214879,0.002081854],"category_scores_gemma":[0.018200835,0.0009524598,0.00079943804,0.0008106542,0.001405413,0.001493522,0.00059498724,0.0012964952,0.0002104498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005782681,0.000018351026,0.00054927584,0.000024705476,0.000012725854,0.00005190705,0.000037593683,0.9847017,0.00032107337,0.0072891237,0.00014692747,0.006788867],"study_design_scores_gemma":[0.000004641573,0.000016147667,0.00029878214,0.00000534336,0.000005040538,0.000007212742,0.000007055772,0.9945592,0.0003492781,0.004655442,0.00008469645,0.0000071437794],"about_ca_topic_score_codex":0.011238048,"about_ca_topic_score_gemma":0.0048536044,"teacher_disagreement_score":0.011238048,"about_ca_system_score_codex":0.0027965778,"about_ca_system_score_gemma":0.0010082615,"threshold_uncertainty_score":0.027561128},"labels":[],"label_agreement":null},{"id":"W2018903348","doi":"10.1142/s0218539303001184","title":"Performance Evaluation of Decreasing Multi-State Consecutive-k-out-of-n: G Systems","year":2003,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Stantec (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"State (computer science); Context (archaeology); Binary number; Path (computing); Mathematics; Combinatorics; Distribution (mathematics); Discrete mathematics; Algorithm; Computer science; Arithmetic; Mathematical analysis","score_opus":0.03541085493033413,"score_gpt":0.29512605410965687,"score_spread":0.25971519917932273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018903348","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85984725,0.0006914342,0.13401474,0.00024703692,0.000033718177,0.00007977763,0.0001350373,0.0010850228,0.003865974],"genre_scores_gemma":[0.9918618,0.000030572806,0.0077825687,0.000011303603,0.000004852485,0.000013501678,0.00005797629,0.00001720557,0.00022013974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843556,0.0005533245,0.00011480923,0.00024286947,0.00035563746,0.00029769196],"domain_scores_gemma":[0.9923132,0.0048583364,0.0006345944,0.0005859498,0.0012151685,0.00039264525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026514865,0.0007996129,0.0008572041,0.0006799871,0.00062334136,0.00079571025,0.0011692552,0.000546365,0.002049688],"category_scores_gemma":[0.00797526,0.00019577789,0.00025462644,0.0004364802,0.0005922825,0.0007947766,0.0007815403,0.00038720784,0.0002536217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001618256,0.00015606736,0.004026746,0.00016709827,0.00005986659,0.00006499009,0.00011709521,0.93321043,0.009779326,0.0029491084,0.00078044314,0.047070537],"study_design_scores_gemma":[0.000018013863,0.0003031696,0.00061878155,0.000004391581,0.00001253044,0.00002545445,0.000017490567,0.99448025,0.003998285,0.00042547382,0.000089526206,0.0000065939657],"about_ca_topic_score_codex":0.005850166,"about_ca_topic_score_gemma":0.00511835,"teacher_disagreement_score":0.005850166,"about_ca_system_score_codex":0.001622366,"about_ca_system_score_gemma":0.0010327032,"threshold_uncertainty_score":0.014022529},"labels":[],"label_agreement":null},{"id":"W2019971886","doi":"10.1109/tr.2014.2337811","title":"Optimal Replacement Last With Continuous and Discrete Policies","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Qatar National Research Fund; Nanjing Tech University; Nanjing University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Mathematical optimization; Discrete time and continuous time; Focus (optics); Unit (ring theory); Operations research; Mathematics; Statistics","score_opus":0.003720249261675196,"score_gpt":0.19211303302457844,"score_spread":0.18839278376290325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019971886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20314926,0.0021431935,0.7772864,0.0011015321,0.00020850572,0.000107140055,0.00033019908,0.0003582782,0.015315374],"genre_scores_gemma":[0.96622354,0.00053971127,0.027580125,0.000087734006,0.000047724676,0.00006287958,0.00007673517,0.00002654751,0.005354954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987405,0.00043544252,0.00005566933,0.00024327054,0.00027752286,0.0002475402],"domain_scores_gemma":[0.99792767,0.0010538993,0.00046819157,0.00018402984,0.00016614883,0.00019993156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018059477,0.00083589356,0.0011041836,0.0005795065,0.00031158808,0.0013804376,0.0015006677,0.0011256991,0.0024981557],"category_scores_gemma":[0.0054529174,0.00048624264,0.00069898035,0.00043300734,0.0012773752,0.0017516286,0.00066713133,0.0009737293,0.00029956063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014874265,0.00008543951,0.00053239946,0.000067719506,0.000018962028,0.00006861369,0.00003573451,0.93547547,0.0009479599,0.05385117,0.0007253629,0.0080425],"study_design_scores_gemma":[0.000033377382,0.000105366744,0.0002895217,0.00001285672,0.000019727695,0.000037963295,0.000026181924,0.97927,0.00046308234,0.018978536,0.0007483944,0.0000150574415],"about_ca_topic_score_codex":0.0033177147,"about_ca_topic_score_gemma":0.0019885132,"teacher_disagreement_score":0.0033177147,"about_ca_system_score_codex":0.0016955183,"about_ca_system_score_gemma":0.0014975271,"threshold_uncertainty_score":0.012301862},"labels":[],"label_agreement":null},{"id":"W2020088934","doi":"10.1108/13552511111157399","title":"Maintenance/production planning with interactive feedback of product quality","year":2011,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Production (economics); Quality (philosophy); Product (mathematics); Control (management); Preventive maintenance; Maintenance actions; Point (geometry); Reliability engineering; Value (mathematics); Operations research; Computer science; Originality; Production planning; Optimal maintenance; Engineering; Risk analysis (engineering); Economics; Mathematics; Business","score_opus":0.029699906106271972,"score_gpt":0.2635196073782785,"score_spread":0.23381970127200655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020088934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06733309,0.00023607563,0.9250598,0.00027736722,0.000040465235,0.00009982153,0.00012560202,0.00027456958,0.006553199],"genre_scores_gemma":[0.9676976,0.00017357034,0.027270561,0.000036998983,0.000021409101,0.00011436283,0.000056846995,0.00002980398,0.0045987153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99904996,0.00023428453,0.00003181155,0.0002432531,0.00031560694,0.00012508062],"domain_scores_gemma":[0.999213,0.00034285054,0.00022569657,0.00005583596,0.0001188988,0.000043664444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009800484,0.0007773645,0.0008591061,0.00044657517,0.00030113984,0.0011396533,0.0011485908,0.0006885881,0.002751126],"category_scores_gemma":[0.0021680766,0.00046961513,0.00079140405,0.00036077385,0.00092019915,0.00096235605,0.00065106,0.0009528424,0.00026801557],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046015433,0.000032431555,0.0003119848,0.000033910048,0.0000106632115,0.00004286264,0.000026619224,0.9859768,0.0014236217,0.005664391,0.00012620578,0.0063044187],"study_design_scores_gemma":[0.000007922597,0.00004682777,0.00018654027,0.000003950707,0.0000085558795,0.000009750669,0.000005344786,0.99710804,0.0003919329,0.0020207695,0.00020501825,0.0000052722808],"about_ca_topic_score_codex":0.007835793,"about_ca_topic_score_gemma":0.0042825253,"teacher_disagreement_score":0.007835793,"about_ca_system_score_codex":0.0015093308,"about_ca_system_score_gemma":0.0015343066,"threshold_uncertainty_score":0.015580416},"labels":[],"label_agreement":null},{"id":"W2021604629","doi":"10.4028/www.scientific.net/amm.471.119","title":"Condition Based Monitoring Application in Predictive Maintenance Strategies for Total Asset and Facility Management Service","year":2013,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MD Precision (Canada)","funders":"","keywords":"Predictive maintenance; Asset management; Facility management; Service (business); Condition monitoring; Business; Condition-based maintenance; Operations management; Reliability engineering; Computer science; Engineering; Finance; Marketing","score_opus":0.005639908924262423,"score_gpt":0.1969614447773484,"score_spread":0.19132153585308598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021604629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3241842,0.0006138735,0.66405416,0.00020970979,0.00011264442,0.000114509785,0.00023601005,0.0025964438,0.0078784255],"genre_scores_gemma":[0.9829754,0.000069255,0.01627391,0.0000151361255,0.000010920249,0.000016359967,0.000033763587,0.000018256644,0.00058695994],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998368,0.000029540179,0.000008944729,0.0000407614,0.000068930225,0.000014989177],"domain_scores_gemma":[0.99946445,0.00026898948,0.000073345574,0.000040622104,0.0001350159,0.000017587114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034037954,0.00039274167,0.00036647217,0.0006761824,0.0002736142,0.000514648,0.0005721661,0.00039512085,0.0016899954],"category_scores_gemma":[0.0014766657,0.00017661744,0.00017353619,0.0004884574,0.00014039133,0.0005158023,0.00020754145,0.00030208187,0.00020761846],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009288312,0.00043512814,0.012968834,0.00017319132,0.0000536089,0.00024725514,0.00013990114,0.3424073,0.061488792,0.0026038315,0.0025002977,0.5760531],"study_design_scores_gemma":[0.0000083997975,0.00013301494,0.004160736,0.0000080719,0.000020500105,0.000060135764,0.000015954372,0.9854167,0.009215758,0.00057384215,0.00037851903,0.000008316249],"about_ca_topic_score_codex":0.0024802962,"about_ca_topic_score_gemma":0.002920656,"teacher_disagreement_score":0.0024802962,"about_ca_system_score_codex":0.00031520482,"about_ca_system_score_gemma":0.00025162735,"threshold_uncertainty_score":0.0056536198},"labels":[],"label_agreement":null},{"id":"W2023345879","doi":"10.1057/palgrave.jors.2601261","title":"Optimal component replacement decisions using vibration monitoring and the proportional-hazards model","year":2002,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":157,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Weibull distribution; Context (archaeology); Purchasing; Computer science; Preventive maintenance; Reliability engineering; Operations research; Operations management; Engineering; Statistics; Mathematics","score_opus":0.07724871446964189,"score_gpt":0.3347167128229625,"score_spread":0.2574679983533206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023345879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21573502,0.00047968456,0.780016,0.0003872542,0.000033023945,0.00017564523,0.00010157929,0.0002091316,0.0028627722],"genre_scores_gemma":[0.970414,0.00013925959,0.027996074,0.000026534493,0.000013630381,0.000073021205,0.000045413854,0.000015966,0.0012761428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873346,0.0006355959,0.0000501208,0.00017365903,0.00022696977,0.00018018547],"domain_scores_gemma":[0.99660444,0.0028192694,0.00024769155,0.00008721566,0.00014408822,0.00009729889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033525384,0.00092427374,0.0012646309,0.00094859104,0.00039124803,0.000934369,0.0011545,0.0010733666,0.0018990339],"category_scores_gemma":[0.008140991,0.00071698596,0.00067972695,0.00044526893,0.0007623669,0.0010116368,0.00076221075,0.0008712922,0.00016618165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009207574,0.000028528902,0.0005748934,0.000018741408,0.000019406416,0.000042643882,0.000023633185,0.9889438,0.00032512916,0.0023143655,0.000094727315,0.007522109],"study_design_scores_gemma":[0.000020778907,0.00008160292,0.0002415632,0.000003313562,0.0000139019685,0.000017246817,0.000013032568,0.9958585,0.0001916262,0.0034526072,0.00009865849,0.000007108975],"about_ca_topic_score_codex":0.0046951557,"about_ca_topic_score_gemma":0.0034495362,"teacher_disagreement_score":0.0046951557,"about_ca_system_score_codex":0.0009161854,"about_ca_system_score_gemma":0.0013429455,"threshold_uncertainty_score":0.017730117},"labels":[],"label_agreement":null},{"id":"W2023857282","doi":"10.1007/s10845-008-0126-0","title":"A virtual collaborative maintenance architecture for manufacturing enterprises","year":2008,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Architecture; Engineering; Reliability (semiconductor); Production (economics); Enterprise architecture; Systems engineering; Enterprise architecture management; Manufacturing engineering; Reliability engineering; Computer science","score_opus":0.009793697706679167,"score_gpt":0.2155327108321344,"score_spread":0.20573901312545523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023857282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11129649,0.00031089046,0.87215316,0.0007765057,0.00010697487,0.00016408264,0.0000707381,0.0044607883,0.010660406],"genre_scores_gemma":[0.7283521,0.00016651675,0.26562205,0.000111210924,0.00004240635,0.00012413078,0.00017495934,0.00013404239,0.0052725356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989679,0.00030705763,0.00009764177,0.00019309485,0.00028691647,0.00014749375],"domain_scores_gemma":[0.99792176,0.00030956077,0.0001293075,0.0009046377,0.00037137285,0.00036338685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023890552,0.00036063112,0.00046193777,0.0007919321,0.0014814078,0.0033120087,0.002752399,0.0019081071,0.0033409789],"category_scores_gemma":[0.0024781642,0.00047252976,0.00061007374,0.00066314824,0.000758614,0.0042854194,0.0038461557,0.00091883994,0.0006790788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014370111,0.0011642321,0.0067628664,0.00020919945,0.00021717377,0.00082603906,0.0024053522,0.34263867,0.03178479,0.16298394,0.012527763,0.43704307],"study_design_scores_gemma":[0.00010008084,0.00029711056,0.0011123981,0.00003523939,0.00011133596,0.000276283,0.00032125358,0.92135346,0.007636372,0.044188857,0.024512578,0.000055003402],"about_ca_topic_score_codex":0.004186337,"about_ca_topic_score_gemma":0.0043016076,"teacher_disagreement_score":0.004186337,"about_ca_system_score_codex":0.0008761733,"about_ca_system_score_gemma":0.001760639,"threshold_uncertainty_score":0.012634695},"labels":[],"label_agreement":null},{"id":"W2024040073","doi":"10.1007/s00170-014-6651-4","title":"Optimal condition-based maintenance policy for a partially observable system with two sampling intervals","year":2014,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partially observable Markov decision process; Sampling (signal processing); Unobservable; Control limits; Mathematics; Bayesian probability; Posterior probability; Observable; Statistics; Markov process; Mathematical optimization; Limit (mathematics); Control theory (sociology); Computer science; Markov decision process; Process (computing); Control chart; Control (management); Econometrics; Filter (signal processing); Artificial intelligence","score_opus":0.008677916299172497,"score_gpt":0.2501384070482084,"score_spread":0.2414604907490359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024040073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28578153,0.00073207304,0.70734006,0.0008353452,0.00012458726,0.00017773232,0.0003276893,0.0009413008,0.0037396965],"genre_scores_gemma":[0.98675007,0.00005426149,0.012562256,0.000035494413,0.00001870476,0.00003943096,0.000048136873,0.0000120422665,0.00047958837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993892,0.00013821892,0.000034209133,0.00016886777,0.00012926961,0.00014029008],"domain_scores_gemma":[0.99686664,0.00199837,0.00038384183,0.00014343517,0.00042744394,0.00018027902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010793307,0.0007730386,0.0010928935,0.00051863905,0.00039852012,0.000995974,0.0007592244,0.001306646,0.0017835685],"category_scores_gemma":[0.0047068195,0.00034641425,0.00032110483,0.0003293156,0.00079598016,0.0007830022,0.0005031015,0.0008358784,0.00016765003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010837191,0.000107672204,0.00087510626,0.00014985848,0.000043484466,0.00010594256,0.00009601154,0.9633367,0.0062161363,0.0064133476,0.0008800664,0.020691872],"study_design_scores_gemma":[0.000047591875,0.000057456436,0.00057107495,0.000007878391,0.00001329766,0.000013885376,0.000007781157,0.99699664,0.00061519723,0.0015939003,0.00006830601,0.000007014093],"about_ca_topic_score_codex":0.008487041,"about_ca_topic_score_gemma":0.004409682,"teacher_disagreement_score":0.008487041,"about_ca_system_score_codex":0.0011388507,"about_ca_system_score_gemma":0.0016769144,"threshold_uncertainty_score":0.016875267},"labels":[],"label_agreement":null},{"id":"W2024154419","doi":"10.1007/s10479-011-1013-1","title":"A Bayesian model and numerical algorithm for CBM availability maximization","year":2011,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Theory of computation; Parameterized complexity; Markov decision process; Computer science; Mathematical optimization; Maximization; Partially observable Markov decision process; Bayesian probability; Expectation–maximization algorithm; Algorithm; Multivariate statistics; Markov chain; Markov process; Markov model; Mathematics; Artificial intelligence; Machine learning; Statistics","score_opus":0.1699342649344517,"score_gpt":0.36933101493084397,"score_spread":0.19939674999639226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024154419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012059973,0.00007250909,0.9975509,0.00013826288,0.000013038032,0.000022215188,0.000028191413,0.00012331166,0.0008455956],"genre_scores_gemma":[0.108723246,0.00022906544,0.887047,0.00015469234,0.000083856416,0.000403077,0.00018521084,0.0002006541,0.002973318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991104,0.0003951075,0.000035400444,0.00012208639,0.00027137322,0.00006570001],"domain_scores_gemma":[0.9955124,0.0034728954,0.00019505202,0.00019268606,0.00049961207,0.00012722368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033561783,0.0009101965,0.0020400418,0.001221855,0.0010867319,0.001634868,0.003072985,0.0031853772,0.006723913],"category_scores_gemma":[0.014131127,0.0015579485,0.0011167513,0.002064461,0.0015454296,0.0027287977,0.0025834744,0.0033993754,0.0013125695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050314717,0.00004681844,0.000163902,0.00005767241,0.000018475273,0.000027942066,0.00005035019,0.91217315,0.0005708151,0.05045698,0.0016905993,0.034693],"study_design_scores_gemma":[0.000010346951,0.0000044335416,0.000020834586,0.000006002198,0.0000027262208,0.0000072348143,0.0000027736298,0.9863114,0.00007581402,0.013254749,0.0002984529,0.0000052892633],"about_ca_topic_score_codex":0.013130889,"about_ca_topic_score_gemma":0.011126608,"teacher_disagreement_score":0.013130889,"about_ca_system_score_codex":0.002066765,"about_ca_system_score_gemma":0.0031935605,"threshold_uncertainty_score":0.02610892},"labels":[],"label_agreement":null},{"id":"W2024245239","doi":"10.1080/0740817x.2014.929363","title":"Effects of subsystem mission time on reliability allocation","year":2014,"lang":"en","type":"article","venue":"IIE Transactions","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Konkuk University","keywords":"Failure rate; Reliability engineering; Reliability (semiconductor); Order (exchange); Factor (programming language); Engineering; Computer science; Business","score_opus":0.0025546932503882846,"score_gpt":0.17398111095123797,"score_spread":0.17142641770084968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024245239","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84266543,0.0013737156,0.1388081,0.00022762988,0.00010432882,0.000082165,0.00011024387,0.0005275045,0.01610093],"genre_scores_gemma":[0.9838309,0.00028237898,0.013367025,0.000042689768,0.000013421229,0.000035400313,0.00006055636,0.0001973266,0.0021702773],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841833,0.0005024052,0.00008038261,0.00017084219,0.000451671,0.00037638677],"domain_scores_gemma":[0.99174833,0.0049206144,0.000925476,0.00062819663,0.0013599194,0.00041745734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025066433,0.000876359,0.00052832754,0.0010311194,0.00059823814,0.0006844094,0.00066463416,0.0003809266,0.0036645718],"category_scores_gemma":[0.009933426,0.00049957004,0.0004934622,0.00076526677,0.0004948546,0.00131127,0.0011150392,0.0008188858,0.0004940774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002127148,0.00027955702,0.0254559,0.0005138242,0.00020859446,0.0008190483,0.0007540823,0.6650659,0.14737146,0.010391276,0.0013448669,0.14566833],"study_design_scores_gemma":[0.00012238006,0.0029860302,0.0776985,0.00011257086,0.0005615167,0.0012682691,0.0011252059,0.6766448,0.21788429,0.009795122,0.011625984,0.00017535067],"about_ca_topic_score_codex":0.002194337,"about_ca_topic_score_gemma":0.0027556915,"teacher_disagreement_score":0.0036645718,"about_ca_system_score_codex":0.000666324,"about_ca_system_score_gemma":0.0010488179,"threshold_uncertainty_score":0.01325655},"labels":[],"label_agreement":null},{"id":"W2025521447","doi":"10.1111/j.1937-5956.2000.tb00465.x","title":"COMBINED PRODUCTION AND MAINTENANCE SCHEDULING FOR A MULTIPLE‐PRODUCT, SINGLE‐ MACHINE PRODUCTION SYSTEM","year":2000,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec","funders":"","keywords":"Computer science; Scheduling (production processes); Markov decision process; Production (economics); Product (mathematics); Reliability engineering; Operations research; Industrial engineering; Markov process; Operations management; Mathematics; Engineering; Economics; Statistics; Microeconomics","score_opus":0.00848559295328552,"score_gpt":0.19346088595215827,"score_spread":0.18497529299887275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025521447","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2593476,0.00052096957,0.7367769,0.0002058001,0.000043875825,0.00013736764,0.00009058153,0.00034393056,0.0025329795],"genre_scores_gemma":[0.9108927,0.0001438482,0.0876488,0.000023106451,0.000031801897,0.00009044126,0.00006234186,0.000028335846,0.0010786059],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986827,0.00041958177,0.00004552157,0.00022847275,0.00041955177,0.00020419456],"domain_scores_gemma":[0.9967405,0.002205,0.00038825622,0.00021746781,0.00023195779,0.00021683038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022993586,0.00069370854,0.0014140173,0.0007775886,0.00050940894,0.00085874146,0.0011186092,0.0007963326,0.001854743],"category_scores_gemma":[0.0037754187,0.0009317571,0.0011485608,0.00065236964,0.0006430743,0.0011848701,0.0010171357,0.0011928674,0.00020273551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011831772,0.000060522485,0.0006351594,0.00003676108,0.000038754562,0.00004135083,0.00002147091,0.9808231,0.0012151201,0.0013244771,0.000121553436,0.015563521],"study_design_scores_gemma":[0.00002349834,0.00007855798,0.00036233506,0.0000025236557,0.000016602417,0.00001676245,0.000004060635,0.9976749,0.00058634946,0.0010808974,0.00014830829,0.000005152177],"about_ca_topic_score_codex":0.0055008526,"about_ca_topic_score_gemma":0.0072302376,"teacher_disagreement_score":0.0055008526,"about_ca_system_score_codex":0.0015107004,"about_ca_system_score_gemma":0.0024162957,"threshold_uncertainty_score":0.012160301},"labels":[],"label_agreement":null},{"id":"W2026087586","doi":"10.1080/0740817x.2013.770188","title":"Multistate degradation and supervised estimation methods for a condition-monitored device","year":2013,"lang":"en","type":"article","venue":"IIE Transactions","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Correctness; Consistency (knowledge bases); Nonparametric statistics; Reliability (semiconductor); Parametric statistics; Degradation (telecommunications); Process (computing); Computer science; Stochastic process; Maximum likelihood; Estimation; Markov chain; Algorithm; Mathematics; Engineering; Statistics; Artificial intelligence; Machine learning","score_opus":0.013895474305160319,"score_gpt":0.2948348266960182,"score_spread":0.2809393523908579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026087586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00791013,0.0001549113,0.9915705,0.00003670371,0.000007036112,0.000010930437,0.00001437981,0.00008675246,0.00020863235],"genre_scores_gemma":[0.7458337,0.00059599435,0.25077984,0.000063614876,0.000080801954,0.00019631797,0.00021845802,0.00008155165,0.002149798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992741,0.00029097436,0.00003683872,0.00020547498,0.00015327657,0.000039339386],"domain_scores_gemma":[0.9961694,0.0024154393,0.000604253,0.00031040597,0.00045382185,0.000046718724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001999218,0.0007534921,0.00087107765,0.00062746607,0.00025283484,0.00064785744,0.001060891,0.0008850014,0.00093684765],"category_scores_gemma":[0.006356408,0.0004710976,0.0009112504,0.00045186467,0.00084146805,0.0014117024,0.0008584183,0.0011441016,0.0002358469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094019095,0.00007930728,0.0022962783,0.00017230285,0.000091466776,0.00005682503,0.0001735534,0.8907359,0.004727131,0.0149152465,0.00046344442,0.08619448],"study_design_scores_gemma":[0.0000015970619,0.000010965507,0.00025751893,0.0000034402542,0.0000044888634,0.000011308541,0.000003391611,0.9972995,0.00039825638,0.001903901,0.000101198384,0.0000044410494],"about_ca_topic_score_codex":0.0021661092,"about_ca_topic_score_gemma":0.001965999,"teacher_disagreement_score":0.0021661092,"about_ca_system_score_codex":0.0005597437,"about_ca_system_score_gemma":0.0007598473,"threshold_uncertainty_score":0.01057297},"labels":[],"label_agreement":null},{"id":"W2026701678","doi":"10.1016/j.spl.2010.07.002","title":"Parameter estimation in a condition-based maintenance model","year":2010,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Unobservable; Mathematics; Maximum likelihood; Autoregressive model; Likelihood function; Estimation theory; Observable; Condition-based maintenance; State (computer science); Applied mathematics; Estimation; Hidden Markov model; Markov process; Statistics; Algorithm; Econometrics; Computer science; Artificial intelligence; Engineering; Reliability engineering","score_opus":0.006651861015694719,"score_gpt":0.2171189491128923,"score_spread":0.21046708809719758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026701678","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1040675,0.00025714847,0.8932205,0.0003962987,0.000024732119,0.00003475635,0.000199805,0.00040600845,0.0013932809],"genre_scores_gemma":[0.97965664,0.00014210971,0.018252557,0.00004284572,0.00002684955,0.000079723395,0.00018850913,0.000047615864,0.0015631987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938715,0.00021353849,0.000032134445,0.00019170222,0.00011617041,0.00005923306],"domain_scores_gemma":[0.9954436,0.0037263897,0.0003315244,0.00018632018,0.00025935928,0.000052749347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015767441,0.00067831425,0.0014349314,0.0005974515,0.00030574974,0.0011756927,0.0014073866,0.0020460326,0.0013862263],"category_scores_gemma":[0.009029717,0.0007766582,0.000556569,0.00072243804,0.0009362413,0.0018628556,0.0006931307,0.0010999689,0.00026445964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034232926,0.000010470975,0.00026301606,0.000012630816,0.000010847173,0.0000117153095,0.000011351642,0.99503833,0.0002445114,0.0016678245,0.000085508116,0.002609495],"study_design_scores_gemma":[0.0000028912837,0.000003937564,0.00009330236,8.479137e-7,0.0000031189595,0.000003029282,6.4929634e-7,0.99905246,0.000052698895,0.00076959847,0.00001525157,0.0000022208296],"about_ca_topic_score_codex":0.009571388,"about_ca_topic_score_gemma":0.0040450008,"teacher_disagreement_score":0.009571388,"about_ca_system_score_codex":0.0009025911,"about_ca_system_score_gemma":0.00081039115,"threshold_uncertainty_score":0.019031346},"labels":[],"label_agreement":null},{"id":"W2027361318","doi":"10.1115/1.4023280","title":"Probability-Based Prediction of Degrading Dynamic Systems","year":2013,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Probabilistic logic; Computer science; Monte Carlo method; Probability distribution; Reliability (semiconductor); Mathematical optimization; Sequence (biology); Reliability engineering; Mathematics; Engineering; Artificial intelligence","score_opus":0.02391900978368653,"score_gpt":0.19662621944276587,"score_spread":0.17270720965907935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027361318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030284882,0.00009222866,0.9684656,0.00006201572,0.000014910609,0.000016448585,0.00006782885,0.0002861011,0.00070988596],"genre_scores_gemma":[0.93005824,0.00019888334,0.06873084,0.000022703067,0.000025322171,0.00006181228,0.0001385859,0.000040219948,0.0007234494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995334,0.00010104978,0.000027119635,0.00010300047,0.00018985108,0.000045547444],"domain_scores_gemma":[0.99698097,0.0021252823,0.00034737238,0.00015953266,0.00032301468,0.00006380116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013160532,0.00066019286,0.0005948934,0.0009704211,0.00026936407,0.00067474716,0.0006581108,0.0005495593,0.00088371796],"category_scores_gemma":[0.0067173583,0.0003357865,0.00039543738,0.0005216733,0.0005489209,0.0009317385,0.00053209864,0.000942947,0.00014026355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000098228165,0.000005802475,0.00044282826,0.000009616947,0.000003910717,0.0000101523,0.000006849229,0.9917338,0.0003840216,0.0022471491,0.000055918816,0.005090124],"study_design_scores_gemma":[7.296296e-7,0.000005613576,0.00013503776,0.0000012668673,0.0000012317391,0.00000455229,0.0000013090618,0.9982439,0.00022246623,0.0013196266,0.00006172223,0.0000025754878],"about_ca_topic_score_codex":0.004130915,"about_ca_topic_score_gemma":0.0022153386,"teacher_disagreement_score":0.004130915,"about_ca_system_score_codex":0.00074865733,"about_ca_system_score_gemma":0.0007007569,"threshold_uncertainty_score":0.008213699},"labels":[],"label_agreement":null},{"id":"W2029536321","doi":"10.1080/00949655.2014.898765","title":"Residual life estimation based on nonlinear-multivariate Wiener processes","year":2014,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National University of Defense Technology; National Natural Science Foundation of China; McMaster University","keywords":"Residual; Degradation (telecommunications); Multivariate statistics; Reliability (semiconductor); Nonlinear system; Population; Wiener process; Product (mathematics); Mathematics; Process (computing); Computer science; Statistics; Mathematical optimization; Applied mathematics; Algorithm","score_opus":0.011211350991326,"score_gpt":0.2706195401285077,"score_spread":0.2594081891371817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029536321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0134389335,0.00024552675,0.9858387,0.000026615393,0.000005934247,0.000011697039,0.000018253682,0.00009112004,0.00032316818],"genre_scores_gemma":[0.7517885,0.0010882233,0.24376675,0.000047491358,0.000055155127,0.00013648396,0.00027577303,0.000074225914,0.0027673845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950933,0.00015436327,0.000036997044,0.00011656173,0.00014568034,0.000037012574],"domain_scores_gemma":[0.99879575,0.0007689093,0.00018245805,0.00005548446,0.00017519982,0.000022234683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014165653,0.000721537,0.00073661486,0.00080902316,0.0001842469,0.0006175204,0.00073136744,0.0006214526,0.0006973419],"category_scores_gemma":[0.0038007153,0.0003509483,0.000801632,0.0004834738,0.0004961771,0.0012383561,0.00082763034,0.00080683787,0.00023343881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067807734,0.00003151098,0.0014813518,0.00012644714,0.00006197437,0.00007054972,0.00009999033,0.92726946,0.0055676657,0.006925468,0.00021153867,0.05808625],"study_design_scores_gemma":[0.0000018422319,0.000011871525,0.00024650118,0.0000038841513,0.0000055406094,0.000013729392,0.000004193338,0.99743783,0.0007720954,0.0013631426,0.00013260863,0.0000067326364],"about_ca_topic_score_codex":0.0023903628,"about_ca_topic_score_gemma":0.0017070961,"teacher_disagreement_score":0.0023903628,"about_ca_system_score_codex":0.00038062915,"about_ca_system_score_gemma":0.00048826536,"threshold_uncertainty_score":0.0074915886},"labels":[],"label_agreement":null},{"id":"W2029706857","doi":"10.1017/s0269964802163042","title":"PRODUCTION-INVENTORY MODELS WITH AN UNRELIABLE FACILITY OPERATING IN A TWO-STATE RANDOM ENVIRONMENT","year":2002,"lang":"en","type":"article","venue":"Probability in the Engineering and Informational Sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Production (economics); Poisson process; Process (computing); Poisson distribution; Computer science; Compound Poisson process; State (computer science); Buffer (optical fiber); Continuous production; Mathematical optimization; Mathematics; Statistics; Environmental science; Algorithm; Economics; Microeconomics","score_opus":0.01637524462792858,"score_gpt":0.18932367316205156,"score_spread":0.17294842853412298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029706857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51907754,0.0014861886,0.45493826,0.002883571,0.00024196098,0.00028211175,0.0026712834,0.0009498712,0.017469207],"genre_scores_gemma":[0.9740063,0.00046991796,0.011568793,0.0000858919,0.000083814484,0.00020641075,0.0004328848,0.000058271155,0.013087812],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810755,0.00070610794,0.000098407916,0.00033706368,0.0002245324,0.0005263287],"domain_scores_gemma":[0.9925851,0.004129579,0.0015091276,0.00038680667,0.00064509694,0.0007441885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003206407,0.0023434479,0.0026355453,0.0014553679,0.0010296111,0.0036271769,0.0047586244,0.0037506393,0.006744664],"category_scores_gemma":[0.006085073,0.0015946347,0.0014730673,0.0020011447,0.0028413518,0.0028932749,0.0021221733,0.00198066,0.0010570841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000256549,0.00010397595,0.0010409855,0.000046071993,0.00004224056,0.00033853587,0.00007872121,0.9648982,0.00045700057,0.03136436,0.00037396073,0.0009994222],"study_design_scores_gemma":[0.00006473107,0.000055924418,0.0002699053,0.0000053764115,0.0000271643,0.000024638126,0.00002684094,0.99358267,0.00008099421,0.0056724227,0.00016614384,0.00002315993],"about_ca_topic_score_codex":0.017094506,"about_ca_topic_score_gemma":0.00799351,"teacher_disagreement_score":0.017094506,"about_ca_system_score_codex":0.0025048947,"about_ca_system_score_gemma":0.0013817614,"threshold_uncertainty_score":0.033989966},"labels":[],"label_agreement":null},{"id":"W2029861596","doi":"10.3166/jds.12.67-77","title":"Optimal Periodic Replacement Strategy Using New and Used Items","year":2003,"lang":"en","type":"article","venue":"Journal of Decision System","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Residual; Block (permutation group theory); Mathematical optimization; Steady state (chemistry); Mathematics; Algorithm; Combinatorics","score_opus":0.02179143330874971,"score_gpt":0.24927530477744328,"score_spread":0.22748387146869356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029861596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31399673,0.0014801477,0.6731931,0.00024144562,0.0000797611,0.00016652759,0.000103602266,0.00038121812,0.010357461],"genre_scores_gemma":[0.91666013,0.0004080779,0.078218624,0.000027680957,0.000022606417,0.000096185875,0.00007539951,0.0000241644,0.0044670966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967134,0.00009685191,0.00001672354,0.000051120376,0.00010650622,0.0000575161],"domain_scores_gemma":[0.9995253,0.00013684199,0.0001027135,0.000063734435,0.00011677411,0.000054677603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051532267,0.0004076458,0.00070387416,0.00040926223,0.00023446398,0.0004912129,0.00088953227,0.0005426337,0.001637132],"category_scores_gemma":[0.0012613294,0.00023768198,0.0002603137,0.00028596554,0.00026774334,0.0004901715,0.00032275572,0.00020798857,0.00041220334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057868776,0.00022770144,0.0021676612,0.00028129388,0.00008101333,0.0003238386,0.00017303854,0.7267068,0.032422647,0.020102417,0.002573565,0.21436137],"study_design_scores_gemma":[0.00008535777,0.00054985465,0.0010288893,0.000027065025,0.000061202794,0.00021560339,0.000043993554,0.98113257,0.005938753,0.008071383,0.0028234296,0.00002190288],"about_ca_topic_score_codex":0.0016998539,"about_ca_topic_score_gemma":0.0018565147,"teacher_disagreement_score":0.0016998539,"about_ca_system_score_codex":0.00045468047,"about_ca_system_score_gemma":0.00095125183,"threshold_uncertainty_score":0.005476773},"labels":[],"label_agreement":null},{"id":"W2030132172","doi":"10.1016/j.amc.2011.10.076","title":"Reliability and non-reliability studies of Poisson variables in series and parallel systems","year":2011,"lang":"en","type":"article","venue":"Applied Mathematics and Computation","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Poisson distribution; Random variable; Mathematics; Monotonic function; Reliability (semiconductor); Series (stratigraphy); Applied mathematics; Hazard; Champion; Stochastic ordering; Failure rate; Variables; Statistics; Mathematical analysis","score_opus":0.019345224360413037,"score_gpt":0.22331343586824748,"score_spread":0.20396821150783445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030132172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39847815,0.01565436,0.5601074,0.001958913,0.00044642662,0.000056856104,0.00008808699,0.000114475544,0.023095297],"genre_scores_gemma":[0.9827313,0.0032469553,0.008977836,0.00008441412,0.00045171025,0.000027903963,0.000041395815,0.00006232729,0.004376191],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989794,0.0004807793,0.000051814448,0.000095562464,0.00028950698,0.000102962134],"domain_scores_gemma":[0.98700714,0.009506244,0.0011531255,0.00049451785,0.0016201816,0.00021889791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039999895,0.00066117675,0.0010385242,0.0019643267,0.00051402656,0.0009793633,0.0013143577,0.0005830937,0.0018214831],"category_scores_gemma":[0.01843901,0.0004378853,0.00087107846,0.0016246808,0.001709513,0.00199782,0.0009018871,0.0011829817,0.00013972798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001295991,0.00010658193,0.003456223,0.00033518867,0.000112486036,0.00032465628,0.0004207559,0.5403835,0.002784801,0.43075004,0.0014156203,0.019780545],"study_design_scores_gemma":[0.0000075919543,0.00005016543,0.0015189168,0.000022573904,0.000040533294,0.00016962364,0.00008957508,0.9051126,0.0007203826,0.09130289,0.000948984,0.000016161284],"about_ca_topic_score_codex":0.0016929841,"about_ca_topic_score_gemma":0.0011416178,"teacher_disagreement_score":0.0039999895,"about_ca_system_score_codex":0.00078742125,"about_ca_system_score_gemma":0.00062512263,"threshold_uncertainty_score":0.021154225},"labels":[],"label_agreement":null},{"id":"W2031070862","doi":"10.1016/j.orl.2008.05.001","title":"The influence of the node criticality relation on some measures of component importance","year":2008,"lang":"en","type":"article","venue":"Operations Research Letters","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"European Regional Development Fund; Royal Canadian Geographical Society","keywords":"Criticality; Component (thermodynamics); Ranking (information retrieval); Reliability (semiconductor); Relation (database); Computer science; Reliability engineering; Node (physics); Mathematics; Statistics; Data mining; Artificial intelligence; Engineering; Physics","score_opus":0.04816845958036342,"score_gpt":0.29232573389454153,"score_spread":0.2441572743141781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031070862","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88986933,0.0015619054,0.09805971,0.00049568136,0.00007395237,0.000023709836,0.00016049006,0.00022438486,0.009530774],"genre_scores_gemma":[0.99802715,0.00012732728,0.00157278,0.000011668244,0.000022177477,0.0000042471456,0.000018149369,0.000028883038,0.00018762541],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985862,0.00072659866,0.000045885543,0.00025289934,0.00023983314,0.00014860339],"domain_scores_gemma":[0.7919413,0.1990034,0.0030931102,0.0030100234,0.001966772,0.0009854161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041897595,0.00052777265,0.0006532544,0.001598948,0.00042973435,0.0011427385,0.0006908696,0.00058999134,0.0016015162],"category_scores_gemma":[0.07107467,0.00030264255,0.00036823738,0.0008840422,0.0012671086,0.0014704403,0.00044320174,0.0008528291,0.000114989496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013331845,0.0001768523,0.032457266,0.00039866386,0.00031315174,0.000909182,0.000525574,0.81277895,0.041904297,0.059325915,0.0014141742,0.04846265],"study_design_scores_gemma":[0.000051850486,0.00048710487,0.044731446,0.00006374909,0.00032237943,0.0007932534,0.00020179474,0.8986533,0.015844285,0.037920855,0.0008226345,0.000107364496],"about_ca_topic_score_codex":0.0011784048,"about_ca_topic_score_gemma":0.0010246275,"teacher_disagreement_score":0.0041897595,"about_ca_system_score_codex":0.0006659168,"about_ca_system_score_gemma":0.0004471462,"threshold_uncertainty_score":0.022157848},"labels":[],"label_agreement":null},{"id":"W2031495046","doi":"10.1007/s10845-009-0364-9","title":"Optimum block replacement policy over a random time horizon","year":2009,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Horizon; Interval (graph theory); Block (permutation group theory); Time horizon; Mathematical optimization; Work (physics); Production (economics); Computer science; Mathematics; Engineering; Economics; Microeconomics","score_opus":0.005360223239951197,"score_gpt":0.2210406414797786,"score_spread":0.2156804182398274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031495046","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50725126,0.002386962,0.47847164,0.0010847612,0.00017782298,0.00019605509,0.00077598105,0.000698717,0.00895673],"genre_scores_gemma":[0.9687275,0.00038425365,0.02578474,0.00007158618,0.000041100844,0.00006530552,0.00017164447,0.000049990183,0.0047037806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992429,0.00026750736,0.000032349522,0.00012104312,0.00013742248,0.000198758],"domain_scores_gemma":[0.9960188,0.002601972,0.0004883837,0.00022896971,0.0003854231,0.00027645795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017684468,0.0006121866,0.001617764,0.0005678304,0.00030354006,0.00072088925,0.0010539267,0.0014972186,0.0033565543],"category_scores_gemma":[0.004994473,0.00060481025,0.00030432187,0.0006640507,0.00053274014,0.0010773158,0.00039652188,0.00070578547,0.0005321641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007432661,0.00006963978,0.0004470152,0.00008678356,0.000031249918,0.00006235954,0.00002707841,0.9716983,0.0036805342,0.006085274,0.0010769954,0.015991544],"study_design_scores_gemma":[0.000063922475,0.00017539991,0.00054241123,0.000011078935,0.000032522643,0.00003570238,0.000009100794,0.99471766,0.0009098365,0.0029758313,0.00051749265,0.000009057899],"about_ca_topic_score_codex":0.0026006957,"about_ca_topic_score_gemma":0.0020677655,"teacher_disagreement_score":0.0033565543,"about_ca_system_score_codex":0.00095111324,"about_ca_system_score_gemma":0.0015952514,"threshold_uncertainty_score":0.01122874},"labels":[],"label_agreement":null},{"id":"W2031614523","doi":"10.1108/13552511311315968","title":"Risk based integrity modeling of offshore process components suffering stochastic degradation","year":2013,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Reliability engineering; Component (thermodynamics); Process (computing); Degradation (telecommunications); Gamma process; Computer science; Submarine pipeline; Bayesian probability; Stochastic process; Engineering; Statistics; Mathematics","score_opus":0.022439706228943312,"score_gpt":0.254651612657843,"score_spread":0.23221190642889966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031614523","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11961094,0.0005510975,0.8699455,0.00039980598,0.000033991902,0.00008201816,0.00022169204,0.00020638367,0.008948596],"genre_scores_gemma":[0.9775808,0.00037374542,0.01603389,0.000042833577,0.00001811159,0.00010557916,0.00015403192,0.000055261386,0.0056358124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990088,0.00026135103,0.00004742533,0.00018681138,0.00036026494,0.00013535444],"domain_scores_gemma":[0.9983808,0.000687642,0.0004480422,0.00010100413,0.00032885582,0.000053627846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016050227,0.0009532406,0.0009718261,0.00086262345,0.00041327413,0.0014275074,0.0015484587,0.0014343037,0.0015545586],"category_scores_gemma":[0.0038521013,0.0006352218,0.0010411865,0.0005238551,0.0011700412,0.0014390567,0.0010468783,0.0012066276,0.0002837743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009925554,0.000007236622,0.00042959946,0.000010481494,0.00000711244,0.000036897887,0.000019617877,0.99447376,0.0005004663,0.0029093476,0.00006791481,0.0015277385],"study_design_scores_gemma":[0.000001586562,0.000009497373,0.000246993,0.000002705355,0.000005819664,0.00001488232,0.0000065919207,0.99838567,0.00016000993,0.0010684134,0.00009389613,0.0000039344404],"about_ca_topic_score_codex":0.011451182,"about_ca_topic_score_gemma":0.0042125364,"teacher_disagreement_score":0.011451182,"about_ca_system_score_codex":0.0017368566,"about_ca_system_score_gemma":0.0014568722,"threshold_uncertainty_score":0.022769034},"labels":[],"label_agreement":null},{"id":"W2032050708","doi":"10.1108/13552510510601311","title":"Joint optimal periodic and conditional maintenance strategy","year":2005,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université de Montréal; Université Laval","funders":"","keywords":"Mathematical optimization; Erlang (programming language); Block (permutation group theory); Time horizon; Steady state (chemistry); Expected value; Computer science; Reliability engineering; Engineering; Mathematics; Statistics","score_opus":0.015813293019501694,"score_gpt":0.24432584496495027,"score_spread":0.2285125519454486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032050708","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16728055,0.000816722,0.8101624,0.00033786037,0.0000900209,0.00015361265,0.00013335297,0.0005724749,0.020452991],"genre_scores_gemma":[0.9629291,0.00016542074,0.03386627,0.000030461777,0.000024223953,0.00004690073,0.000059045957,0.000018425553,0.0028600967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999608,0.000084540654,0.000022256709,0.00007549012,0.0001379189,0.000071906616],"domain_scores_gemma":[0.9995067,0.00015590696,0.000097305674,0.00007188816,0.00012623637,0.00004193719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005450571,0.0004057275,0.00052960165,0.00041925506,0.00023743733,0.0005472192,0.0007748688,0.00032021216,0.0030671428],"category_scores_gemma":[0.0015998549,0.00018338056,0.00025682102,0.00031706746,0.0003101397,0.0005517579,0.00038267643,0.00025074676,0.00027038326],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004100622,0.0001684439,0.002488755,0.0002543388,0.00007429192,0.00031769622,0.000105197505,0.7315821,0.00933195,0.058083218,0.0038953996,0.19328858],"study_design_scores_gemma":[0.000028629423,0.00014919466,0.000848363,0.000013873084,0.000031307067,0.00015126864,0.000018313991,0.9876216,0.0014473543,0.008542237,0.0011370922,0.000010680419],"about_ca_topic_score_codex":0.0021454785,"about_ca_topic_score_gemma":0.0016997489,"teacher_disagreement_score":0.0030671428,"about_ca_system_score_codex":0.00046842295,"about_ca_system_score_gemma":0.0010474743,"threshold_uncertainty_score":0.010260582},"labels":[],"label_agreement":null},{"id":"W2035398748","doi":"10.1155/2007/43565","title":"Reliability of Modules with Load-Sharing Components","year":2007,"lang":"en","type":"article","venue":"Journal of Applied Mathematics and Decision Sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Component (thermodynamics); Reliability (semiconductor); Load sharing; Computer science; Reliability engineering; Power (physics); Residual; Electronic component; Distributed computing; Electrical engineering; Engineering","score_opus":0.020063214432126403,"score_gpt":0.25613038554541334,"score_spread":0.23606717111328693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035398748","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.817657,0.00029974748,0.1772718,0.00011438109,0.000019159144,0.000032543627,0.00012747264,0.00013877978,0.004339163],"genre_scores_gemma":[0.994926,0.00008818926,0.0032591016,0.0000093305525,0.00001552005,0.000022312499,0.000066413006,0.000018994686,0.0015940992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995937,0.00010472313,0.00001950352,0.00007093318,0.00010942961,0.00010177137],"domain_scores_gemma":[0.99874276,0.00042924567,0.00032639224,0.00016067082,0.00022873374,0.00011233127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093837664,0.0004874218,0.00045992676,0.00059166324,0.00028869478,0.00064119394,0.00081470236,0.00060678663,0.0018917683],"category_scores_gemma":[0.0040478315,0.00022713646,0.00041119312,0.00049746665,0.00086466916,0.00094240095,0.0007196214,0.0003300155,0.00034198962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030838719,0.000082845574,0.009764193,0.00011054881,0.00009908501,0.0006994961,0.00034152152,0.87285936,0.020402899,0.06916369,0.0010743106,0.025093699],"study_design_scores_gemma":[0.000018925994,0.00014676132,0.0043752557,0.000010697114,0.000037103993,0.00024322965,0.00006239687,0.95763636,0.0029956927,0.03362132,0.0008348307,0.000017369968],"about_ca_topic_score_codex":0.0019204636,"about_ca_topic_score_gemma":0.0006843989,"teacher_disagreement_score":0.0019204636,"about_ca_system_score_codex":0.000529992,"about_ca_system_score_gemma":0.00028377215,"threshold_uncertainty_score":0.0063285828},"labels":[],"label_agreement":null},{"id":"W2035508719","doi":"10.1002/nav.20023","title":"A unified model incorporating yield, burn‐in, and reliability","year":2004,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Science Foundation","keywords":"Reliability (semiconductor); Yield (engineering); Reliability engineering; Burn-in; Function (biology); Poisson distribution; Computer science; Mathematics; Applied mathematics; Statistics; Engineering; Physics; Thermodynamics","score_opus":0.07724833844691953,"score_gpt":0.3232252662938739,"score_spread":0.24597692784695435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035508719","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076758,0.0009636859,0.89481413,0.0010200831,0.00014169203,0.00021471552,0.0009725125,0.00070559786,0.024409575],"genre_scores_gemma":[0.90280765,0.001382351,0.046270814,0.00016609415,0.00011209463,0.0006433877,0.00058461016,0.00018616855,0.047846716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914455,0.00024307809,0.000040095016,0.00020935785,0.00022041811,0.00014236421],"domain_scores_gemma":[0.99870825,0.00060263154,0.00020776705,0.00010073323,0.0002901749,0.00009049841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019247632,0.0014765721,0.0018750947,0.0012281442,0.0005539812,0.0022561818,0.003826257,0.0031678139,0.0049219313],"category_scores_gemma":[0.0042388933,0.0010346358,0.0013288232,0.0011082514,0.0013214842,0.0030551096,0.001172775,0.0014321608,0.00095854426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023789395,0.000022451202,0.00019215613,0.000027877946,0.00001409475,0.0000802084,0.00003248965,0.97974604,0.0006716692,0.016670268,0.00032071976,0.002198252],"study_design_scores_gemma":[0.000008700694,0.00002447962,0.000085171196,0.000004833972,0.000015507587,0.000018756544,0.000006354779,0.99561846,0.00014029907,0.003668046,0.0004013283,0.000008104666],"about_ca_topic_score_codex":0.008599743,"about_ca_topic_score_gemma":0.004940446,"teacher_disagreement_score":0.008599743,"about_ca_system_score_codex":0.0019135881,"about_ca_system_score_gemma":0.0016950639,"threshold_uncertainty_score":0.01709938},"labels":[],"label_agreement":null},{"id":"W2035818340","doi":"10.1239/jap/1025131440","title":"On the behaviour of some new ageing properties based upon the residual life of <i>k</i>-out-of-<i>n</i> systems","year":2002,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Lanzhou University","keywords":"Mathematics; Residual; Monotonic function; Complement (music); Independent and identically distributed random variables; Pure mathematics; Combinatorics; Discrete mathematics; Statistics; Random variable; Mathematical analysis; Algorithm; Chemistry","score_opus":0.0297263792729597,"score_gpt":0.19250349013627618,"score_spread":0.16277711086331648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035818340","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88552326,0.0011697658,0.10979349,0.00021917658,0.000026873586,0.000042100404,0.000118861426,0.00012673288,0.002979703],"genre_scores_gemma":[0.9942363,0.00033408092,0.004842021,0.000026345564,0.0000422566,0.000016881095,0.000091672846,0.00003905347,0.0003713257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99962854,0.00010971035,0.000025279915,0.00007483658,0.00009738958,0.00006422789],"domain_scores_gemma":[0.98758686,0.0073064766,0.003159272,0.0005539685,0.0010088084,0.0003846325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024045499,0.0005179363,0.00054781104,0.0011588477,0.00036440798,0.0006988718,0.0006135844,0.00056328217,0.000851508],"category_scores_gemma":[0.011828388,0.0001793078,0.0005664663,0.0004119973,0.001424511,0.0017338392,0.00047259242,0.0006350935,0.00010445898],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076513673,0.000283304,0.052664857,0.00091652456,0.00023693992,0.0016218023,0.0019566456,0.5485263,0.11173395,0.22383343,0.0014772268,0.055983994],"study_design_scores_gemma":[0.000017082142,0.00028620483,0.017094258,0.000049987866,0.000046984114,0.0007537617,0.00017246364,0.94231415,0.008760887,0.029694049,0.0007522536,0.000057977588],"about_ca_topic_score_codex":0.00060444314,"about_ca_topic_score_gemma":0.0004044373,"teacher_disagreement_score":0.0024045499,"about_ca_system_score_codex":0.0004529313,"about_ca_system_score_gemma":0.00022124905,"threshold_uncertainty_score":0.012716651},"labels":[],"label_agreement":null},{"id":"W2036815968","doi":"10.1016/j.jhazmat.2004.01.011","title":"Risk-based maintenance of ethylene oxide production facilities","year":2004,"lang":"en","type":"review","venue":"Journal of Hazardous Materials","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":130,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Fault tree analysis; Reliability engineering; Reliability (semiconductor); Engineering; Risk assessment; Interval (graph theory); Risk analysis (engineering); Schedule; Preventive maintenance; Computer science; Mathematics","score_opus":0.015527947834879072,"score_gpt":0.24546311308246835,"score_spread":0.22993516524758928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036815968","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014220623,0.9946526,0.0027335484,0.00014618952,0.00007702081,0.000007126947,0.000015948335,0.000015635407,0.0009298506],"genre_scores_gemma":[0.016177494,0.97972804,0.0027728819,0.00008510416,0.00015987175,0.000012704152,0.00007299205,0.0000043714945,0.000986538],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996687,0.00006109417,0.000040365998,0.000046903813,0.00016485961,0.000018118748],"domain_scores_gemma":[0.9990808,0.00048135023,0.00021355144,0.000026556647,0.00018286798,0.0000149586285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064788,0.00088655594,0.0015403624,0.0013436498,0.00014887298,0.0006480977,0.0018332842,0.0009607619,0.0012111651],"category_scores_gemma":[0.0013428708,0.00033490316,0.00044033,0.0016344415,0.00019981599,0.0008509111,0.00031388056,0.00046529248,0.0003612431],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000782292,0.0000771322,0.000399333,0.007617738,0.00014384766,0.00011001585,0.000021060525,0.0072823954,0.0016347267,0.0022238612,0.0043344502,0.97607726],"study_design_scores_gemma":[0.00014891382,0.0017188772,0.011810178,0.010811934,0.0013558706,0.0048554116,0.00023458866,0.022129659,0.012907775,0.013546209,0.92033666,0.00014411588],"about_ca_topic_score_codex":0.00133223,"about_ca_topic_score_gemma":0.002522606,"teacher_disagreement_score":0.0018332842,"about_ca_system_score_codex":0.0004172599,"about_ca_system_score_gemma":0.0005715556,"threshold_uncertainty_score":0.004051745},"labels":[],"label_agreement":null},{"id":"W2038983816","doi":"10.1080/00207543.2015.1030468","title":"Joint production, setup and preventive maintenance policies of unreliable two-product manufacturing systems","year":2015,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université du Québec à Montréal; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preventive maintenance; Robustness (evolution); Production (economics); Reliability engineering; Computer science; Production control; Product (mathematics); Production planning; Control (management); Industrial engineering; Engineering; Operations research; Risk analysis (engineering); Mathematical optimization; Manufacturing engineering; Mathematics; Artificial intelligence","score_opus":0.07025828378083719,"score_gpt":0.34127322008021244,"score_spread":0.27101493629937523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038983816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.319521,0.0006435644,0.6768084,0.00020709561,0.00003274998,0.00015973773,0.00008510087,0.00022092188,0.0023213967],"genre_scores_gemma":[0.98812985,0.000076451535,0.0114418315,0.000007770633,0.000005274758,0.00004267291,0.000019899328,0.000009076736,0.0002671622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987268,0.00048981985,0.000066544635,0.0001931374,0.00027422412,0.00024955586],"domain_scores_gemma":[0.9963003,0.0022347786,0.0009136323,0.00018579853,0.00020716217,0.00015831682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022487778,0.0010075921,0.0011488447,0.0005508996,0.00041942592,0.0012251554,0.001221883,0.0010443944,0.0008812018],"category_scores_gemma":[0.0062676743,0.00053643796,0.00058411993,0.0004838246,0.00085172994,0.00086703786,0.0011617817,0.0008883617,0.000086758795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015541131,0.000041378626,0.00048285045,0.00006694825,0.000019192978,0.00008157681,0.000036331465,0.9902873,0.0023203008,0.0013082147,0.000054338652,0.005146182],"study_design_scores_gemma":[0.000016384396,0.00012071694,0.0004887714,0.0000075876555,0.000015110217,0.000023594703,0.000020138936,0.99644655,0.0017298189,0.0010236021,0.000100782876,0.0000070214846],"about_ca_topic_score_codex":0.0026225022,"about_ca_topic_score_gemma":0.0012842219,"teacher_disagreement_score":0.0026225022,"about_ca_system_score_codex":0.00069924654,"about_ca_system_score_gemma":0.0009466832,"threshold_uncertainty_score":0.011892796},"labels":[],"label_agreement":null},{"id":"W2039700046","doi":"10.1002/net.21530","title":"Nonexistence of optimal graphs for all terminal reliability","year":2013,"lang":"en","type":"article","venue":"Networks","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Combinatorics; Conjecture; Mathematics; Graph; Undirected graph; Discrete mathematics; Multigraph","score_opus":0.007172499954143852,"score_gpt":0.2076871176198089,"score_spread":0.20051461766566506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039700046","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67647827,0.00058659207,0.3016655,0.0021057145,0.00008250348,0.00011938061,0.0007484032,0.00046580762,0.017747864],"genre_scores_gemma":[0.9551897,0.00018297056,0.042836346,0.00024277462,0.00005377478,0.000090557645,0.00023907663,0.00007292482,0.001091841],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99823606,0.0005534731,0.00008522443,0.0005025339,0.00037768247,0.00024505422],"domain_scores_gemma":[0.9815677,0.011612587,0.0023763406,0.0018295229,0.0014111695,0.0012026067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021549412,0.00065195665,0.0011359444,0.0012897281,0.0010032706,0.001282639,0.0010900788,0.001076029,0.0022770066],"category_scores_gemma":[0.017604778,0.00090883556,0.0007591068,0.0006437273,0.002058435,0.002349215,0.0014281336,0.00161951,0.00027304504],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007468721,0.00026764165,0.009105949,0.0007623812,0.00025036212,0.00091981294,0.0006932473,0.21915242,0.021869168,0.69651216,0.011062557,0.038657464],"study_design_scores_gemma":[0.0000915441,0.00017409267,0.003139263,0.00007576963,0.00009995136,0.0009303853,0.0002337832,0.19354284,0.0075654327,0.7903074,0.00378912,0.00005040405],"about_ca_topic_score_codex":0.00040969715,"about_ca_topic_score_gemma":0.0005461552,"teacher_disagreement_score":0.0022770066,"about_ca_system_score_codex":0.0010474275,"about_ca_system_score_gemma":0.0008810469,"threshold_uncertainty_score":0.011396587},"labels":[],"label_agreement":null},{"id":"W2040350164","doi":"10.1109/rams.2013.6517618","title":"Selective preventive maintenance scheduling under imperfect repair","year":2013,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preventive maintenance; Time horizon; Optimal maintenance; Schedule; Reliability engineering; Scheduling (production processes); Maintenance engineering; Computer science; Planned maintenance; Reliability (semiconductor); Interval (graph theory); Imperfect; Maintenance actions; Cost reduction; Predictive maintenance; Operations research; Mathematical optimization; Engineering; Mathematics","score_opus":0.004345606374028291,"score_gpt":0.1938696546397489,"score_spread":0.1895240482657206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040350164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37515432,0.0005693184,0.61878526,0.00014707986,0.000029456398,0.000059282826,0.00017131722,0.00026929166,0.004814728],"genre_scores_gemma":[0.9880117,0.000113205446,0.011000024,0.000008603789,0.000007161597,0.000019926685,0.000043890686,0.0000072975554,0.0007881417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997435,0.0000513388,0.000011777209,0.00005395618,0.00007449909,0.000064870415],"domain_scores_gemma":[0.9993629,0.00026538107,0.00018366655,0.00006868408,0.00006443651,0.00005496435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046176076,0.00043752437,0.0006790157,0.00038889865,0.00024135657,0.00044470603,0.001107808,0.00039986556,0.000858386],"category_scores_gemma":[0.0010947046,0.00032765363,0.00037557053,0.000426444,0.00045212792,0.00053175265,0.00041090377,0.00029520862,0.00009614008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000871957,0.000029186715,0.0005494824,0.000053756412,0.000019492616,0.00013462109,0.000033585795,0.9784064,0.0033635492,0.00493541,0.00020412945,0.012183217],"study_design_scores_gemma":[0.000011043625,0.00006843264,0.00042783847,0.0000027742933,0.000013645123,0.000043614782,0.000010225106,0.9968636,0.00066125963,0.0016866439,0.00020793286,0.000003102649],"about_ca_topic_score_codex":0.0050031673,"about_ca_topic_score_gemma":0.004373186,"teacher_disagreement_score":0.0050031673,"about_ca_system_score_codex":0.00062893296,"about_ca_system_score_gemma":0.00081716035,"threshold_uncertainty_score":0.009948075},"labels":[],"label_agreement":null},{"id":"W2040405051","doi":"10.1016/j.ress.2010.12.023","title":"Condition based maintenance optimization for multi-component systems using proportional hazards model","year":2011,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":267,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Condition-based maintenance; Component (thermodynamics); Preventive maintenance; Reliability engineering; Condition monitoring; Dependency (UML); Engineering; Work (physics); Maintenance actions; Unit (ring theory); Computer science; Operations research; Mathematical optimization; Systems engineering; Mathematics","score_opus":0.024953193101157803,"score_gpt":0.2221492380271124,"score_spread":0.1971960449259546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040405051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06396613,0.0003514606,0.93266726,0.00015290786,0.000036272453,0.00006133186,0.000087871515,0.0002841146,0.002392704],"genre_scores_gemma":[0.95827085,0.00017961407,0.03840057,0.000025925361,0.000024186353,0.0001209592,0.00011381699,0.000057459594,0.0028066442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996039,0.00013944496,0.000014133252,0.000072169176,0.00011953665,0.00005083991],"domain_scores_gemma":[0.9991211,0.00064989214,0.00006585328,0.000038852842,0.00009756571,0.000026691378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001115942,0.00086452166,0.0014698284,0.00060560135,0.0003630235,0.00070235913,0.0012304567,0.00086145906,0.002515575],"category_scores_gemma":[0.0021757467,0.0006067149,0.0008781051,0.0005575699,0.00049751403,0.001145283,0.00070884445,0.00077428453,0.000145014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035519126,0.000016946979,0.00009923439,0.000023722045,0.000016195614,0.000012905868,0.000009772577,0.99423015,0.00042350768,0.0012081893,0.00010919834,0.0038145948],"study_design_scores_gemma":[0.0000046363725,0.000009324984,0.000047957732,5.402677e-7,0.00000367742,0.0000019290926,0.0000010102658,0.9992287,0.000062345054,0.0006172491,0.00002136675,0.0000012508463],"about_ca_topic_score_codex":0.006757489,"about_ca_topic_score_gemma":0.0032476948,"teacher_disagreement_score":0.006757489,"about_ca_system_score_codex":0.00069981854,"about_ca_system_score_gemma":0.00091461145,"threshold_uncertainty_score":0.013436317},"labels":[],"label_agreement":null},{"id":"W2040526657","doi":"10.1016/s0951-8320(02)00204-1","title":"A method for evaluation of reliability indices for repairable circular consecutive-k-out-of-n:F systems","year":2003,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Component (thermodynamics); Reliability (semiconductor); Reliability engineering; Key (lock); Exponential distribution; Mathematics; State (computer science); Statistics; Computer science; Applied mathematics; Engineering; Algorithm; Physics","score_opus":0.015121153096440897,"score_gpt":0.2571845445431579,"score_spread":0.242063391446717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040526657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028447006,0.00009699212,0.99545383,0.000009130332,0.000021547503,0.00006584054,0.000078104546,0.00056180067,0.0008681031],"genre_scores_gemma":[0.055852447,0.00013541871,0.941676,0.000012716248,0.000021707943,0.00029981325,0.00020119031,0.00016592242,0.0016349007],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987237,0.00031649595,0.00007874887,0.00018873188,0.00063709833,0.000055302226],"domain_scores_gemma":[0.99741155,0.0009105273,0.0002533969,0.00030082956,0.0010674096,0.000056394332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019252049,0.0012519332,0.0010950587,0.003184858,0.00072395045,0.0008575574,0.0012093327,0.0006732165,0.0030664094],"category_scores_gemma":[0.004267772,0.00040886764,0.00081647857,0.0018244253,0.00046562613,0.0011153447,0.0005761912,0.0010142805,0.0011209544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032247402,0.00020198587,0.0027800594,0.00050239934,0.00014169177,0.00008716707,0.00031099305,0.06191869,0.06552,0.026901826,0.0057225456,0.83559024],"study_design_scores_gemma":[0.000055572003,0.00036167292,0.00442611,0.00007175658,0.00009400273,0.0004867122,0.00007571766,0.93144816,0.039134532,0.011043927,0.012653825,0.00014802505],"about_ca_topic_score_codex":0.0023039235,"about_ca_topic_score_gemma":0.0028957026,"teacher_disagreement_score":0.003184858,"about_ca_system_score_codex":0.0006049558,"about_ca_system_score_gemma":0.0008675076,"threshold_uncertainty_score":0.010258198},"labels":[],"label_agreement":null},{"id":"W2041628555","doi":"10.1016/j.ijpe.2011.12.021","title":"Integrating noncyclical preventive maintenance scheduling and production planning for a single machine","year":2011,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":126,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preventive maintenance; Time horizon; Production (economics); Sizing; Production planning; Scheduling (production processes); Computer science; Operations research; Planned maintenance; Holding cost; Corrective maintenance; Reliability engineering; Total cost; Constraint (computer-aided design); Operations management; Mathematical optimization; Engineering; Economics; Mathematics; Microeconomics","score_opus":0.02160655366941448,"score_gpt":0.23157043459629947,"score_spread":0.209963880926885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041628555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21239953,0.0005891127,0.78080535,0.00033051378,0.00013325181,0.00013148654,0.00016219799,0.00045163793,0.004996924],"genre_scores_gemma":[0.96130204,0.0001218759,0.03723874,0.000018216599,0.000036661193,0.000040385396,0.00004357575,0.000033479362,0.0011649448],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993357,0.00019257664,0.000032783562,0.00015096454,0.00015978415,0.00012824454],"domain_scores_gemma":[0.9982697,0.0010229851,0.0002798883,0.00016179208,0.00016751734,0.00009808283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014220489,0.00076934794,0.0015554641,0.0005072617,0.0004820445,0.0011684907,0.0018203927,0.00093998737,0.0017158809],"category_scores_gemma":[0.0033378617,0.00090488204,0.00081992697,0.0010142989,0.0005140858,0.0012469129,0.0007089623,0.00089567446,0.00017110968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074126816,0.000057104433,0.00027727304,0.00003007399,0.000025117073,0.000033692773,0.0000125511315,0.9863781,0.00076148025,0.0014135554,0.00009891416,0.010837943],"study_design_scores_gemma":[0.0000061107717,0.0000400558,0.00021047328,0.0000015227008,0.000012356194,0.000006608183,0.0000029238408,0.9984719,0.00018589337,0.0010038659,0.000054788383,0.0000034411134],"about_ca_topic_score_codex":0.013516937,"about_ca_topic_score_gemma":0.015655564,"teacher_disagreement_score":0.013516937,"about_ca_system_score_codex":0.0012245687,"about_ca_system_score_gemma":0.002335068,"threshold_uncertainty_score":0.02687651},"labels":[],"label_agreement":null},{"id":"W2041759495","doi":"10.1108/13552510810861923","title":"Optimal inspection and preventive maintenance policy for systems with self‐announcing and non‐self‐announcing failures","year":2008,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Uniqueness; Preventive maintenance; Originality; Idle; Order (exchange); Computer science; Value (mathematics); Reliability engineering; Optimal maintenance; Operations research; Mathematical optimization; Risk analysis (engineering); Engineering; Mathematics; Economics; Business; Finance","score_opus":0.007945176398540704,"score_gpt":0.2233356510639563,"score_spread":0.2153904746654156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041759495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44312456,0.00041511486,0.5512047,0.00046317535,0.000029556151,0.0001292518,0.000098379955,0.00016019557,0.004375085],"genre_scores_gemma":[0.9898189,0.00006671287,0.008954777,0.000011709829,0.00000624227,0.000037211914,0.000015501791,0.0000066017274,0.001082328],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949837,0.00015856953,0.00002185991,0.00011555922,0.000080062935,0.00012556174],"domain_scores_gemma":[0.9987985,0.00055469136,0.000384726,0.000058071342,0.00012421157,0.000079879705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011664301,0.0005361021,0.0006101473,0.0005592537,0.00030854656,0.0006858795,0.00083525537,0.0007364942,0.0011008311],"category_scores_gemma":[0.0028381785,0.00035184188,0.00045531418,0.00024061487,0.0009093111,0.00064311596,0.00047298337,0.0005156401,0.0001353986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000104421386,0.0000856448,0.0016648445,0.000034927718,0.000021442178,0.00009565089,0.000059674578,0.9779854,0.0027904492,0.011446461,0.0001808143,0.005530343],"study_design_scores_gemma":[0.000015520576,0.00007233584,0.0005974358,0.000004643747,0.0000139037065,0.000022529774,0.000020955405,0.9956962,0.0004270744,0.0030271898,0.00009641114,0.0000058237247],"about_ca_topic_score_codex":0.004940823,"about_ca_topic_score_gemma":0.0030266708,"teacher_disagreement_score":0.004940823,"about_ca_system_score_codex":0.0014168452,"about_ca_system_score_gemma":0.0012333308,"threshold_uncertainty_score":0.010280013},"labels":[],"label_agreement":null},{"id":"W2042776149","doi":"10.1017/s0269964800143074","title":"SOME CONJECTURED UNIFORMLY OPTIMAL RELIABLE NETWORKS","year":2000,"lang":"en","type":"article","venue":"Probability in the Engineering and Informational Sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Vermont; University of Victoria","keywords":"Conjecture; Mathematics; Combinatorics; Reliability (semiconductor); Graph; Discrete mathematics; Physics","score_opus":0.006028857549030835,"score_gpt":0.1803667193359299,"score_spread":0.17433786178689906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042776149","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5789227,0.0021241237,0.3686791,0.0062181633,0.0001639143,0.000085248575,0.0009365613,0.000689269,0.042180948],"genre_scores_gemma":[0.9680806,0.0007689435,0.02782989,0.00044973852,0.000107265216,0.00010026528,0.0003183745,0.00006854238,0.0022764942],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99883264,0.00038832158,0.00006010552,0.00022123028,0.0002498416,0.0002479217],"domain_scores_gemma":[0.99102,0.005528888,0.0011255045,0.0009533967,0.0008848824,0.00048733348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002062775,0.00053850043,0.0007556947,0.0011630689,0.0014428983,0.0016469322,0.0019206008,0.0015502498,0.0030122274],"category_scores_gemma":[0.01945773,0.0006080333,0.0006734847,0.0008778207,0.0025329792,0.0036847075,0.0011353267,0.001476156,0.00023204632],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001894702,0.00005183607,0.0021052074,0.00016270294,0.000046859117,0.00024489328,0.00043777667,0.09895047,0.002015525,0.8778984,0.005613391,0.012283548],"study_design_scores_gemma":[0.000058504265,0.000066452834,0.0008668514,0.000055827073,0.000033622713,0.00025622037,0.00021968674,0.21360107,0.001708368,0.77917486,0.003928057,0.000030574294],"about_ca_topic_score_codex":0.0014572158,"about_ca_topic_score_gemma":0.0011719163,"teacher_disagreement_score":0.0030122274,"about_ca_system_score_codex":0.0018476099,"about_ca_system_score_gemma":0.0006107998,"threshold_uncertainty_score":0.013405383},"labels":[],"label_agreement":null},{"id":"W2042937152","doi":"10.1002/qre.1114","title":"Artificial neural network application of modeling failure rate for Boeing 737 tires","year":2010,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"King Fahd University of Petroleum and Minerals","keywords":"Weibull distribution; Artificial neural network; Failure rate; Reliability (semiconductor); Engineering; Computer science; Reliability engineering; Artificial intelligence; Statistics; Mathematics","score_opus":0.01135295607308184,"score_gpt":0.24936207230611585,"score_spread":0.238009116233034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042937152","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6124294,0.00066479103,0.38064417,0.00022861862,0.000070885624,0.00006189438,0.0002755481,0.0009289644,0.004695777],"genre_scores_gemma":[0.9808648,0.00015167696,0.01739028,0.000011761573,0.000010987692,0.000039249273,0.00012586625,0.0000137178085,0.0013915979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978036,0.0000768434,0.000014044185,0.00003386949,0.00007252436,0.00002226144],"domain_scores_gemma":[0.999297,0.00039740885,0.00006549962,0.000034981338,0.00019414855,0.000010847445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079268636,0.00066887157,0.0004058238,0.00065145135,0.00019336227,0.00035604739,0.00045469284,0.00068937696,0.00096622756],"category_scores_gemma":[0.0022068447,0.00019782115,0.0003450395,0.00056079176,0.00017069481,0.0003859212,0.00019060622,0.00037368466,0.00020444609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045844907,0.00002479876,0.0017297282,0.000020625406,0.00002404596,0.00003319311,0.000014711173,0.9779017,0.0010585573,0.00024005506,0.00015526565,0.018751489],"study_design_scores_gemma":[9.383953e-7,0.000009281215,0.0003293966,0.000001550758,0.0000026042385,0.000003596506,0.000001869585,0.9991665,0.0003405279,0.00009162309,0.0000503651,0.0000018346536],"about_ca_topic_score_codex":0.015147955,"about_ca_topic_score_gemma":0.007975041,"teacher_disagreement_score":0.015147955,"about_ca_system_score_codex":0.0006247379,"about_ca_system_score_gemma":0.00033133832,"threshold_uncertainty_score":0.030119538},"labels":[],"label_agreement":null},{"id":"W2043129860","doi":"10.1023/b:lida.0000036389.14073.dd","title":"Covariates and Random Effects in a Gamma Process Model with Application to Degradation and Failure","year":2004,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":572,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gamma process; Covariate; Degradation (telecommunications); Process (computing); Random effects model; Econometrics; Goodness of fit; Computer science; Statistics; Sequence (biology); Mathematics; Chemistry","score_opus":0.003535797549385565,"score_gpt":0.21039448970278896,"score_spread":0.20685869215340338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043129860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03468103,0.0011452595,0.9604408,0.00084785634,0.00015808447,0.0001763595,0.0011070287,0.0007685441,0.0006750217],"genre_scores_gemma":[0.6495124,0.0053297756,0.30887356,0.0006609425,0.0007895742,0.0021934623,0.003906576,0.00084780465,0.02788588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9864861,0.009525668,0.00047409974,0.0019671626,0.0007488667,0.000798032],"domain_scores_gemma":[0.88034016,0.106747225,0.004070381,0.0055479347,0.0021844914,0.0011097778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03936784,0.003244552,0.0058633117,0.003072091,0.0017200566,0.0049250405,0.00700836,0.007999039,0.008627102],"category_scores_gemma":[0.09091551,0.0035783164,0.004482674,0.005411151,0.0055272593,0.007405141,0.003733291,0.0068994197,0.0015380029],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013287761,0.00034898735,0.010942139,0.00050490577,0.0008475206,0.0011205804,0.0010871829,0.5040122,0.0009608055,0.4338749,0.0033974457,0.041574493],"study_design_scores_gemma":[0.00035797516,0.0002921092,0.0030381219,0.00010711004,0.00041333504,0.0002568067,0.00013788135,0.8038163,0.00027825916,0.18789308,0.0032910355,0.000117946845],"about_ca_topic_score_codex":0.012816061,"about_ca_topic_score_gemma":0.011462188,"teacher_disagreement_score":0.03936784,"about_ca_system_score_codex":0.002015021,"about_ca_system_score_gemma":0.0038824824,"threshold_uncertainty_score":0.2081995},"labels":[],"label_agreement":null},{"id":"W2043475556","doi":"10.1016/j.ress.2005.12.002","title":"Coupling ant colony and the degraded ceiling algorithm for the redundancy allocation problem of series–parallel systems","year":2006,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":105,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Redundancy (engineering); Computer science; Ant colony optimization algorithms; Heuristics; Mathematical optimization; Metaheuristic; Distributed computing; Algorithm; Mathematics","score_opus":0.0032395356757796557,"score_gpt":0.16683589792059644,"score_spread":0.1635963622448168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043475556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066902116,0.00045682443,0.9280883,0.00024342326,0.00009901226,0.00005242258,0.00003230145,0.00027884563,0.0038467355],"genre_scores_gemma":[0.80061084,0.00023670669,0.19594735,0.000103422695,0.00006876906,0.00011609888,0.00007826988,0.00009139982,0.0027471944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944514,0.00023403212,0.000030757645,0.0000704925,0.00015297085,0.00006666303],"domain_scores_gemma":[0.9988702,0.0006210393,0.00013692286,0.00008760991,0.0002063408,0.00007789675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012472675,0.0006911633,0.0013540211,0.0005245258,0.00045221628,0.000766841,0.0013064961,0.00071437587,0.0017559547],"category_scores_gemma":[0.003239159,0.0004662408,0.00043528332,0.0006634994,0.00066888274,0.0008361469,0.001073854,0.0009127481,0.00018238244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007914463,0.00003330142,0.00021727516,0.00004375035,0.00002940508,0.000023611177,0.000022454547,0.9727041,0.0008304052,0.003323733,0.0005201256,0.022172589],"study_design_scores_gemma":[0.0000088004035,0.000017547836,0.000038667902,0.000001598754,0.0000036477163,0.000006413491,0.0000021213764,0.9984571,0.00010637776,0.0012506768,0.00010529797,0.0000017488087],"about_ca_topic_score_codex":0.004371913,"about_ca_topic_score_gemma":0.0032279708,"teacher_disagreement_score":0.004371913,"about_ca_system_score_codex":0.00056086626,"about_ca_system_score_gemma":0.001243987,"threshold_uncertainty_score":0.00869292},"labels":[],"label_agreement":null},{"id":"W2043530653","doi":"10.1016/j.ress.2005.11.012","title":"Analysis of on-line maintenance strategies for -out-of- standby safety systems","year":2006,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Ontario Institute of Technology","funders":"","keywords":"Unavailability; Reliability engineering; Corrective maintenance; Preventive maintenance; Predictive maintenance; Engineering; Shutdown; Planned maintenance; Computer science; Nuclear engineering","score_opus":0.00779113425052394,"score_gpt":0.20922983936556577,"score_spread":0.20143870511504183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043530653","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77005094,0.0006339617,0.20753017,0.00026958867,0.000036301954,0.00016824236,0.00020629591,0.00032924896,0.020775259],"genre_scores_gemma":[0.99557614,0.000059251186,0.002837298,0.000015693746,0.000008393506,0.000014961158,0.000036634065,0.00002487803,0.0014268353],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997123,0.0000937733,0.000007851303,0.000028839027,0.00007166621,0.00008557582],"domain_scores_gemma":[0.9988564,0.00072114693,0.00014263186,0.000045534787,0.00019622436,0.000038061855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007009777,0.0009840896,0.0007977825,0.00069690245,0.0003214197,0.0008591327,0.00089530036,0.000637872,0.0045738644],"category_scores_gemma":[0.0019376774,0.00031787498,0.00044838764,0.0003247258,0.00028327288,0.0007746812,0.0003177141,0.00037095693,0.00021554992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022411083,0.0001078183,0.00095177494,0.00008266953,0.000048480266,0.00008299451,0.000043294523,0.97104305,0.0046526254,0.0022677174,0.0005157374,0.01997966],"study_design_scores_gemma":[0.0000068266577,0.00007128122,0.0006000054,0.0000030027657,0.00001978319,0.000013633013,0.000016035987,0.9982773,0.0005149977,0.0004173972,0.00005772182,0.0000020540474],"about_ca_topic_score_codex":0.0064006206,"about_ca_topic_score_gemma":0.0058113774,"teacher_disagreement_score":0.0064006206,"about_ca_system_score_codex":0.0009761559,"about_ca_system_score_gemma":0.0007441616,"threshold_uncertainty_score":0.015301049},"labels":[],"label_agreement":null},{"id":"W2047017350","doi":"10.1115/icone12-49074","title":"Stochastic Maintenance Optimization at Candu Power Plants","year":2004,"lang":"en","type":"article","venue":"12th International Conference on Nuclear Engineering, Volume 2","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; University of Toronto; Université Laval; Bruce Power (Canada)","funders":"","keywords":"Reliability engineering; Computer science; Boom; Stochastic optimization; Preventive maintenance; Risk analysis (engineering); Phase (matter); Operations research; Competition (biology); Engineering; Mathematical optimization; Business; Mathematics","score_opus":0.008169902028429304,"score_gpt":0.19192558455813852,"score_spread":0.18375568252970922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047017350","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.699445,0.0007993966,0.2751493,0.0015197821,0.00006689124,0.000083940104,0.00049241155,0.00038989063,0.0220534],"genre_scores_gemma":[0.9832945,0.00012932558,0.013013894,0.000024542698,0.000015958012,0.000039025555,0.00014018062,0.000022331425,0.0033203263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995585,0.00013965649,0.000016649265,0.00008191634,0.00012514881,0.000078061865],"domain_scores_gemma":[0.9989759,0.00054119114,0.00014763142,0.000055606353,0.00017518095,0.00010451498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014666322,0.0005000231,0.00086603215,0.0006242376,0.0006737849,0.0009652909,0.0008177801,0.0008314759,0.002183589],"category_scores_gemma":[0.003192914,0.0003721689,0.0005258151,0.0006051257,0.00054722704,0.0005560004,0.0007234239,0.0008758569,0.00018563098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000076300494,0.000029736204,0.00094678276,0.000018149005,0.00001472824,0.000060195922,0.0000149368525,0.98266554,0.0004997013,0.008263554,0.0006128149,0.006797558],"study_design_scores_gemma":[0.000012640774,0.000046470468,0.00059938064,0.0000027874253,0.0000037449179,0.000011713012,0.000012149699,0.9949903,0.00030039478,0.0037504134,0.00026280803,0.000007225109],"about_ca_topic_score_codex":0.013808255,"about_ca_topic_score_gemma":0.012338965,"teacher_disagreement_score":0.013808255,"about_ca_system_score_codex":0.001674464,"about_ca_system_score_gemma":0.0012307046,"threshold_uncertainty_score":0.027455747},"labels":[],"label_agreement":null},{"id":"W2047416222","doi":"10.1002/qre.816","title":"An optimal burn‐in preventive‐replacement model associated with a mixture distribution","year":2007,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; University of Toronto","funders":"","keywords":"Mean time between failures; Burn-in; Preventive maintenance; Reliability engineering; Failure rate; Hazard; Population; Computer science; Operations research; Engineering; Environmental health; Medicine","score_opus":0.007036526222194247,"score_gpt":0.24777187613956475,"score_spread":0.2407353499173705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047416222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33615255,0.0013295716,0.642138,0.0021589322,0.00021332232,0.00025876873,0.000751573,0.00052736036,0.016469944],"genre_scores_gemma":[0.9622182,0.00028698758,0.02102511,0.00012700768,0.000044785163,0.0002067859,0.00022769326,0.000064202635,0.015799161],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990884,0.00035770776,0.00003613161,0.00017141085,0.00011515864,0.0002312261],"domain_scores_gemma":[0.99631125,0.0022959472,0.0004559111,0.00010716773,0.0003690518,0.0004606724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034116374,0.0012058044,0.0030473252,0.0014479334,0.00070084305,0.0023560321,0.0031563083,0.0032072163,0.0061756526],"category_scores_gemma":[0.006864129,0.0014559646,0.0013552972,0.00092524866,0.0021486518,0.0017843727,0.0014554018,0.0019379173,0.00058595114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015705467,0.000051570085,0.00048810744,0.000026127693,0.00002655826,0.00008813776,0.000024378063,0.99006224,0.00023939574,0.0072402996,0.00029334793,0.0013028202],"study_design_scores_gemma":[0.000021773121,0.00002167275,0.00011557669,0.0000039551833,0.000011155804,0.00000798697,0.000008846293,0.9977418,0.00004255882,0.0019203328,0.000097828866,0.000006551388],"about_ca_topic_score_codex":0.022624481,"about_ca_topic_score_gemma":0.008395628,"teacher_disagreement_score":0.022624481,"about_ca_system_score_codex":0.0023060292,"about_ca_system_score_gemma":0.0015148591,"threshold_uncertainty_score":0.044985592},"labels":[],"label_agreement":null},{"id":"W2048468995","doi":"10.1080/0740817x.2013.770185","title":"A pseudo-likelihood analysis for incomplete warranty data with a time usage rate variable and production counts","year":2013,"lang":"en","type":"article","venue":"IIE Transactions","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo","keywords":"Warranty; Failure rate; Reliability (semiconductor); Reliability engineering; Computer science; Product (mathematics); Missing data; Production (economics); Variable (mathematics); Econometrics; Statistics; Engineering; Mathematics; Economics","score_opus":0.011579722917857124,"score_gpt":0.20032744114857773,"score_spread":0.18874771823072062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048468995","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00871049,0.00020243881,0.98986816,0.00025671156,0.000015319703,0.00004784913,0.00023261512,0.0001488464,0.00051764585],"genre_scores_gemma":[0.42409796,0.0014239838,0.55697954,0.00030016928,0.00038536216,0.0008409437,0.003299264,0.0004171446,0.012255648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9932766,0.00414664,0.00033677305,0.0007897264,0.0011337642,0.00031654706],"domain_scores_gemma":[0.9454134,0.04637958,0.0033808392,0.0021670847,0.002222784,0.00043623446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015499071,0.0013294562,0.0020105736,0.0026067346,0.00079752674,0.0026964853,0.0037823264,0.0018123094,0.0052729836],"category_scores_gemma":[0.05607591,0.0014662859,0.0022488956,0.003692888,0.002095895,0.0047499696,0.0019291395,0.0027386919,0.0010729765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037014994,0.00018735063,0.009291775,0.0005196551,0.0002461715,0.0010336854,0.000579623,0.6865611,0.0022129903,0.2091226,0.0036103704,0.086264506],"study_design_scores_gemma":[0.000015524029,0.000041230083,0.0012919599,0.000027064734,0.000022281982,0.00015339271,0.000038451413,0.97334576,0.00042865882,0.023266386,0.0013362733,0.000033073804],"about_ca_topic_score_codex":0.005151281,"about_ca_topic_score_gemma":0.0038642115,"teacher_disagreement_score":0.015499071,"about_ca_system_score_codex":0.0014391663,"about_ca_system_score_gemma":0.0026963686,"threshold_uncertainty_score":0.08196789},"labels":[],"label_agreement":null},{"id":"W2049556091","doi":"10.1115/icone18-30130","title":"An Accurate Probabilistic Model for Estimating the Life Cycle Cost of Degrading Components in Nuclear Power Plants","year":2010,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University Network of Excellence in Nuclear Engineering","keywords":"Reliability engineering; Reliability (semiconductor); Nuclear power; Nuclear power plant; Probabilistic logic; Time horizon; Computer science; System lifecycle; Engineering; Power (physics); Mathematical optimization; Mathematics; Software","score_opus":0.02501531525728651,"score_gpt":0.24752439416667352,"score_spread":0.222509078909387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049556091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071813256,0.000616494,0.923861,0.00034750026,0.00002629623,0.00007006866,0.00055126677,0.00020795435,0.0025062456],"genre_scores_gemma":[0.95497453,0.0008480306,0.039386995,0.000066378656,0.00005175534,0.00023974235,0.0005964357,0.000070670634,0.00376538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989949,0.000342304,0.000054014592,0.00019633054,0.00027379178,0.00013857371],"domain_scores_gemma":[0.99722326,0.0018827149,0.00041721447,0.00012616688,0.0002902767,0.000060284405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021179707,0.0011996861,0.0014253278,0.0009902149,0.0004114396,0.0012323245,0.0023113757,0.0020611628,0.001675727],"category_scores_gemma":[0.006423414,0.0010845802,0.00092234666,0.0015908448,0.0010462955,0.00184471,0.00071912305,0.0015699248,0.00026476264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007956076,0.000004404889,0.0001219599,0.000009139964,0.0000036162137,0.000014408295,0.000004546189,0.99722457,0.00014260407,0.0015668692,0.000055797278,0.00084418064],"study_design_scores_gemma":[0.0000015327523,0.0000062299882,0.00012560481,0.0000018135643,0.000004271563,0.000009346517,0.0000019266556,0.99867094,0.00006348162,0.0010610684,0.000050496918,0.0000032559867],"about_ca_topic_score_codex":0.013383376,"about_ca_topic_score_gemma":0.008318885,"teacher_disagreement_score":0.013383376,"about_ca_system_score_codex":0.0018678256,"about_ca_system_score_gemma":0.0011077889,"threshold_uncertainty_score":0.02661097},"labels":[],"label_agreement":null},{"id":"W2049626324","doi":"10.1002/asmb.949","title":"Foreword: Special issue on statistical reliability and maintenance modeling","year":2013,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Warranty; Operations research; Reliability (semiconductor); Maintainability; Computer science; Special section; China; Engineering; Reliability engineering; Political science","score_opus":0.01105427262832692,"score_gpt":0.20255331207912286,"score_spread":0.19149903945079594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049626324","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016518936,0.10990287,0.047159426,0.05509097,0.6704701,0.0003190846,0.0040935166,0.001247792,0.11006425],"genre_scores_gemma":[0.012964438,0.09665363,0.010229238,0.014722871,0.62094295,0.00032863446,0.0068252534,0.0015637046,0.2357693],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984457,0.00026461837,0.00014132266,0.0003354136,0.0007304257,0.00008251639],"domain_scores_gemma":[0.99523807,0.0018837665,0.0003241374,0.00038763124,0.0018606014,0.0003057006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022584435,0.0016994381,0.0016846547,0.0026808504,0.0007606775,0.004047883,0.001574155,0.0026848349,0.066940814],"category_scores_gemma":[0.006791096,0.000604436,0.001317464,0.0027969596,0.0006788944,0.003258391,0.0010112865,0.0046582925,0.045676786],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020116597,0.000026257463,0.00014514453,0.00023160102,0.00002512882,0.000040583316,0.000016318392,0.0004387343,0.00017957637,0.00479991,0.95911646,0.03496027],"study_design_scores_gemma":[0.000010744761,0.000065982036,0.00080309866,0.0003266365,0.000025938682,0.00019107126,0.00002781212,0.0024905223,0.00020222974,0.010712088,0.98512506,0.000018732137],"about_ca_topic_score_codex":0.001333577,"about_ca_topic_score_gemma":0.0013444147,"teacher_disagreement_score":0.066940814,"about_ca_system_score_codex":0.0015032558,"about_ca_system_score_gemma":0.0013942344,"threshold_uncertainty_score":0.22393936},"labels":[],"label_agreement":null},{"id":"W2051382092","doi":"10.1016/j.jlp.2014.09.005","title":"Improving safety and availability of complex systems using a risk-based failure assessment approach","year":2014,"lang":"en","type":"article","venue":"Journal of Loss Prevention in the Process Industries","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Root cause analysis; Root cause; Risk analysis (engineering); Reliability engineering; Identification (biology); Engineering; Failure mode and effects analysis; Risk assessment; Task (project management); Plan (archaeology); Computer science; Systems engineering; Computer security; Business","score_opus":0.02051308838613477,"score_gpt":0.26644730204714173,"score_spread":0.24593421366100696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051382092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08172005,0.00033416375,0.9157363,0.00024546927,0.000023848252,0.00007513326,0.000044991928,0.0002464051,0.0015736867],"genre_scores_gemma":[0.8708526,0.0001734027,0.12786835,0.000043505323,0.00003529518,0.00006818881,0.000046483125,0.000035247886,0.0008769033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894077,0.0003567981,0.000060956718,0.0001401564,0.00040886857,0.00009252674],"domain_scores_gemma":[0.9967847,0.0021161882,0.0004156732,0.00014700896,0.00045077113,0.00008558959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002106845,0.001117831,0.0011343753,0.00217567,0.00043343313,0.0011535287,0.0011544054,0.0008563555,0.0013121514],"category_scores_gemma":[0.006609536,0.0005401928,0.00097421365,0.00066014257,0.0004913187,0.0018501574,0.0011720167,0.0009441211,0.00010355557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007194204,0.00013728069,0.002388341,0.000053522523,0.000108273765,0.000042019525,0.00005069315,0.9451565,0.0030444204,0.0032480627,0.00020879273,0.045490187],"study_design_scores_gemma":[0.0000034514194,0.000031643158,0.00042647644,0.0000038088772,0.000016969245,0.000010924773,0.000008076677,0.9971698,0.00038496134,0.0018765697,0.000061373226,0.0000059889026],"about_ca_topic_score_codex":0.0042038066,"about_ca_topic_score_gemma":0.004518456,"teacher_disagreement_score":0.0042038066,"about_ca_system_score_codex":0.0007949546,"about_ca_system_score_gemma":0.0011264263,"threshold_uncertainty_score":0.011142194},"labels":[],"label_agreement":null},{"id":"W2052062282","doi":"10.1016/j.ijpe.2006.09.018","title":"Optimal safety stocks and preventive maintenance periods in unreliable manufacturing systems","year":2006,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Preventive maintenance; Corrective maintenance; Production (economics); Computer science; Planned maintenance; Safety stock; Context (archaeology); Operations research; Stock (firearms); Reliability engineering; Predictive maintenance; Order (exchange); Point (geometry); Work (physics); Operations management; Proactive maintenance; Risk analysis (engineering); Business; Economics; Engineering; Mathematics; Microeconomics","score_opus":0.0031789850827178786,"score_gpt":0.18253321131498043,"score_spread":0.17935422623226255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052062282","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50482565,0.0014909144,0.48854434,0.0006014178,0.000045880715,0.00008662923,0.00022774385,0.00016870727,0.004008638],"genre_scores_gemma":[0.990449,0.0003992331,0.008402172,0.000014809491,0.000014298626,0.000031593758,0.00004355592,0.000017471959,0.0006278926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944156,0.00021896735,0.00002770768,0.000058281,0.000115990086,0.00013738743],"domain_scores_gemma":[0.9973035,0.0015834215,0.0006422329,0.00009720125,0.0002110679,0.00016262413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016648688,0.0011457453,0.0010048293,0.0011603046,0.00047360008,0.0013779865,0.0010469307,0.0008880473,0.0012758553],"category_scores_gemma":[0.0072115455,0.0008201849,0.00052460335,0.00075558375,0.0011961939,0.001343243,0.00079328596,0.00072119944,0.00010487788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005393317,0.000012661453,0.0003246924,0.000028706723,0.0000133640315,0.000071564544,0.000027504497,0.9907851,0.0007056586,0.006116446,0.00007978038,0.0017805172],"study_design_scores_gemma":[0.000020054933,0.000055447483,0.00039236422,0.00000988059,0.000019631001,0.000020433521,0.000025267573,0.9897215,0.0005356159,0.009078366,0.00011184528,0.0000095356045],"about_ca_topic_score_codex":0.005439306,"about_ca_topic_score_gemma":0.0024582515,"teacher_disagreement_score":0.005439306,"about_ca_system_score_codex":0.001507338,"about_ca_system_score_gemma":0.0012428897,"threshold_uncertainty_score":0.010936558},"labels":[],"label_agreement":null},{"id":"W2052193358","doi":"10.1016/j.ress.2007.03.021","title":"Dynamic reliability networks with self-healing units","year":2007,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Toronto Metropolitan University","funders":"","keywords":"Self-healing; Reliability (semiconductor); Reliability engineering; Computer science; Limit (mathematics); Graph; Graph theory; Series and parallel circuits; Engineering; Mathematics; Theoretical computer science; Voltage","score_opus":0.002387837913940618,"score_gpt":0.16789948498220103,"score_spread":0.1655116470682604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052193358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16213422,0.00054606836,0.8279597,0.0005026666,0.000114994,0.00010103281,0.00017837033,0.0005955961,0.007867356],"genre_scores_gemma":[0.9495638,0.00030373863,0.04413391,0.00008219889,0.00006637145,0.00012906245,0.00009948001,0.000045632743,0.005575865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950504,0.00013820094,0.000024803265,0.00015211037,0.00009701308,0.00008286186],"domain_scores_gemma":[0.99751174,0.0012456623,0.00036001406,0.00040035584,0.0003226471,0.00015960906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000987368,0.00057663344,0.00069606944,0.00088642206,0.0005625801,0.0010146183,0.0017318644,0.0007716623,0.0035185418],"category_scores_gemma":[0.0047408394,0.0006411982,0.0004231149,0.00077674177,0.00082142814,0.0023953961,0.0011257216,0.00081092847,0.0004324206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003024269,0.00007463472,0.0007030999,0.00007044197,0.00004125299,0.00012595528,0.00007835,0.89256424,0.0033966342,0.060431052,0.0014210365,0.040790915],"study_design_scores_gemma":[0.000016726934,0.000052925996,0.00015412518,0.000007042244,0.000018430252,0.000051309966,0.000017177901,0.97198886,0.00075870496,0.026192194,0.0007349294,0.000007687761],"about_ca_topic_score_codex":0.0013372074,"about_ca_topic_score_gemma":0.0014954053,"teacher_disagreement_score":0.0035185418,"about_ca_system_score_codex":0.0010369478,"about_ca_system_score_gemma":0.0005034589,"threshold_uncertainty_score":0.011770725},"labels":[],"label_agreement":null},{"id":"W2052304942","doi":"10.1080/02664760903406488","title":"Optimal design of accelerated degradation tests based on Wiener process models","year":2010,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":170,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Quantile; Wiener process; Estimator; Mathematics; Robustness (evolution); Delta method; Statistics; Random variable; Sensitivity (control systems); Mathematical optimization; Range (aeronautics); Computer science; Applied mathematics; Engineering","score_opus":0.019967688203337306,"score_gpt":0.2430872403094555,"score_spread":0.2231195521061182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052304942","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085544154,0.00040722097,0.9108085,0.000103380495,0.00002201316,0.00016037833,0.000051336116,0.00025085275,0.002652152],"genre_scores_gemma":[0.9211235,0.00018436571,0.077086106,0.000029575045,0.000012488086,0.0002939022,0.000058172467,0.000033404744,0.001178474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847466,0.00061704445,0.00006653101,0.00024459534,0.00037974896,0.00021747833],"domain_scores_gemma":[0.99733424,0.0015360774,0.00053879543,0.00008691273,0.00037721102,0.00012676156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023368406,0.0011998793,0.0014843626,0.0010098832,0.0003400933,0.0012190668,0.0008862186,0.00082708,0.0010839723],"category_scores_gemma":[0.0049103517,0.00082907075,0.00067431584,0.00051030633,0.0010018799,0.001037478,0.00092681916,0.000825298,0.0001999715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016314322,0.000039862134,0.00030191286,0.00003464323,0.000021516256,0.00003404121,0.000028397802,0.9808851,0.0034043859,0.0034967442,0.00008837925,0.011501918],"study_design_scores_gemma":[0.000024836801,0.00020637171,0.00026785836,0.000007965726,0.000014189178,0.000011470819,0.000011242004,0.9945368,0.0019689975,0.0027209017,0.00021917492,0.000010137242],"about_ca_topic_score_codex":0.0014427712,"about_ca_topic_score_gemma":0.0011635824,"teacher_disagreement_score":0.0023368406,"about_ca_system_score_codex":0.000998286,"about_ca_system_score_gemma":0.0013532232,"threshold_uncertainty_score":0.012358546},"labels":[],"label_agreement":null},{"id":"W2052461311","doi":"10.1016/j.ress.2009.08.004","title":"Performance evaluation of multi-state degraded systems with minimal repairs and imperfect preventive maintenance","year":2009,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":137,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Preventive maintenance; Reliability engineering; Reliability (semiconductor); Imperfect; Maintenance actions; Limit (mathematics); Markov process; Failure rate; State (computer science); Degradation (telecommunications); Process (computing); Function (biology); Renewal theory; Markov chain; Mathematical optimization; Inspection time; Production (economics); Computer science; Engineering; Mathematics; Statistics; Algorithm; Economics","score_opus":0.006999054080612516,"score_gpt":0.19787099688141388,"score_spread":0.19087194280080136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052461311","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94436276,0.0006916897,0.051186096,0.00023445462,0.00003337426,0.00004569327,0.0001271335,0.00039924603,0.0029196737],"genre_scores_gemma":[0.9985697,0.00002561747,0.001163567,0.0000052392,0.0000043212826,0.0000054647917,0.000025566535,0.0000076215592,0.00019295826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987324,0.00047704089,0.000077682,0.00018540744,0.0002472881,0.00028031354],"domain_scores_gemma":[0.9924388,0.0049743038,0.0007068212,0.0004934172,0.0009969203,0.00038977052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002438647,0.00082835415,0.0010296411,0.00070351874,0.000514687,0.0010452186,0.000768599,0.0008387719,0.001518534],"category_scores_gemma":[0.0073742457,0.0002515453,0.0004092297,0.00046348813,0.00077087147,0.0009642555,0.00073961064,0.00046838273,0.00017388238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016600393,0.000113484806,0.0013162686,0.00012086157,0.000060476876,0.0000770613,0.000054542492,0.98056924,0.0044727167,0.0008722123,0.0001934009,0.010489616],"study_design_scores_gemma":[0.000027143527,0.00049098814,0.0011533696,0.0000052631876,0.000030760813,0.00003531029,0.000025567799,0.99547654,0.0023361526,0.0003637197,0.000045955683,0.000009220644],"about_ca_topic_score_codex":0.004928868,"about_ca_topic_score_gemma":0.0020276061,"teacher_disagreement_score":0.004928868,"about_ca_system_score_codex":0.0011328824,"about_ca_system_score_gemma":0.0007691441,"threshold_uncertainty_score":0.012896955},"labels":[],"label_agreement":null},{"id":"W2053135815","doi":"10.1007/s00170-014-6175-y","title":"Production and maintenance planning for a failure-prone deteriorating manufacturing system: a hierarchical control approach","year":2014,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Corrective maintenance; Preventive maintenance; Reliability engineering; Imperfect; Production (economics); Sensitivity (control systems); Operations research; Engineering; Control (management); Failure rate; Planned maintenance; Problem statement; Production planning; Optimal maintenance; Duration (music); Operations management; Risk analysis (engineering); Computer science; Economics; Business; Management science","score_opus":0.00515628301583451,"score_gpt":0.20825656791295963,"score_spread":0.20310028489712512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053135815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05514323,0.00028191402,0.940312,0.00027821754,0.00004194528,0.000081879705,0.00009216814,0.00030040173,0.003468257],"genre_scores_gemma":[0.9514682,0.00019998946,0.045979965,0.00007443399,0.000047470137,0.00010939034,0.000105681924,0.000049962044,0.0019648133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989806,0.00025686063,0.000058230344,0.00022329744,0.00026920895,0.00021168854],"domain_scores_gemma":[0.9984452,0.00080692547,0.0002687869,0.00007470966,0.00030185902,0.00010256728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016684153,0.0013301844,0.0014851304,0.0012152224,0.001113594,0.0019630194,0.0022315588,0.001230104,0.0023009465],"category_scores_gemma":[0.0027990178,0.001196921,0.0012473969,0.0012794044,0.0013496155,0.0011493175,0.0013353957,0.0010314259,0.00020577537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026597489,0.00003335793,0.00019647712,0.000028034123,0.000020311561,0.000038231774,0.00003658917,0.99209595,0.0007524896,0.001907085,0.0001306474,0.0047340607],"study_design_scores_gemma":[0.0000036500833,0.000014652684,0.000076941695,0.000001281824,0.0000073205765,0.0000019853487,0.000003832316,0.99907446,0.00009554285,0.0006927513,0.000024743305,0.0000028650568],"about_ca_topic_score_codex":0.040435567,"about_ca_topic_score_gemma":0.027553663,"teacher_disagreement_score":0.040435567,"about_ca_system_score_codex":0.0019773177,"about_ca_system_score_gemma":0.0023940133,"threshold_uncertainty_score":0.08040041},"labels":[],"label_agreement":null},{"id":"W2053510719","doi":"10.1016/j.ress.2010.04.003","title":"Periodic inspection optimization model for a complex repairable system","year":2010,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":116,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Interval (graph theory); Reliability engineering; Computer science; Mathematical optimization; Mathematics; Engineering","score_opus":0.006478532688058146,"score_gpt":0.18556821105774196,"score_spread":0.1790896783696838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053510719","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16037509,0.00092419743,0.8049621,0.0013696671,0.00014084447,0.00021979075,0.0011191185,0.0006891205,0.03020012],"genre_scores_gemma":[0.94400585,0.00043225274,0.027958991,0.00012788031,0.000052256888,0.0002585859,0.0004387232,0.00013501743,0.026590485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942434,0.00017185371,0.000019929474,0.00014995623,0.00012704913,0.00010695661],"domain_scores_gemma":[0.9987832,0.00069094694,0.00020991378,0.00005973329,0.00018317353,0.00007310695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011530234,0.0012578447,0.0019161972,0.0008437326,0.0005187117,0.0015918162,0.0021850304,0.0032593652,0.007373513],"category_scores_gemma":[0.002963801,0.0010664627,0.0010516249,0.0009157193,0.0010853388,0.0011983532,0.00096569926,0.0014791806,0.00058745756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024364084,0.000016486632,0.00008839015,0.000021692384,0.000011397087,0.000052760595,0.000012006823,0.9955106,0.00024275198,0.0027821662,0.00023555024,0.0010019285],"study_design_scores_gemma":[0.0000072312655,0.000009604295,0.000064799664,0.0000017183643,0.000005854477,0.0000057976213,0.000002519663,0.998978,0.000032531527,0.0008230748,0.00006589692,0.0000029350501],"about_ca_topic_score_codex":0.022424234,"about_ca_topic_score_gemma":0.010350436,"teacher_disagreement_score":0.022424234,"about_ca_system_score_codex":0.0016157404,"about_ca_system_score_gemma":0.0016462816,"threshold_uncertainty_score":0.044587374},"labels":[],"label_agreement":null},{"id":"W2054201783","doi":"10.1049/iet-rsn.2010.0237","title":"Scheduling for multifunction radar via two-slope benefit functions","year":2011,"lang":"en","type":"article","venue":"IET Radar Sonar & Navigation","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Scheduling (production processes); Radar; Computer science; Real-time computing; Schedule; Function (biology); Operations research; Mathematical optimization; Engineering; Telecommunications; Mathematics; Operating system","score_opus":0.01690419178995547,"score_gpt":0.21781974201199306,"score_spread":0.20091555022203758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054201783","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09791097,0.00026786857,0.89684373,0.00014136049,0.000027408378,0.00008534537,0.00007450454,0.00027883347,0.0043699415],"genre_scores_gemma":[0.820271,0.00018798439,0.17556296,0.000035365476,0.000023459144,0.000114879906,0.00009341059,0.000081373066,0.003629517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996841,0.000089600915,0.00000970454,0.000037114132,0.000106789616,0.00007274762],"domain_scores_gemma":[0.99958163,0.00021684043,0.000058573973,0.000029557772,0.00006943862,0.000043852437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076073944,0.00051758345,0.00042287193,0.00040838303,0.00033340047,0.00044757457,0.00059276476,0.00032839354,0.0028730757],"category_scores_gemma":[0.0011328371,0.0002522721,0.0003545359,0.00047838478,0.00030585704,0.0004704563,0.0005313894,0.0005426226,0.00031164332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021845286,0.00007091672,0.00068316894,0.00008220908,0.000016911705,0.000081933336,0.00012076234,0.9017553,0.012514492,0.01541813,0.0008956328,0.068142116],"study_design_scores_gemma":[0.000020726788,0.00012970362,0.0004547766,0.0000038758335,0.0000073444835,0.000025991489,0.00001925592,0.9897181,0.0019805592,0.006223866,0.0014083902,0.000007361467],"about_ca_topic_score_codex":0.0025262104,"about_ca_topic_score_gemma":0.0033748331,"teacher_disagreement_score":0.0028730757,"about_ca_system_score_codex":0.00076518836,"about_ca_system_score_gemma":0.0010361193,"threshold_uncertainty_score":0.009611368},"labels":[],"label_agreement":null},{"id":"W2054423156","doi":"10.1007/s00170-014-5721-y","title":"Joint production and setup control policies: an extensive study addressing implementation issues via quantitative and qualitative criteria","year":2014,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; École de Technologie Supérieure; Université du Québec; Université du Québec à Montréal","funders":"","keywords":"Generality; Customer satisfaction; Computer science; Control (management); Operations research; Production (economics); Industrial engineering; Constraint (computer-aided design); Total cost; Mathematical optimization; Engineering; Mathematics; Economics","score_opus":0.024734643387805944,"score_gpt":0.36473597569041727,"score_spread":0.3400013323026113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054423156","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9302815,0.00034002162,0.057268556,0.00025196,0.000013117987,0.00080046913,0.00068785663,0.00009914303,0.010257371],"genre_scores_gemma":[0.9899479,0.00008875882,0.0088793235,0.000023635172,0.000003435388,0.0002357701,0.00012827611,0.000012897489,0.00067995786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9848162,0.009297715,0.0008325793,0.00084357813,0.0032799805,0.0009299359],"domain_scores_gemma":[0.80759764,0.1655392,0.010451721,0.0058356794,0.0098085115,0.00076726405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024457505,0.0005533398,0.0007018145,0.0017204243,0.0008114881,0.002125777,0.0018420087,0.0010567256,0.002844017],"category_scores_gemma":[0.06774689,0.0005547836,0.00062847155,0.0023356758,0.0012956397,0.0031613894,0.00095030526,0.0012304392,0.00026206087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026424963,0.006402558,0.13971081,0.002471612,0.00060429884,0.00036678766,0.0051110378,0.569191,0.011644926,0.04368604,0.0018994601,0.21626893],"study_design_scores_gemma":[0.00043430875,0.010407785,0.16437274,0.00067199854,0.00087405404,0.00021446639,0.015131494,0.73936534,0.030776009,0.026875982,0.010578532,0.00029725066],"about_ca_topic_score_codex":0.010313165,"about_ca_topic_score_gemma":0.010697148,"teacher_disagreement_score":0.024457505,"about_ca_system_score_codex":0.005425358,"about_ca_system_score_gemma":0.005498461,"threshold_uncertainty_score":0.12934518},"labels":[],"label_agreement":null},{"id":"W2055296793","doi":"10.1007/s00170-011-3327-1","title":"Production planning and repair/replacement switching policy for deteriorating manufacturing systems","year":2011,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dassault Systèmes (Canada); École de Technologie Supérieure; Université du Québec; Université du Québec à Montréal","funders":"","keywords":"Markov decision process; Production (economics); Time horizon; Reliability engineering; Dynamic programming; Product (mathematics); Operations research; Stochastic programming; Markov chain; Sensitivity (control systems); Engineering; Mathematical optimization; Production planning; Markov process; Computer science; Economics; Mathematics; Microeconomics","score_opus":0.013466748939622148,"score_gpt":0.24331809984161062,"score_spread":0.22985135090198847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055296793","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6469664,0.000787919,0.3439126,0.00066921365,0.00007266653,0.00022052691,0.0004589842,0.00039722,0.006514444],"genre_scores_gemma":[0.98397714,0.0001302248,0.01449449,0.00002584124,0.000011911953,0.00002695981,0.00011799584,0.000020407977,0.0011950276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953663,0.0001626603,0.000024093972,0.000080185586,0.000075453434,0.00012105504],"domain_scores_gemma":[0.998197,0.0012148923,0.00022164176,0.00006890925,0.00019972229,0.00009779503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016869322,0.00065382494,0.0010220965,0.0007270236,0.00050242007,0.0010581233,0.0010263338,0.0008532298,0.0031674933],"category_scores_gemma":[0.0027504798,0.0006632367,0.000481048,0.00062090973,0.00061682635,0.00069922785,0.00034118185,0.00088453724,0.00021976302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019267727,0.000049867864,0.0005662484,0.000034263223,0.000012811733,0.00005654489,0.000023503917,0.98913133,0.0012977114,0.0017979906,0.00031309313,0.0065239742],"study_design_scores_gemma":[0.000013915888,0.000057327517,0.0006760246,0.0000041511403,0.000011704403,0.000009262495,0.000015996206,0.9967301,0.00055742014,0.0018362984,0.00008289615,0.0000048491775],"about_ca_topic_score_codex":0.008735947,"about_ca_topic_score_gemma":0.007157788,"teacher_disagreement_score":0.008735947,"about_ca_system_score_codex":0.0014387271,"about_ca_system_score_gemma":0.0013349768,"threshold_uncertainty_score":0.017370224},"labels":[],"label_agreement":null},{"id":"W2056808619","doi":"10.1007/s11107-005-4528-z","title":"Mesh-Restorable Networks with Enhanced Dual-Failure Restorability Properties","year":2005,"lang":"en","type":"article","venue":"Photonic Network Communications","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Spare part; Computer science; Dual (grammatical number); TRACE (psycholinguistics); Limit (mathematics); Reliability engineering; Operations management","score_opus":0.013650188404251003,"score_gpt":0.20517866981042185,"score_spread":0.19152848140617085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056808619","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37998313,0.00047402913,0.6096049,0.0004187152,0.00006046589,0.000026944172,0.000113246795,0.0003899051,0.00892866],"genre_scores_gemma":[0.9651229,0.00012323253,0.032945864,0.000034409688,0.000032821306,0.000025302594,0.000048387494,0.000035811307,0.0016312624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998541,0.000037127884,0.000006629787,0.000022461414,0.00005396606,0.00002560037],"domain_scores_gemma":[0.9991374,0.00042018518,0.00015573199,0.0001323947,0.000115506795,0.00003867949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004683803,0.00035784717,0.0003498666,0.00036604077,0.0002574754,0.00042058164,0.0006545225,0.0003583244,0.0015466204],"category_scores_gemma":[0.0014590042,0.00018177029,0.0001628971,0.00036543165,0.0002542017,0.0009369325,0.00038108023,0.00042091473,0.00016903238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063476356,0.00023941223,0.0016057534,0.0002414564,0.00006694692,0.0002416492,0.00010750801,0.6604455,0.11361108,0.12415422,0.003409866,0.09524181],"study_design_scores_gemma":[0.000033533044,0.00011211,0.00054166134,0.0000068357285,0.000030443443,0.00015551578,0.000015757923,0.96495503,0.012079491,0.020999009,0.0010606349,0.000009974422],"about_ca_topic_score_codex":0.00019670786,"about_ca_topic_score_gemma":0.0006935724,"teacher_disagreement_score":0.0015466204,"about_ca_system_score_codex":0.00033894405,"about_ca_system_score_gemma":0.00021424534,"threshold_uncertainty_score":0.005173981},"labels":[],"label_agreement":null},{"id":"W2057320483","doi":"10.1016/j.ress.2005.12.001","title":"Reliability optimization using multiobjective ant colony system approaches","year":2006,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":87,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Mathematical optimization; Redundancy (engineering); Ant colony optimization algorithms; Computer science; Reliability (semiconductor); Probabilistic logic; Multi-objective optimization; Heuristic; Optimization problem; Mathematics; Artificial intelligence","score_opus":0.007849150253308916,"score_gpt":0.16968875815111537,"score_spread":0.16183960789780644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057320483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027515732,0.00046582552,0.9620903,0.00014464711,0.00007653718,0.00006518009,0.00002655366,0.00019544062,0.009419703],"genre_scores_gemma":[0.67509365,0.0005001346,0.3152391,0.00008267743,0.00006710688,0.000278802,0.000061492115,0.0001715999,0.008505366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994672,0.00024566625,0.000018980292,0.000048582642,0.00018636583,0.000033308464],"domain_scores_gemma":[0.9991473,0.00053919735,0.00008354032,0.00004458667,0.00015748109,0.000027904262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000937562,0.0009659331,0.0008851599,0.0008762322,0.0004376422,0.0010382348,0.0010558375,0.0010343154,0.002387115],"category_scores_gemma":[0.0025451875,0.00064549915,0.0006131369,0.0008967081,0.00047164146,0.0008257626,0.00075503276,0.0007618854,0.0003072368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017941786,0.000018552799,0.0000761742,0.00002631122,0.000019034962,0.000018946888,0.000013614784,0.98617005,0.000655142,0.002781088,0.00018070637,0.010022397],"study_design_scores_gemma":[0.0000039097276,0.000008953749,0.000019017447,0.0000015531442,0.0000030564927,0.0000036663466,0.000001893365,0.99877125,0.00012443951,0.0009586168,0.000102360355,0.0000013116444],"about_ca_topic_score_codex":0.0018586452,"about_ca_topic_score_gemma":0.001776822,"teacher_disagreement_score":0.002387115,"about_ca_system_score_codex":0.00052343257,"about_ca_system_score_gemma":0.0005560621,"threshold_uncertainty_score":0.0079856515},"labels":[],"label_agreement":null},{"id":"W2057460724","doi":"10.1080/01966324.2003.10737613","title":"Standby System Availability Using Gamma Approximation","year":2003,"lang":"en","type":"article","venue":"American Journal of Mathematical and Management Sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Unit (ring theory); Function (biology); Reliability engineering; Point (geometry); Mathematical optimization; Real-time computing; Applied mathematics; Algorithm; Control theory (sociology); Mathematics; Artificial intelligence; Engineering","score_opus":0.01340954413482042,"score_gpt":0.22402838494451965,"score_spread":0.21061884080969923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057460724","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1438075,0.00043873425,0.8487638,0.00019912772,0.00002218937,0.000024268948,0.00014715672,0.0003221699,0.006275059],"genre_scores_gemma":[0.98299855,0.00036844463,0.01412697,0.000037571168,0.000027679238,0.000037188987,0.00012576133,0.00005174325,0.0022261394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961746,0.00013966579,0.000010339203,0.000046674602,0.00012049157,0.00006538723],"domain_scores_gemma":[0.9983612,0.0010099194,0.00019534453,0.00013852886,0.0002417429,0.000053338008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011763144,0.0005243516,0.0005707015,0.00095355714,0.00024510224,0.0009178695,0.0011040715,0.0005214228,0.0025648936],"category_scores_gemma":[0.005578767,0.00030693816,0.00043711788,0.0006848498,0.0006755898,0.001752612,0.00086025486,0.00065840525,0.0004032416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005434326,0.000010140741,0.0013240963,0.000036930214,0.000018817373,0.00015628256,0.00008469914,0.91687274,0.001340585,0.07099924,0.0005519839,0.008550183],"study_design_scores_gemma":[0.0000017049155,0.0000059135145,0.00024251943,0.0000043234086,0.0000024016258,0.000025069276,0.00001132766,0.98486346,0.00016497335,0.014545318,0.00012872533,0.00000420985],"about_ca_topic_score_codex":0.00507377,"about_ca_topic_score_gemma":0.00213457,"teacher_disagreement_score":0.00507377,"about_ca_system_score_codex":0.0010063191,"about_ca_system_score_gemma":0.0005253101,"threshold_uncertainty_score":0.010088444},"labels":[],"label_agreement":null},{"id":"W2058724121","doi":"10.1016/j.ress.2014.06.018","title":"Joint optimal inspection and inventory for a k-out-of-n system","year":2014,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"Barrick Gold Corporation","keywords":"Spare part; Joint (building); Reliability engineering; Homogeneous; Process (computing); Poisson process; Computer science; Poisson distribution; Mathematical optimization; Reliability (semiconductor); Engineering; Mathematics; Operations management; Statistics; Structural engineering; Combinatorics","score_opus":0.0058618685047008515,"score_gpt":0.1743362015413462,"score_spread":0.16847433303664533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058724121","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69064224,0.00076617976,0.28867224,0.0013854479,0.00013702585,0.00023275963,0.0006604215,0.0008055051,0.016698238],"genre_scores_gemma":[0.9881616,0.000061692954,0.008716528,0.000032131913,0.000014532854,0.000019076351,0.00006737782,0.000018639388,0.0029083176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993536,0.0001472844,0.000033582284,0.00014159422,0.00009007301,0.00023388932],"domain_scores_gemma":[0.99849904,0.000732696,0.00026201105,0.000069101356,0.0002778771,0.00015924286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011347936,0.0009144102,0.0019014983,0.0008254208,0.0012404071,0.0015696073,0.0013022523,0.0018694648,0.0035499614],"category_scores_gemma":[0.002728951,0.0008181655,0.00076150935,0.00069102366,0.0012631123,0.001022244,0.0010879993,0.00077624264,0.0003440531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043189974,0.00006338623,0.0012800464,0.000079977006,0.000033285334,0.00025681147,0.00005394192,0.9876579,0.0024679604,0.0016158258,0.0006148725,0.005444204],"study_design_scores_gemma":[0.000021382712,0.0000668349,0.0007069554,0.0000049973387,0.000020042822,0.000030337042,0.000027946486,0.9977071,0.00032639602,0.0010002054,0.000076118646,0.00001149269],"about_ca_topic_score_codex":0.044047624,"about_ca_topic_score_gemma":0.034242496,"teacher_disagreement_score":0.044047624,"about_ca_system_score_codex":0.0020992355,"about_ca_system_score_gemma":0.0024004357,"threshold_uncertainty_score":0.08758247},"labels":[],"label_agreement":null},{"id":"W2059259361","doi":"10.1108/ijqrm-04-2012-0056","title":"A risk-based availability estimation using Markov method","year":2014,"lang":"en","type":"article","venue":"International Journal of Quality & Reliability Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Reliability engineering; Maintainability; Reliability (semiconductor); Risk analysis (engineering); Markov chain; Process (computing); Markov process; Computer science; Reliability block diagram; Markov model; Engineering; Fault tree analysis; Machine learning","score_opus":0.01513269320747995,"score_gpt":0.3131529363610952,"score_spread":0.29802024315361525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059259361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012330585,0.0001978699,0.985885,0.00008712184,0.00001693781,0.000040781702,0.000087769906,0.00016828511,0.0011856082],"genre_scores_gemma":[0.7961635,0.000679278,0.19905274,0.00007989349,0.00006880856,0.00023923964,0.0004949366,0.000066571316,0.0031549544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991032,0.0003604093,0.000045980458,0.00016697077,0.00022664966,0.00009680517],"domain_scores_gemma":[0.9973334,0.0018874652,0.0002989266,0.00007569813,0.00032545923,0.000079117286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014220097,0.0007166031,0.00089031836,0.0013955827,0.000521146,0.0010310638,0.0010790963,0.0007715777,0.0034372495],"category_scores_gemma":[0.0037774893,0.00057891774,0.0013351649,0.0007789247,0.00048654887,0.0011974826,0.00086288323,0.0010806961,0.0003653226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049691884,0.000032006385,0.001924926,0.00006926796,0.00004613229,0.000066448236,0.000042451175,0.9642455,0.0010573643,0.009897728,0.00039256373,0.022175957],"study_design_scores_gemma":[0.0000016712071,0.000009382489,0.00015365797,0.000005803843,0.0000057365273,0.000013249008,0.000004573109,0.9978123,0.00014401623,0.0017250935,0.00011956047,0.000004965152],"about_ca_topic_score_codex":0.012563417,"about_ca_topic_score_gemma":0.007524164,"teacher_disagreement_score":0.012563417,"about_ca_system_score_codex":0.001046639,"about_ca_system_score_gemma":0.001740257,"threshold_uncertainty_score":0.024980545},"labels":[],"label_agreement":null},{"id":"W2059795097","doi":"10.1109/24.855543","title":"Generalized multi-state k-out-of-n:G systems","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":176,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"State (computer science); Reliability (semiconductor); Component (thermodynamics); Mathematics; Binary number; Discrete mathematics; Reliability engineering; Computer science; Combinatorics; Algorithm; Physics; Engineering; Arithmetic; Thermodynamics","score_opus":0.014918359927543007,"score_gpt":0.2291471955575747,"score_spread":0.2142288356300317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059795097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34607145,0.0009116343,0.635561,0.0005213699,0.00018146429,0.00022076906,0.000862313,0.002371595,0.013298439],"genre_scores_gemma":[0.9415013,0.00017502844,0.05378284,0.00014545947,0.000046644236,0.0001209029,0.0003080179,0.000074525626,0.0038452407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990343,0.00017679608,0.00006937432,0.00031437364,0.00017765112,0.00022741736],"domain_scores_gemma":[0.99868757,0.00029986879,0.0002538774,0.00031365556,0.00033898762,0.0001061126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007812082,0.0007193266,0.0010043336,0.00067779276,0.00091687473,0.001447344,0.0015924332,0.00097353116,0.003736533],"category_scores_gemma":[0.001679778,0.00029559503,0.0005692461,0.00073723536,0.0014208136,0.002112971,0.0014989745,0.0007564776,0.0007233077],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013966013,0.00018096792,0.005326974,0.0006188947,0.00012945708,0.0016812083,0.0012168799,0.6400896,0.038211647,0.21107982,0.0072626746,0.09280526],"study_design_scores_gemma":[0.000058222784,0.00018410769,0.0015480958,0.000034340796,0.000043437005,0.0003246703,0.000097407596,0.90188235,0.0058114748,0.0839677,0.005982545,0.00006560212],"about_ca_topic_score_codex":0.0037307579,"about_ca_topic_score_gemma":0.005573859,"teacher_disagreement_score":0.003736533,"about_ca_system_score_codex":0.0012458848,"about_ca_system_score_gemma":0.0007464603,"threshold_uncertainty_score":0.0124999285},"labels":[],"label_agreement":null},{"id":"W2060250681","doi":"10.1016/j.ress.2013.09.003","title":"Selective maintenance for multi-state series–parallel systems under economic dependence","year":2013,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":138,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Component (thermodynamics); Series and parallel circuits; Reliability engineering; Series (stratigraphy); State (computer science); Maintenance actions; Resource (disambiguation); Computer science; Genetic algorithm; Mathematical optimization; Operations research; Engineering; Algorithm; Mathematics","score_opus":0.007771529503925715,"score_gpt":0.19491509697366544,"score_spread":0.18714356746973973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060250681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7442911,0.0004928232,0.24750327,0.00069867773,0.000051332398,0.00005535597,0.00019351355,0.00019750703,0.0065164287],"genre_scores_gemma":[0.99724483,0.00007079776,0.0016664017,0.000009968324,0.0000136009485,0.00001326593,0.000027108732,0.0000116009805,0.0009425209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99963593,0.000109713146,0.000018295797,0.000060141127,0.00006691348,0.000108840046],"domain_scores_gemma":[0.9969483,0.002015892,0.00046614144,0.00016846965,0.0002714155,0.00012980723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014029419,0.0007035834,0.001247631,0.0007388371,0.0005261357,0.0007542206,0.0010600721,0.000594981,0.0017292202],"category_scores_gemma":[0.004330246,0.000454086,0.0006994208,0.0005363848,0.0010292424,0.0011855401,0.0009380468,0.00063422247,0.00010887822],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003326542,0.00006596721,0.0012182494,0.000071571776,0.000074906784,0.00029264245,0.00003893246,0.9727165,0.0031566622,0.014768602,0.00051013567,0.0067530912],"study_design_scores_gemma":[0.000008252327,0.000022821156,0.00048159456,0.0000014177486,0.000013932409,0.000025355157,0.000008052802,0.9961506,0.00020142259,0.0030505073,0.00003260377,0.0000034021407],"about_ca_topic_score_codex":0.0038973836,"about_ca_topic_score_gemma":0.004013889,"teacher_disagreement_score":0.0038973836,"about_ca_system_score_codex":0.0010181415,"about_ca_system_score_gemma":0.0006409807,"threshold_uncertainty_score":0.0077493787},"labels":[],"label_agreement":null},{"id":"W2061057166","doi":"10.1080/00207720500139930","title":"Forecasting warranty performance in the presence of the ‘maturing data’ phenomenon","year":2005,"lang":"en","type":"article","venue":"International Journal of Systems Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Warranty; Reliability (semiconductor); Artificial neural network; Computer science; Quality (philosophy); Reliability engineering; Process (computing); Operations research; Engineering; Artificial intelligence","score_opus":0.028686692503136053,"score_gpt":0.24078535858414535,"score_spread":0.2120986660810093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061057166","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9724893,0.00016017708,0.02541226,0.00017793593,0.0000213957,0.000013609496,0.0004399849,0.0001942447,0.0010910963],"genre_scores_gemma":[0.99688894,0.000038858205,0.00256147,0.00000433272,0.000003862052,0.0000035790872,0.00028295827,0.000004422007,0.00021165791],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996704,0.000075618846,0.000028631088,0.00007239884,0.00011599817,0.00003700502],"domain_scores_gemma":[0.99779713,0.0009748745,0.00046097816,0.00017513317,0.0005075767,0.0000843587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001296786,0.0005084642,0.0003234044,0.00068203313,0.00016989654,0.0005389166,0.000470037,0.00069081003,0.00044798432],"category_scores_gemma":[0.0043658605,0.00019163585,0.00028309875,0.0007618255,0.00017661408,0.00081704016,0.00025541813,0.0006745597,0.00015092346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022236434,0.000080112186,0.047983076,0.00006112973,0.00004331296,0.00019578701,0.00008532713,0.91180474,0.007520954,0.00037805,0.0005427352,0.031082457],"study_design_scores_gemma":[0.0000014775936,0.000040522955,0.015613096,0.0000045609377,0.000004585562,0.000013577154,0.000026187727,0.98201615,0.0020160663,0.00012502738,0.0001314278,0.000007280646],"about_ca_topic_score_codex":0.017769184,"about_ca_topic_score_gemma":0.015745621,"teacher_disagreement_score":0.017769184,"about_ca_system_score_codex":0.00077814574,"about_ca_system_score_gemma":0.00039020955,"threshold_uncertainty_score":0.035331547},"labels":[],"label_agreement":null},{"id":"W2061267743","doi":"10.1007/s10845-009-0358-7","title":"Condition based maintenance optimization considering multiple objectives","year":2009,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Concordia University","funders":"","keywords":"Reliability (semiconductor); Condition-based maintenance; Reliability engineering; Multi-objective optimization; Decision maker; Process (computing); Optimization problem; Computer science; Mathematical optimization; Production (economics); Preventive maintenance; Engineering; Operations research; Mathematics","score_opus":0.008533576907598112,"score_gpt":0.21593241347614187,"score_spread":0.20739883656854374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061267743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23832625,0.00095646473,0.75298536,0.00040892328,0.00015183415,0.00020145356,0.00024125646,0.00047455708,0.0062538423],"genre_scores_gemma":[0.947861,0.0001413063,0.049901955,0.00003944269,0.00006728266,0.00014046949,0.00014531046,0.000048673926,0.0016546191],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999193,0.00025088916,0.00003844671,0.00014562008,0.00024609308,0.0001259707],"domain_scores_gemma":[0.9977336,0.0015278348,0.00023900576,0.00009536086,0.00031669825,0.00008752256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018342999,0.0015361446,0.0025415402,0.001383597,0.00048220306,0.0014903169,0.0015971275,0.0023392,0.0030258805],"category_scores_gemma":[0.0038427408,0.0008309749,0.001043806,0.001339716,0.0006221893,0.001910847,0.00074460456,0.00096454576,0.00021352913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014420041,0.00010923211,0.0004417082,0.0000595421,0.00006241988,0.000052206015,0.000015875537,0.9809039,0.0011232531,0.0015946325,0.0003523219,0.015140696],"study_design_scores_gemma":[0.0000200561,0.00007158589,0.0002391662,0.0000030407657,0.000015930087,0.00001114669,0.0000038715543,0.99850816,0.000238279,0.00083780836,0.000047323196,0.000003542196],"about_ca_topic_score_codex":0.0028202944,"about_ca_topic_score_gemma":0.0016073495,"teacher_disagreement_score":0.0030258805,"about_ca_system_score_codex":0.00093472743,"about_ca_system_score_gemma":0.00082298846,"threshold_uncertainty_score":0.010122597},"labels":[],"label_agreement":null},{"id":"W2061509544","doi":"10.1002/prs.11722","title":"A risk‐based methodology to estimate shutdown interval considering system availability","year":2014,"lang":"en","type":"article","venue":"Process Safety Progress","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Shutdown; Reliability engineering; Engineering; Interval (graph theory); Original equipment manufacturer; Schedule; Process (computing); Markov chain; Risk analysis (engineering); Computer science; Mathematics","score_opus":0.01988833933123652,"score_gpt":0.29434886524890935,"score_spread":0.27446052591767284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061509544","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034798984,0.000114130686,0.99584466,0.000022571761,0.0000089554915,0.000035797926,0.000035498044,0.00013016073,0.0003284139],"genre_scores_gemma":[0.44136557,0.00046942057,0.55599713,0.000057072582,0.00007098673,0.0003969492,0.00045694198,0.00010756955,0.0010785116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978496,0.00065358385,0.00015601928,0.0003974756,0.00082635396,0.000116992196],"domain_scores_gemma":[0.99459875,0.0034869157,0.0008119005,0.00021995539,0.0007915008,0.0000908452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028237016,0.0015229456,0.0012146241,0.0029254395,0.00045886217,0.0010806862,0.0015708116,0.0008865944,0.0018146029],"category_scores_gemma":[0.009969455,0.0006929804,0.0014599999,0.0009970403,0.00041405158,0.0016080391,0.0010508242,0.0011557213,0.0003204403],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049355393,0.000047111236,0.0021334845,0.000114576804,0.0000892904,0.00005687444,0.00005869248,0.93372303,0.0024119054,0.007791037,0.00035659235,0.053168047],"study_design_scores_gemma":[0.0000057394614,0.000057951416,0.0006427947,0.000015728681,0.000024801084,0.00005599024,0.0000085161655,0.994294,0.0010503279,0.0033200812,0.0005066412,0.000017444134],"about_ca_topic_score_codex":0.0038917733,"about_ca_topic_score_gemma":0.0023133438,"teacher_disagreement_score":0.0038917733,"about_ca_system_score_codex":0.0012522223,"about_ca_system_score_gemma":0.0016222633,"threshold_uncertainty_score":0.014933288},"labels":[],"label_agreement":null},{"id":"W2061758728","doi":"10.1016/j.jspi.2012.02.045","title":"Optimal design and maintenance of a repairable multi-state system with standby components","year":2012,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability engineering; Markov model; Markov process; Mathematical optimization; State (computer science); Markov chain; Process (computing); Mathematics; Electric power system; Work (physics); Power (physics); Preventive maintenance; Reliability (semiconductor); Control theory (sociology); Computer science; Engineering; Statistics; Algorithm","score_opus":0.027634950945616535,"score_gpt":0.2504595994827245,"score_spread":0.22282464853710798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061758728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36947212,0.0006285625,0.62371814,0.00071309437,0.00007013319,0.00023910377,0.00025028602,0.00046371235,0.00444483],"genre_scores_gemma":[0.9865303,0.000068642454,0.0122524,0.000024360857,0.000012548903,0.00006729046,0.00004440774,0.000016613802,0.0009833819],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995239,0.00015716213,0.000023616614,0.0001093134,0.000085207925,0.00010092102],"domain_scores_gemma":[0.9987685,0.00071771105,0.00019159779,0.000048728518,0.00019787347,0.000075587384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011950914,0.0008620966,0.0014639713,0.00065587636,0.00067122694,0.001333866,0.00092843524,0.001326412,0.0018258481],"category_scores_gemma":[0.0028120966,0.00088206877,0.00059563125,0.0003425884,0.0010467826,0.0009101112,0.00083460496,0.00077872176,0.00018227499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015510511,0.000027455857,0.0003154409,0.00004295021,0.000028473738,0.000057753125,0.000035268848,0.9902844,0.0028541218,0.0014932402,0.00011484912,0.004590858],"study_design_scores_gemma":[0.000019571036,0.00005246654,0.00014836804,0.0000025809768,0.000016464723,0.00000524274,0.000006486558,0.99876046,0.00036472437,0.0005714866,0.000048534217,0.0000036531771],"about_ca_topic_score_codex":0.009336054,"about_ca_topic_score_gemma":0.008419993,"teacher_disagreement_score":0.009336054,"about_ca_system_score_codex":0.0011411258,"about_ca_system_score_gemma":0.0017389538,"threshold_uncertainty_score":0.01856345},"labels":[],"label_agreement":null},{"id":"W2064473318","doi":"10.1002/atr.5670420307","title":"Network reliability‐based optimal toll design","year":2008,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Toll; Reliability (semiconductor); Computer science; Flow network; Network planning and design; Toll road; Monte Carlo method; Transport engineering; Reliability engineering; Operations research; Engineering; Mathematical optimization; Computer network; Mathematics","score_opus":0.010734809258512692,"score_gpt":0.2049281615626612,"score_spread":0.1941933523041485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064473318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04253113,0.00015293993,0.9479798,0.0001969953,0.00004815755,0.000065074884,0.00008193595,0.00020990388,0.00873399],"genre_scores_gemma":[0.9543046,0.00013296622,0.041748125,0.000030582723,0.000017321592,0.00008077548,0.00007420826,0.000056827168,0.0035547006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993655,0.00022038823,0.000019226187,0.00010115506,0.00014348021,0.00015029316],"domain_scores_gemma":[0.9991738,0.00031857195,0.00010156481,0.000074879186,0.0002726892,0.000058406138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083024363,0.0006967912,0.0006850209,0.0010609315,0.00038846623,0.000973803,0.0013483211,0.0007873733,0.0045138854],"category_scores_gemma":[0.0028206594,0.00040600027,0.000439409,0.00050822314,0.00073018356,0.0011560704,0.0010167432,0.0006885047,0.00035679777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048884827,0.000012673684,0.00018414226,0.000030178766,0.0000069847238,0.000024764578,0.000014034191,0.9790679,0.0010510606,0.010772035,0.00038433445,0.008403046],"study_design_scores_gemma":[0.000006435313,0.00001925138,0.000050481573,0.0000047327917,0.000006172076,0.000009550989,0.00001170907,0.9925668,0.00062544574,0.006229915,0.00046484228,0.000004670788],"about_ca_topic_score_codex":0.0028655191,"about_ca_topic_score_gemma":0.002037056,"teacher_disagreement_score":0.0045138854,"about_ca_system_score_codex":0.0014297428,"about_ca_system_score_gemma":0.0012055,"threshold_uncertainty_score":0.015100479},"labels":[],"label_agreement":null},{"id":"W2065240344","doi":"10.1017/s0269964800144031","title":"RELIABILITY EVALUATION OF A LINEAR <i>k</i>-WITHIN-(<i>r</i>,<i>s</i>)-OUT-OF-(<i>m</i>,<i>n</i>):F LATTICE SYSTEM","year":2000,"lang":"en","type":"article","venue":"Probability in the Engineering and Informational Sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lattice (music); Combinatorics; Mathematics; Rectangle; Dimension (graph theory); Crystal system; Discrete mathematics; Physics; Crystallography; Geometry; Crystal structure; Chemistry","score_opus":0.018821878345554873,"score_gpt":0.23109833343607655,"score_spread":0.21227645509052168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065240344","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57477564,0.0001526008,0.41895887,0.0001987328,0.000015145823,0.00005686353,0.00012523543,0.0006721573,0.0050448347],"genre_scores_gemma":[0.97484004,0.000023900118,0.024319803,0.0000110115825,0.00000644453,0.000017509914,0.00007580593,0.000023899594,0.0006816274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996325,0.00008914686,0.00001678666,0.00006404617,0.00012137844,0.0000760586],"domain_scores_gemma":[0.9985967,0.0007263125,0.00013711768,0.00008760279,0.00036276775,0.00008953718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006305571,0.0002578028,0.00033449248,0.0004732395,0.00032419158,0.00052736024,0.00060558604,0.00028498462,0.002148619],"category_scores_gemma":[0.004132926,0.00017285845,0.00025374454,0.00026453554,0.0005606271,0.00071827,0.00042388515,0.00025090986,0.0003149504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044768208,0.000048719023,0.0077016256,0.000115103954,0.00003928866,0.00021222545,0.00013880664,0.91762936,0.018162033,0.007982475,0.0011232306,0.04639948],"study_design_scores_gemma":[0.000005609824,0.000041674935,0.0006016133,0.0000022232853,0.000007558423,0.000031014162,0.000015293268,0.9950558,0.0030706364,0.001072059,0.0000896888,0.0000068067893],"about_ca_topic_score_codex":0.006694969,"about_ca_topic_score_gemma":0.004716719,"teacher_disagreement_score":0.006694969,"about_ca_system_score_codex":0.001110753,"about_ca_system_score_gemma":0.00086782593,"threshold_uncertainty_score":0.013311982},"labels":[],"label_agreement":null},{"id":"W2065694111","doi":"10.1177/1748006x11422623","title":"Reliability analysis of generalized multi-state <i>k</i> -out-of- <i>n</i> systems","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reliability (semiconductor); State (computer science); Component (thermodynamics); Integer (computer science); Computer science; Flexibility (engineering); Independent and identically distributed random variables; Algorithm; Reliability theory; Mathematics; Random variable; Statistics; Physics; Failure rate","score_opus":0.014941330809820356,"score_gpt":0.21171552099841387,"score_spread":0.19677419018859352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065694111","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22310218,0.00049439206,0.77293915,0.00021291948,0.000029917508,0.000036810994,0.00011054874,0.00019665557,0.0028774436],"genre_scores_gemma":[0.98602575,0.00016270725,0.012968851,0.000021255288,0.000016286054,0.00002537153,0.000057222533,0.000021561345,0.00070098176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955195,0.00015037466,0.000017990293,0.000090219335,0.000107611406,0.00008181828],"domain_scores_gemma":[0.99897254,0.00052612904,0.0001988995,0.00011706602,0.00014562864,0.00003981414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009353453,0.00047946718,0.0006949102,0.0004390627,0.00028634473,0.0005113222,0.0007939769,0.00039828164,0.0008989427],"category_scores_gemma":[0.0023854293,0.00029219346,0.00074236,0.00036895843,0.00085408223,0.0009123586,0.0006230361,0.000489919,0.00009764172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024682051,0.000005391238,0.0005369406,0.000020171432,0.000014768913,0.00005804502,0.000030238745,0.9876957,0.00088130747,0.0077683125,0.00013523662,0.0028292434],"study_design_scores_gemma":[0.0000016318658,0.000007835765,0.00031538756,0.0000018783842,0.0000031944744,0.0000138831,0.000006694829,0.9948521,0.00011222647,0.0046163676,0.00006572435,0.0000031497211],"about_ca_topic_score_codex":0.0060217576,"about_ca_topic_score_gemma":0.003047293,"teacher_disagreement_score":0.0060217576,"about_ca_system_score_codex":0.000712664,"about_ca_system_score_gemma":0.000580892,"threshold_uncertainty_score":0.011973441},"labels":[],"label_agreement":null},{"id":"W2065809635","doi":"10.1108/13552510410539204","title":"Stochastic analysis of a maintainable robot‐safety system with common‐cause failures","year":2004,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Reliability (semiconductor); Common cause failure; Reliability engineering; Variance (accounting); Mean time between failures; Constant (computer programming); Markov model; Robot; Markov chain; Markov process; Variable (mathematics); Engineering; State (computer science); Failure rate; Computer science; Common cause and special cause; Statistics; Mathematics; Algorithm; Artificial intelligence; Operations management","score_opus":0.0086000420875686,"score_gpt":0.23262024396924885,"score_spread":0.22402020188168026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065809635","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38506,0.0006271684,0.609405,0.00047433507,0.00003672944,0.000049673137,0.00018159562,0.0002049278,0.0039605773],"genre_scores_gemma":[0.99417347,0.00019379541,0.004397346,0.000017418432,0.000024591467,0.00003387421,0.00006959012,0.000015968222,0.0010739387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990514,0.00031051913,0.000027323795,0.000098803735,0.00035440727,0.00015758706],"domain_scores_gemma":[0.99648935,0.0023398935,0.0005722659,0.000104126455,0.00038354765,0.00011078222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018635517,0.00055829156,0.0007314403,0.00079787907,0.00042936235,0.00063586904,0.0007217423,0.00055457663,0.0010166686],"category_scores_gemma":[0.004778482,0.00043584823,0.0007040362,0.0004462073,0.0011676708,0.00071365316,0.0006728958,0.00057739817,0.000093767805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039158207,0.0000112692,0.0005704361,0.000030566385,0.000027730544,0.00008905498,0.00003676918,0.97869736,0.0015778313,0.016949652,0.00014764321,0.0018224706],"study_design_scores_gemma":[0.0000052449423,0.00001729728,0.00046435653,0.0000020259774,0.000008677734,0.000015979102,0.0000081481285,0.99542975,0.00019797216,0.0037683684,0.000076720455,0.0000053996655],"about_ca_topic_score_codex":0.011069352,"about_ca_topic_score_gemma":0.0045935735,"teacher_disagreement_score":0.011069352,"about_ca_system_score_codex":0.0010942963,"about_ca_system_score_gemma":0.0010639937,"threshold_uncertainty_score":0.02200985},"labels":[],"label_agreement":null},{"id":"W2066074156","doi":"10.1109/tr.2015.2421819","title":"Periodic Inspection Optimization of a k-Out-of-n Load-Sharing System","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Load sharing; Interval (graph theory); Component (thermodynamics); Reliability engineering; Poisson distribution; Function (biology); Hazard; Process (computing); Moment (physics); Poisson process; Mathematical optimization; Interval arithmetic; Computer science; Mathematics; Engineering; Distributed computing; Statistics","score_opus":0.016838392383250127,"score_gpt":0.22050464138447964,"score_spread":0.2036662490012295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066074156","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51630276,0.00059505546,0.47404218,0.00059582526,0.0000751448,0.0001522165,0.00023118808,0.0003896538,0.007615984],"genre_scores_gemma":[0.9884382,0.000083949024,0.008879723,0.000024042283,0.000012396224,0.00004486736,0.000056664943,0.00002332183,0.0024369129],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993591,0.00018383037,0.00002693768,0.0001537395,0.00010802874,0.0001683968],"domain_scores_gemma":[0.99857426,0.0006097897,0.00036437184,0.00007703617,0.00018803717,0.00018644508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015292896,0.0010595113,0.0018163258,0.00059583737,0.00066229014,0.0009864734,0.0016479196,0.001298588,0.003305943],"category_scores_gemma":[0.0032055215,0.0007044493,0.0007491705,0.0006216641,0.00095427607,0.0010975631,0.0010620361,0.0006707774,0.0002577507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089387,0.000027799948,0.00054862886,0.000029900802,0.000019967221,0.00014608854,0.000031424654,0.9938983,0.00078877434,0.0017704932,0.00018978078,0.0024594415],"study_design_scores_gemma":[0.000008763232,0.000030216415,0.00023321563,0.0000017835721,0.0000071980985,0.00001656993,0.000011351144,0.99880326,0.0000849081,0.0007489741,0.00005038738,0.0000034639243],"about_ca_topic_score_codex":0.01090549,"about_ca_topic_score_gemma":0.005446784,"teacher_disagreement_score":0.01090549,"about_ca_system_score_codex":0.0013770674,"about_ca_system_score_gemma":0.0009866247,"threshold_uncertainty_score":0.02168405},"labels":[],"label_agreement":null},{"id":"W2067640696","doi":"10.1080/0740817x.2012.761371","title":"Selective maintenance modeling for a multistate system with multistate components under imperfect maintenance","year":2013,"lang":"en","type":"article","venue":"IIE Transactions","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":116,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability engineering; Imperfect; Component (thermodynamics); Maintenance actions; Reliability (semiconductor); Function (biology); Computer science; Predictive maintenance; Engineering","score_opus":0.010196622769053712,"score_gpt":0.1972156953615536,"score_spread":0.1870190725924999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067640696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24591759,0.0006125231,0.7431317,0.0003352143,0.000039950435,0.00008327506,0.0003196515,0.00040576875,0.009154384],"genre_scores_gemma":[0.9893713,0.000172294,0.00772187,0.000016714786,0.000010049164,0.00004834421,0.00007399955,0.000016812068,0.0025686547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966,0.000067559646,0.00001876375,0.000097824755,0.00008784205,0.00006810771],"domain_scores_gemma":[0.9993549,0.0003104047,0.0001574021,0.000043671815,0.0001048464,0.000028773125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007995189,0.0007618981,0.0006375414,0.00069624226,0.00042137862,0.0007779025,0.0009679754,0.00071617466,0.001842562],"category_scores_gemma":[0.001278204,0.0003079074,0.00070743065,0.0005090624,0.0008856736,0.00093510957,0.0006111006,0.000560332,0.0001848288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003686889,0.0000131289935,0.00051056995,0.00002547844,0.000013644222,0.000082502454,0.000050905128,0.98882943,0.0010501418,0.006509059,0.00013211394,0.002746211],"study_design_scores_gemma":[0.0000021218427,0.000013179704,0.0001700999,0.0000016792267,0.0000063902626,0.000008699504,0.0000066943257,0.9983222,0.0001157939,0.0012680396,0.00008291706,0.0000021683338],"about_ca_topic_score_codex":0.010944878,"about_ca_topic_score_gemma":0.007150991,"teacher_disagreement_score":0.010944878,"about_ca_system_score_codex":0.0009687767,"about_ca_system_score_gemma":0.00057948683,"threshold_uncertainty_score":0.021762311},"labels":[],"label_agreement":null},{"id":"W2067960619","doi":"10.1080/0740817x.2010.540638","title":"On the investment in a reliability improvement program for warranted second-hand items","year":2011,"lang":"en","type":"article","venue":"IIE Transactions","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Warranty; Reliability (semiconductor); Upgrade; Investment (military); Reliability engineering; Product (mathematics); Action (physics); Computer science; State (computer science); Return on investment; Operations research; Risk analysis (engineering); Actuarial science; Engineering; Operations management; Business; Economics; Microeconomics; Production (economics); Mathematics; Power (physics)","score_opus":0.017931730525427892,"score_gpt":0.2154855122191438,"score_spread":0.1975537816937159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067960619","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38799712,0.00093528815,0.55764925,0.003246519,0.00019907452,0.00045477183,0.00091331813,0.0004000065,0.048204694],"genre_scores_gemma":[0.97027135,0.0004865798,0.01428436,0.00006860767,0.00003878204,0.00012499384,0.00017099317,0.000038998158,0.014515312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989705,0.0003051894,0.000026097674,0.00017909808,0.00028292215,0.00023616204],"domain_scores_gemma":[0.9983954,0.0009610776,0.0002875075,0.00009639989,0.00013845028,0.00012116466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015897561,0.0010255573,0.0012675746,0.0007669846,0.00066274137,0.0014289512,0.0016020569,0.0023897216,0.0076140473],"category_scores_gemma":[0.0037091626,0.0009228095,0.0008395811,0.000794911,0.0008107283,0.0022487233,0.0007757838,0.00202345,0.0006786295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026400608,0.00011065294,0.0012407991,0.000100731624,0.000039652554,0.0002703506,0.0000496697,0.9512137,0.0024871528,0.027304431,0.0008221707,0.016096668],"study_design_scores_gemma":[0.000035840905,0.0002565848,0.0018955301,0.000033101267,0.000061574836,0.00012559815,0.00006214982,0.98764485,0.0011066815,0.0070642913,0.0016850159,0.00002883792],"about_ca_topic_score_codex":0.007443573,"about_ca_topic_score_gemma":0.008904509,"teacher_disagreement_score":0.0076140473,"about_ca_system_score_codex":0.0026640464,"about_ca_system_score_gemma":0.0022456057,"threshold_uncertainty_score":0.025471509},"labels":[],"label_agreement":null},{"id":"W2069052392","doi":"10.1080/00207543.2010.492798","title":"Preventive maintenance and replacement policies for deteriorating production systems subject to imperfect repairs","year":2010,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Preventive maintenance; Reliability engineering; Failure rate; Proactive maintenance; Corrective maintenance; Planned maintenance; Engineering; Imperfect; Failure mode and effects analysis; Production (economics); Maintenance actions; Risk analysis (engineering); Operations research; Computer science; Operations management; Business; Economics","score_opus":0.026883871398125647,"score_gpt":0.3480416117801532,"score_spread":0.32115774038202755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069052392","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21302752,0.0012874139,0.78064024,0.0003733199,0.00007627835,0.00011857521,0.00013856185,0.00036460848,0.0039734696],"genre_scores_gemma":[0.9818013,0.00029461406,0.016455691,0.000029172665,0.00002408073,0.000047810845,0.000052940966,0.000018442042,0.0012760203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984621,0.0006128646,0.00006767887,0.00021142639,0.00028728248,0.00035854944],"domain_scores_gemma":[0.9965364,0.0020158864,0.00079570594,0.00020827891,0.0002572841,0.00018643544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034083948,0.0011159829,0.001517674,0.00096770807,0.0005198511,0.0011208161,0.0013824899,0.0011267032,0.0017863171],"category_scores_gemma":[0.0057552215,0.0006586171,0.0008553475,0.00069204066,0.0014483643,0.0007459014,0.00082939473,0.0010728126,0.00020565906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010842588,0.000045356148,0.0004862535,0.00006218819,0.000027935279,0.000115979594,0.00004929156,0.9828355,0.0015153021,0.008818922,0.00018279592,0.0057521025],"study_design_scores_gemma":[0.000024571485,0.000093286464,0.0005251619,0.0000095129135,0.000027647524,0.000035680932,0.00001219271,0.9936613,0.0004601042,0.004952451,0.00018860151,0.000009469412],"about_ca_topic_score_codex":0.005647601,"about_ca_topic_score_gemma":0.0028419907,"teacher_disagreement_score":0.005647601,"about_ca_system_score_codex":0.0012960896,"about_ca_system_score_gemma":0.001214837,"threshold_uncertainty_score":0.018025517},"labels":[],"label_agreement":null},{"id":"W2069478775","doi":"10.1016/j.cie.2008.11.023","title":"Optimal maintenance policy for a multi-state deteriorating system with two types of failures under general repair","year":2008,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Canada Research Chairs","funders":"","keywords":"Markov decision process; Minification; Mathematical optimization; Optimal maintenance; Computer science; Process (computing); State (computer science); Markov process; Reliability engineering; Computational complexity theory; Algorithm; Engineering; Mathematics","score_opus":0.028704046744751417,"score_gpt":0.22784030444860026,"score_spread":0.19913625770384885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069478775","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6729375,0.0007970038,0.32091606,0.0012094491,0.00009193929,0.0001224896,0.00021088109,0.0004178433,0.0032969],"genre_scores_gemma":[0.99422705,0.000077694305,0.0048127146,0.00003122655,0.0000148775125,0.000017656888,0.000030549243,0.000012633836,0.0007755359],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957865,0.00013807896,0.000020813552,0.00009037022,0.000045908902,0.00012618648],"domain_scores_gemma":[0.997741,0.0013147992,0.0003119524,0.000108188324,0.00033095744,0.00019310028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014221721,0.0009608265,0.0013623098,0.0007966008,0.00062223885,0.00101206,0.001137877,0.0019776432,0.0015026074],"category_scores_gemma":[0.002765988,0.00063649204,0.0005133858,0.00048157285,0.0010297607,0.0009580444,0.0007370513,0.0010272162,0.00014509643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004893115,0.000058984366,0.00066406955,0.00009250756,0.000042178035,0.00016636917,0.00008097831,0.9858732,0.0035766894,0.0036443248,0.000438674,0.0048726564],"study_design_scores_gemma":[0.000029905344,0.00006472905,0.00049225264,0.000004604302,0.000028782242,0.000020966843,0.000017155075,0.9974565,0.00031878657,0.0015186332,0.000040132352,0.0000076186257],"about_ca_topic_score_codex":0.007206898,"about_ca_topic_score_gemma":0.0035495718,"teacher_disagreement_score":0.007206898,"about_ca_system_score_codex":0.0012778541,"about_ca_system_score_gemma":0.0009226288,"threshold_uncertainty_score":0.014329851},"labels":[],"label_agreement":null},{"id":"W2070599978","doi":"10.1108/13552511111134619","title":"Reliability and availability analysis of a robot‐safety system","year":2011,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Backup; Reliability engineering; Robot; Reliability (semiconductor); Process (computing); Engineering; System safety; Markov process; Mean time between failures; Markov chain; Markov model; Computer science; Simulation; Failure rate; Artificial intelligence","score_opus":0.016369480219774617,"score_gpt":0.22683976336942027,"score_spread":0.21047028314964566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070599978","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6897754,0.0006125228,0.30400455,0.00022650317,0.0000248792,0.000045998353,0.0001292367,0.00022135249,0.0049595027],"genre_scores_gemma":[0.99632084,0.000046224373,0.0030732958,0.000005640289,0.000004813949,0.00001142669,0.000024463341,0.000007713788,0.00050549634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934417,0.00020475582,0.000021568634,0.00010596043,0.00024570464,0.0000778598],"domain_scores_gemma":[0.997097,0.0018743553,0.00039448318,0.00016219832,0.00041376802,0.000058148602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008238562,0.00029508423,0.00040049173,0.00047997475,0.00029473254,0.00039936975,0.00042012418,0.0003119882,0.0020005761],"category_scores_gemma":[0.0035724351,0.00016942868,0.00045418882,0.00023099955,0.0005710122,0.00056735787,0.0003385436,0.000413167,0.00018536266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017899854,0.000039848397,0.005566822,0.00011922042,0.000056680245,0.00019398652,0.00013660558,0.958624,0.011824703,0.010039826,0.00027728753,0.012942039],"study_design_scores_gemma":[0.000009200636,0.00015187719,0.0041551576,0.0000079023985,0.000023039056,0.000093901544,0.0000416804,0.9894364,0.0023220207,0.003485271,0.00026324546,0.00001034119],"about_ca_topic_score_codex":0.004546608,"about_ca_topic_score_gemma":0.00178116,"teacher_disagreement_score":0.004546608,"about_ca_system_score_codex":0.0007938512,"about_ca_system_score_gemma":0.0007747193,"threshold_uncertainty_score":0.009040296},"labels":[],"label_agreement":null},{"id":"W2070991662","doi":"10.1108/13552510310466927","title":"Hierarchical control of production and maintenance rates in manufacturing systems","year":2003,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; École de Technologie Supérieure; Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Preventive maintenance; Optimal control; Production control; Limiting; Mathematical optimization; Production (economics); Reduction (mathematics); Control (management); Optimal maintenance; Stochastic control; Control theory (sociology); Production planning; Engineering; Computer science; Reliability engineering; Mathematics; Economics","score_opus":0.009706710725806434,"score_gpt":0.23311181342613108,"score_spread":0.22340510270032465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070991662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19103275,0.00030694803,0.8050335,0.00022861378,0.000030409163,0.000048110407,0.000053379008,0.00041650163,0.0028498073],"genre_scores_gemma":[0.9871236,0.00007535796,0.012101435,0.000015417327,0.000010326291,0.000033707693,0.000027522334,0.0000106440675,0.0006020257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917585,0.00025335874,0.000035255594,0.00016222925,0.00024840358,0.00012492113],"domain_scores_gemma":[0.998643,0.00066686247,0.0003284889,0.00011902776,0.00016304968,0.00007958124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001593664,0.0005869116,0.0005380356,0.0003434041,0.00034881302,0.00082101964,0.00121735,0.0005792196,0.0009077646],"category_scores_gemma":[0.0045961994,0.00036405263,0.0004811122,0.00027761166,0.0008441375,0.0010088264,0.00083798415,0.00082111434,0.00013975517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029618757,0.00003004241,0.0003406464,0.000023407252,0.000007791155,0.000026401467,0.000041923067,0.97653925,0.002746291,0.012910769,0.00011898104,0.0071848296],"study_design_scores_gemma":[0.0000049648465,0.000016260548,0.00014092684,0.0000012050618,0.000002189458,0.0000027925532,0.0000032628584,0.99651474,0.00036878037,0.0028727413,0.00006957237,0.000002573504],"about_ca_topic_score_codex":0.008084724,"about_ca_topic_score_gemma":0.0037754457,"teacher_disagreement_score":0.008084724,"about_ca_system_score_codex":0.0015232293,"about_ca_system_score_gemma":0.0009928831,"threshold_uncertainty_score":0.016075313},"labels":[],"label_agreement":null},{"id":"W2072863921","doi":"10.1108/13552511111180203","title":"Optimal replacement with minimal repair policy for a system operating over a random time horizon","year":2011,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Time horizon; Mathematical optimization; Simple (philosophy); Function (biology); Horizon; Reliability engineering; Operations research; Originality; Computer science; Value (mathematics); Operations management; Engineering; Mathematics","score_opus":0.012745199050193093,"score_gpt":0.2387178765165174,"score_spread":0.22597267746632432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072863921","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39641318,0.0015951522,0.59494925,0.00072130765,0.0000710763,0.0001559685,0.00025080802,0.00029808062,0.0055452026],"genre_scores_gemma":[0.9844743,0.00019912943,0.013375537,0.000024089664,0.0000138626365,0.000040291612,0.0000674623,0.000024181458,0.0017812767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990841,0.00034609027,0.000032578173,0.00016722956,0.0001571333,0.00021280134],"domain_scores_gemma":[0.99808747,0.0010894244,0.00045949288,0.00009294931,0.00014928723,0.00012135414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014824218,0.0007333422,0.0010683694,0.0006190139,0.00036196597,0.0008328271,0.0007796959,0.0007760877,0.0016638824],"category_scores_gemma":[0.0040821005,0.00054758467,0.00061730447,0.00040505044,0.0008798908,0.0008610934,0.000446905,0.0007262764,0.00018237477],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001233401,0.000031135394,0.00048393168,0.000046427496,0.000024949682,0.000054270444,0.000017276736,0.9912236,0.0014727735,0.002659686,0.0001400734,0.0037226044],"study_design_scores_gemma":[0.000022494594,0.00016241815,0.00066993246,0.000007576112,0.000029672485,0.00003274093,0.000017184895,0.9958865,0.0007169503,0.0022312254,0.00021436231,0.000008976475],"about_ca_topic_score_codex":0.0046626832,"about_ca_topic_score_gemma":0.002813307,"teacher_disagreement_score":0.0046626832,"about_ca_system_score_codex":0.0014143236,"about_ca_system_score_gemma":0.0013063039,"threshold_uncertainty_score":0.010261714},"labels":[],"label_agreement":null},{"id":"W2074236332","doi":"10.1016/j.ress.2015.03.029","title":"Optimal preventive maintenance and repair policies for multi-state systems","year":2015,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Imperfect; Schedule; Preventive maintenance; Reliability engineering; State (computer science); Optimal maintenance; State space; Mathematical optimization; Markov process; Computer science; Homogeneous; Operations research; Engineering; Mathematics; Algorithm; Statistics","score_opus":0.013753730155873762,"score_gpt":0.22184226134202556,"score_spread":0.2080885311861518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074236332","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3644869,0.0015236945,0.6258532,0.0015662772,0.00014263563,0.00016975337,0.0004213112,0.0005880229,0.0052482206],"genre_scores_gemma":[0.98519623,0.00022472849,0.012607358,0.00005199348,0.000042430456,0.00005050186,0.00008671428,0.00003643123,0.0017036059],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989574,0.00033007003,0.000059967762,0.00019355708,0.00014747701,0.0003115301],"domain_scores_gemma":[0.992268,0.005948386,0.00073937554,0.00025945986,0.00046334116,0.00032141016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026417598,0.0013397206,0.001884591,0.0014844143,0.0006677856,0.00160591,0.0014478823,0.0015634394,0.0023257467],"category_scores_gemma":[0.009339715,0.0012180674,0.0007961148,0.0007669934,0.0015597577,0.002043208,0.0013215208,0.0015230392,0.00021901166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002052826,0.0000657739,0.0002804535,0.000047729292,0.00003616673,0.0000334126,0.000030673265,0.9884952,0.00083346426,0.0043347385,0.00030991723,0.00532731],"study_design_scores_gemma":[0.00002326243,0.00003731893,0.00023945703,0.000005227918,0.0000152400535,0.000008008481,0.000010384303,0.99529785,0.0002130742,0.004096947,0.000047356076,0.000005963926],"about_ca_topic_score_codex":0.008094404,"about_ca_topic_score_gemma":0.006287831,"teacher_disagreement_score":0.008094404,"about_ca_system_score_codex":0.0022469459,"about_ca_system_score_gemma":0.0021448275,"threshold_uncertainty_score":0.016302824},"labels":[],"label_agreement":null},{"id":"W2074611292","doi":"10.1016/j.ress.2015.01.014","title":"An extended optimal replacement model for a deteriorating system with inspections","year":2015,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Reliability engineering; Type (biology); Catastrophic failure; Failure rate; Computer science; Engineering","score_opus":0.009850551881605635,"score_gpt":0.20929682419414772,"score_spread":0.19944627231254208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074611292","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1805467,0.0022764753,0.7852272,0.001648743,0.00030312315,0.00020256515,0.0014048711,0.00071400445,0.027676374],"genre_scores_gemma":[0.94881034,0.00076052913,0.02453416,0.00013542986,0.00008948635,0.00018007142,0.00043842298,0.00012553231,0.024926122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999186,0.0002500299,0.000041767616,0.00020935056,0.00014190111,0.00017101709],"domain_scores_gemma":[0.9987332,0.0006022998,0.00020585713,0.00007187802,0.00028429014,0.000102403515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015553604,0.001596729,0.0028157374,0.0011469874,0.0007069039,0.002193286,0.0037894137,0.0040627085,0.0062575047],"category_scores_gemma":[0.0033681442,0.0013887964,0.0014765081,0.001230952,0.0014439918,0.0019247532,0.0012095965,0.0019110561,0.00070447376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003490562,0.000016717917,0.0001424298,0.000042360127,0.00001549458,0.000093670096,0.000024587856,0.9934447,0.00033837673,0.004334065,0.0002669077,0.0012458126],"study_design_scores_gemma":[0.000010632512,0.0000145180165,0.00009726484,0.0000044230096,0.000014410684,0.000013833767,0.000006481661,0.9979836,0.000040596045,0.0016602557,0.00014743416,0.000006477854],"about_ca_topic_score_codex":0.027544247,"about_ca_topic_score_gemma":0.012719408,"teacher_disagreement_score":0.027544247,"about_ca_system_score_codex":0.0018480583,"about_ca_system_score_gemma":0.00199623,"threshold_uncertainty_score":0.054767847},"labels":[],"label_agreement":null},{"id":"W2074982381","doi":"10.1016/j.ress.2008.01.009","title":"Optimal design of multi-state weighted k-out-of-n systems based on component design","year":2008,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reliability (semiconductor); Component (thermodynamics); Mathematical optimization; Genetic algorithm; Reliability engineering; Selection (genetic algorithm); Optimization problem; State (computer science); Computer science; Mathematics; Engineering; Algorithm; Artificial intelligence","score_opus":0.0196878663532699,"score_gpt":0.19926874268139405,"score_spread":0.17958087632812414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074982381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10360002,0.00039549705,0.8885808,0.00023931212,0.00009379233,0.00011494285,0.0000773762,0.00023611005,0.006662233],"genre_scores_gemma":[0.9572965,0.0001230781,0.040062677,0.00005141405,0.000023878718,0.00011955812,0.00006189877,0.000031642972,0.0022293995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908423,0.00031461855,0.000048641945,0.00021626076,0.00016683261,0.00016943706],"domain_scores_gemma":[0.99879223,0.00054927776,0.00023685514,0.00007079322,0.0002758921,0.00007500351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013694024,0.0013118958,0.0017233749,0.0007294227,0.00083106477,0.0014298,0.0014642578,0.0013024544,0.0021419444],"category_scores_gemma":[0.003126844,0.00093601475,0.0007267988,0.00059860514,0.0010746615,0.0013441049,0.0014421586,0.0007689726,0.00027573117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013578891,0.00002830798,0.0002209139,0.00005505851,0.000041311392,0.000028862865,0.00003965659,0.9847787,0.0016924558,0.0035953764,0.00016660134,0.009217067],"study_design_scores_gemma":[0.000012702192,0.00004468107,0.0001008693,0.000004739031,0.0000131621555,0.0000049602186,0.0000063342773,0.9978728,0.00033971653,0.0014762497,0.000118821124,0.000005042628],"about_ca_topic_score_codex":0.0065852376,"about_ca_topic_score_gemma":0.010555234,"teacher_disagreement_score":0.0065852376,"about_ca_system_score_codex":0.0012812817,"about_ca_system_score_gemma":0.0014028697,"threshold_uncertainty_score":0.013093829},"labels":[],"label_agreement":null},{"id":"W2075090660","doi":"10.1002/asmb.1920","title":"Parameter estimation for partially observable systems subject to random failure","year":2012,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Unobservable; Observable; Expectation–maximization algorithm; State (computer science); State vector; Computer science; Multivariate statistics; Maximization; Markov chain; Applied mathematics; Mathematics; Mathematical optimization; Algorithm; Statistics; Econometrics; Maximum likelihood","score_opus":0.02336913945640077,"score_gpt":0.22104290994442488,"score_spread":0.19767377048802412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075090660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016204955,0.00013703699,0.98303694,0.00011298142,0.000007864402,0.000022673197,0.00006347423,0.00016909228,0.00024496324],"genre_scores_gemma":[0.87427276,0.0005320808,0.120619714,0.00009703636,0.000075299686,0.00033539202,0.0007798901,0.00014897286,0.0031388653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99842405,0.00070000655,0.00007289426,0.00041351127,0.00025520846,0.0001344581],"domain_scores_gemma":[0.9879401,0.009722988,0.0012247977,0.00049606984,0.0005055766,0.00011051557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003977027,0.0012914785,0.0019791552,0.00090720516,0.0004859528,0.001244333,0.0017359209,0.0016190237,0.0016095457],"category_scores_gemma":[0.022227112,0.0014074289,0.0012128067,0.000742653,0.0017373157,0.0021990205,0.0014895067,0.0020320108,0.00033936568],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034650686,0.000012286557,0.0006598669,0.000033580785,0.00004493533,0.000042471227,0.00003678163,0.98555815,0.00031848988,0.005968042,0.00012703301,0.0071636653],"study_design_scores_gemma":[0.0000052314995,0.000009249433,0.00019863219,0.0000044032217,0.00000513558,0.000009782633,0.0000041937856,0.99462986,0.00013817562,0.0049003377,0.000087664994,0.0000073069737],"about_ca_topic_score_codex":0.010263423,"about_ca_topic_score_gemma":0.0053532147,"teacher_disagreement_score":0.010263423,"about_ca_system_score_codex":0.0011646476,"about_ca_system_score_gemma":0.0014609005,"threshold_uncertainty_score":0.02103281},"labels":[],"label_agreement":null},{"id":"W2075221061","doi":"10.1016/j.ress.2009.10.004","title":"Discounted cost model for condition-based maintenance optimization","year":2009,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preventive maintenance; Reliability (semiconductor); Reliability engineering; Condition-based maintenance; Optimal maintenance; Mathematical optimization; Computer science; Process (computing); Point (geometry); Engineering; Mathematics","score_opus":0.005114522545990426,"score_gpt":0.20226969651187382,"score_spread":0.1971551739658834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075221061","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029788537,0.001468279,0.952401,0.0009932988,0.00025268248,0.00012657972,0.000994846,0.0004636963,0.013511158],"genre_scores_gemma":[0.8976174,0.0009493824,0.064601615,0.0002343973,0.00016678945,0.0003710775,0.0009496251,0.0002222514,0.034887414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884063,0.00043849467,0.000044088625,0.00016880728,0.00029804284,0.00021003118],"domain_scores_gemma":[0.99705184,0.00203815,0.00018505416,0.00014505799,0.00038629372,0.00019357294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028266755,0.0017159024,0.003180781,0.0012769153,0.00063050445,0.0024912662,0.004619202,0.0032183349,0.008807428],"category_scores_gemma":[0.008573294,0.0016271872,0.0011129333,0.0016225382,0.0015830352,0.0025953935,0.0012867881,0.0029027313,0.0009903348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051599214,0.000020613257,0.000081312006,0.000038322687,0.000017832415,0.000027602398,0.000012376573,0.9831655,0.00014900471,0.01288462,0.00061996817,0.0029312943],"study_design_scores_gemma":[0.00000786606,0.00000789513,0.000040698575,0.0000040767345,0.0000071234517,0.000004983201,0.0000014707856,0.9938605,0.00003334359,0.00582978,0.00019760823,0.000004576193],"about_ca_topic_score_codex":0.015833365,"about_ca_topic_score_gemma":0.00916501,"teacher_disagreement_score":0.015833365,"about_ca_system_score_codex":0.0038440756,"about_ca_system_score_gemma":0.0020670935,"threshold_uncertainty_score":0.031482458},"labels":[],"label_agreement":null},{"id":"W2075291636","doi":"10.1016/j.ress.2011.12.011","title":"Ergodicity of forward times of the renewal process in a block-based inspection model using the delay time concept","year":2011,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Engineering and Physical Sciences Research Council; Ontario Centres of Excellence","keywords":"Schedule; Reliability engineering; Component (thermodynamics); Renewal theory; Inspection time; Interval (graph theory); Block (permutation group theory); Process (computing); Computer science; Service (business); Ergodicity; Operations research; Engineering; Industrial engineering; Mathematics; Statistics","score_opus":0.006888763576153668,"score_gpt":0.18320534178493375,"score_spread":0.17631657820878008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075291636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18523401,0.0008935642,0.8080234,0.0004769859,0.000074107666,0.000037404596,0.00017948025,0.00015964294,0.0049214843],"genre_scores_gemma":[0.97779524,0.00088235096,0.01363546,0.00005667469,0.0000834617,0.00006251291,0.00016709806,0.00010994316,0.0072071482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933356,0.00024049725,0.0000337548,0.000107794636,0.00015187063,0.00013254605],"domain_scores_gemma":[0.9928047,0.005049815,0.0009170629,0.0003359766,0.0006221893,0.000270256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033699258,0.0008827165,0.0010821327,0.0015662549,0.0004804767,0.0016583991,0.0014910749,0.0010841349,0.0022510076],"category_scores_gemma":[0.011049268,0.00075898354,0.0012260327,0.0008475464,0.001964984,0.0030876058,0.0012257607,0.0015055377,0.00024104954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105287814,0.0000466507,0.0015989582,0.000101048245,0.00007150396,0.00017597919,0.00015902746,0.60175246,0.003044357,0.38827568,0.0005513833,0.004117676],"study_design_scores_gemma":[0.0000071236086,0.000014925822,0.0003310406,0.0000086204545,0.000021840815,0.000033034583,0.000013611635,0.9621975,0.0003471374,0.036831222,0.00017695811,0.000017052022],"about_ca_topic_score_codex":0.0059443284,"about_ca_topic_score_gemma":0.0035152044,"teacher_disagreement_score":0.0059443284,"about_ca_system_score_codex":0.0017519873,"about_ca_system_score_gemma":0.0016154904,"threshold_uncertainty_score":0.017822087},"labels":[],"label_agreement":null},{"id":"W2076133331","doi":"10.1016/j.ress.2014.07.025","title":"Relevance of control theory to design and maintenance problems in time-variant reliability: The case of stochastic viability","year":2014,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Reliability (semiconductor); Mathematical optimization; Set (abstract data type); Computer science; Reliability theory; Relevance (law); Markov process; Stochastic process; Reliability engineering; Mathematics; Engineering; Statistics; Failure rate","score_opus":0.0030728032511846354,"score_gpt":0.1693172500791967,"score_spread":0.16624444682801207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076133331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044281717,0.0024032476,0.91826624,0.008403941,0.00036060732,0.00003916317,0.000078716825,0.000050195376,0.026116174],"genre_scores_gemma":[0.96501756,0.0017731495,0.02559457,0.00050141825,0.00070338836,0.0000867574,0.000054036114,0.000048720776,0.0062204413],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981122,0.0010442616,0.000066972636,0.0002576359,0.00034851505,0.00017044552],"domain_scores_gemma":[0.98783433,0.010312462,0.00056408875,0.00033399503,0.00067058275,0.0002845734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043608746,0.0010595703,0.0015479671,0.0010779373,0.00084478,0.0031429643,0.001543489,0.0026449075,0.0026173003],"category_scores_gemma":[0.01723554,0.0006155282,0.0015153844,0.0007417723,0.0052390355,0.0038956997,0.0016233742,0.0029606463,0.00012094106],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019206986,0.00002934276,0.00024726705,0.000059472808,0.00003299064,0.00008488246,0.000052126514,0.16737166,0.00025725292,0.8271651,0.0007037751,0.0039768172],"study_design_scores_gemma":[0.000014612887,0.000022589647,0.00013272357,0.000018444729,0.000010452466,0.000024680294,0.000026448964,0.39653182,0.00007747797,0.6025901,0.0005371218,0.0000135465825],"about_ca_topic_score_codex":0.0035597086,"about_ca_topic_score_gemma":0.0018319103,"teacher_disagreement_score":0.0043608746,"about_ca_system_score_codex":0.0019130581,"about_ca_system_score_gemma":0.0019461754,"threshold_uncertainty_score":0.023062766},"labels":[],"label_agreement":null},{"id":"W2076529707","doi":"10.1080/00207540600596882","title":"Optimal condition based maintenance with imperfect information and the proportional hazards model","year":2006,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":124,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Condition-based maintenance; Optimal maintenance; Partially observable Markov decision process; Markov decision process; Dynamic programming; Reliability engineering; Preventive maintenance; Mathematical optimization; Maintenance actions; Markov process; State (computer science); Process (computing); Computer science; Engineering; Mathematics; Statistics; Algorithm","score_opus":0.010453693590908326,"score_gpt":0.27767389506815426,"score_spread":0.26722020147724596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076529707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04141643,0.00063671864,0.9524753,0.00076222466,0.00006753583,0.000082163315,0.00038200073,0.00027392394,0.0039036865],"genre_scores_gemma":[0.9512989,0.0005023638,0.04351786,0.00010476208,0.00006380982,0.00017717092,0.00028368508,0.000038620172,0.0040126485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981951,0.0006071621,0.00006488376,0.00037475833,0.00045773428,0.00030039737],"domain_scores_gemma":[0.9947885,0.0040642093,0.0004889445,0.00021227838,0.00030126216,0.00014487756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028303945,0.0010507897,0.0015889684,0.00071191357,0.00036773502,0.0012720416,0.0016415461,0.0013683445,0.0034233774],"category_scores_gemma":[0.009198781,0.00085610495,0.0008601726,0.000686596,0.0016417791,0.0020774691,0.0012887162,0.001523325,0.00024948383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087213186,0.000035459398,0.0005312933,0.000050370865,0.000033530112,0.00008924002,0.000037915506,0.9656145,0.00037223008,0.02551344,0.00049041444,0.007144378],"study_design_scores_gemma":[0.000023821825,0.000035045447,0.00023186867,0.000005576379,0.0000130844055,0.000021082516,0.000006914956,0.9784757,0.00013765541,0.020828666,0.0002104639,0.000010113226],"about_ca_topic_score_codex":0.0087537095,"about_ca_topic_score_gemma":0.0041017365,"teacher_disagreement_score":0.0087537095,"about_ca_system_score_codex":0.0017878609,"about_ca_system_score_gemma":0.0019815057,"threshold_uncertainty_score":0.01740551},"labels":[],"label_agreement":null},{"id":"W2078436846","doi":"10.1007/s00170-013-5183-7","title":"Joint control of production, overhaul, and preventive maintenance for a production system subject to quality and reliability deteriorations","year":2013,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec; Université du Québec à Montréal","funders":"","keywords":"Preventive maintenance; Reliability engineering; Quality (philosophy); Production (economics); Reliability (semiconductor); Control (management); Engineering; Function (biology); Time horizon; Plan (archaeology); Operations management; Operations research; Risk analysis (engineering); Computer science; Mathematical optimization; Business; Economics; Mathematics; Artificial intelligence; Microeconomics","score_opus":0.007383374905245211,"score_gpt":0.23427630868537527,"score_spread":0.22689293378013006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078436846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4476865,0.0006141283,0.5457678,0.000557565,0.00015835684,0.00008341167,0.00005159833,0.00050475815,0.00457586],"genre_scores_gemma":[0.99667156,0.000042470572,0.002672341,0.0000111624395,0.000015153911,0.000012888706,0.0000063879097,0.000009444285,0.000558589],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952173,0.000101396225,0.000024067755,0.00011384562,0.00011682113,0.00012220458],"domain_scores_gemma":[0.9986355,0.0006034891,0.0003329686,0.000062544386,0.00026895507,0.00009658118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010983868,0.00080595457,0.0007503558,0.00033879318,0.0003763027,0.0013595743,0.00077422,0.00050831784,0.00090152066],"category_scores_gemma":[0.0026351055,0.00030181042,0.0003282361,0.00023461472,0.00067499606,0.00050407427,0.0006187907,0.00067726005,0.00009074683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008938137,0.00020954461,0.0014226459,0.00015299939,0.00007331693,0.0001272409,0.00017748223,0.9120091,0.031008717,0.0064124535,0.00083832396,0.04667429],"study_design_scores_gemma":[0.000030397592,0.00018964989,0.0011474723,0.000004069795,0.0000292804,0.000018305094,0.000013112688,0.9950767,0.0023361053,0.0010249164,0.00012207056,0.000007881629],"about_ca_topic_score_codex":0.0050593764,"about_ca_topic_score_gemma":0.003147665,"teacher_disagreement_score":0.0050593764,"about_ca_system_score_codex":0.00061008654,"about_ca_system_score_gemma":0.0010334477,"threshold_uncertainty_score":0.0100598335},"labels":[],"label_agreement":null},{"id":"W2078861181","doi":"10.1016/j.apm.2013.03.017","title":"Age replacement policy with lead-time for a system subject to non-homogeneous pure birth shocks","year":2013,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Council","keywords":"Spare part; Homogeneous; Generalization; Shock (circulatory); Catastrophic failure; Computer science; Mathematics; Engineering; Operations management; Medicine; Physics; Thermodynamics; Mathematical analysis; Internal medicine","score_opus":0.008178268067143805,"score_gpt":0.19662308730658995,"score_spread":0.18844481923944614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078861181","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58654505,0.0018955917,0.39371604,0.004404973,0.0002961988,0.00019818655,0.0011383285,0.0007908591,0.011014845],"genre_scores_gemma":[0.9815045,0.0005079016,0.006209948,0.00011850249,0.00007218121,0.000050767907,0.00012440635,0.000043376993,0.011368386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937576,0.00023124275,0.000028535816,0.00009605302,0.000066994005,0.00020137409],"domain_scores_gemma":[0.9960601,0.0024958178,0.0005881746,0.00015865857,0.00038503273,0.00031222295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002402908,0.001005256,0.0018541586,0.0010960517,0.0007418561,0.0017847329,0.0019063015,0.002773779,0.004293309],"category_scores_gemma":[0.0062405467,0.00062344415,0.0008165502,0.00090460794,0.0015734275,0.0011899179,0.0013571868,0.0012140659,0.00059297157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039386764,0.000050176182,0.0009833521,0.000110653906,0.00004354791,0.00030217343,0.00009379574,0.96899855,0.0017127261,0.022680687,0.0010679908,0.0035624416],"study_design_scores_gemma":[0.000046919195,0.000073181945,0.00063332607,0.000011902942,0.00004392975,0.000053580843,0.000043077816,0.98983276,0.00036121384,0.008677615,0.00020729357,0.000015215112],"about_ca_topic_score_codex":0.009307515,"about_ca_topic_score_gemma":0.004657552,"teacher_disagreement_score":0.009307515,"about_ca_system_score_codex":0.0017457089,"about_ca_system_score_gemma":0.0013901832,"threshold_uncertainty_score":0.018506646},"labels":[],"label_agreement":null},{"id":"W2080015090","doi":"10.1142/s0217595911003235","title":"OPTIMAL DESIGN OF A MULTI-STATE WEIGHTED SERIES-PARALLEL SYSTEM USING PHYSICAL PROGRAMMING AND GENETIC ALGORITHMS","year":2011,"lang":"en","type":"article","venue":"Asia Pacific Journal of Operational Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Component (thermodynamics); Reliability (semiconductor); Computer science; Mathematical optimization; Genetic algorithm; Series (stratigraphy); Optimization problem; Flexibility (engineering); Series and parallel circuits; Optimal design; Selection (genetic algorithm); State (computer science); Algorithm; Mathematics; Engineering; Artificial intelligence; Machine learning","score_opus":0.08133626238716024,"score_gpt":0.31160211068398724,"score_spread":0.230265848296827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080015090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049389485,0.0002901552,0.9446664,0.00019551512,0.00003642985,0.00009471378,0.0000369443,0.00016390378,0.005126519],"genre_scores_gemma":[0.7950022,0.00038786067,0.19944796,0.00008068095,0.0000357653,0.00035624762,0.00007541874,0.000073023795,0.0045410073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999481,0.00016115727,0.000021182399,0.00012748942,0.00014779568,0.00006137642],"domain_scores_gemma":[0.9993443,0.00033508314,0.00014861328,0.000030374624,0.000110069996,0.00003155944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011069524,0.0010691263,0.001152874,0.00072721415,0.00045597763,0.0010091008,0.0007831376,0.0009858729,0.0016995473],"category_scores_gemma":[0.0016833873,0.0007007943,0.0008851885,0.00065225124,0.0009990147,0.00083491206,0.0007176871,0.00077025354,0.00018081538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014738257,0.000013217843,0.0000839584,0.00002199106,0.000012735657,0.000020522471,0.000010840315,0.99309087,0.00069210917,0.0022032417,0.00006394277,0.0037718427],"study_design_scores_gemma":[0.000008581127,0.000031923613,0.00004576101,0.0000028861286,0.000006940942,0.0000065964737,0.0000057379566,0.9975937,0.00021565927,0.0019258786,0.00015337049,0.0000029869163],"about_ca_topic_score_codex":0.0034416723,"about_ca_topic_score_gemma":0.0034941183,"teacher_disagreement_score":0.0034416723,"about_ca_system_score_codex":0.0011222996,"about_ca_system_score_gemma":0.0013939748,"threshold_uncertainty_score":0.008142889},"labels":[],"label_agreement":null},{"id":"W2081075072","doi":"10.1177/1748006x15573166","title":"A multi-constrained maintenance scheduling optimization model for a hydrocarbon processing facility","year":2015,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Scheduling (production processes); Reliability engineering; Preventive maintenance; Computer science; Reliability (semiconductor); Flexibility (engineering); Predictive maintenance; Optimal maintenance; Mathematical optimization; Engineering; Operations research; Operations management","score_opus":0.016711110142128848,"score_gpt":0.22234054406329892,"score_spread":0.20562943392117009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081075072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06403396,0.0008433196,0.91755056,0.00064799434,0.000089656634,0.00015631922,0.0007714787,0.00034660797,0.0155601],"genre_scores_gemma":[0.9145668,0.00088611775,0.065708354,0.00010687361,0.00005838536,0.00052489096,0.00055753364,0.000088726316,0.0175023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995129,0.00014232342,0.000021603571,0.00011458606,0.00011313559,0.000095469266],"domain_scores_gemma":[0.99959403,0.00018480323,0.00009314814,0.000015201825,0.000078517405,0.00003429432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006957133,0.0010604257,0.0011762399,0.0006614642,0.0005055156,0.0013287744,0.0017368583,0.0019480287,0.0036602863],"category_scores_gemma":[0.00096838456,0.00069952285,0.0008169323,0.0010477738,0.000561759,0.00083738274,0.000744738,0.001229974,0.00040925032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017849388,0.000015730053,0.000096243915,0.000029357103,0.000008566713,0.000049149832,0.000013431653,0.9952649,0.0004379618,0.0022827778,0.0002008039,0.0015833097],"study_design_scores_gemma":[0.0000070760293,0.000016952314,0.00008323502,0.000002595981,0.000006432324,0.000008401927,0.000005468797,0.99908817,0.0000757253,0.00048261278,0.00022011201,0.000003191202],"about_ca_topic_score_codex":0.017550817,"about_ca_topic_score_gemma":0.009686925,"teacher_disagreement_score":0.017550817,"about_ca_system_score_codex":0.0014952337,"about_ca_system_score_gemma":0.0019744197,"threshold_uncertainty_score":0.034897327},"labels":[],"label_agreement":null},{"id":"W2081222230","doi":"10.1115/imece2013-64799","title":"Aircraft Fleet Maintenance Planning Using Combined Cost Benefit Model and Branch and Bound","year":2013,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Downtime; Aircraft maintenance; Scheduling (production processes); Reliability engineering; Optimal maintenance; Maintenance engineering; Preventive maintenance; Planned maintenance; Computer science; Operations research; Engineering; Operations management; Aeronautics","score_opus":0.013443095398736128,"score_gpt":0.215809605181749,"score_spread":0.20236650978301288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081222230","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045902282,0.0008418622,0.94398147,0.00042689955,0.000042620286,0.00017275513,0.00043977544,0.00031144594,0.007880824],"genre_scores_gemma":[0.8194945,0.0014108048,0.15757976,0.00013093185,0.00008649355,0.0007004859,0.0010034604,0.00020063897,0.019392932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925846,0.00030439536,0.000026852178,0.00010251348,0.00016841455,0.00013943149],"domain_scores_gemma":[0.9985298,0.0011389301,0.0000893962,0.00003600024,0.00014846667,0.00005739024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016702462,0.0011914275,0.0018688954,0.0014610821,0.0006424806,0.0015539859,0.001677305,0.0016186222,0.00575338],"category_scores_gemma":[0.0029976547,0.0013900945,0.0011226428,0.0017511068,0.0007122597,0.0014699958,0.0007349948,0.0014802628,0.0006922639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011714368,0.00000640554,0.000077915494,0.0000092505015,0.000007435135,0.000012991106,0.0000048696397,0.99710506,0.000042031697,0.0010072876,0.00012390404,0.0015911452],"study_design_scores_gemma":[0.0000030132533,0.000007690027,0.00004345568,0.0000021086696,0.0000037713908,0.000003285637,0.0000022062911,0.99861526,0.000021657304,0.001218377,0.000077341305,0.0000017791323],"about_ca_topic_score_codex":0.03123265,"about_ca_topic_score_gemma":0.025582692,"teacher_disagreement_score":0.03123265,"about_ca_system_score_codex":0.0019255375,"about_ca_system_score_gemma":0.0019347186,"threshold_uncertainty_score":0.06210172},"labels":[],"label_agreement":null},{"id":"W2081666985","doi":"10.5539/mas.v2n3p113","title":"The Reliability Analysis of N-Unit Series Repairable System With One Replaceable Repair Facility and a Repairman Doing Other Work","year":2008,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reliability engineering; Reliability (semiconductor); Unit (ring theory); Work (physics); Series (stratigraphy); Computer science; Exponential distribution; Markov chain; Markov model; Statistics; Engineering; Mathematics; Physics; Mechanical engineering","score_opus":0.009551192879457942,"score_gpt":0.17966961199169093,"score_spread":0.170118419112233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081666985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44388127,0.0021202378,0.54008424,0.00093133334,0.00015397748,0.00008287764,0.00035986025,0.00026139375,0.012124853],"genre_scores_gemma":[0.98837775,0.0005979166,0.005086478,0.00002229297,0.000058537764,0.000047614834,0.00009911564,0.000028267466,0.005682006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934214,0.00026124806,0.000023559462,0.00014323024,0.000115371266,0.00011444582],"domain_scores_gemma":[0.99827445,0.00088373444,0.00037557996,0.000081057326,0.00027207792,0.00011312824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015854782,0.0013182014,0.0018626899,0.0008136809,0.00060737965,0.00082271954,0.0017942882,0.0012125919,0.0027095398],"category_scores_gemma":[0.0028725825,0.0006413491,0.0015585991,0.000789942,0.0012872405,0.001111269,0.0007544743,0.0009839578,0.00026348516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058091373,0.000017111586,0.00047973514,0.000046234956,0.000029849594,0.00016982056,0.000044161694,0.9891389,0.000994201,0.007265225,0.00025201804,0.0015045622],"study_design_scores_gemma":[0.000005671295,0.00003376908,0.00024223246,0.0000030875078,0.00001562443,0.000029963636,0.000015229223,0.997347,0.00010485951,0.0021112897,0.00008544601,0.0000058754554],"about_ca_topic_score_codex":0.013247558,"about_ca_topic_score_gemma":0.0053538545,"teacher_disagreement_score":0.013247558,"about_ca_system_score_codex":0.0014349245,"about_ca_system_score_gemma":0.0010160112,"threshold_uncertainty_score":0.026340902},"labels":[],"label_agreement":null},{"id":"W2081767320","doi":"10.1142/s0218539308003143","title":"OPTIMAL DESIGN OF BINARY WEIGHTED k-OUT-OF-n SYSTEMS","year":2008,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Genetic algorithm; Tabu search; Reliability (semiconductor); Binary number; Mathematical optimization; Computer science; Key (lock); Computation; Algorithm; Function (biology); Optimal design; Process (computing); Value (mathematics); Reliability engineering; Mathematics; Engineering; Arithmetic","score_opus":0.023184973315361374,"score_gpt":0.2511000430412425,"score_spread":0.22791506972588113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081767320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14201555,0.00065920356,0.844388,0.00032536872,0.00012570068,0.00019261544,0.000098095035,0.00026150295,0.011933949],"genre_scores_gemma":[0.925014,0.00021463571,0.07100825,0.000081557555,0.000032412194,0.00015729298,0.000068722315,0.000048172133,0.0033749796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987631,0.00039028298,0.00006104756,0.00026101503,0.00031332826,0.00021114672],"domain_scores_gemma":[0.99882287,0.00040533108,0.0003629122,0.000065088476,0.00025843602,0.00008542536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013371081,0.0010447328,0.0013695034,0.0007223499,0.00065733097,0.0012943378,0.0012426766,0.0011715725,0.0029818746],"category_scores_gemma":[0.003351876,0.0005982182,0.0005149733,0.0005244426,0.00090368785,0.0011227609,0.0011011605,0.0005018933,0.00039937542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031116582,0.00008388311,0.0006007856,0.00025153282,0.000057237197,0.00013544239,0.00009287654,0.94804347,0.0072872695,0.011292842,0.0005922734,0.03125121],"study_design_scores_gemma":[0.00005162377,0.0001666779,0.0003014755,0.000020189647,0.000025064697,0.000040917872,0.00002907578,0.9912271,0.0014884053,0.005860385,0.00077516894,0.000013924115],"about_ca_topic_score_codex":0.0020749213,"about_ca_topic_score_gemma":0.0025169153,"teacher_disagreement_score":0.0029818746,"about_ca_system_score_codex":0.000985426,"about_ca_system_score_gemma":0.00090113585,"threshold_uncertainty_score":0.009975374},"labels":[],"label_agreement":null},{"id":"W2082731959","doi":"10.1108/13552511011048896","title":"Proportional hazards modeling of engine failures in military vehicles","year":2010,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Christian ministry; Covariate; Reliability engineering; Engineering; Markov model; Decision model; Diesel fuel; Hazard; Operations research; Markov chain; Risk analysis (engineering); Computer science; Automotive engineering; Business","score_opus":0.009135250757017778,"score_gpt":0.23933906711570685,"score_spread":0.23020381635868908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082731959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34231707,0.000402138,0.6504762,0.0006725946,0.00008180497,0.00022402442,0.0005230943,0.00045960757,0.0048433784],"genre_scores_gemma":[0.97877,0.00015942643,0.015687142,0.00003214097,0.000027966857,0.0001327468,0.000189706,0.000034611712,0.0049663577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981875,0.0010093427,0.000047673173,0.0002662701,0.0002499404,0.00023924452],"domain_scores_gemma":[0.9937171,0.00496382,0.00063412526,0.00021848986,0.00033134702,0.00013514355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046164803,0.00074007985,0.0008077733,0.0010313699,0.00046138128,0.00095571234,0.0019124028,0.00077221805,0.0050104437],"category_scores_gemma":[0.01119746,0.0005664026,0.001119904,0.0005563699,0.00090766384,0.00078014925,0.0010055054,0.001234097,0.00043371605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001301878,0.00006216373,0.0052746427,0.00003570716,0.00004928119,0.00014914523,0.00011857738,0.9713752,0.00028573588,0.013426022,0.00031331915,0.008779991],"study_design_scores_gemma":[0.000011542728,0.000042380892,0.0006469893,0.000003908511,0.000009829409,0.000024808578,0.00001931123,0.993455,0.00009412301,0.005446363,0.0002391726,0.00000652617],"about_ca_topic_score_codex":0.017000722,"about_ca_topic_score_gemma":0.005723065,"teacher_disagreement_score":0.017000722,"about_ca_system_score_codex":0.0011953487,"about_ca_system_score_gemma":0.0012716474,"threshold_uncertainty_score":0.033803582},"labels":[],"label_agreement":null},{"id":"W2083665485","doi":"10.1108/13552510810899490","title":"Probabilistic analysis of a repairable robot‐safety system composed of (<i>n</i>−1) standby robots, a safety unit, and a switch","year":2008,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Robot; Reliability engineering; Reliability (semiconductor); Engineering; Process (computing); Probabilistic logic; Markov process; System safety; Simulation; Computer science; Artificial intelligence","score_opus":0.016482154997588642,"score_gpt":0.23158909906306072,"score_spread":0.21510694406547207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083665485","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56049097,0.00030541222,0.43574995,0.00021176966,0.000016954802,0.00006925034,0.0001351415,0.00016627762,0.002854209],"genre_scores_gemma":[0.99460804,0.00007348467,0.0043035094,0.00000955303,0.00000671882,0.000026202615,0.00004738291,0.000011607474,0.00091345364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993973,0.00018348693,0.000027892775,0.0001276427,0.00015503519,0.00010874632],"domain_scores_gemma":[0.9964903,0.0021820762,0.0007704087,0.00012800556,0.00033369692,0.00009554743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014277903,0.0004566621,0.00055099564,0.0006011827,0.00031741406,0.0005399622,0.0006403002,0.00044313882,0.002409871],"category_scores_gemma":[0.0031680022,0.00040070142,0.00086239004,0.00029337982,0.0008551674,0.00072854734,0.00048291625,0.000455756,0.00014749194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007343358,0.000016681619,0.0023975552,0.00004192187,0.000035982022,0.00012242985,0.000054632696,0.9830268,0.0038263584,0.0068183634,0.00014361643,0.0034422844],"study_design_scores_gemma":[0.000004254528,0.00004115813,0.0018192581,0.0000030560218,0.000020615495,0.00003327824,0.000022204416,0.9955337,0.000517536,0.0019174201,0.00008058555,0.000006889966],"about_ca_topic_score_codex":0.007161114,"about_ca_topic_score_gemma":0.0033733726,"teacher_disagreement_score":0.007161114,"about_ca_system_score_codex":0.0010309176,"about_ca_system_score_gemma":0.00075306103,"threshold_uncertainty_score":0.014238834},"labels":[],"label_agreement":null},{"id":"W2083847842","doi":"10.1016/j.ress.2010.12.001","title":"Availability of a general k-out-of-n:G system with non-identical components considering shut-off rules using quasi-birth–death process","year":2010,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Correctness; Redundancy (engineering); Algorithm; Monte Carlo method; Process (computing); Birth–death process; Computer science; Mathematical optimization; State (computer science); Mathematics; Applied mathematics; Reliability engineering; Engineering; Population; Statistics","score_opus":0.008699082593434302,"score_gpt":0.21235982774218046,"score_spread":0.20366074514874616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083847842","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7082067,0.0008256217,0.28205958,0.0010604176,0.0001615226,0.000060631315,0.00043768226,0.0005118085,0.0066760434],"genre_scores_gemma":[0.99619067,0.00006791738,0.0027116067,0.000021674332,0.000024125427,0.000009612241,0.000037431106,0.000015915562,0.0009210304],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933237,0.00014533044,0.00003555937,0.00018554184,0.000119203796,0.0001819422],"domain_scores_gemma":[0.99643,0.002097285,0.00049179036,0.0001746302,0.00057674013,0.00022964562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014709332,0.0006354807,0.0017758176,0.00076220924,0.0009292729,0.0013827124,0.0017343953,0.0020314741,0.0025013776],"category_scores_gemma":[0.003941541,0.0005117999,0.0008187048,0.0007049173,0.0015387755,0.0014891685,0.0011289637,0.00067411823,0.00030062036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005265972,0.00004425301,0.0030795967,0.00024310368,0.0000789349,0.00093903625,0.00015617117,0.9688137,0.005987544,0.015832772,0.0007167364,0.003581619],"study_design_scores_gemma":[0.000015036754,0.000029959649,0.00065931666,0.0000055979654,0.000027361972,0.00007887176,0.000022867647,0.9954052,0.00028625713,0.0033774285,0.000079687634,0.000012410712],"about_ca_topic_score_codex":0.007002264,"about_ca_topic_score_gemma":0.004088598,"teacher_disagreement_score":0.007002264,"about_ca_system_score_codex":0.00095910334,"about_ca_system_score_gemma":0.0010547638,"threshold_uncertainty_score":0.013922989},"labels":[],"label_agreement":null},{"id":"W2085667230","doi":"10.1007/s10845-009-0347-x","title":"Accuracy and robustness of decision making techniques in condition based maintenance","year":2009,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Robustness (evolution); Taguchi methods; Monte Carlo method; Computer science; Reliability engineering; Engineering; Machine learning; Statistics; Mathematics","score_opus":0.00844890463435562,"score_gpt":0.25370633959892985,"score_spread":0.24525743496457422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085667230","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5426292,0.0017761039,0.4519335,0.0005728622,0.00018600916,0.00007054521,0.00020290774,0.000744411,0.0018844137],"genre_scores_gemma":[0.973678,0.00016957065,0.025633747,0.000050218812,0.00005635709,0.000017859784,0.00013350828,0.000043839827,0.00021682438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9921038,0.0036933399,0.000680606,0.0011053138,0.0019120536,0.00050479465],"domain_scores_gemma":[0.776184,0.2085975,0.0037281597,0.005827195,0.0050151274,0.00064799824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021112476,0.0009364166,0.0014940326,0.002017702,0.0006468811,0.0025320782,0.001559772,0.0022557266,0.0008324509],"category_scores_gemma":[0.11399145,0.00058804353,0.0008617514,0.0013090563,0.0014530573,0.0029299327,0.0011989989,0.0023887905,0.00022045602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0043719215,0.00033002978,0.015838899,0.00015673925,0.00036488243,0.00008243021,0.00022525575,0.7965918,0.0043072435,0.004806109,0.0006588919,0.17226572],"study_design_scores_gemma":[0.000034140547,0.0001466541,0.0029853608,0.000013948948,0.00004404552,0.000024815068,0.000018895149,0.9919803,0.0023481266,0.0023291647,0.00005810843,0.000016454918],"about_ca_topic_score_codex":0.0066467426,"about_ca_topic_score_gemma":0.0022130937,"teacher_disagreement_score":0.021112476,"about_ca_system_score_codex":0.0014359376,"about_ca_system_score_gemma":0.0013601098,"threshold_uncertainty_score":0.11165476},"labels":[],"label_agreement":null},{"id":"W2086250731","doi":"10.1002/nav.20068","title":"Shock model in Markovian environment","year":2005,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Shock (circulatory); Markov process; Reliability (semiconductor); Exponential function; Applied mathematics; Exponential distribution; Statistical physics; Markov chain; Mathematics; Magnitude (astronomy); Phase-type distribution; Markovian arrival process; Statistics; Physics; Mathematical analysis; Thermodynamics","score_opus":0.06579873709004619,"score_gpt":0.31701362946509354,"score_spread":0.25121489237504735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086250731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3291459,0.0013022296,0.6452354,0.0019709405,0.00038821297,0.00014537536,0.00096970587,0.00062858337,0.020213645],"genre_scores_gemma":[0.98392856,0.00050635904,0.0044934293,0.00013527737,0.00009024884,0.000089351684,0.00019179433,0.000027156084,0.010537926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990621,0.00026868843,0.000038896294,0.00017263099,0.00019088692,0.0002667348],"domain_scores_gemma":[0.998251,0.00069473375,0.00043726747,0.000107809705,0.00031467888,0.00019461884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008381096,0.0008172842,0.0013363353,0.00082447584,0.0006381728,0.0016313088,0.0015453361,0.0015464275,0.0043980847],"category_scores_gemma":[0.0028301657,0.00046726834,0.0007482522,0.00085916906,0.0011409862,0.0016883244,0.0012471178,0.0014386626,0.00057430915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002032957,0.00007449177,0.0017075224,0.00008769125,0.00007955307,0.0006233337,0.0001485346,0.85333115,0.0019979149,0.13508272,0.0020620686,0.0046016634],"study_design_scores_gemma":[0.000032755448,0.00004398887,0.00031784707,0.000006888608,0.000019993464,0.000054800043,0.00002702246,0.97609895,0.00017013986,0.022676213,0.0005355182,0.000015768539],"about_ca_topic_score_codex":0.010072536,"about_ca_topic_score_gemma":0.0038818738,"teacher_disagreement_score":0.010072536,"about_ca_system_score_codex":0.0011189502,"about_ca_system_score_gemma":0.0009358248,"threshold_uncertainty_score":0.020027816},"labels":[],"label_agreement":null},{"id":"W2087033015","doi":"10.1115/1.533558","title":"Reliability Analysis of Non-Constant-Size Part Populations in Design for Remanufacture","year":2000,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Reliability engineering; Population; Constant (computer programming); Computer science; Failure rate; Work (physics); Engineering; Mechanical engineering","score_opus":0.03300391590878424,"score_gpt":0.2642141217130745,"score_spread":0.23121020580429028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087033015","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84197766,0.00013645155,0.15661539,0.00009718274,0.000008728066,0.000024846262,0.000021969407,0.00007673153,0.0010410047],"genre_scores_gemma":[0.9937317,0.000032403732,0.005762661,0.000006284015,0.0000020994005,0.000014471004,0.000017110076,0.0000079100155,0.0004252375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996724,0.00016413779,0.00001008458,0.000040410596,0.000076349505,0.000036571426],"domain_scores_gemma":[0.99706715,0.0021596446,0.0003209578,0.00015989009,0.00023320713,0.0000591454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015522401,0.00022918549,0.00027522218,0.00032584934,0.00016705372,0.0002523106,0.0005458247,0.00036491363,0.00045494863],"category_scores_gemma":[0.0051809466,0.0002443707,0.00042074305,0.00014029711,0.00046131862,0.00028155744,0.00023810861,0.00035147314,0.000071510534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003625118,0.00001547658,0.0024340653,0.000009801339,0.000012000905,0.000038419752,0.000046167184,0.99117565,0.0015854596,0.00068744284,0.000047261063,0.003912127],"study_design_scores_gemma":[0.0000033511549,0.000057053665,0.0010843277,0.0000014093876,0.000006769913,0.000013102697,0.000011466979,0.9977943,0.00067518925,0.0002956338,0.00005510221,0.0000024017809],"about_ca_topic_score_codex":0.0039794585,"about_ca_topic_score_gemma":0.0017607119,"teacher_disagreement_score":0.0039794585,"about_ca_system_score_codex":0.000765102,"about_ca_system_score_gemma":0.0004243135,"threshold_uncertainty_score":0.008209109},"labels":[],"label_agreement":null},{"id":"W2088495516","doi":"10.1093/imaman/dpn016","title":"A maintenance model with minimal and general repair","year":2008,"lang":"en","type":"article","venue":"IMA Journal of Management Mathematics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Optimal maintenance; Preventive maintenance; Computer science; Reliability engineering; Maintenance actions; Mathematical optimization; Minification; Markov decision process; Extension (predicate logic); Operations research; Markov process; Mathematics; Engineering","score_opus":0.009291288303857488,"score_gpt":0.18768075393343964,"score_spread":0.17838946562958216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088495516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23681627,0.0017426498,0.7079095,0.0029643613,0.0002573321,0.00022340787,0.002876409,0.0010480323,0.04616203],"genre_scores_gemma":[0.9476594,0.00044754412,0.021727871,0.00017255647,0.000101468475,0.00021402111,0.0005558943,0.00006991747,0.029051393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897504,0.00021555788,0.000052230516,0.00033995588,0.00021498837,0.00020224198],"domain_scores_gemma":[0.9986657,0.000620441,0.00023385607,0.00014119051,0.00018890171,0.00014995162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00114515,0.0010233695,0.0014729026,0.0010927843,0.0006185631,0.0016811674,0.0034202586,0.0028134515,0.00707183],"category_scores_gemma":[0.0031658858,0.000720883,0.0010552553,0.0010145666,0.0014818572,0.0024469576,0.0015156672,0.0016662579,0.0009917425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015331636,0.000074998716,0.00044496226,0.00008854723,0.000026172493,0.00023475719,0.000094709925,0.91987777,0.0012724667,0.07147324,0.0014061006,0.0048529324],"study_design_scores_gemma":[0.000044767883,0.00006240142,0.00023532001,0.000009111816,0.00001808655,0.00006944413,0.000014893816,0.9781229,0.00013614044,0.020320838,0.000952257,0.000013855409],"about_ca_topic_score_codex":0.008604733,"about_ca_topic_score_gemma":0.0047688247,"teacher_disagreement_score":0.008604733,"about_ca_system_score_codex":0.0018471964,"about_ca_system_score_gemma":0.0013001796,"threshold_uncertainty_score":0.02365762},"labels":[],"label_agreement":null},{"id":"W2090937711","doi":"10.1080/00949655.2013.824448","title":"Residual life estimation based on bivariate non-stationary gamma degradation process","year":2013,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Residual; Gamma process; Bivariate analysis; Copula (linguistics); Degradation (telecommunications); Computer science; Dependency (UML); Mathematics; Bayesian probability; Mathematical optimization; Algorithm; Statistics; Econometrics; Artificial intelligence","score_opus":0.00970243384331821,"score_gpt":0.2624789842705734,"score_spread":0.2527765504272552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090937711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02085354,0.00015964203,0.97859555,0.00002979448,0.0000041064127,0.000013249191,0.000022442084,0.00010732567,0.00021441493],"genre_scores_gemma":[0.81893367,0.000645453,0.17866358,0.000045317807,0.00003073204,0.000093041366,0.00029018405,0.00006544867,0.0012324892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993837,0.00021702008,0.000036158563,0.00017358577,0.00013708475,0.00005230164],"domain_scores_gemma":[0.9978923,0.0012535934,0.00028550078,0.00016142675,0.00035947128,0.000047735222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018179268,0.0005824907,0.0007327016,0.00095713435,0.00016023521,0.0005868747,0.00083241134,0.0005958609,0.0006046993],"category_scores_gemma":[0.005235202,0.00033589103,0.00072064885,0.00065869774,0.0005345163,0.0011122406,0.0007459579,0.00082270853,0.00018566425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098079414,0.0000433311,0.004327576,0.0001221581,0.000053205476,0.00012047211,0.00017124515,0.9145453,0.007146271,0.007283241,0.00032410808,0.06576508],"study_design_scores_gemma":[0.000002827475,0.000018296205,0.0005326556,0.0000031139978,0.0000071921436,0.000021257392,0.0000067724955,0.9972519,0.0008706031,0.0011586059,0.00011917115,0.0000074981995],"about_ca_topic_score_codex":0.0028141353,"about_ca_topic_score_gemma":0.0017587953,"teacher_disagreement_score":0.0028141353,"about_ca_system_score_codex":0.00045915294,"about_ca_system_score_gemma":0.000552067,"threshold_uncertainty_score":0.009614229},"labels":[],"label_agreement":null},{"id":"W2093664936","doi":"10.1016/j.ijpe.2011.07.011","title":"Simultaneous control of production, repair/replacement and preventive maintenance of deteriorating manufacturing systems","year":2011,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; École de Technologie Supérieure","funders":"","keywords":"Preventive maintenance; Reliability engineering; Production (economics); Reliability (semiconductor); Maintenance actions; Computer science; Markov decision process; Time horizon; Sensitivity (control systems); Order (exchange); Failure rate; Operations research; Planned maintenance; Markov process; Risk analysis (engineering); Mathematical optimization; Engineering; Business; Economics; Mathematics","score_opus":0.007545413346551625,"score_gpt":0.19014030657910524,"score_spread":0.18259489323255362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093664936","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66132677,0.0005896926,0.33366668,0.00027412848,0.00012303214,0.0000535669,0.000057864854,0.0003346431,0.0035735928],"genre_scores_gemma":[0.9970386,0.000044925815,0.0025076028,0.000006688831,0.000016160819,0.000007681577,0.000007462656,0.0000060318002,0.00036476235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957436,0.00008722499,0.000025684361,0.00010394065,0.000116293275,0.00009246234],"domain_scores_gemma":[0.99867886,0.0005820108,0.00034064293,0.00008491703,0.00022846033,0.00008507859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090088823,0.0007445414,0.00064187014,0.0003195903,0.00027952364,0.0009644341,0.0007921933,0.00035267894,0.00093905226],"category_scores_gemma":[0.0020274858,0.00033109204,0.00030888207,0.0002487812,0.0004315731,0.00048633188,0.00046104714,0.0004822134,0.00010711622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018597733,0.0004271003,0.0030230642,0.0003077365,0.00013664806,0.00023236217,0.00017171082,0.7464084,0.12766859,0.008018055,0.0008001432,0.110946395],"study_design_scores_gemma":[0.000071059985,0.00046139583,0.00385682,0.000005788803,0.00008427862,0.000056591754,0.00001722823,0.98110044,0.011556179,0.0024123834,0.00036384814,0.000013985978],"about_ca_topic_score_codex":0.0017192365,"about_ca_topic_score_gemma":0.0016172811,"teacher_disagreement_score":0.0017192365,"about_ca_system_score_codex":0.00040481362,"about_ca_system_score_gemma":0.0006383467,"threshold_uncertainty_score":0.0047644377},"labels":[],"label_agreement":null},{"id":"W2093687145","doi":"10.1016/j.dam.2014.05.048","title":"The average reliability of a graph","year":2014,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Mathematics; Reliability (semiconductor); Bounded function; Graph; Combinatorics; Discrete mathematics","score_opus":0.004022009903919438,"score_gpt":0.1857318155078487,"score_spread":0.18170980560392927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093687145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3533727,0.0030686336,0.62751174,0.0019098751,0.00028682148,0.00004499935,0.001437129,0.0007767549,0.011591317],"genre_scores_gemma":[0.95018774,0.0026707174,0.0399274,0.00020267555,0.0005964941,0.00010012635,0.00087213656,0.0002488515,0.0051938877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99889743,0.00038042458,0.00004701881,0.00034681335,0.00022554805,0.00010267884],"domain_scores_gemma":[0.9856865,0.010266338,0.0012577978,0.00093120337,0.001258441,0.0005997852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015933871,0.0011787411,0.0015951496,0.0030390413,0.00058607926,0.002116663,0.0022311893,0.0014585763,0.0033110518],"category_scores_gemma":[0.014652276,0.00080438674,0.0009674371,0.0022043209,0.0016058626,0.004229958,0.0008481317,0.0016405478,0.0005381687],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023684178,0.0000623768,0.0036103197,0.00054341584,0.00029759356,0.00032563013,0.0003918038,0.47205973,0.0077482187,0.46404555,0.0067005637,0.04397794],"study_design_scores_gemma":[0.000017559176,0.000084064726,0.0018712986,0.000040012834,0.00008491933,0.00024556796,0.000056526784,0.5705376,0.0009672991,0.4237763,0.0022837983,0.000035021443],"about_ca_topic_score_codex":0.0018823337,"about_ca_topic_score_gemma":0.0011598323,"teacher_disagreement_score":0.0033110518,"about_ca_system_score_codex":0.0012966847,"about_ca_system_score_gemma":0.0006945428,"threshold_uncertainty_score":0.01107651},"labels":[],"label_agreement":null},{"id":"W2094377372","doi":"10.1016/j.ejor.2011.07.056","title":"A note on replacement policy for a system subject to non-homogeneous pure birth shocks","year":2011,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Homogeneous; Computer science; Subject (documents); Type (biology); Process (computing); Operations research; Mathematics; Geology","score_opus":0.050028334555752896,"score_gpt":0.31568662808074194,"score_spread":0.26565829352498904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094377372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0829218,0.01356122,0.77951306,0.031856436,0.004521903,0.00034654848,0.001163093,0.0009302624,0.08518579],"genre_scores_gemma":[0.8684632,0.0098836385,0.0574695,0.0039980267,0.0035582387,0.000271025,0.00036598614,0.0005275053,0.05546282],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908066,0.00040235117,0.0000642393,0.000158735,0.00016668318,0.00012727766],"domain_scores_gemma":[0.9958568,0.0030408227,0.00022434955,0.0002641891,0.00039968095,0.0002140195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039552213,0.0012659855,0.002341529,0.0007398461,0.00091882725,0.0024331422,0.0023511695,0.0034300063,0.008263196],"category_scores_gemma":[0.011167153,0.0005980292,0.0016522912,0.0008730245,0.001855451,0.0033586302,0.002110713,0.0038548766,0.0007127275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007185556,0.000103841965,0.0009152991,0.00098978,0.0002519646,0.0013726979,0.00037850454,0.3577811,0.010661771,0.5652989,0.030652475,0.030875089],"study_design_scores_gemma":[0.00013284071,0.00015786527,0.0012380086,0.00014691582,0.00017937267,0.00022694301,0.00010441166,0.7019194,0.0012287333,0.284399,0.010195057,0.00007150142],"about_ca_topic_score_codex":0.007920615,"about_ca_topic_score_gemma":0.003951954,"teacher_disagreement_score":0.008263196,"about_ca_system_score_codex":0.0020280848,"about_ca_system_score_gemma":0.001991515,"threshold_uncertainty_score":0.027643144},"labels":[],"label_agreement":null},{"id":"W2095150328","doi":"10.1002/nav.20312","title":"Replacing nonidentical vital components to extend system life","year":2008,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"U.S. National Library of Medicine; Agency for Healthcare Research and Quality; National Science Foundation","keywords":"Spare part; Component (thermodynamics); Counterintuitive; Computer science; Independent and identically distributed random variables; Scheduling (production processes); A priori and a posteriori; Mathematical optimization; Distributed computing; Mathematics; Random variable; Operations management; Statistics; Economics","score_opus":0.1468013988448786,"score_gpt":0.3308095670622263,"score_spread":0.1840081682173477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095150328","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7502976,0.00024995845,0.24639347,0.0002604226,0.00004141782,0.000030089606,0.00004346623,0.00015374563,0.002529826],"genre_scores_gemma":[0.9921176,0.000048530117,0.0072156293,0.000021335778,0.000011435084,0.000006918025,0.000011922252,0.000013193773,0.0005533876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99948454,0.00017300813,0.0000242633,0.00007948501,0.00013453854,0.00010414791],"domain_scores_gemma":[0.9975183,0.0010162949,0.00061122066,0.0003075706,0.00021890436,0.00032771175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016441561,0.00053299713,0.0005284293,0.00041093002,0.00029127006,0.00047991646,0.00080517586,0.00052915554,0.001229527],"category_scores_gemma":[0.0038339163,0.00022075829,0.00024406242,0.0003742688,0.00079305697,0.00088448584,0.0005590343,0.00048292865,0.0001927232],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039003242,0.00017403235,0.004580166,0.00007580466,0.00004483972,0.00021132512,0.000117085736,0.91190314,0.023116983,0.028434398,0.00058358925,0.030368619],"study_design_scores_gemma":[0.000044381613,0.00061714713,0.0015778083,0.000011802798,0.000045037035,0.00026210526,0.000059254347,0.9562106,0.009019971,0.030826103,0.0013081225,0.000017573913],"about_ca_topic_score_codex":0.00042877274,"about_ca_topic_score_gemma":0.0006176378,"teacher_disagreement_score":0.0016441561,"about_ca_system_score_codex":0.0005257642,"about_ca_system_score_gemma":0.0005493237,"threshold_uncertainty_score":0.008695185},"labels":[],"label_agreement":null},{"id":"W2095978076","doi":"10.1061/41182(416)5","title":"Condition-Based Maintenance in Facilities Management","year":2011,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Preventive maintenance; Spare part; Condition monitoring; Condition-based maintenance; Maintenance actions; Work (physics); Reliability engineering; Asset (computer security); Risk analysis (engineering); Planned maintenance; Asset management; Intervention (counseling); Predictive maintenance; Computer science; Engineering; Operations management; Business; Computer security; Mechanical engineering","score_opus":0.011015515011882505,"score_gpt":0.17773255359050982,"score_spread":0.16671703857862732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095978076","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026187645,0.050047867,0.8440571,0.006970224,0.0015559762,0.000464694,0.0004833772,0.0018563126,0.06837685],"genre_scores_gemma":[0.77579767,0.01442358,0.18931408,0.0007769233,0.0017408256,0.00026229327,0.0004881016,0.00012223484,0.017074328],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985246,0.00040009044,0.00008065087,0.00031376322,0.00053000567,0.00015084071],"domain_scores_gemma":[0.9982666,0.00076920097,0.00026117428,0.00026143395,0.00030256985,0.00013897344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018523223,0.0005403128,0.0006527898,0.0014789897,0.00071942294,0.0022576067,0.0026984094,0.002108366,0.0056571774],"category_scores_gemma":[0.0045466744,0.0003215328,0.00046005083,0.002109494,0.0013708071,0.0032570998,0.0015351365,0.0014489092,0.0014058234],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010807704,0.00020319619,0.002091791,0.00059571763,0.000040895684,0.0003203028,0.00030381946,0.08860403,0.002255367,0.27302936,0.024691409,0.60775614],"study_design_scores_gemma":[0.000084560896,0.0004485048,0.0055602444,0.00064140477,0.00008590146,0.0014205293,0.00037773798,0.4950731,0.0025524513,0.30196482,0.19164181,0.00014892647],"about_ca_topic_score_codex":0.005187892,"about_ca_topic_score_gemma":0.0026448295,"teacher_disagreement_score":0.0056571774,"about_ca_system_score_codex":0.002293349,"about_ca_system_score_gemma":0.0010062644,"threshold_uncertainty_score":0.01892513},"labels":[],"label_agreement":null},{"id":"W2096845859","doi":"10.1287/ijoc.1110.0493","title":"Static Network Reliability Estimation via Generalized Splitting","year":2012,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Reliability (semiconductor); Monte Carlo method; Computer science; Graph; Algorithm; Set (abstract data type); Exploit; Enhanced Data Rates for GSM Evolution; Mathematical optimization; Mathematics; Theoretical computer science; Statistics; Artificial intelligence","score_opus":0.008376867683933403,"score_gpt":0.22905180316735826,"score_spread":0.22067493548342487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096845859","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014000422,0.00006242118,0.9850144,0.000043450684,0.000009907909,0.00001848432,0.000026311709,0.00031089742,0.00051372295],"genre_scores_gemma":[0.64009184,0.00016061593,0.35802665,0.00007037236,0.00004299127,0.00013935176,0.00027440814,0.0001494895,0.0010443104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992386,0.0002746351,0.000027920494,0.00016881723,0.00022930541,0.00006072478],"domain_scores_gemma":[0.9976547,0.0011065131,0.00035970885,0.00043516577,0.00034133924,0.000102559025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011551423,0.0010427654,0.0011855988,0.0013256699,0.00041494836,0.0006331936,0.0019486748,0.0007918257,0.0010170605],"category_scores_gemma":[0.00569737,0.0006019802,0.0007362492,0.0009271981,0.0008409823,0.0016270183,0.0013792325,0.0008957684,0.00026588503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048062262,0.000015346555,0.0009720662,0.000022662747,0.000031164695,0.00004694684,0.000047842615,0.9589395,0.0023562869,0.0076607126,0.00038449664,0.02947499],"study_design_scores_gemma":[0.0000026080327,0.0000074135482,0.00006871845,0.0000017633644,0.000002337647,0.000009932608,0.000002294292,0.9957806,0.00028255716,0.0037314177,0.00010772982,0.0000026187822],"about_ca_topic_score_codex":0.004263365,"about_ca_topic_score_gemma":0.0024530734,"teacher_disagreement_score":0.004263365,"about_ca_system_score_codex":0.00078879937,"about_ca_system_score_gemma":0.0008503631,"threshold_uncertainty_score":0.008477092},"labels":[],"label_agreement":null},{"id":"W2101926044","doi":"10.1109/rams.2007.328069","title":"Joint Optimization of Inventory Control and Maintenance Policy","year":2007,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Preventive maintenance; Corrective maintenance; Inventory control; Safety stock; Control (management); Computer science; Production (economics); Reliability engineering; Optimization problem; Inventory theory; Operations research; Holding cost; Work (physics); Operations management; Engineering; Business; Economics; Supply chain; Microeconomics","score_opus":0.005637931692480186,"score_gpt":0.19662337504824473,"score_spread":0.19098544335576453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101926044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1718744,0.0016213957,0.81336355,0.00068525225,0.00008882445,0.00020397372,0.0002492298,0.0005903405,0.011323032],"genre_scores_gemma":[0.9711646,0.0003120136,0.025275046,0.00003781684,0.000029442543,0.00010302599,0.000086908956,0.000036739533,0.0029544942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989128,0.0003134132,0.000054321543,0.00020538855,0.00023376053,0.0002803492],"domain_scores_gemma":[0.9986958,0.0007032074,0.0002731093,0.00008806559,0.00015080816,0.000089020534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015187706,0.001479108,0.0017842327,0.0008423678,0.00034333798,0.0017896548,0.0010502762,0.0011488742,0.0017734828],"category_scores_gemma":[0.0039010546,0.0008029692,0.00063719763,0.000884992,0.0007739109,0.0014785771,0.0007425935,0.0008770963,0.00032676395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098297576,0.000052625695,0.0004051142,0.00004062439,0.000028857134,0.000038503324,0.000015021268,0.983307,0.0014283989,0.00363309,0.0002846715,0.0106678335],"study_design_scores_gemma":[0.00002050745,0.00008395891,0.00044155517,0.0000047930703,0.00002631655,0.000019805357,0.000011032638,0.99538475,0.0009480429,0.0027408353,0.0003109982,0.0000074398827],"about_ca_topic_score_codex":0.004273192,"about_ca_topic_score_gemma":0.0025427458,"teacher_disagreement_score":0.004273192,"about_ca_system_score_codex":0.0013965586,"about_ca_system_score_gemma":0.002068405,"threshold_uncertainty_score":0.01013273},"labels":[],"label_agreement":null},{"id":"W2101936477","doi":"10.1002/nav.10008","title":"Optimal switchover times between two activities utilizing the same resource","year":2002,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Switchover; Time horizon; Computer science; Mathematical optimization; Resource (disambiguation); Sequence (biology); Function (biology); Variable (mathematics); Operations research; Order (exchange); Nonlinear system; Horizon; Component (thermodynamics); Production (economics); Fraction (chemistry); Mathematics; Economics","score_opus":0.1037950778199091,"score_gpt":0.334109150956844,"score_spread":0.23031407313693492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101936477","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86403304,0.0005706861,0.11898741,0.00079574797,0.00009217779,0.00018194201,0.00026095938,0.0002851525,0.01479284],"genre_scores_gemma":[0.99292964,0.00007504022,0.0051485267,0.000028302802,0.00000657248,0.000030334433,0.00006969528,0.000016552991,0.0016953527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944335,0.00015726987,0.000026474121,0.00009445957,0.00006326873,0.00021521821],"domain_scores_gemma":[0.9983388,0.0008409845,0.0002399382,0.000092373855,0.000119234486,0.00036865208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013433588,0.0006156834,0.00086942327,0.00062140287,0.000566527,0.0010871087,0.0010617883,0.0009791842,0.009603219],"category_scores_gemma":[0.003844709,0.000550178,0.0004965173,0.00043107476,0.0009047208,0.0015022977,0.00066121964,0.0010979709,0.0004956642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017363335,0.0003631628,0.0022770213,0.000120453136,0.000068030524,0.00019085036,0.000096114425,0.94367856,0.0041864174,0.017987305,0.0016252554,0.027670505],"study_design_scores_gemma":[0.00013712152,0.00044543235,0.0016042197,0.000025671809,0.000048957507,0.000048625185,0.00019251194,0.979502,0.0019295188,0.01521325,0.0008295042,0.000023161425],"about_ca_topic_score_codex":0.00680572,"about_ca_topic_score_gemma":0.0047305194,"teacher_disagreement_score":0.009603219,"about_ca_system_score_codex":0.0016483818,"about_ca_system_score_gemma":0.0013953886,"threshold_uncertainty_score":0.03212601},"labels":[],"label_agreement":null},{"id":"W2102189742","doi":"10.1109/tr.2007.896747","title":"A Computational Model for Determining the Optimal Preventive Maintenance Policy With Random Breakdowns and Imperfect Repairs","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Preventive maintenance; Operations research; Reliability engineering; Variable (mathematics); Imperfect; Computer science; Decision model; Optimal decision; Random variable; Operations management; Engineering; Mathematics; Statistics; Decision tree","score_opus":0.005919531378681124,"score_gpt":0.22770419811472417,"score_spread":0.22178466673604305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102189742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18732758,0.0003642127,0.79001254,0.0018300349,0.000112000525,0.00022713494,0.0014911018,0.00082078244,0.017814677],"genre_scores_gemma":[0.8027859,0.00028852947,0.18812588,0.00020995218,0.00006275961,0.0005948427,0.0008704808,0.00010562517,0.006956157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995987,0.00011687716,0.000020556947,0.00008359305,0.0000850959,0.000095219126],"domain_scores_gemma":[0.9972799,0.0021266597,0.00019646864,0.00010439129,0.00018452227,0.000107990745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008338661,0.0007286935,0.0012355673,0.0005658552,0.0005949833,0.0015813909,0.002252831,0.0017355912,0.0059302766],"category_scores_gemma":[0.004168065,0.00077263796,0.0008404264,0.0006712618,0.00093309593,0.001310275,0.0009262894,0.0012973301,0.00044176576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021416841,0.00001300409,0.0001575093,0.000012671308,0.000004020308,0.000025955438,0.000007289515,0.9950536,0.00005894962,0.003598221,0.00015932115,0.00088796637],"study_design_scores_gemma":[0.000005387803,0.0000032878772,0.000015203904,0.0000010042743,0.0000012006266,0.0000023219839,0.000002694247,0.9988721,0.00002407185,0.0010055577,0.00006594809,0.0000011739431],"about_ca_topic_score_codex":0.023083817,"about_ca_topic_score_gemma":0.016900701,"teacher_disagreement_score":0.023083817,"about_ca_system_score_codex":0.0016697063,"about_ca_system_score_gemma":0.003189865,"threshold_uncertainty_score":0.045898914},"labels":[],"label_agreement":null},{"id":"W2102661347","doi":"","title":"Stochastic Justification of Some Simple Reliability Models","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Simple (philosophy); Infinitesimal; Reliability (semiconductor); Function (biology); Mathematics; Applied mathematics; Econometrics; Transformation (genetics); Event (particle physics); Reliability theory; Calculus (dental); Statistics; Mathematical analysis; Failure rate; Epistemology; Thermodynamics; Physics","score_opus":0.019009688516801723,"score_gpt":0.1939846044518412,"score_spread":0.1749749159350395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102661347","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058586802,0.0010688767,0.89616644,0.0042027435,0.0002477535,0.00009577882,0.00070107804,0.00030079178,0.03862989],"genre_scores_gemma":[0.89384717,0.001591082,0.08764875,0.00078335474,0.0007408606,0.00027798343,0.0005646497,0.00016223494,0.014383942],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980318,0.0007370518,0.00013246862,0.00028506914,0.0006631888,0.00015044036],"domain_scores_gemma":[0.9920312,0.004967055,0.001134275,0.0007872956,0.0008436446,0.00023653389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045321668,0.00073864247,0.00084716483,0.0012137688,0.000629449,0.0014755593,0.0016458263,0.0018922518,0.006045201],"category_scores_gemma":[0.018024411,0.00042906712,0.0014251847,0.00086045614,0.0020680537,0.0023296324,0.001665307,0.0022084161,0.00060438784],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059017984,0.000012459577,0.00020508711,0.00003473347,0.000011300724,0.00006712294,0.000059606624,0.029992789,0.00016709775,0.9664728,0.00076255307,0.0022084592],"study_design_scores_gemma":[0.000016299295,0.000017544102,0.00021589526,0.00001611555,0.000007867209,0.00006694186,0.000015979222,0.18610236,0.00009046735,0.81055766,0.002881034,0.000011787475],"about_ca_topic_score_codex":0.0027627952,"about_ca_topic_score_gemma":0.0021371534,"teacher_disagreement_score":0.006045201,"about_ca_system_score_codex":0.0013466175,"about_ca_system_score_gemma":0.0010009377,"threshold_uncertainty_score":0.023968697},"labels":[],"label_agreement":null},{"id":"W2105076454","doi":"10.1239/aap/1059486827","title":"On the collapsibility of lifetime regression models","year":2003,"lang":"en","type":"article","venue":"Advances in Applied Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Université Laval","funders":"","keywords":"Mathematics; Simple (philosophy); Infinitesimal; Function (biology); Applied mathematics; Covariate; Transformation (genetics); Econometrics; Mathematical analysis","score_opus":0.009192959822511398,"score_gpt":0.2176656447050595,"score_spread":0.2084726848825481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105076454","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04089783,0.0013293939,0.9537318,0.0009871696,0.00004403779,0.0000625979,0.00028686223,0.00022764545,0.0024327103],"genre_scores_gemma":[0.8716637,0.0051340433,0.10822053,0.00061155495,0.0006677084,0.00060939643,0.0015949241,0.0003711747,0.011127036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99251163,0.003515148,0.00045114197,0.0018398045,0.0010827159,0.0005995225],"domain_scores_gemma":[0.8981682,0.082105696,0.01086927,0.004473672,0.0033768236,0.0010063342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020011142,0.0028636164,0.003070155,0.0027768235,0.0010148546,0.0025313413,0.0026694261,0.0024937333,0.003758455],"category_scores_gemma":[0.080267586,0.0017037279,0.0034085421,0.0018807926,0.004762782,0.0063002226,0.005482183,0.0048682583,0.00082948443],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009073716,0.000056814468,0.004211065,0.00020410761,0.0002708291,0.00064680836,0.0005772117,0.5233556,0.0009448052,0.4539072,0.001234724,0.0145000685],"study_design_scores_gemma":[0.000025390003,0.00010502068,0.00069371256,0.000059511858,0.000047780693,0.00017338613,0.000058909125,0.6981917,0.00036077132,0.2986308,0.0016001988,0.00005282655],"about_ca_topic_score_codex":0.0055725113,"about_ca_topic_score_gemma":0.002386862,"teacher_disagreement_score":0.020011142,"about_ca_system_score_codex":0.0017302107,"about_ca_system_score_gemma":0.001632642,"threshold_uncertainty_score":0.10583031},"labels":[],"label_agreement":null},{"id":"W2105340209","doi":"10.1109/tr.2007.911248","title":"A New Methodology for Risk-Based Availability Analysis","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Reliability engineering; Maintenance engineering; Computer science; Thermal power station; Reliability (semiconductor); Scheme (mathematics); Engineering; Risk analysis (engineering); Power (physics); Mathematics","score_opus":0.034061724494652786,"score_gpt":0.25719324200133886,"score_spread":0.22313151750668608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105340209","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018898028,0.00006596559,0.99919826,0.00001940795,0.000018019893,0.000013188213,0.000017055605,0.00007223213,0.00040686392],"genre_scores_gemma":[0.06944282,0.0005580482,0.92629987,0.00012720877,0.00025670548,0.00033064646,0.00020136654,0.0002959024,0.0024872886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99726945,0.0008532704,0.00014987912,0.0004029238,0.001225299,0.000099127465],"domain_scores_gemma":[0.9962901,0.0021682696,0.0003524859,0.00045769147,0.0006550552,0.000076511555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029648764,0.0015438542,0.0011172104,0.0027179746,0.0006562565,0.0015809656,0.0021414633,0.00087557867,0.0046006017],"category_scores_gemma":[0.0081121335,0.00073872134,0.001863503,0.0013130396,0.00093455514,0.002746227,0.0017230297,0.002526399,0.0011913094],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003566125,0.00007271509,0.0007704097,0.0002630074,0.00021631591,0.00016694481,0.00017128799,0.47975203,0.007210197,0.31974846,0.003948866,0.18764406],"study_design_scores_gemma":[0.000012710233,0.000045087567,0.00023093543,0.000045968904,0.00004142739,0.00019868238,0.000016369537,0.87534183,0.0017543636,0.10851408,0.01376234,0.000036298912],"about_ca_topic_score_codex":0.0016366349,"about_ca_topic_score_gemma":0.0010565534,"teacher_disagreement_score":0.0046006017,"about_ca_system_score_codex":0.00094032235,"about_ca_system_score_gemma":0.0012865044,"threshold_uncertainty_score":0.015679896},"labels":[],"label_agreement":null},{"id":"W2105647959","doi":"10.1108/jqme-12-2012-0047","title":"Risk-based maintenance and remaining life assessment for gas turbines","year":2015,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Nalcor Energy (Canada)","funders":"","keywords":"Reliability engineering; Weibull distribution; Risk analysis (engineering); Engineering; Preventive maintenance; Gas turbines; Risk assessment; Interval (graph theory); Risk management; Work (physics); Turbine; Operations research; Operations management; Computer science; Business; Statistics","score_opus":0.02507879782168979,"score_gpt":0.28493431160258476,"score_spread":0.25985551378089494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105647959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10514479,0.0017093462,0.884972,0.000397764,0.000051596715,0.000110381305,0.00038260798,0.00036397326,0.0068674847],"genre_scores_gemma":[0.9742309,0.0005339319,0.02170319,0.00004137875,0.000026993876,0.00012537639,0.00023165853,0.000048262144,0.0030583465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992555,0.00025921452,0.000037816644,0.00012637409,0.00025137284,0.000069792666],"domain_scores_gemma":[0.9987412,0.00067848776,0.00026225738,0.00006283573,0.00021310516,0.00004210203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012060078,0.0010212332,0.00072115887,0.0010714807,0.00025968236,0.0010900851,0.0015548164,0.0012059652,0.0014765658],"category_scores_gemma":[0.0033719302,0.00038759268,0.00093064894,0.0005393084,0.0004927961,0.0012661947,0.0006064909,0.00080254267,0.0002103816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014902925,0.000015004462,0.0007765617,0.000028882827,0.000014032082,0.000048159054,0.000022568012,0.99061966,0.0005153496,0.0031592383,0.00016696668,0.0046187406],"study_design_scores_gemma":[0.0000020355142,0.000021134869,0.00042714336,0.000008385363,0.000009777095,0.00004328016,0.0000088776615,0.9968266,0.00015358585,0.002230359,0.00026290168,0.000005850008],"about_ca_topic_score_codex":0.0055733277,"about_ca_topic_score_gemma":0.0033011027,"teacher_disagreement_score":0.0055733277,"about_ca_system_score_codex":0.0014487677,"about_ca_system_score_gemma":0.00092798605,"threshold_uncertainty_score":0.011081755},"labels":[],"label_agreement":null},{"id":"W2106129728","doi":"10.1177/1748006x11421265","title":"A condition- and age-based replacement model using delay time modelling","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Residual; Computer science; Condition-based maintenance; Reliability engineering; Process (computing); Operations research; Order (exchange); Preventive maintenance; Mathematical optimization; Engineering; Mathematics; Algorithm; Economics","score_opus":0.015353892874597546,"score_gpt":0.20182504819341937,"score_spread":0.18647115531882183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106129728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07496504,0.0010302186,0.90746367,0.0008374001,0.00019171488,0.000171124,0.001774565,0.0005413777,0.01302485],"genre_scores_gemma":[0.9124756,0.0014443904,0.041130587,0.00011767401,0.00010657637,0.0004838129,0.00094053283,0.00009581419,0.043205008],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993197,0.00013928971,0.00004911433,0.00021767027,0.00014022802,0.00013409586],"domain_scores_gemma":[0.99865097,0.0006885753,0.0002561439,0.000060478298,0.00024214816,0.00010161907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015275045,0.0011941742,0.0016091242,0.001181058,0.0005135207,0.0020666646,0.003554356,0.003322365,0.0068024457],"category_scores_gemma":[0.0032138298,0.0009654325,0.0014263069,0.0014487416,0.00086794834,0.0019313011,0.0010520879,0.0016510535,0.0010857184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034597182,0.000019865009,0.00038690015,0.000029210618,0.000016673894,0.000069032394,0.000031946944,0.9898031,0.0005304937,0.0068865577,0.00018887072,0.0020027307],"study_design_scores_gemma":[0.000008446077,0.00001605216,0.00013304784,0.0000030495933,0.0000121944995,0.000016094498,0.000004335756,0.9979937,0.000065697466,0.0014868437,0.0002536686,0.000006868695],"about_ca_topic_score_codex":0.01783308,"about_ca_topic_score_gemma":0.008762029,"teacher_disagreement_score":0.01783308,"about_ca_system_score_codex":0.0015990416,"about_ca_system_score_gemma":0.0014800007,"threshold_uncertainty_score":0.035458565},"labels":[],"label_agreement":null},{"id":"W2106610814","doi":"10.5539/mas.v5n6p232","title":"Reliability Assessment Based on Multivariate Degradation Measures and Competing Failure Analysis","year":2011,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multivariate statistics; Reliability (semiconductor); Reliability engineering; Computer science; Degradation (telecommunications); Dimensionality reduction; Multivariate analysis; Statistics; Mathematics; Artificial intelligence; Machine learning; Engineering","score_opus":0.01743615965233424,"score_gpt":0.22503728446192334,"score_spread":0.2076011248095891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106610814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027542593,0.0003573636,0.97036195,0.00008316313,0.000019666342,0.00003030195,0.00007182633,0.00026734633,0.0012658039],"genre_scores_gemma":[0.9219348,0.00042611675,0.07545327,0.000032833676,0.000053541135,0.00012485974,0.000166986,0.00007429248,0.0017333727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786985,0.0006293773,0.000089734254,0.00022881493,0.001057674,0.00012461017],"domain_scores_gemma":[0.99690956,0.0014623073,0.00045485687,0.00026200718,0.00081502483,0.000096280295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026270198,0.0011037225,0.0008699769,0.0020983266,0.00027620557,0.0007511586,0.0009176107,0.00052956026,0.00077205617],"category_scores_gemma":[0.0063582207,0.00030577715,0.0012204773,0.0010989142,0.0007296851,0.0011962617,0.0006778254,0.0010063589,0.00025039032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116129675,0.00007050159,0.0036534907,0.00012641595,0.00008774115,0.00014268945,0.00011822457,0.88828284,0.0085555315,0.038370237,0.0012246388,0.059251674],"study_design_scores_gemma":[0.0000026687837,0.000054741533,0.0010425758,0.0000042235174,0.000013001857,0.00006422556,0.0000064138735,0.9904136,0.00086165173,0.0072039,0.0003123289,0.000020666796],"about_ca_topic_score_codex":0.0024350977,"about_ca_topic_score_gemma":0.0012511214,"teacher_disagreement_score":0.0026270198,"about_ca_system_score_codex":0.00088168134,"about_ca_system_score_gemma":0.0005885858,"threshold_uncertainty_score":0.013893127},"labels":[],"label_agreement":null},{"id":"W2106687296","doi":"10.1109/tr.2009.2034947","title":"Evaluating the Reliability Function and the Mean Residual Life for Equipment With Unobservable States","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Unobservable; Residual; Moment (physics); Markov process; Reliability (semiconductor); Degradation (telecommunications); Markov chain; State (computer science); Stochastic process; Continuous-time Markov chain; Bayes' theorem; Applied mathematics; Computer science; Mathematics; Reliability engineering; Markov model; Statistics; Econometrics; Markov property; Algorithm; Engineering; Bayesian probability","score_opus":0.022287473417226165,"score_gpt":0.2583616720662186,"score_spread":0.2360741986489924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106687296","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1341203,0.00054124166,0.863787,0.0001889201,0.000012602031,0.000021597614,0.00009904989,0.00020961824,0.0010196652],"genre_scores_gemma":[0.93734753,0.00039586567,0.06070716,0.000016729022,0.000020155432,0.00007420723,0.00018136067,0.000050179526,0.0012067828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990294,0.00041031349,0.00003884505,0.00016853515,0.00025551935,0.00009739973],"domain_scores_gemma":[0.99537235,0.0036316053,0.0004325543,0.00023908317,0.00026828036,0.000056120498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003369734,0.0007700073,0.0010891161,0.0009328235,0.00023357886,0.00090511877,0.0013232909,0.0012528731,0.00091840787],"category_scores_gemma":[0.013574326,0.00046209584,0.0009280236,0.0005860382,0.000782104,0.0017163858,0.0005915975,0.00066108827,0.00021441946],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024974257,0.000011838637,0.0010912912,0.00002650408,0.00001590567,0.000037075162,0.000023679633,0.98759115,0.00043482464,0.0047413907,0.00007576117,0.0059255264],"study_design_scores_gemma":[0.0000018818429,0.000013891405,0.00049200223,0.0000031816714,0.000005215978,0.000011593527,0.0000055101686,0.9965576,0.00027488815,0.0025695155,0.00005956462,0.0000051629445],"about_ca_topic_score_codex":0.008305655,"about_ca_topic_score_gemma":0.0043413844,"teacher_disagreement_score":0.008305655,"about_ca_system_score_codex":0.001387806,"about_ca_system_score_gemma":0.001295628,"threshold_uncertainty_score":0.017821074},"labels":[],"label_agreement":null},{"id":"W2108014179","doi":"10.7202/705349ar","title":"Modélisation d'une politique d'autocontrôle sur un réseau d'eau potable","year":2005,"lang":"fr","type":"article","venue":"Revue des sciences de l eau","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Humanities; Political science; Philosophy","score_opus":0.037004163545336116,"score_gpt":0.26865076765349377,"score_spread":0.23164660410815766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108014179","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30417633,0.0007618101,0.67553145,0.0010469062,0.00009858268,0.00026594615,0.0013534408,0.00074129447,0.016024249],"genre_scores_gemma":[0.92781067,0.0007650966,0.051818144,0.000096525255,0.000026364813,0.0005067686,0.0005892974,0.000104753984,0.018282462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991702,0.00018778584,0.000036889807,0.00031095408,0.000172733,0.00012143795],"domain_scores_gemma":[0.9978757,0.0014206829,0.00025690364,0.000114533505,0.00026927536,0.00006279559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015126078,0.00087220885,0.0011160647,0.0007847348,0.00064261537,0.0022588745,0.0015848752,0.0015644402,0.006211218],"category_scores_gemma":[0.0034761853,0.0008764555,0.0018660076,0.00085713336,0.0012022622,0.0022031895,0.000920917,0.0013933304,0.0005949354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055317614,0.000026830228,0.0017147766,0.000055135384,0.00003205057,0.00003725722,0.00008326012,0.9855379,0.0016261039,0.0061609237,0.00014976207,0.0045207217],"study_design_scores_gemma":[0.000015722837,0.00005431276,0.00103589,0.000013058255,0.000029530547,0.000016770313,0.00004273205,0.9925321,0.0010070099,0.0043204087,0.000915718,0.000016799077],"about_ca_topic_score_codex":0.041093137,"about_ca_topic_score_gemma":0.022472924,"teacher_disagreement_score":0.041093137,"about_ca_system_score_codex":0.002860243,"about_ca_system_score_gemma":0.0026032447,"threshold_uncertainty_score":0.081707895},"labels":[],"label_agreement":null},{"id":"W2108249402","doi":"10.5539/cis.v5n5p93","title":"The Age or Excess of the M|G|? Queue Busy Cycle Mean Value","year":2012,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia","keywords":"Measure (data warehouse); Queue; Reliability (semiconductor); Exponential function; Simple (philosophy); Exponential distribution; Value (mathematics)","score_opus":0.006943614961703254,"score_gpt":0.20810805541264155,"score_spread":0.2011644404509383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108249402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42951307,0.004308774,0.54926956,0.0014787068,0.00044842274,0.000048174872,0.0006829061,0.001032903,0.013217438],"genre_scores_gemma":[0.97195286,0.0010595534,0.023465425,0.00016886405,0.00028132455,0.00003130357,0.00014424107,0.00012177863,0.0027747673],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99953294,0.00007171268,0.000027052152,0.00012825592,0.0001283374,0.00011178063],"domain_scores_gemma":[0.99654764,0.0019940096,0.0004959419,0.00036273457,0.00037974367,0.00021987918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012032905,0.000491426,0.00058877486,0.0008354636,0.0003252017,0.0014454989,0.0007644268,0.0008073724,0.0024135916],"category_scores_gemma":[0.009367206,0.00018434304,0.00023596881,0.00046783214,0.00077300175,0.0021769302,0.00079032447,0.0007498372,0.00061617064],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016703192,0.00011851303,0.03480134,0.0006783162,0.00013687859,0.0008979124,0.0006900049,0.27535424,0.07451179,0.4016782,0.005864202,0.20359826],"study_design_scores_gemma":[0.000039942333,0.00045140216,0.01878029,0.00017934268,0.00009604064,0.0020289286,0.00027022045,0.7150338,0.033581488,0.21310672,0.016234638,0.00019725268],"about_ca_topic_score_codex":0.00048403934,"about_ca_topic_score_gemma":0.0004581264,"teacher_disagreement_score":0.0024135916,"about_ca_system_score_codex":0.00081662525,"about_ca_system_score_gemma":0.0006436718,"threshold_uncertainty_score":0.008074284},"labels":[],"label_agreement":null},{"id":"W2110098267","doi":"10.1109/tr.2005.853440","title":"Reliability Optimization of Distributed Access Networks With Constrained Total Cost","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematical optimization; Reliability (semiconductor); Constraint (computer-aided design); Upper and lower bounds; Computer science; Tree (set theory); Branch and bound; Process (computing); Optimization problem; Combinatorial optimization; Reliability theory; Search tree; Mathematics; Search algorithm; Reliability engineering; Engineering; Failure rate","score_opus":0.007269141267441066,"score_gpt":0.2175768517132472,"score_spread":0.21030771044580612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110098267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0939682,0.0011007745,0.8991303,0.00032071568,0.00003936919,0.000048596663,0.0000925496,0.00016532425,0.0051342193],"genre_scores_gemma":[0.9326807,0.0008630345,0.062416352,0.000047951115,0.00005082472,0.00015078153,0.00013415825,0.000092179514,0.0035638746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999218,0.00032230394,0.000025941537,0.00010737047,0.00022456108,0.000101874],"domain_scores_gemma":[0.99848,0.0010527307,0.00014595731,0.000056390498,0.00022289479,0.0000420329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001091969,0.001018268,0.0010417352,0.0006869782,0.00031531602,0.0008202924,0.0007299828,0.00058255764,0.0012182954],"category_scores_gemma":[0.0031905852,0.00041366258,0.0004856724,0.0010240283,0.00061610836,0.0011370643,0.00067701715,0.0006037289,0.0001448371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026669839,0.000012688115,0.00018063183,0.00004790258,0.000016431375,0.00004448444,0.000018158946,0.9830661,0.00095459324,0.008113896,0.00022489837,0.007293545],"study_design_scores_gemma":[0.000008560468,0.00002531492,0.00009435083,0.0000036489846,0.0000074198088,0.000017239501,0.000008154034,0.99412966,0.00030291246,0.005144419,0.00025546885,0.0000029678004],"about_ca_topic_score_codex":0.0026952804,"about_ca_topic_score_gemma":0.0017000369,"teacher_disagreement_score":0.0026952804,"about_ca_system_score_codex":0.0009270581,"about_ca_system_score_gemma":0.0008455951,"threshold_uncertainty_score":0.0067263246},"labels":[],"label_agreement":null},{"id":"W2110132105","doi":"10.1109/tr.2011.2161703","title":"Reliability and Availability Analysis of a Repairable $k$-out-of-$n:G$ System With $R$ Repairmen Subject to Shut-Off Rules","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science","score_opus":0.011722100197533322,"score_gpt":0.20313197455620555,"score_spread":0.19140987435867224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110132105","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8673389,0.0004970586,0.11280646,0.0007990196,0.00010407171,0.00014282539,0.0018982308,0.002429177,0.013984162],"genre_scores_gemma":[0.9945979,0.000050080907,0.0028817733,0.000017617615,0.00001160761,0.000024562627,0.00033674095,0.000059150138,0.0020206198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987564,0.00026046028,0.00006270918,0.00025026491,0.00033530628,0.00033480552],"domain_scores_gemma":[0.99573106,0.0021087474,0.0004278595,0.00036819285,0.0011253724,0.00023863162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017463762,0.0006694344,0.00078713585,0.0009422177,0.0006044103,0.0008926875,0.001400425,0.00072917005,0.0075619025],"category_scores_gemma":[0.003911701,0.00032122608,0.00076268264,0.00050023024,0.001044571,0.0009361611,0.00059013837,0.00060701364,0.00093180593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009621737,0.00006810082,0.0058093965,0.00018522813,0.000081787955,0.0007694109,0.00017751638,0.9578338,0.01135885,0.00718354,0.004435121,0.01113497],"study_design_scores_gemma":[0.00002105829,0.00012655993,0.002631867,0.000008616528,0.0000434459,0.00010050487,0.000054266937,0.9928757,0.002308942,0.0014756442,0.00033904743,0.0000143271545],"about_ca_topic_score_codex":0.019576617,"about_ca_topic_score_gemma":0.012117127,"teacher_disagreement_score":0.019576617,"about_ca_system_score_codex":0.0015434972,"about_ca_system_score_gemma":0.001579024,"threshold_uncertainty_score":0.03892535},"labels":[],"label_agreement":null},{"id":"W2111658085","doi":"10.1109/rams.2011.5754467","title":"Trend analysis of the power law process with censored data","year":2011,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Censoring (clinical trials); Computer science; Statistics; Maximum likelihood; Likelihood function; Poisson distribution; Counting process; Poisson process; Accelerated life testing; Econometrics; Algorithm; Mathematics; Weibull distribution","score_opus":0.016647932310426845,"score_gpt":0.21138659027655438,"score_spread":0.19473865796612752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111658085","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05473579,0.00037317327,0.943109,0.00017856622,0.000045003133,0.00007573598,0.0002680177,0.0003290486,0.0008856498],"genre_scores_gemma":[0.8527928,0.0016120921,0.13764353,0.00020173563,0.00021683484,0.0004643959,0.0018100357,0.00025692864,0.005001674],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99721175,0.0009225105,0.00020389419,0.00071608345,0.0006801719,0.00026557586],"domain_scores_gemma":[0.9808965,0.012474826,0.0023504316,0.0018429433,0.0021955953,0.00023967243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008533959,0.00080461975,0.001295628,0.0029305038,0.00047801738,0.0012936686,0.0018681919,0.0012736777,0.0024461942],"category_scores_gemma":[0.041753482,0.00045408992,0.0014506308,0.0025619795,0.0010735737,0.003663611,0.0010246714,0.0017498278,0.0006180144],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042874194,0.00023469563,0.05354332,0.0007161773,0.00052298803,0.0016797112,0.0009358769,0.5764784,0.008163194,0.21454768,0.0028979038,0.13985129],"study_design_scores_gemma":[0.000018184135,0.000086474705,0.0045311404,0.00003385161,0.000045606488,0.00018389881,0.00007103765,0.9557786,0.0008348967,0.036810767,0.0015730757,0.00003244088],"about_ca_topic_score_codex":0.0029947546,"about_ca_topic_score_gemma":0.0013490076,"teacher_disagreement_score":0.008533959,"about_ca_system_score_codex":0.0009264611,"about_ca_system_score_gemma":0.000977451,"threshold_uncertainty_score":0.0451324},"labels":[],"label_agreement":null},{"id":"W2114161156","doi":"10.1504/ijor.2012.045185","title":"Joint job scheduling and preventive maintenance on a single machine","year":2012,"lang":"en","type":"article","venue":"International Journal of Operational Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Preventive maintenance; Computer science; Scheduling (production processes); Single-machine scheduling; Schedule; Operations research; Job shop scheduling; Mathematical optimization; Reliability engineering; Operations management; Mathematics; Engineering; Operating system","score_opus":0.05168019472083722,"score_gpt":0.3379157525354672,"score_spread":0.28623555781462995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114161156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2652497,0.0011233445,0.72746414,0.00027759434,0.00011985163,0.00013837502,0.0001245566,0.00034603215,0.005156466],"genre_scores_gemma":[0.93555915,0.00037840378,0.05940313,0.000029173134,0.00008581799,0.000097521,0.00008972128,0.000058499012,0.0042986553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988953,0.00035054272,0.00003975301,0.00022644029,0.00025651924,0.0002314523],"domain_scores_gemma":[0.9987382,0.00069197355,0.00023119421,0.00011996383,0.00008312299,0.00013553695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013561492,0.001023901,0.0018458545,0.0005926586,0.0005113744,0.0011672038,0.001351097,0.0009966728,0.0017050495],"category_scores_gemma":[0.0022163442,0.0007752975,0.0013032648,0.00079236814,0.00093981985,0.0011056946,0.0007644275,0.0008291044,0.00033753167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022983298,0.00009132234,0.0004305922,0.000107832275,0.00004977232,0.00014049288,0.000050572173,0.97455716,0.0041800607,0.0055582016,0.0002257025,0.014378405],"study_design_scores_gemma":[0.000025273563,0.00017525686,0.0003551039,0.0000047692533,0.000031443138,0.000041132964,0.000012614042,0.99475044,0.0010722545,0.00318723,0.00033519042,0.000009294646],"about_ca_topic_score_codex":0.0036015352,"about_ca_topic_score_gemma":0.0026019388,"teacher_disagreement_score":0.0036015352,"about_ca_system_score_codex":0.0006193913,"about_ca_system_score_gemma":0.0014044323,"threshold_uncertainty_score":0.0071721077},"labels":[],"label_agreement":null},{"id":"W2115781301","doi":"10.1016/j.cie.2015.09.013","title":"Optimal opportunistic indirect grouping of preventive replacements in multicomponent systems","year":2015,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mathematical optimization; Time horizon; Tree (set theory); Preventive maintenance; Heuristic; Interval (graph theory); Reliability engineering; Mathematics; Engineering; Artificial intelligence","score_opus":0.04690434959953443,"score_gpt":0.2193259444428086,"score_spread":0.17242159484327416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115781301","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5678365,0.00093298237,0.42515963,0.0003059709,0.00013758459,0.00021562642,0.00013375723,0.00048110026,0.004796758],"genre_scores_gemma":[0.973527,0.000072549985,0.024945697,0.000028639111,0.0000250765,0.000045651657,0.000049415423,0.000023266628,0.0012826709],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991954,0.0002712201,0.000045022225,0.00014105486,0.000116642084,0.0002307015],"domain_scores_gemma":[0.997375,0.0014089734,0.00039168898,0.00029190278,0.00024966622,0.00028277296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014090925,0.0009934447,0.0024488217,0.0007856925,0.0009418736,0.0011778432,0.0022571802,0.001024816,0.0021993772],"category_scores_gemma":[0.0034844342,0.00084053806,0.0005683841,0.0010511702,0.0009932054,0.0009230597,0.0011360343,0.00061538245,0.00022853957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074917835,0.00022232051,0.001361351,0.000088460314,0.00006854517,0.00009211901,0.00009606087,0.9495623,0.00234081,0.0028146391,0.0006969459,0.041907318],"study_design_scores_gemma":[0.000038012837,0.00021595751,0.00067837985,0.000009460166,0.000039829323,0.000030074956,0.000045012777,0.99563783,0.0006537411,0.0024564709,0.00018680566,0.00000833894],"about_ca_topic_score_codex":0.0042570503,"about_ca_topic_score_gemma":0.005903223,"teacher_disagreement_score":0.0042570503,"about_ca_system_score_codex":0.0008267137,"about_ca_system_score_gemma":0.001259417,"threshold_uncertainty_score":0.008464575},"labels":[],"label_agreement":null},{"id":"W2115800482","doi":"10.1109/glocom.1990.116591","title":"Using and abusing bounds for network reliability","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reliability (semiconductor); Bounding overwatch; Computer science; Network topology; Process (computing); Selection (genetic algorithm); Reliability engineering; Reliability theory; Data mining; Computer network; Artificial intelligence; Engineering","score_opus":0.030298134573398406,"score_gpt":0.2199269307596478,"score_spread":0.1896287961862494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115800482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024952134,0.005659923,0.97466236,0.0012369951,0.00034682243,0.000027013946,0.000039386712,0.00020464163,0.015327653],"genre_scores_gemma":[0.39033103,0.01953691,0.5738137,0.0012878716,0.0020788216,0.00055389584,0.00020140523,0.0009882904,0.011208068],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97868663,0.0103733465,0.0010220516,0.0024286842,0.006583825,0.0009054224],"domain_scores_gemma":[0.93777716,0.047886927,0.002789878,0.007119442,0.0039099935,0.00051661726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015858075,0.003160216,0.0015613685,0.0036626295,0.0015596324,0.008082395,0.002808737,0.0031051224,0.003614409],"category_scores_gemma":[0.0855025,0.0010933164,0.0013249177,0.0026249166,0.0058669043,0.01609888,0.004902348,0.0069286893,0.0013333807],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000550339,0.000020819958,0.0002334783,0.0001655712,0.000037412683,0.00006864766,0.0002649052,0.09064184,0.0013744712,0.8451673,0.0025882616,0.0593823],"study_design_scores_gemma":[0.000010039643,0.000049362312,0.00012588194,0.00021100548,0.00003505684,0.00010659139,0.00006926911,0.17382696,0.00253708,0.80460644,0.018368958,0.00005333492],"about_ca_topic_score_codex":0.0026518828,"about_ca_topic_score_gemma":0.0019405892,"teacher_disagreement_score":0.015858075,"about_ca_system_score_codex":0.0035697136,"about_ca_system_score_gemma":0.0020408167,"threshold_uncertainty_score":0.08386648},"labels":[],"label_agreement":null},{"id":"W2116097629","doi":"10.1007/s10732-009-9117-3","title":"A heuristic method for non-homogeneous redundancy optimization of series-parallel multi-state systems","year":2009,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université de Montréal; Computer Research Institute of Montréal","funders":"","keywords":"Mathematical optimization; Redundancy (engineering); Computer science; Disjoint sets; Piecewise; Linear subspace; Series and parallel circuits; Algorithm; Heuristic; Series (stratigraphy); State space; Mathematics","score_opus":0.01103870433776294,"score_gpt":0.2578407229845529,"score_spread":0.24680201864678997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116097629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020099726,0.0002991515,0.97543746,0.000079628364,0.00008460581,0.0001082323,0.000051944397,0.00033999057,0.0034992837],"genre_scores_gemma":[0.44637698,0.00026876607,0.54951537,0.000113566144,0.00008846506,0.00035749338,0.00014169449,0.00014567074,0.0029920312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973005,0.00010905238,0.00001300123,0.00003245561,0.00006912746,0.000046262823],"domain_scores_gemma":[0.99929655,0.0004369303,0.000059733567,0.000050008457,0.000120083445,0.000036754765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010427451,0.00079306756,0.0013620853,0.0010965029,0.00063737226,0.0006613699,0.0014407907,0.00089086057,0.0028649224],"category_scores_gemma":[0.0015700848,0.00057831494,0.00086094823,0.0010444352,0.0005996667,0.0006504209,0.00066116476,0.0007585739,0.0002677612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007697789,0.000061914856,0.0001366124,0.000071066745,0.000041016876,0.000045874425,0.000030229789,0.9451809,0.001474307,0.0046155676,0.0007971085,0.0474685],"study_design_scores_gemma":[0.000017732249,0.00002425348,0.000031566084,0.000003989073,0.000008649924,0.0000070746155,0.0000043256236,0.99821246,0.0002445071,0.001198334,0.00024341918,0.0000035876826],"about_ca_topic_score_codex":0.005356031,"about_ca_topic_score_gemma":0.0055848486,"teacher_disagreement_score":0.005356031,"about_ca_system_score_codex":0.00074444985,"about_ca_system_score_gemma":0.0012451605,"threshold_uncertainty_score":0.010649741},"labels":[],"label_agreement":null},{"id":"W2117574960","doi":"10.5539/mas.v5n6p86","title":"Developing Decision Making Grid for Maintenance Policy Making Based on Estimated Range of Overall Equipment Effectiveness","year":2011,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Overall equipment effectiveness; Profitability index; Total productive maintenance; Business; Grid; Computer science; Competition (biology); Range (aeronautics); Perspective (graphical); Operations management; Operations research; Environmental economics; Production (economics); Economics; Engineering; Microeconomics; Mathematics; Finance","score_opus":0.03481548078866492,"score_gpt":0.288895434499786,"score_spread":0.2540799537111211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117574960","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22630128,0.00029153554,0.75870645,0.0009534041,0.00007122439,0.00082093664,0.0009569868,0.0012644177,0.010633832],"genre_scores_gemma":[0.705844,0.00017344549,0.29218766,0.00008385093,0.000020777732,0.00048691873,0.00072454807,0.000040733852,0.00043796797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995698,0.0024102265,0.00038117415,0.00052939664,0.00067428785,0.00030700202],"domain_scores_gemma":[0.99057674,0.0060840677,0.0010102844,0.00047168837,0.0015913153,0.0002658541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0086815115,0.0008358701,0.0014527265,0.004019171,0.0005597555,0.0030546724,0.0010023849,0.00095570873,0.0020599999],"category_scores_gemma":[0.018888399,0.00052300916,0.00066862337,0.002722967,0.0006340113,0.003119571,0.001390031,0.0009042649,0.0003963556],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069824443,0.00030410392,0.056227524,0.00037167926,0.00021678432,0.0004475016,0.0007759414,0.6964971,0.0017622215,0.027312113,0.0043964144,0.21099035],"study_design_scores_gemma":[0.00010173739,0.00012542323,0.0052932543,0.000066681314,0.000045111952,0.000056287583,0.00075628463,0.96772,0.0015772032,0.02246545,0.0017512316,0.000041465366],"about_ca_topic_score_codex":0.0053156177,"about_ca_topic_score_gemma":0.0033815957,"teacher_disagreement_score":0.0086815115,"about_ca_system_score_codex":0.0019975272,"about_ca_system_score_gemma":0.0029005003,"threshold_uncertainty_score":0.045912743},"labels":[],"label_agreement":null},{"id":"W2117639993","doi":"10.1287/moor.25.1.141.15207","title":"Optimal Preventive Replacement Under Minimal Repair and Random Repair Cost","year":2000,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Optimal stopping; Mathematical optimization; Mathematics; Maximization; Limit (mathematics); Semimartingale; Unit (ring theory); Utility maximization problem; Applied mathematics; Mathematical economics; Utility maximization","score_opus":0.031193077777050486,"score_gpt":0.3199220425933819,"score_spread":0.2887289648163314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117639993","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43951142,0.0019533816,0.5505457,0.0010637096,0.00010497365,0.00010556386,0.00022749622,0.00035946572,0.0061282767],"genre_scores_gemma":[0.98046255,0.00031311635,0.01686572,0.00004973601,0.000028633394,0.00005086853,0.00008470124,0.00004101607,0.002103645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99898475,0.00039423472,0.00005182713,0.00017854366,0.00013243232,0.00025823127],"domain_scores_gemma":[0.9970396,0.0019316495,0.00041791805,0.00011240257,0.00024417674,0.0002543229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017440335,0.0009326306,0.0016609534,0.0007238956,0.0003458432,0.00094886945,0.0011232959,0.0013510043,0.0016621833],"category_scores_gemma":[0.0051034777,0.0007832737,0.00064141944,0.00039890458,0.0011897926,0.00086416263,0.0006787381,0.0008887707,0.00019734509],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020198942,0.00007142319,0.0004941326,0.00010926616,0.00003461044,0.00012501504,0.000042948195,0.9732842,0.0019593185,0.017272476,0.00045523033,0.0059493645],"study_design_scores_gemma":[0.000038614016,0.00008226085,0.00027857747,0.000010549843,0.000022992288,0.000028342694,0.00001655054,0.9887905,0.0005078196,0.010032932,0.000181951,0.000008928014],"about_ca_topic_score_codex":0.0055023083,"about_ca_topic_score_gemma":0.0021717842,"teacher_disagreement_score":0.0055023083,"about_ca_system_score_codex":0.0013554334,"about_ca_system_score_gemma":0.0014816808,"threshold_uncertainty_score":0.010940552},"labels":[],"label_agreement":null},{"id":"W2117854991","doi":"10.1007/s10845-013-0752-z","title":"An integrated GA-DEA algorithm for determining the most effective maintenance policy for a k -out-of- n problem","year":2013,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"University of Tehran","keywords":"Genetic algorithm; Mathematical optimization; Computation; Pareto principle; Flexibility (engineering); Data envelopment analysis; Computer science; Production (economics); Algorithm; Pareto optimal; Reliability (semiconductor); Queue; Multi-objective optimization; Mathematics; Economics","score_opus":0.008782547401022432,"score_gpt":0.2455464937929538,"score_spread":0.23676394639193135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117854991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05403325,0.00096959644,0.9369004,0.00029345124,0.00014108735,0.00022341078,0.00016918538,0.00080299046,0.0064666],"genre_scores_gemma":[0.41721106,0.00033425083,0.5784601,0.00021493081,0.000066804474,0.0004050156,0.00031068816,0.00009166197,0.0029054512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995914,0.000101635545,0.000027313128,0.00009904739,0.00009712043,0.00008350926],"domain_scores_gemma":[0.9992834,0.0004254962,0.000062118124,0.00003164449,0.00015657878,0.000040871695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009476525,0.0012076705,0.0016684614,0.0011908392,0.000619453,0.0009817381,0.0018477478,0.0020599794,0.0024033755],"category_scores_gemma":[0.0023179883,0.00071580487,0.00096146524,0.0009954936,0.0004823545,0.00077951833,0.00088425155,0.0010484873,0.00034287153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009294494,0.00011553656,0.00063033495,0.000071953014,0.000067087676,0.000055750075,0.000028294395,0.9298565,0.0013599799,0.0017870225,0.0011027068,0.06483192],"study_design_scores_gemma":[0.00002100493,0.000033208664,0.000108524386,0.000006556309,0.000016547605,0.000015522564,0.0000069406433,0.99888736,0.00020551225,0.00049351616,0.00020165839,0.0000036469712],"about_ca_topic_score_codex":0.0118732555,"about_ca_topic_score_gemma":0.015420027,"teacher_disagreement_score":0.0118732555,"about_ca_system_score_codex":0.0010081994,"about_ca_system_score_gemma":0.0026302163,"threshold_uncertainty_score":0.023608267},"labels":[],"label_agreement":null},{"id":"W2120269763","doi":"10.1109/rams.2005.1408395","title":"Component repairs: when to perform and what to do?","year":2005,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Component (thermodynamics); Reliability engineering; Computer science; Process (computing); Function (biology); Set (abstract data type); Reduction (mathematics); Interval (graph theory); Maintenance engineering; State (computer science); Engineering; Mathematics; Algorithm","score_opus":0.005397761441237632,"score_gpt":0.19289854459029204,"score_spread":0.1875007831490544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120269763","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5397406,0.020667842,0.305602,0.07817395,0.0025741712,0.0006230665,0.003319282,0.0017891094,0.047509898],"genre_scores_gemma":[0.9493002,0.0043412787,0.03510705,0.0010955848,0.00047997443,0.00011117743,0.000644833,0.00022775782,0.008692114],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99771035,0.0008122896,0.00014505775,0.0003897575,0.0006069112,0.00033560256],"domain_scores_gemma":[0.9939129,0.0021736405,0.0015417183,0.00032903557,0.0011051719,0.00093744777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004091795,0.0007147183,0.0011397857,0.00097189314,0.0005228108,0.0026158951,0.0012257496,0.0016977934,0.005342407],"category_scores_gemma":[0.014079743,0.00035694073,0.0005687119,0.0007930925,0.0012167478,0.0032368738,0.00050733954,0.0012240275,0.002348128],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014189933,0.0010190696,0.12280235,0.0015645262,0.0003952097,0.0004849133,0.0014418548,0.022311347,0.0058775856,0.049138665,0.026641633,0.7669039],"study_design_scores_gemma":[0.0002537852,0.0018182984,0.3712146,0.0020226354,0.0008479914,0.002929919,0.021348856,0.15299326,0.014181303,0.302112,0.12961873,0.0006585202],"about_ca_topic_score_codex":0.004086162,"about_ca_topic_score_gemma":0.0070306086,"teacher_disagreement_score":0.005342407,"about_ca_system_score_codex":0.001255888,"about_ca_system_score_gemma":0.0023604275,"threshold_uncertainty_score":0.021639764},"labels":[],"label_agreement":null},{"id":"W2123870732","doi":"10.1057/jors.2010.49","title":"Optimization models for critical spare parts inventories—a reliability approach","year":2010,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Spare part; Reliability (semiconductor); Computer science; Project management; Operations research; Reliability engineering; Purchasing; Scheduling (production processes); Systems engineering; Operations management; Engineering","score_opus":0.053585881235284384,"score_gpt":0.3332193809567216,"score_spread":0.2796334997214372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123870732","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019871274,0.0025485961,0.96525973,0.0011920484,0.00012220645,0.00010025402,0.00047063341,0.00022751208,0.010207748],"genre_scores_gemma":[0.78062695,0.004750517,0.17879933,0.00050462445,0.00038802347,0.0008160487,0.0012712606,0.00038202616,0.03246128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926525,0.00037544715,0.000030603314,0.00011551288,0.00011420326,0.00009894418],"domain_scores_gemma":[0.9970271,0.0022346072,0.00024332659,0.00007485854,0.000339693,0.00008042707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025704291,0.0017780724,0.0017882242,0.0017041132,0.0005097324,0.0017239338,0.0019121899,0.0021039594,0.005054269],"category_scores_gemma":[0.0057648094,0.0014293581,0.0015498092,0.0014363294,0.000952975,0.0016244247,0.0012975471,0.0018237812,0.00073233794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008180453,0.0000071655163,0.00010195239,0.000020169073,0.000014336343,0.0000142942135,0.000009192838,0.9923724,0.000057355388,0.0049396497,0.0003456341,0.0021096708],"study_design_scores_gemma":[0.0000019547495,0.0000054437137,0.000043519878,0.0000074257314,0.0000052583555,0.000004183891,0.0000065270765,0.99557805,0.000026120631,0.003990013,0.0003284497,0.0000031707086],"about_ca_topic_score_codex":0.015525199,"about_ca_topic_score_gemma":0.010846842,"teacher_disagreement_score":0.015525199,"about_ca_system_score_codex":0.0017868283,"about_ca_system_score_gemma":0.0022431551,"threshold_uncertainty_score":0.030869663},"labels":[],"label_agreement":null},{"id":"W2124019770","doi":"10.1109/tr.2006.874916","title":"Performance Evaluation of Generalized Multi-State&lt;tex&gt;$k$&lt;/tex&gt;-Out-of-&lt;tex&gt;$n$&lt;/tex&gt;Systems","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Alberta","funders":"","keywords":"State (computer science); Mathematics; Monotonic function; State vector; Discrete mathematics; Algorithm; Applied mathematics; Mathematical analysis; Physics","score_opus":0.020659968326457098,"score_gpt":0.24549270156631528,"score_spread":0.2248327332398582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124019770","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58700013,0.0003934921,0.40270898,0.00015788295,0.000033685566,0.00008645424,0.00012254834,0.00082843885,0.00866841],"genre_scores_gemma":[0.9903847,0.00004006525,0.0091486685,0.00000822999,0.0000028009572,0.000014242841,0.00005838378,0.000017381582,0.00032549328],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955827,0.0001711079,0.000021852049,0.00007309841,0.00010511425,0.000070562826],"domain_scores_gemma":[0.99920195,0.00035061451,0.00009249181,0.00011563959,0.00020410928,0.000035272587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009592249,0.0006224384,0.000567023,0.00034158622,0.00034895758,0.0006120399,0.0006802518,0.00044476474,0.0014671687],"category_scores_gemma":[0.0015189246,0.000106627056,0.00040331198,0.000310195,0.00040402522,0.00068349193,0.00060822285,0.0003653207,0.0001545737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011186152,0.00002417277,0.00089574605,0.000039374,0.000019882536,0.000041114603,0.000035023997,0.9734441,0.0034239944,0.0026864244,0.00026274915,0.019015389],"study_design_scores_gemma":[0.0000019250845,0.000033559394,0.00019507884,0.0000012554618,0.0000033967174,0.0000053379804,0.0000056602435,0.99873716,0.00061347126,0.00035007164,0.000050775485,0.0000022878041],"about_ca_topic_score_codex":0.007167212,"about_ca_topic_score_gemma":0.0053148414,"teacher_disagreement_score":0.007167212,"about_ca_system_score_codex":0.00091877725,"about_ca_system_score_gemma":0.0005172712,"threshold_uncertainty_score":0.014250994},"labels":[],"label_agreement":null},{"id":"W2126691367","doi":"10.1109/tr.2010.2056412","title":"An Integrated Model for Production and Preventive Maintenance Planning in Multi-State Systems","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Université Laval","keywords":"Preventive maintenance; Corrective maintenance; Time horizon; Production (economics); Sizing; Reliability engineering; Operations research; Holding cost; State (computer science); Engineering; Planned maintenance; Genetic algorithm; Computer science; Production planning; Total cost; Operations management; Mathematical optimization; Mathematics; Economics","score_opus":0.01622437822571341,"score_gpt":0.2547837867690253,"score_spread":0.2385594085433119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126691367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011467276,0.00024430064,0.9822917,0.0002575417,0.00004822075,0.00007373256,0.0002103771,0.00036033368,0.0050464147],"genre_scores_gemma":[0.72427917,0.00079237315,0.26049772,0.000118218566,0.00008725232,0.000676745,0.00060460315,0.00012834191,0.012815455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992867,0.00019841747,0.00003569797,0.0001708299,0.00020654243,0.00010172033],"domain_scores_gemma":[0.999393,0.0003106582,0.000079877194,0.00006491462,0.00010357332,0.00004802809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000865033,0.0010026232,0.001126832,0.0006787941,0.00064990105,0.00199311,0.002164773,0.0017037027,0.0045136604],"category_scores_gemma":[0.0014719331,0.000719132,0.0014099227,0.001120642,0.0009853345,0.0019827292,0.0011052849,0.0016534905,0.0006093058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001951886,0.000022541291,0.00011598588,0.000025053772,0.000013068798,0.000045739172,0.000026954785,0.97765726,0.00042739077,0.017438365,0.00027623872,0.0039319387],"study_design_scores_gemma":[0.000009629303,0.000017724076,0.000041657564,0.0000037672698,0.0000079664305,0.000009762249,0.000004785873,0.9945234,0.00012848809,0.0045581735,0.00068987225,0.000004768806],"about_ca_topic_score_codex":0.011759488,"about_ca_topic_score_gemma":0.010747946,"teacher_disagreement_score":0.011759488,"about_ca_system_score_codex":0.0019478338,"about_ca_system_score_gemma":0.0021264593,"threshold_uncertainty_score":0.023382068},"labels":[],"label_agreement":null},{"id":"W2127308214","doi":"","title":"Estimating the value of reliability for business customers","year":2004,"lang":"en","type":"article","venue":"IEEE International Conference on Probabilistic Methods Applied to Power Systems","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Hydro (Canada)","funders":"","keywords":"Liberian dollar; Reliability (semiconductor); Valuation (finance); Activity-based costing; Computer science; Business; Actuarial science; Operations research; Reliability engineering; Marketing; Engineering; Finance","score_opus":0.04168765933937114,"score_gpt":0.33197769961434476,"score_spread":0.2902900402749736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127308214","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8459151,0.0013543541,0.13897961,0.0008699638,0.00003524076,0.00011426067,0.0006652389,0.00010447697,0.011961837],"genre_scores_gemma":[0.98624367,0.0004902721,0.0117738275,0.000016702963,0.000032110253,0.000044471453,0.00035126298,0.000016988939,0.0010307399],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99767715,0.0012806439,0.000089042944,0.00020317972,0.0005706596,0.0001792935],"domain_scores_gemma":[0.9780391,0.018146819,0.0015795334,0.00061620376,0.0013516837,0.0002666561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026542898,0.0006646696,0.0006095922,0.0021130072,0.0002736348,0.0018233644,0.0009232677,0.0012043134,0.0026651616],"category_scores_gemma":[0.028782774,0.0005188415,0.00046264962,0.0023055978,0.0005308864,0.0025908279,0.0007254667,0.0010449861,0.0004203644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006868694,0.00030247023,0.16671407,0.0002519195,0.00033574758,0.0005735189,0.0005869589,0.5973402,0.0023866196,0.055682205,0.0025496138,0.17258969],"study_design_scores_gemma":[0.000020298705,0.00022011973,0.047160562,0.00005401406,0.00008625827,0.00027803564,0.0006564962,0.91362184,0.0016481718,0.03402551,0.0021491428,0.00007963766],"about_ca_topic_score_codex":0.002995149,"about_ca_topic_score_gemma":0.0024339524,"teacher_disagreement_score":0.002995149,"about_ca_system_score_codex":0.0015444146,"about_ca_system_score_gemma":0.0005280094,"threshold_uncertainty_score":0.014037371},"labels":[],"label_agreement":null},{"id":"W2130058925","doi":"10.1613/jair.3902","title":"Scheduling a Dynamic Aircraft Repair Shop with Limited Repair Resources","year":2013,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Schedule; Time horizon; Computer science; Scheduling (production processes); Integer programming; Job shop scheduling; Operations research; Dynamic programming; Mathematical optimization; Dynamic priority scheduling; Reliability engineering; Engineering; Mathematics; Algorithm","score_opus":0.043717456545048716,"score_gpt":0.3167900718857077,"score_spread":0.27307261534065896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130058925","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26250678,0.0004103116,0.73225635,0.00030434437,0.00005065365,0.00014749229,0.0003437973,0.00042603037,0.0035542678],"genre_scores_gemma":[0.8474843,0.00037017482,0.1474343,0.00005910392,0.000038243936,0.00014493526,0.00040277766,0.00007568516,0.003990388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946755,0.0001659276,0.00002863287,0.0001382134,0.00008178173,0.00011796364],"domain_scores_gemma":[0.99889094,0.00069564703,0.00016203224,0.000069621514,0.00008118183,0.00010057722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094657054,0.00088173425,0.0011080074,0.0004957077,0.0004939759,0.00076797814,0.00108799,0.0008723219,0.0024555859],"category_scores_gemma":[0.0015130909,0.00071774353,0.00093584886,0.0005334515,0.00071401644,0.0009732085,0.0005657673,0.00067945296,0.0002292222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015712777,0.000073059564,0.00029878927,0.00010077788,0.000030187093,0.00016165085,0.00006428217,0.97806203,0.0047057355,0.0054950356,0.00030890098,0.010542392],"study_design_scores_gemma":[0.00004775106,0.00013745336,0.00016713138,0.000006631294,0.000017863222,0.000040415875,0.000027714315,0.99366426,0.0010571061,0.0043720366,0.00045321576,0.00000845039],"about_ca_topic_score_codex":0.0038330804,"about_ca_topic_score_gemma":0.0027243893,"teacher_disagreement_score":0.0038330804,"about_ca_system_score_codex":0.00070813345,"about_ca_system_score_gemma":0.0012290142,"threshold_uncertainty_score":0.008214772},"labels":[],"label_agreement":null},{"id":"W2133020076","doi":"10.1243/1748006xjrr93","title":"Maintenance modelling and scheduling in fault-tolerant control","year":2008,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fault tolerance; Scheduling (production processes); Markov chain; Computer science; Optimal maintenance; Fault detection and isolation; Mathematical optimization; Control theory (sociology); Control (management); Distributed computing; Engineering; Reliability engineering; Mathematics; Actuator; Artificial intelligence; Machine learning","score_opus":0.007165396534082781,"score_gpt":0.1837241743250642,"score_spread":0.17655877779098142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133020076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023567908,0.00048219107,0.97421366,0.00020806442,0.000034202672,0.000015570491,0.000035237706,0.00007593509,0.0013673453],"genre_scores_gemma":[0.9462266,0.0008794867,0.0500235,0.000046346726,0.000080016456,0.000093090996,0.00009812997,0.00003508214,0.0025177323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994717,0.00017994038,0.000026858244,0.000095106836,0.00016495008,0.000061566265],"domain_scores_gemma":[0.99907047,0.0005850978,0.00017659165,0.00004663216,0.00008896912,0.000032258642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008828646,0.00064884487,0.00071837136,0.0003745799,0.00030969622,0.00089998776,0.00096398214,0.00085452385,0.00077915034],"category_scores_gemma":[0.0026438083,0.00037253185,0.00055140915,0.00049694575,0.00070616225,0.00096460595,0.00037437922,0.00066690444,0.00014022396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000109550965,0.00000723828,0.00012477017,0.000016974867,0.0000072891257,0.000018118244,0.000018196723,0.9810207,0.00038923472,0.014810072,0.00009632429,0.0034801813],"study_design_scores_gemma":[0.0000019669303,0.000005902229,0.0000364274,0.000001791293,0.000001818669,0.000004196062,0.0000024720741,0.9947949,0.00009675834,0.0049010525,0.0001508807,0.0000018397789],"about_ca_topic_score_codex":0.008552276,"about_ca_topic_score_gemma":0.0040009925,"teacher_disagreement_score":0.008552276,"about_ca_system_score_codex":0.0011370752,"about_ca_system_score_gemma":0.0010945518,"threshold_uncertainty_score":0.017005026},"labels":[],"label_agreement":null},{"id":"W2133729278","doi":"10.1108/jqme-03-2014-0013","title":"Age replacement policies for two-component systems with stochastic dependence","year":2015,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université Laval; Cegep de Sainte Foy","funders":"","keywords":"Component (thermodynamics); Constant (computer programming); Reliability (semiconductor); Function (biology); Random variable; Stochastic modelling; Preventive maintenance; Domino effect; Probability density function; Computer science; Reliability engineering; Mathematical optimization; Mathematics; Engineering; Statistics","score_opus":0.02909560418926364,"score_gpt":0.2763367829980244,"score_spread":0.24724117880876076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133729278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46538988,0.0017021738,0.527652,0.00046166434,0.000081106366,0.0001310646,0.00017673612,0.00031676693,0.0040885652],"genre_scores_gemma":[0.99287647,0.00021867779,0.0051009753,0.000016353932,0.00001624563,0.00001910623,0.000029898505,0.000010777996,0.0017115286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992374,0.00021235592,0.00004514614,0.00014408899,0.00020046275,0.00016058599],"domain_scores_gemma":[0.9973475,0.00130475,0.00073186104,0.00014587381,0.00031798694,0.00015192074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016460153,0.00068055274,0.0008488165,0.00066817744,0.00039675707,0.00074545655,0.00090174185,0.0006780134,0.0020438228],"category_scores_gemma":[0.0036226597,0.00038142808,0.0007261138,0.00038374512,0.00058916333,0.0007826792,0.0006155255,0.0006379225,0.00019301043],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017973607,0.00007340144,0.0028685993,0.00009329628,0.000041082025,0.00024062561,0.00006551617,0.97367054,0.0042239227,0.007821244,0.00033416005,0.010387902],"study_design_scores_gemma":[0.000009626948,0.000114001276,0.0010995164,0.000004621135,0.000021644133,0.00008773326,0.000015430925,0.9957528,0.0006396164,0.0020073862,0.00024045391,0.0000072102366],"about_ca_topic_score_codex":0.0031381173,"about_ca_topic_score_gemma":0.0018269201,"teacher_disagreement_score":0.0031381173,"about_ca_system_score_codex":0.0010781977,"about_ca_system_score_gemma":0.00062562927,"threshold_uncertainty_score":0.00870502},"labels":[],"label_agreement":null},{"id":"W2134021005","doi":"10.1109/pes.2006.1708961","title":"Deriving asset probabilities of failure: effect of condition and maintenance levels","year":2006,"lang":"en","type":"article","venue":"2006 IEEE Power Engineering Society General Meeting","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kinectrics (Canada)","funders":"","keywords":"Asset (computer security); Risk analysis (engineering); Actuarial science; Life expectancy; Expectancy theory; Computer science; Focus (optics); Reliability engineering; Business; Economics; Engineering; Computer security","score_opus":0.0024247046734488854,"score_gpt":0.17916500210146924,"score_spread":0.17674029742802036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134021005","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30335924,0.00058279943,0.68923,0.0002271856,0.0000355146,0.000079547906,0.0005400206,0.00038989144,0.005555744],"genre_scores_gemma":[0.9598435,0.0004441086,0.038561992,0.000039541654,0.000023706796,0.00004275203,0.00036389907,0.000069243266,0.0006112029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988337,0.0003931247,0.000057292797,0.00018025977,0.000429304,0.00010641931],"domain_scores_gemma":[0.97497296,0.022011565,0.0013736908,0.000900617,0.00060301716,0.0001381733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024627564,0.00084434834,0.00065832277,0.0011378748,0.00020931214,0.0010842754,0.00074088044,0.0008466899,0.0014655147],"category_scores_gemma":[0.02330271,0.0005041319,0.0008317033,0.0006784442,0.00071020727,0.0019378323,0.0009582923,0.0012788349,0.00034512667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067622,0.000059279035,0.011573147,0.000064941574,0.000068359026,0.00010912455,0.000080999336,0.9423954,0.0024104377,0.008179274,0.0002264294,0.034764998],"study_design_scores_gemma":[0.000010063247,0.00013194418,0.008871618,0.000022167125,0.00006867333,0.00017114983,0.000027850298,0.96327466,0.006583722,0.020183409,0.0006114971,0.000043231343],"about_ca_topic_score_codex":0.0024571118,"about_ca_topic_score_gemma":0.001707682,"teacher_disagreement_score":0.0024627564,"about_ca_system_score_codex":0.0005050867,"about_ca_system_score_gemma":0.0005495939,"threshold_uncertainty_score":0.013024449},"labels":[],"label_agreement":null},{"id":"W2136354326","doi":"10.1002/nav.20037","title":"Warranty costs: An age‐dependent failure/repair model","year":2004,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Office of Naval Research","keywords":"Warranty; Product (mathematics); Failure rate; Hazard; Function (biology); Poisson distribution; Computer science; Poisson process; Reliability engineering; Operations research; Mathematics; Engineering; Statistics","score_opus":0.07931213403529234,"score_gpt":0.3429327634649323,"score_spread":0.26362062942963993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136354326","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29962122,0.002548507,0.66682285,0.0022867708,0.0002175008,0.00019478766,0.0016232964,0.00045915952,0.02622597],"genre_scores_gemma":[0.9695143,0.00074202847,0.011197881,0.00006661473,0.00007564407,0.00010111316,0.00029568354,0.000051350453,0.017955292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990959,0.0003088869,0.000050923805,0.00013565352,0.00023423298,0.00017430338],"domain_scores_gemma":[0.99818295,0.0008157927,0.0004777033,0.000110762325,0.00025146705,0.00016124877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023013833,0.0011807751,0.001092409,0.0015905004,0.00047274376,0.0016157735,0.0028888283,0.0022392438,0.005707195],"category_scores_gemma":[0.0043330775,0.00077113224,0.000890822,0.001159482,0.0009191389,0.0016108251,0.00066080823,0.001366138,0.00082004257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007193753,0.000047319587,0.00041793814,0.000034997785,0.000015107815,0.00016730827,0.000031604206,0.97106475,0.00045511904,0.02302101,0.0005176046,0.0041552894],"study_design_scores_gemma":[0.000013339162,0.000037200305,0.0002827996,0.0000074671443,0.00001612128,0.000046464793,0.000011644536,0.9910704,0.00012552147,0.007843563,0.0005360307,0.000009371061],"about_ca_topic_score_codex":0.007924202,"about_ca_topic_score_gemma":0.0045052716,"teacher_disagreement_score":0.007924202,"about_ca_system_score_codex":0.0020082204,"about_ca_system_score_gemma":0.001102565,"threshold_uncertainty_score":0.0190925},"labels":[],"label_agreement":null},{"id":"W2137704118","doi":"10.1109/tr.2013.2241196","title":"Comparative Analysis of Optimal Maintenance Policies Under General Repair With Underlying Weibull Distributions","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Health Services and Policy Research","funders":"","keywords":"Weibull distribution; Preventive maintenance; Mathematical optimization; Monte Carlo method; Function (biology); Reliability engineering; Computer science; Maintenance engineering; Mathematics; Applied mathematics; Engineering; Statistics","score_opus":0.017235598939832373,"score_gpt":0.2512624210522543,"score_spread":0.23402682211242196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137704118","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7642761,0.0034559574,0.22299981,0.00049264514,0.00005608588,0.000101575264,0.00021967223,0.00032080655,0.008077384],"genre_scores_gemma":[0.9867491,0.00062800373,0.01200819,0.000019050536,0.000013535185,0.000043324737,0.00006592998,0.000027751401,0.00044507848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991227,0.00040679594,0.000034500838,0.00009183111,0.00015888269,0.00018530733],"domain_scores_gemma":[0.9877787,0.009705207,0.0010116269,0.00042756108,0.0007975071,0.00027942422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004534269,0.0006157996,0.0011056962,0.0017052753,0.0003634875,0.00087942183,0.0008622372,0.0009225963,0.0014133259],"category_scores_gemma":[0.01432927,0.00032995795,0.00048295787,0.0010159807,0.0006875582,0.0013186057,0.0004750553,0.0005768712,0.00013136983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034173575,0.000065315086,0.0011792987,0.00010893008,0.00004394844,0.00004672865,0.00006324569,0.9667613,0.001546505,0.016409183,0.0004125746,0.013021321],"study_design_scores_gemma":[0.00003181072,0.00023867759,0.0015925283,0.000020423697,0.00005945672,0.00004316013,0.000081490056,0.98950595,0.0013000545,0.006785957,0.00032629955,0.0000141151],"about_ca_topic_score_codex":0.0017860016,"about_ca_topic_score_gemma":0.0015085018,"teacher_disagreement_score":0.004534269,"about_ca_system_score_codex":0.001805499,"about_ca_system_score_gemma":0.0015826713,"threshold_uncertainty_score":0.023979843},"labels":[],"label_agreement":null},{"id":"W2137812345","doi":"10.1590/s0101-74382003000100011","title":"Allotment of aircraft spare parts using genetic alorithms","year":2003,"lang":"en","type":"article","venue":"Pesquisa Operacional","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Allotment; Spare part; Computer science; Operations research; Task (project management); Mathematical optimization; Genetic algorithm; Class (philosophy); Population; Resource allocation; Order (exchange); Resource (disambiguation); Operations management; Mathematics; Artificial intelligence; Engineering; Economics; Machine learning; Systems engineering","score_opus":0.014464576646005216,"score_gpt":0.22155411303688913,"score_spread":0.2070895363908839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137812345","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14139533,0.0005158641,0.8535769,0.00017664219,0.000030436668,0.00006025707,0.000025488744,0.00036260358,0.0038565907],"genre_scores_gemma":[0.754364,0.0004917351,0.24248384,0.00006504611,0.000026560077,0.00010992165,0.00006456737,0.000069922186,0.0023244475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998311,0.000049125872,0.0000051066145,0.000030713294,0.000058091086,0.000025897642],"domain_scores_gemma":[0.9997925,0.000114596034,0.000037192956,0.000019853902,0.000027442893,0.000008416843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000481048,0.0005774562,0.0005174103,0.00084259576,0.0003064598,0.00053732016,0.00062220887,0.0005824005,0.0008520087],"category_scores_gemma":[0.0012340136,0.00026679743,0.0004723213,0.0005331966,0.00066682976,0.00052266376,0.0003266459,0.0004110219,0.00015146885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028926012,0.000024703604,0.0005050491,0.000026666374,0.000020657333,0.000040542447,0.000049457205,0.951498,0.0033292128,0.0051987707,0.00017002966,0.039107993],"study_design_scores_gemma":[0.000015145678,0.000043705393,0.00024377542,0.000007752006,0.000023154209,0.000027111248,0.000022173457,0.9923185,0.002168722,0.004214671,0.0009084163,0.0000067871265],"about_ca_topic_score_codex":0.0040498446,"about_ca_topic_score_gemma":0.0036904488,"teacher_disagreement_score":0.0040498446,"about_ca_system_score_codex":0.00060258526,"about_ca_system_score_gemma":0.0006209855,"threshold_uncertainty_score":0.008052528},"labels":[],"label_agreement":null},{"id":"W2138120567","doi":"10.1109/ptc.2001.964775","title":"Application of probabilistic health analysis in generating facilities maintenance scheduling","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Probabilistic logic; Reliability engineering; Scheduling (production processes); Preventive maintenance; Computer science; Economic shortage; Reliability (semiconductor); Schedule; Operations research; Risk analysis (engineering); Engineering; Operations management","score_opus":0.011886713746827258,"score_gpt":0.21160193446443354,"score_spread":0.19971522071760628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138120567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05462736,0.00027482954,0.9406991,0.00030486783,0.000022046926,0.000060087255,0.00008749207,0.0001965381,0.0037277287],"genre_scores_gemma":[0.941849,0.00018419763,0.05717205,0.000039261275,0.000038417096,0.000055785084,0.00006978238,0.00001818106,0.0005734036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992853,0.00033486434,0.000023103787,0.000079672835,0.00022084013,0.00005626036],"domain_scores_gemma":[0.9966133,0.002688707,0.00032641244,0.00013056914,0.00020669021,0.000034283225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016056586,0.000353071,0.00032369283,0.000815589,0.0003117753,0.0006350759,0.00051253213,0.0004872081,0.0010504558],"category_scores_gemma":[0.005276076,0.00039367782,0.0005708636,0.00056153524,0.0005607468,0.0006166174,0.00047941922,0.00057628896,0.00009713152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021169946,0.0000125880515,0.0010966498,0.000012458814,0.000019445653,0.000027641086,0.000018860588,0.97619253,0.0005289245,0.010174948,0.00012751487,0.011767185],"study_design_scores_gemma":[0.0000043876403,0.000017188055,0.00047601797,0.0000021185376,0.000008939371,0.000017997283,0.0000057053526,0.9921314,0.00025730734,0.0068662097,0.00020713887,0.000005612145],"about_ca_topic_score_codex":0.0043824217,"about_ca_topic_score_gemma":0.0032117455,"teacher_disagreement_score":0.0043824217,"about_ca_system_score_codex":0.0008217553,"about_ca_system_score_gemma":0.0009135463,"threshold_uncertainty_score":0.008713841},"labels":[],"label_agreement":null},{"id":"W2138758440","doi":"10.1109/rams.2000.816319","title":"Multi-state k-out-of-n system model and its applications","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"State (computer science); Binary number; Binary system; Computer science; Mathematics; Algorithm; Arithmetic","score_opus":0.023613871298707147,"score_gpt":0.21183471217950983,"score_spread":0.18822084088080268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138758440","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033040836,0.0019214603,0.9317113,0.0006475504,0.00024098807,0.00012102303,0.00043036754,0.0006445594,0.031241873],"genre_scores_gemma":[0.9189599,0.0022892295,0.06528283,0.00017742287,0.00015788176,0.00023425205,0.00038841635,0.00010133421,0.012408709],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99937856,0.00011776026,0.000042083164,0.00016307287,0.00022064999,0.00007790377],"domain_scores_gemma":[0.9995303,0.00011513763,0.00010175619,0.000071494534,0.00014550304,0.000035795212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050677214,0.0007938653,0.00075304596,0.00069111824,0.0007690573,0.00094805344,0.0016024619,0.0009969115,0.0029635543],"category_scores_gemma":[0.0009813794,0.0003021127,0.0009273033,0.0009304624,0.0007726902,0.0016389007,0.00092534034,0.001058323,0.00063592143],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014258946,0.00010156123,0.0021032728,0.0003692216,0.00006246339,0.0010025076,0.0005303978,0.7204594,0.010296235,0.22537792,0.003843142,0.035711225],"study_design_scores_gemma":[0.000008309718,0.000058597147,0.00030823055,0.000019252562,0.000018652094,0.00016235701,0.000035482244,0.96253014,0.00064099825,0.03181075,0.0043863934,0.000020739602],"about_ca_topic_score_codex":0.008533907,"about_ca_topic_score_gemma":0.005596091,"teacher_disagreement_score":0.008533907,"about_ca_system_score_codex":0.0011201906,"about_ca_system_score_gemma":0.000863835,"threshold_uncertainty_score":0.01696843},"labels":[],"label_agreement":null},{"id":"W2140635263","doi":"10.5267/j.msl.2014.3.028","title":"Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process","year":2014,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":373,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pairwise comparison; Quality (philosophy); Reliability (semiconductor); Production (economics); Analytic hierarchy process; Computer science; Reliability engineering; Selection (genetic algorithm); Process (computing); Rank (graph theory); Fuzzy logic; Cost reduction; Reduction (mathematics); Operations research; Risk analysis (engineering); Business; Mathematics; Engineering; Marketing; Artificial intelligence","score_opus":0.004386457513619024,"score_gpt":0.20783876900555082,"score_spread":0.2034523114919318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140635263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17278892,0.0005377258,0.81963664,0.00021545752,0.000023829816,0.00044595325,0.000067062094,0.00023709165,0.006047351],"genre_scores_gemma":[0.68851316,0.00024937265,0.3103089,0.000034268145,0.000012922621,0.00027078556,0.000071364775,0.000025190033,0.00051408785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987545,0.000442717,0.00007417215,0.0001323614,0.0004601189,0.00013612975],"domain_scores_gemma":[0.99875057,0.00074213644,0.00013862547,0.00003696829,0.00028573925,0.00004602955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018994433,0.0008216414,0.0012079541,0.002942266,0.0008058686,0.001352684,0.0008359652,0.0007894205,0.0015502127],"category_scores_gemma":[0.005547573,0.00047175185,0.0009853842,0.0010991169,0.00042692616,0.0009387187,0.00048534106,0.0005090614,0.00017029293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028361267,0.00040905984,0.0058195903,0.0005806939,0.00023844403,0.00022637531,0.0012368633,0.6046635,0.011991622,0.017475745,0.0013297648,0.35574472],"study_design_scores_gemma":[0.00005876424,0.00035110334,0.001542411,0.000047351827,0.000085044594,0.000052861364,0.00024724135,0.98755985,0.002131384,0.007062925,0.0008368822,0.000024141991],"about_ca_topic_score_codex":0.0040672743,"about_ca_topic_score_gemma":0.003406447,"teacher_disagreement_score":0.0040672743,"about_ca_system_score_codex":0.0012533141,"about_ca_system_score_gemma":0.00199152,"threshold_uncertainty_score":0.01004529},"labels":[],"label_agreement":null},{"id":"W2141173738","doi":"10.1109/icsmc.1995.538043","title":"System design with deteriorative components for minimal life cycle costs","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reliability engineering; Failure rate; Reliability (semiconductor); Computer science; System lifecycle; Maintenance engineering; Genetic algorithm; Preventive maintenance; Mean time between failures; Systems design; Series (stratigraphy); Engineering","score_opus":0.021514430210979752,"score_gpt":0.19392031279999614,"score_spread":0.17240588258901637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141173738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09405566,0.00093688216,0.893522,0.00025606435,0.000045213048,0.00020665611,0.00010571463,0.00040701212,0.01046487],"genre_scores_gemma":[0.8267397,0.0008092986,0.1650955,0.00010683012,0.00007516989,0.00040689122,0.00014311096,0.00011111265,0.006512275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995171,0.00016435944,0.000019491055,0.000067223016,0.00017380208,0.000057959915],"domain_scores_gemma":[0.9996668,0.00010442163,0.0000925172,0.000024073568,0.00009208157,0.000020112626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005340452,0.0010331101,0.0008135895,0.0006173642,0.00038980262,0.0008474139,0.0008772576,0.0005974552,0.002931003],"category_scores_gemma":[0.0011393348,0.00041176192,0.0005431041,0.0003945623,0.00044898278,0.00055288663,0.00050443236,0.0005621181,0.0004008187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015153445,0.000054271404,0.00041795892,0.00024133985,0.00004624295,0.000086296204,0.00007572168,0.9498498,0.011626419,0.0100172255,0.0006558192,0.02677739],"study_design_scores_gemma":[0.00008809468,0.00056172983,0.0005082885,0.000028852166,0.00010914967,0.000091520844,0.00002925224,0.98172486,0.00480486,0.007939118,0.0040991777,0.000015209419],"about_ca_topic_score_codex":0.0011127213,"about_ca_topic_score_gemma":0.0017473074,"teacher_disagreement_score":0.002931003,"about_ca_system_score_codex":0.00081712106,"about_ca_system_score_gemma":0.00093472074,"threshold_uncertainty_score":0.009805143},"labels":[],"label_agreement":null},{"id":"W2141504626","doi":"10.1108/13552510510601320","title":"To maintain or not to maintain? What should a risk‐averse decision maker do?","year":2005,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Risk aversion (psychology); Originality; Decision maker; Actuarial science; Computer science; Expected utility hypothesis; Economics; Monotone polygon; Component (thermodynamics); Value (mathematics); Operations research; Risk analysis (engineering); Econometrics; Engineering; Mathematics; Business; Mathematical economics","score_opus":0.02399852375832842,"score_gpt":0.3048529495930108,"score_spread":0.28085442583468234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141504626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5262442,0.02280279,0.3004582,0.088825114,0.0011856707,0.00029852396,0.00043801367,0.00025515133,0.059492365],"genre_scores_gemma":[0.9810998,0.002598039,0.0132705495,0.0013445289,0.00022384123,0.000037650425,0.000025224996,0.000013790531,0.0013864603],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980716,0.0012216248,0.000080578466,0.00019720152,0.00024410288,0.00018491197],"domain_scores_gemma":[0.9908206,0.005966992,0.0016153426,0.00029686297,0.0006587028,0.0006415126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004933613,0.00049144047,0.000600563,0.00039519524,0.0005268257,0.0028209423,0.0006874013,0.0020911868,0.0029758713],"category_scores_gemma":[0.01632319,0.00018378142,0.00034915953,0.00033940113,0.0015361176,0.002453419,0.0006383236,0.0012886113,0.0005518563],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018591906,0.0011230847,0.054760255,0.0026318482,0.0005431618,0.0012236629,0.0059597134,0.06467664,0.009705812,0.2434501,0.017637419,0.59642917],"study_design_scores_gemma":[0.00021872915,0.0015181502,0.024995444,0.0016499306,0.00037449476,0.0017423009,0.012226901,0.16306478,0.0049471585,0.74442714,0.044538535,0.0002964405],"about_ca_topic_score_codex":0.00087894354,"about_ca_topic_score_gemma":0.00091935747,"teacher_disagreement_score":0.004933613,"about_ca_system_score_codex":0.0007165369,"about_ca_system_score_gemma":0.0012653045,"threshold_uncertainty_score":0.026091754},"labels":[],"label_agreement":null},{"id":"W2143706300","doi":"10.1109/tr.2004.833311","title":"Dominant Multi-State Systems","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Binary number; Reliability (semiconductor); State (computer science); Component (thermodynamics); Image (mathematics); Computer science; Binary image; Algorithm; Reliability theory; Function (biology); Theoretical computer science; Mathematics; Artificial intelligence; Image processing; Statistics; Arithmetic","score_opus":0.010373014174989339,"score_gpt":0.21417426784483098,"score_spread":0.20380125366984164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143706300","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029301696,0.0009399855,0.9452252,0.0003835872,0.00018083902,0.00010047503,0.0002587925,0.0002900266,0.023319367],"genre_scores_gemma":[0.8433391,0.0012764793,0.13603786,0.00039059372,0.0002278968,0.00030413858,0.0005118059,0.00010874656,0.017803337],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99869424,0.00019752825,0.00008305697,0.00034946352,0.00049817027,0.00017750972],"domain_scores_gemma":[0.99831885,0.00047543345,0.00023123006,0.00026334997,0.0005883091,0.00012281694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009417995,0.0006559935,0.0007317791,0.0012184405,0.00090362417,0.0021514774,0.0010201707,0.0007514617,0.004774171],"category_scores_gemma":[0.0025291329,0.00035036178,0.00053308276,0.00092967285,0.0015614422,0.0031520172,0.0021689124,0.0014402157,0.00076782564],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005016895,0.00003003557,0.0005941152,0.00009862459,0.00002853771,0.00020439464,0.00020100715,0.044639707,0.003809941,0.9258291,0.0018020374,0.022712316],"study_design_scores_gemma":[0.000023862218,0.00007075254,0.0004551362,0.000034697274,0.000033002452,0.00025797458,0.00008309723,0.41991803,0.0027845358,0.55596584,0.020325797,0.000047229576],"about_ca_topic_score_codex":0.0016939098,"about_ca_topic_score_gemma":0.0011282456,"teacher_disagreement_score":0.004774171,"about_ca_system_score_codex":0.0012886751,"about_ca_system_score_gemma":0.0008773609,"threshold_uncertainty_score":0.015971243},"labels":[],"label_agreement":null},{"id":"W2147049291","doi":"10.1109/icmla.2007.59","title":"Model evaluation for prognostics: estimating cost saving for the end users","year":2007,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Prognostics; Computer science; Train; Reliability engineering; Risk analysis (engineering); Engineering; Data mining","score_opus":0.04532904582113712,"score_gpt":0.29861170931666503,"score_spread":0.2532826634955279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147049291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10918212,0.0019046722,0.88240397,0.0009756414,0.00008249055,0.00019524909,0.000562823,0.0005704738,0.0041226144],"genre_scores_gemma":[0.8171628,0.0012463421,0.17941159,0.00007754232,0.00007062625,0.00029234402,0.00056389114,0.000082289414,0.0010925531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984475,0.00092448696,0.000058420927,0.00010010406,0.00040225688,0.00006721387],"domain_scores_gemma":[0.9934094,0.005262013,0.00036809623,0.0003953301,0.00048785572,0.00007735631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032212194,0.0013410654,0.0010696338,0.0016065852,0.00034964638,0.001318936,0.0009195515,0.0011743137,0.0019150322],"category_scores_gemma":[0.0146817025,0.00037596276,0.0006817144,0.0012313401,0.00040333695,0.0020705264,0.00080164656,0.0009122134,0.00020002451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003412316,0.00012027737,0.0064113014,0.0002671513,0.00013304075,0.00008750541,0.000055839733,0.8470251,0.0015660544,0.013504487,0.0016352498,0.12885274],"study_design_scores_gemma":[0.000013550496,0.0001110101,0.00092058524,0.00002735647,0.000031763942,0.000042797554,0.00002537079,0.9906702,0.001293869,0.0061213095,0.00072978495,0.000012445086],"about_ca_topic_score_codex":0.0032684712,"about_ca_topic_score_gemma":0.001965609,"teacher_disagreement_score":0.0032684712,"about_ca_system_score_codex":0.0015344387,"about_ca_system_score_gemma":0.0012691112,"threshold_uncertainty_score":0.017035604},"labels":[],"label_agreement":null},{"id":"W2147811293","doi":"10.1111/poms.12386","title":"Inspecting a Vital Component Needed upon Emergency","year":2015,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Component (thermodynamics); Computer science; Mathematical optimization; Operations research; Reliability engineering; Risk analysis (engineering); Business; Mathematics; Engineering","score_opus":0.016058105151821597,"score_gpt":0.21985340673866385,"score_spread":0.20379530158684225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147811293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2498575,0.00025483995,0.74200666,0.0011729992,0.00010321723,0.00008810747,0.0001935792,0.0002522763,0.00607094],"genre_scores_gemma":[0.96080214,0.00017259142,0.034333106,0.000055303277,0.000033932185,0.000043354798,0.00008110184,0.000028862047,0.004449651],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996482,0.000083096274,0.000009794585,0.000103482664,0.000064620224,0.000090821544],"domain_scores_gemma":[0.99891007,0.0005809625,0.00021962075,0.000064483895,0.00008135002,0.00014358945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007523969,0.00076992257,0.0008131198,0.00045681116,0.00058537396,0.00081411796,0.0014745418,0.0018705131,0.0032895866],"category_scores_gemma":[0.00279653,0.0005290703,0.0006318029,0.00044782314,0.0013469683,0.0009239066,0.0010483315,0.0010801799,0.00021055015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001464977,0.000062618026,0.0008772602,0.000062235806,0.000025453597,0.00042425495,0.000041834577,0.9738399,0.004161116,0.014332377,0.00054770824,0.0054787653],"study_design_scores_gemma":[0.000017693932,0.00007248274,0.00031142597,0.000005491454,0.000014477689,0.00007557777,0.000033389995,0.99237376,0.0008119753,0.0059713093,0.00030003118,0.00001240761],"about_ca_topic_score_codex":0.0050870115,"about_ca_topic_score_gemma":0.0043790718,"teacher_disagreement_score":0.0050870115,"about_ca_system_score_codex":0.0009465835,"about_ca_system_score_gemma":0.0010804202,"threshold_uncertainty_score":0.011004746},"labels":[],"label_agreement":null},{"id":"W2148495027","doi":"10.5539/mas.v2n4p33","title":"Manpower Management Benefits Predictor Method for Aircraft Two Level Maintenance Concept","year":2008,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computerized maintenance management system; Aircraft maintenance; Computer science; Operations management; Operations research; Management system; Planned maintenance; Process (computing); Reliability engineering; Risk analysis (engineering); Business; Preventive maintenance; Engineering; Aeronautics","score_opus":0.023017314076241382,"score_gpt":0.2458947059188435,"score_spread":0.2228773918426021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148495027","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029051175,0.00017273502,0.9681485,0.00013391636,0.000052300904,0.000039074446,0.00008607638,0.00041644028,0.0018996921],"genre_scores_gemma":[0.90482116,0.00033571594,0.08846498,0.00008228932,0.00008930641,0.00026168584,0.0002948052,0.00006056193,0.005589474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99975973,0.00006353955,0.000007935286,0.00006597831,0.00007408936,0.000028649109],"domain_scores_gemma":[0.99958247,0.00020884107,0.000048090278,0.00003127468,0.0001042936,0.000025071435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005362163,0.0005397078,0.00037312487,0.00068633654,0.0002513684,0.0004983767,0.00069979456,0.00039151087,0.0037870225],"category_scores_gemma":[0.0016738494,0.00018232194,0.0004411785,0.0005492514,0.0002737449,0.00089099264,0.00039500202,0.0010189426,0.00040589616],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096768395,0.00012297189,0.00609417,0.00008229547,0.000052244373,0.00009273001,0.000098746175,0.80688196,0.0027449552,0.02709601,0.0020496785,0.15458758],"study_design_scores_gemma":[0.0000040991495,0.00002453324,0.0004885219,0.000003875264,0.000006125763,0.000010722645,0.0000043844366,0.9963225,0.00038078267,0.00238948,0.00036000946,0.0000050143426],"about_ca_topic_score_codex":0.0048503783,"about_ca_topic_score_gemma":0.0028244455,"teacher_disagreement_score":0.0048503783,"about_ca_system_score_codex":0.00059210666,"about_ca_system_score_gemma":0.0007919423,"threshold_uncertainty_score":0.012668788},"labels":[],"label_agreement":null},{"id":"W2149335246","doi":"10.1109/tr.2008.916888","title":"Identifying Optimal Components in a Reliability System","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Generalitat de Catalunya; European Regional Development Fund; Royal Canadian Geographical Society","keywords":"Reliability (semiconductor); Component (thermodynamics); Reliability theory; Reliability engineering; Measure (data warehouse); Computer science; Value (mathematics); Process (computing); Failure rate; Data mining; Engineering; Machine learning","score_opus":0.02361647586340717,"score_gpt":0.21876260256756855,"score_spread":0.19514612670416137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149335246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08069288,0.00033089894,0.914791,0.0002090214,0.000015878664,0.00014072514,0.00008590075,0.0003570794,0.0033765356],"genre_scores_gemma":[0.5240478,0.0004559397,0.47263315,0.00004895243,0.00002058396,0.00035585085,0.00016223105,0.00016420224,0.0021113453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896395,0.00035374548,0.000062210594,0.00021404024,0.00027250792,0.00013367587],"domain_scores_gemma":[0.99910694,0.0004548301,0.00011607196,0.000055494656,0.00022316963,0.00004343058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013996616,0.0012314339,0.0011978911,0.0024298886,0.0008699943,0.0013600852,0.00069038785,0.0011316851,0.0023791783],"category_scores_gemma":[0.004753002,0.000987991,0.0005513968,0.0011685381,0.0011288464,0.0012826297,0.0010210775,0.00084887916,0.0005016685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022664336,0.00008560494,0.0031371524,0.000316531,0.00007265307,0.00017735234,0.00036815944,0.8082607,0.014779526,0.06806366,0.0014627796,0.10304926],"study_design_scores_gemma":[0.00003826039,0.00017375339,0.001274896,0.000055779397,0.00007014851,0.00007220263,0.00016752302,0.9382631,0.004833372,0.051917665,0.0030970166,0.000036307323],"about_ca_topic_score_codex":0.004347467,"about_ca_topic_score_gemma":0.0029414434,"teacher_disagreement_score":0.004347467,"about_ca_system_score_codex":0.0012724464,"about_ca_system_score_gemma":0.0022372482,"threshold_uncertainty_score":0.009232223},"labels":[],"label_agreement":null},{"id":"W2151665069","doi":"10.1002/9780470400531.eorms0446","title":"<i>k</i>‐out‐of‐<i>n</i>Systems","year":2010,"lang":"en","type":"other","venue":"Wiley Encyclopedia of Operations Research and Management Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Alberta","funders":"","keywords":"Redundancy (engineering); Binary number; Focus (optics); State (computer science); Component (thermodynamics); Binary system; Computer science; Algorithm; Mathematics; Discrete mathematics; Physics; Thermodynamics; Arithmetic","score_opus":0.015850900345760557,"score_gpt":0.28234459399611633,"score_spread":0.2664936936503558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151665069","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21009731,0.0009318924,0.7094588,0.0009233587,0.0003571183,0.00018683226,0.0008018183,0.000944173,0.07629861],"genre_scores_gemma":[0.9393212,0.00035181383,0.044600677,0.000103894854,0.000068142406,0.00006647774,0.00042030573,0.00007085446,0.014996585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991646,0.00018775703,0.00005884538,0.00020493488,0.00021298375,0.00017099125],"domain_scores_gemma":[0.9988656,0.00026072882,0.00027112153,0.0002929966,0.0002521998,0.000057414523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058476883,0.000673351,0.00070338737,0.0005925967,0.0008199583,0.0016331527,0.001414628,0.0005950221,0.007420041],"category_scores_gemma":[0.0019609642,0.0002634658,0.00047976626,0.0008781152,0.00070713036,0.0014473909,0.0013273293,0.00067081675,0.001263959],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044877583,0.00023711787,0.005770097,0.00037486004,0.00012568782,0.0007040074,0.00018314942,0.67113346,0.010987813,0.14066142,0.01172367,0.15764993],"study_design_scores_gemma":[0.000025485764,0.000118310956,0.0015690286,0.00003989674,0.00003393469,0.00046149967,0.00007077657,0.9257385,0.00350439,0.058891065,0.009507641,0.000039428953],"about_ca_topic_score_codex":0.0033756,"about_ca_topic_score_gemma":0.0043232897,"teacher_disagreement_score":0.007420041,"about_ca_system_score_codex":0.0009435626,"about_ca_system_score_gemma":0.00083010166,"threshold_uncertainty_score":0.024822474},"labels":[],"label_agreement":null},{"id":"W2152104303","doi":"10.1239/jap/1053003548","title":"On exact and large deviation approximation for the distribution of the longest run in a sequence of two-state Markov dependent trials","year":2003,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Manitoba","funders":"","keywords":"Mathematics; Markov chain; Sequence (biology); Applied mathematics; Variable-order Markov model; Markov process; Markov model; Markov chain mixing time; Distribution (mathematics); Statistics; Mathematical analysis","score_opus":0.022231204504160832,"score_gpt":0.25457408826277816,"score_spread":0.23234288375861734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152104303","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024739197,0.0008072782,0.97138447,0.00030637946,0.000044610755,0.000051160634,0.00009387974,0.00019585651,0.0023772004],"genre_scores_gemma":[0.73129046,0.0027520417,0.25836867,0.00034202187,0.0002154761,0.0006791805,0.00068877294,0.00043120608,0.005232191],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974022,0.001247067,0.00010806395,0.00036654106,0.0006449333,0.00023124942],"domain_scores_gemma":[0.95059747,0.04153136,0.0024483586,0.002303678,0.002361198,0.0007578652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012270559,0.0012643358,0.0019020911,0.0020071226,0.000646641,0.002036928,0.0029905704,0.0016978614,0.0032535212],"category_scores_gemma":[0.05901421,0.0007849722,0.0011156439,0.002144452,0.0041198833,0.006030752,0.0024103732,0.0033173254,0.0007386031],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012167762,0.000056279445,0.0014335781,0.00017215763,0.00005028722,0.00019250516,0.00021214518,0.70022684,0.00096565456,0.28199372,0.0009374842,0.013637632],"study_design_scores_gemma":[0.000009917199,0.000014786721,0.00016835758,0.000029923658,0.000005996128,0.00003614497,0.00001907562,0.93574387,0.00029521226,0.06339148,0.0002676665,0.00001760487],"about_ca_topic_score_codex":0.0027949084,"about_ca_topic_score_gemma":0.002061821,"teacher_disagreement_score":0.012270559,"about_ca_system_score_codex":0.0025921331,"about_ca_system_score_gemma":0.0019657575,"threshold_uncertainty_score":0.06489366},"labels":[],"label_agreement":null},{"id":"W2152511683","doi":"10.1109/icqr2mse.2011.5976671","title":"Optimising burn-in procedure and warranty policy in lifecycle costing","year":2011,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Warranty; Burn-in; Activity-based costing; Sensitivity (control systems); Product (mathematics); Reliability engineering; Computer science; Function (biology); Optimal maintenance; Risk analysis (engineering); Business; Engineering; Mathematics","score_opus":0.011962214143052471,"score_gpt":0.20283548020918254,"score_spread":0.1908732660661301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152511683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14641358,0.0013679164,0.8464707,0.00040741987,0.000037009126,0.00015871415,0.00012611003,0.00026289202,0.0047556087],"genre_scores_gemma":[0.9191666,0.00055479,0.0779164,0.000037622063,0.00001430194,0.00010735634,0.000080363876,0.00007756029,0.002044973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990941,0.0004476723,0.000030005422,0.00009712165,0.00018669911,0.00014438371],"domain_scores_gemma":[0.998207,0.0012628292,0.00028092082,0.00006756859,0.000108349566,0.000073218514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027469439,0.001003057,0.0016797027,0.0009031292,0.00048170527,0.0012843332,0.001247552,0.001575533,0.0017750394],"category_scores_gemma":[0.0062920037,0.0009937055,0.0007279106,0.0008120915,0.0007659288,0.0021930637,0.00067614537,0.0010687129,0.00021767183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030464531,0.000020098032,0.00015662704,0.000034307737,0.000006645165,0.000018779341,0.000016713113,0.9930987,0.00056901696,0.0022421211,0.00008002839,0.0037265513],"study_design_scores_gemma":[0.000008389686,0.000042207535,0.00011923698,0.000009022863,0.000007654347,0.000009726314,0.000011201507,0.9973928,0.00042356714,0.0017701611,0.00019999653,0.0000059560184],"about_ca_topic_score_codex":0.0052331067,"about_ca_topic_score_gemma":0.004695022,"teacher_disagreement_score":0.0052331067,"about_ca_system_score_codex":0.0020689513,"about_ca_system_score_gemma":0.0019464764,"threshold_uncertainty_score":0.01501137},"labels":[],"label_agreement":null},{"id":"W2152769971","doi":"10.1007/s00170-014-6454-7","title":"Environmental issue in an alternative production–maintenance control for unreliable manufacturing system subject to degradation","year":2014,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Robustness (evolution); Production (economics); Preventive maintenance; Computer science; Control (management); Degradation (telecommunications); Reliability engineering; Time horizon; Optimal maintenance; Mathematical optimization; Control theory (sociology); Engineering; Mathematics; Economics; Microeconomics","score_opus":0.004399107925735802,"score_gpt":0.2121133482822539,"score_spread":0.2077142403565181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152769971","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8054824,0.00054325914,0.19068061,0.00031154059,0.000065658336,0.00003148336,0.000025476194,0.00009705163,0.0027624073],"genre_scores_gemma":[0.99838793,0.000025155576,0.0012656566,0.0000070634132,0.0000057148804,0.0000027743147,0.00000319206,0.0000025642757,0.0002999581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973255,0.00006073794,0.0000138202795,0.000056548804,0.00007853547,0.000057840207],"domain_scores_gemma":[0.99947375,0.00020313836,0.00009879941,0.000021725447,0.00017791428,0.000024744204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005408443,0.0003614365,0.00043759422,0.0002662648,0.00054664345,0.0007459184,0.0005649078,0.0005274568,0.0005887437],"category_scores_gemma":[0.0008122354,0.00014622507,0.0002798212,0.00022301242,0.0003512127,0.0003630863,0.00037066036,0.00027670187,0.00003315318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009916477,0.00016384416,0.0046819574,0.00028304494,0.000101141886,0.00092005945,0.00029582737,0.8935315,0.0602281,0.0040629706,0.0006009814,0.034138806],"study_design_scores_gemma":[0.000011568634,0.0002556999,0.002883103,0.0000052787773,0.00004404121,0.000093323935,0.000047982136,0.9912224,0.0046183723,0.0005929799,0.00021576861,0.00000949851],"about_ca_topic_score_codex":0.0031608215,"about_ca_topic_score_gemma":0.0024318208,"teacher_disagreement_score":0.0031608215,"about_ca_system_score_codex":0.0003498721,"about_ca_system_score_gemma":0.00035809868,"threshold_uncertainty_score":0.0062848926},"labels":[],"label_agreement":null},{"id":"W2153245253","doi":"10.5267/j.msl.2011.12.014","title":"A multi-objective robust optimization model for the capacitated P-hub location problem under uncertainty","year":2012,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Robust optimization; Mathematical optimization; Operations research; Mathematics","score_opus":0.02238090355653857,"score_gpt":0.2242412586068319,"score_spread":0.20186035505029332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153245253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008058668,0.00014566419,0.98886526,0.0001618496,0.00001503565,0.000031891726,0.00007800716,0.00008616246,0.0025574747],"genre_scores_gemma":[0.85146195,0.0004882677,0.13949503,0.00009660534,0.000046943056,0.0004018898,0.00024350637,0.00008213644,0.007683731],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931324,0.00026116028,0.000028913666,0.00016313813,0.00015227884,0.00008129472],"domain_scores_gemma":[0.9994253,0.0003018791,0.00011339462,0.000029221885,0.00010079488,0.000029396188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012464141,0.0011982152,0.001170787,0.00061197166,0.00040211744,0.0013051181,0.0016363513,0.0017506118,0.0028292965],"category_scores_gemma":[0.001894165,0.0005579242,0.0010531357,0.0009498056,0.00082733575,0.0014019836,0.0010391596,0.0015829576,0.00038578323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010511421,0.0000056172494,0.000039458886,0.000017387534,0.00000785909,0.000025003617,0.000009961101,0.9929202,0.00036145578,0.00495587,0.000108379296,0.0015382833],"study_design_scores_gemma":[0.0000023595621,0.0000115578105,0.000021809308,0.0000019841937,0.0000030483186,0.0000049789564,0.0000030281954,0.9980952,0.00008494441,0.0016470869,0.00012093593,0.0000029980406],"about_ca_topic_score_codex":0.005575921,"about_ca_topic_score_gemma":0.0034382557,"teacher_disagreement_score":0.005575921,"about_ca_system_score_codex":0.0010710552,"about_ca_system_score_gemma":0.001021097,"threshold_uncertainty_score":0.011086941},"labels":[],"label_agreement":null},{"id":"W2153591206","doi":"10.1109/tr.2010.2103596","title":"Periodic Inspection Optimization Models for a Repairable System Subject to Hidden Failures","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":130,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Interval (graph theory); Component (thermodynamics); Reliability engineering; Maintenance engineering; Mathematical optimization; Computer science; Algorithm; Engineering; Mathematics","score_opus":0.017869024274027624,"score_gpt":0.20251278706911474,"score_spread":0.18464376279508712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153591206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066603534,0.00061353424,0.9265379,0.0005082799,0.000041087813,0.000052441672,0.0002133539,0.0002287369,0.0052011022],"genre_scores_gemma":[0.9363633,0.00067124056,0.050196804,0.000078370635,0.000049708677,0.00023590549,0.0002664679,0.00011512092,0.0120231975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992798,0.00025475214,0.000028885166,0.00015847711,0.00015591022,0.00012221334],"domain_scores_gemma":[0.99799097,0.0012735904,0.00038225422,0.000089908004,0.00017199357,0.000091262904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018888023,0.0012304578,0.0014534503,0.0007070498,0.0003763574,0.00094902224,0.002045435,0.0016814555,0.0035079513],"category_scores_gemma":[0.004365384,0.00084111217,0.0011411948,0.0005725889,0.0011575309,0.0013770452,0.0008309662,0.0014843491,0.0003780426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018349649,0.000010423397,0.00010798366,0.000016176175,0.000008479191,0.000036649722,0.000019102665,0.99032253,0.00019340981,0.0080122845,0.00014997822,0.0011047322],"study_design_scores_gemma":[0.000006207768,0.000011505551,0.00007480064,0.0000025939994,0.0000045080064,0.0000068377917,0.0000046941345,0.9957184,0.00004472369,0.0040301895,0.000091941016,0.0000036118745],"about_ca_topic_score_codex":0.0074815904,"about_ca_topic_score_gemma":0.0040545478,"teacher_disagreement_score":0.0074815904,"about_ca_system_score_codex":0.0013405221,"about_ca_system_score_gemma":0.0010557555,"threshold_uncertainty_score":0.014876127},"labels":[],"label_agreement":null},{"id":"W2156384856","doi":"10.1109/icqr2mse.2011.5976648","title":"Managing performance based logistics by balancing reliability and spare parts stocking","year":2011,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Spare part; Reliability (semiconductor); Computer science; Service (business); Capital equipment; Reliability engineering; Metric (unit); Product (mathematics); Service provider; Operations research; Operations management; Manufacturing engineering; Business; Engineering; Marketing","score_opus":0.012888770497590905,"score_gpt":0.1797272647860098,"score_spread":0.1668384942884189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156384856","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16661225,0.0008919322,0.81968653,0.0012994268,0.0000635652,0.00020545631,0.00007269031,0.00033904947,0.0108290585],"genre_scores_gemma":[0.9783925,0.00024636384,0.02045149,0.00004059775,0.000051145496,0.000045892284,0.000026493326,0.000028733779,0.0007166968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964641,0.001512956,0.0001400797,0.00032708643,0.0009784873,0.00057742425],"domain_scores_gemma":[0.9936388,0.0030574468,0.001968753,0.00030274442,0.0007159788,0.00031621588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005635286,0.0021302428,0.0010448671,0.0017175325,0.00071633066,0.0039650807,0.0018756482,0.0010997568,0.0012031049],"category_scores_gemma":[0.010523161,0.0005005009,0.0004607464,0.0012568418,0.0011657287,0.005148054,0.0017640298,0.0008045151,0.00022619052],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010759622,0.00010745521,0.0015208211,0.00010483047,0.00009791031,0.00008931898,0.00012257478,0.9412093,0.0042033447,0.02265031,0.0002988727,0.029487623],"study_design_scores_gemma":[0.000017969245,0.00037452372,0.0012563746,0.000036957237,0.000045032295,0.00008866688,0.0001990647,0.9696609,0.002683931,0.02465487,0.0009337501,0.000047954283],"about_ca_topic_score_codex":0.001251675,"about_ca_topic_score_gemma":0.0011183295,"teacher_disagreement_score":0.005635286,"about_ca_system_score_codex":0.0022643546,"about_ca_system_score_gemma":0.0020410868,"threshold_uncertainty_score":0.02980262},"labels":[],"label_agreement":null},{"id":"W2159217028","doi":"10.1109/icsmc.1997.626217","title":"Defining quality and reliability: a linear graph model perspective","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reliability (semiconductor); Computer science; Quality (philosophy); Perspective (graphical); Graph; Reliability theory; Software quality; Reliability engineering; Graph theory; Theoretical computer science; Mathematics; Artificial intelligence; Engineering; Epistemology; Failure rate; Programming language","score_opus":0.02021219434849232,"score_gpt":0.24794163178318795,"score_spread":0.22772943743469565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159217028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003997019,0.0010660832,0.978246,0.002184916,0.000082470244,0.000045464116,0.00016492045,0.00017197836,0.014041244],"genre_scores_gemma":[0.5135086,0.008026418,0.45574698,0.0020540578,0.00078377535,0.0006873532,0.00076276244,0.0004207492,0.018009244],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99734074,0.0012836204,0.00009409926,0.00034745893,0.00074508635,0.00018905237],"domain_scores_gemma":[0.99572915,0.0026981863,0.00046916344,0.0003117976,0.00064285327,0.0001487172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022014303,0.0013916423,0.0007911032,0.00264752,0.00055859576,0.0029867454,0.0024207872,0.0019438678,0.0041352184],"category_scores_gemma":[0.006461478,0.0006443169,0.0010130304,0.003101427,0.002701594,0.007185541,0.0015340361,0.0027540016,0.0009869947],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019143039,0.000035086356,0.00044065117,0.000115059425,0.00003842253,0.000052004674,0.00018252216,0.09988113,0.0007012889,0.87893724,0.0031226622,0.016474757],"study_design_scores_gemma":[0.000010481444,0.000045872235,0.00020938995,0.000036683858,0.000028967088,0.000058685077,0.0000886365,0.18558504,0.0004335866,0.8026114,0.010867898,0.000023451217],"about_ca_topic_score_codex":0.008073661,"about_ca_topic_score_gemma":0.006293035,"teacher_disagreement_score":0.008073661,"about_ca_system_score_codex":0.0027967028,"about_ca_system_score_gemma":0.0014526927,"threshold_uncertainty_score":0.020291626},"labels":[],"label_agreement":null},{"id":"W2160178178","doi":"10.1109/dcds.2011.5970319","title":"Production, preventive and corrective maintenance planning in manufacturing systems under imperfect repairs","year":2011,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Corrective maintenance; Preventive maintenance; Imperfect; Reliability engineering; Production (economics); Control (management); Sensitivity (control systems); Operations research; Failure rate; Planned maintenance; Maintenance actions; Problem statement; Computer science; Proactive maintenance; Optimal maintenance; Risk analysis (engineering); Operations management; Engineering; Business; Economics","score_opus":0.011493317137998277,"score_gpt":0.19749914000595487,"score_spread":0.18600582286795658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160178178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2293107,0.0013761974,0.7649346,0.0005021524,0.00004237689,0.00010188009,0.0001468413,0.00024808894,0.0033371607],"genre_scores_gemma":[0.97977805,0.00029236288,0.018831283,0.000021199025,0.000016002352,0.00005046067,0.000052886913,0.000017072456,0.00094069843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986267,0.00053335226,0.000050976643,0.00018727855,0.0003116968,0.00029008923],"domain_scores_gemma":[0.99724615,0.0019101872,0.00047079506,0.00008998916,0.00017706529,0.00010588086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027234177,0.000987037,0.0010993017,0.00081795146,0.0004590286,0.00121533,0.0009532688,0.0010049442,0.00088285154],"category_scores_gemma":[0.005365283,0.0009317279,0.0007042774,0.0007255072,0.0014902346,0.0011706309,0.0006379985,0.0007653335,0.00008768818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016247077,0.000009283793,0.00015878472,0.000013418643,0.000007282952,0.00002125278,0.000008862298,0.99617827,0.00019982921,0.001991467,0.000034676465,0.0013606066],"study_design_scores_gemma":[0.0000063900166,0.000026470078,0.0003524932,0.0000033836811,0.000008635145,0.0000055382684,0.000008207761,0.99652123,0.0002565178,0.0027608667,0.00004571168,0.000004572352],"about_ca_topic_score_codex":0.020711178,"about_ca_topic_score_gemma":0.010934743,"teacher_disagreement_score":0.020711178,"about_ca_system_score_codex":0.0022544758,"about_ca_system_score_gemma":0.0021954505,"threshold_uncertainty_score":0.041181266},"labels":[],"label_agreement":null},{"id":"W2161675288","doi":"10.1109/rams.2011.5754498","title":"Incorporating repair information into maintenance optimization models for repairable systems","year":2011,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Risk analysis (engineering); Maintenance engineering; Point (geometry); Maintenance actions; Computer science; Reliability engineering; Engineering; Operations research; Business","score_opus":0.015234137557907899,"score_gpt":0.18291786263755852,"score_spread":0.16768372507965063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161675288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054735415,0.0019426182,0.919693,0.0014419546,0.0001442687,0.00018076222,0.0009424697,0.00053106726,0.020388475],"genre_scores_gemma":[0.89811885,0.0016480403,0.07463244,0.0003455508,0.00013603037,0.0004995433,0.0011249093,0.00023056875,0.023263924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999413,0.00019907054,0.000035577024,0.000099170145,0.00013550093,0.0001176551],"domain_scores_gemma":[0.9977406,0.0016105225,0.00025305228,0.00005737158,0.0002735865,0.00006478201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019143814,0.0013557012,0.00187505,0.0012045979,0.00066761667,0.0018884282,0.0021427392,0.0026160246,0.0057731727],"category_scores_gemma":[0.0044432366,0.0010866931,0.0015955536,0.0011452372,0.0007730095,0.001737452,0.0011228748,0.002863355,0.00073999277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054628886,0.000006745033,0.00011558369,0.000011939945,0.000006122265,0.000018841869,0.0000066140683,0.9974644,0.000048592454,0.0011270663,0.00012710338,0.0010614236],"study_design_scores_gemma":[0.0000020310167,0.000004147927,0.000046553014,0.0000045732495,0.0000034839986,0.000004347359,0.000004156286,0.9982998,0.000025770729,0.0014234561,0.00017912911,0.000002517398],"about_ca_topic_score_codex":0.02456077,"about_ca_topic_score_gemma":0.022485055,"teacher_disagreement_score":0.02456077,"about_ca_system_score_codex":0.0020064006,"about_ca_system_score_gemma":0.0017126986,"threshold_uncertainty_score":0.048835635},"labels":[],"label_agreement":null},{"id":"W2163028993","doi":"10.1109/tr.2006.890892","title":"System Stress-Strength Reliability: The Multivariate Case","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Combinatorics; Type (biology); Mathematics; Infant formula","score_opus":0.00814317000322719,"score_gpt":0.2215150192967542,"score_spread":0.213371849293527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163028993","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44133806,0.0011095923,0.5162588,0.0025222145,0.00015146563,0.000075770775,0.0012709643,0.0010351094,0.036237977],"genre_scores_gemma":[0.9843427,0.00040239244,0.009256673,0.00006833534,0.000131037,0.000048766655,0.00024027356,0.0001360175,0.0053737946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998728,0.00038811727,0.00004790774,0.00023282097,0.00030637934,0.00029686242],"domain_scores_gemma":[0.9945543,0.0028378735,0.0008392923,0.0008361418,0.0006073639,0.00032496423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027114928,0.0010484114,0.0009994988,0.001783499,0.0006008604,0.0013388902,0.0013510346,0.00093322754,0.0069684098],"category_scores_gemma":[0.015801223,0.0004434029,0.0010236986,0.0018456717,0.0017296647,0.002230749,0.0013031922,0.0017856344,0.0008929219],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014915645,0.00006988929,0.010111198,0.00008868169,0.0000844429,0.00073423755,0.00019545421,0.7756401,0.0008673093,0.18842517,0.0042655277,0.019368801],"study_design_scores_gemma":[0.000009880814,0.000015636917,0.0023788193,0.0000105872305,0.000018678564,0.00012617047,0.00004102864,0.9291419,0.00025341232,0.06738412,0.00060169405,0.00001811537],"about_ca_topic_score_codex":0.010399113,"about_ca_topic_score_gemma":0.0056744264,"teacher_disagreement_score":0.010399113,"about_ca_system_score_codex":0.001053048,"about_ca_system_score_gemma":0.000661787,"threshold_uncertainty_score":0.023311675},"labels":[],"label_agreement":null},{"id":"W2163232988","doi":"10.1109/ccece.1999.804927","title":"Selective maintenance optimization for multi-state systems","year":2003,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"State (computer science); Minification; Computer science; Integer programming; Mathematical optimization; Component (thermodynamics); Sequence (biology); Series (stratigraphy); Nonlinear programming; Optimization problem; Nonlinear system; Distributed computing; Algorithm; Mathematics","score_opus":0.014881636692563887,"score_gpt":0.2224213448412019,"score_spread":0.207539708148638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163232988","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20132142,0.0011442797,0.7885748,0.0005529922,0.000048201797,0.000076533244,0.0003062384,0.00035487485,0.007620667],"genre_scores_gemma":[0.9752694,0.00034275858,0.020310972,0.00004553773,0.00002142766,0.000088662695,0.0002046828,0.00004153009,0.0036749665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968207,0.00009413918,0.000012005469,0.0000714437,0.000072269584,0.00006813871],"domain_scores_gemma":[0.9992685,0.00047439625,0.00010164953,0.000031857795,0.00008631969,0.000037351325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006421026,0.0008016312,0.00079946936,0.00050233456,0.0003082393,0.0005962495,0.00057397323,0.00059711165,0.0021366603],"category_scores_gemma":[0.0015528174,0.00043913932,0.00045969483,0.0005106683,0.0006891842,0.0007584807,0.0006047468,0.00052025216,0.0001788379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040627852,0.00001729411,0.00023604132,0.0000437838,0.000019704083,0.000052808053,0.000021264535,0.98658365,0.0007946197,0.0058707776,0.000375721,0.005943715],"study_design_scores_gemma":[0.00000914727,0.00002519729,0.00015052226,0.0000021048677,0.000005420158,0.000008959438,0.0000068668714,0.9956262,0.00017890582,0.0037754166,0.00020926904,0.0000020061757],"about_ca_topic_score_codex":0.004672149,"about_ca_topic_score_gemma":0.0029865394,"teacher_disagreement_score":0.004672149,"about_ca_system_score_codex":0.0009255052,"about_ca_system_score_gemma":0.00058455236,"threshold_uncertainty_score":0.009289861},"labels":[],"label_agreement":null},{"id":"W2165933339","doi":"10.1023/a:1009616111968","title":"Alternative time scales and failure time models.","year":2000,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":109,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto","funders":"","keywords":"Reliability (semiconductor); Scale (ratio); Computer science; Measure (data warehouse); Accelerated failure time model; Statistics; Reliability engineering; Econometrics; Survival analysis; Data mining; Mathematics; Engineering; Geography; Cartography","score_opus":0.006880506656244254,"score_gpt":0.1997418170113651,"score_spread":0.19286131035512083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165933339","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03756141,0.005835136,0.9316247,0.0019257768,0.000603251,0.00008704963,0.0008007333,0.00038033104,0.02118159],"genre_scores_gemma":[0.8173877,0.005411941,0.1315891,0.00052068545,0.00072568445,0.0004871086,0.0015854563,0.00041867784,0.041873615],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99801433,0.0009861208,0.00007833443,0.00027138848,0.00041683024,0.00023302075],"domain_scores_gemma":[0.99032027,0.0069276565,0.0009481616,0.0008605055,0.00063307746,0.00031028493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004685034,0.0011095749,0.0008116613,0.0016558495,0.0004519256,0.0022322787,0.0030706425,0.0017905324,0.009608925],"category_scores_gemma":[0.020558864,0.00055395614,0.0013371438,0.0021099036,0.0010673816,0.004036858,0.0010708276,0.0029409316,0.0021511384],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015644185,0.00011650055,0.0013644203,0.0001503514,0.00010820248,0.00011396625,0.00019749792,0.296321,0.00062846614,0.66492933,0.006940394,0.028973408],"study_design_scores_gemma":[0.000022867178,0.000039450548,0.0006031704,0.000026164347,0.00004281193,0.000057668683,0.000045305824,0.7541376,0.00016287535,0.23904887,0.005793886,0.000019340276],"about_ca_topic_score_codex":0.0035657468,"about_ca_topic_score_gemma":0.0026483487,"teacher_disagreement_score":0.009608925,"about_ca_system_score_codex":0.0016712825,"about_ca_system_score_gemma":0.00094874157,"threshold_uncertainty_score":0.032145083},"labels":[],"label_agreement":null},{"id":"W2167252691","doi":"10.1287/opre.1060.0327","title":"Efficient Supply Chain Management at the U.S. Coast Guard Using Part-Age Dependent Supply Replenishment Policies","year":2006,"lang":"en","type":"article","venue":"Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Pan-American Association of Ophthalmology","keywords":"Computer science; Supply chain; Homeland security; Operations research; Supply chain management; Merge (version control); Coast guard; Service (business); Database; Business; Environmental science","score_opus":0.029091919467867575,"score_gpt":0.2984447453519369,"score_spread":0.2693528258840693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167252691","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61314124,0.00059077836,0.38146317,0.00057458907,0.00001817442,0.00018845379,0.00038531155,0.0005273405,0.0031109753],"genre_scores_gemma":[0.95267767,0.00023826603,0.046272628,0.000021311527,0.0000052002047,0.000041149113,0.00020713788,0.000015839,0.0005207907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990055,0.00043656983,0.00007004549,0.00017791506,0.00020073011,0.00010918067],"domain_scores_gemma":[0.99812204,0.0009333071,0.00038375496,0.000224574,0.000243074,0.00009323841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023390062,0.00046115948,0.00051021244,0.0009315512,0.00045637853,0.0017359129,0.0007218878,0.00041610518,0.0007858138],"category_scores_gemma":[0.0042812848,0.0005600792,0.0002996246,0.001460112,0.0005049808,0.0022929148,0.0007397333,0.0004881446,0.00013797607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001001054,0.00006669641,0.0031659966,0.00003120523,0.000025572965,0.00004380327,0.00009436221,0.94530934,0.0018870572,0.005692246,0.00040584023,0.04317786],"study_design_scores_gemma":[0.000018944665,0.00005074291,0.00092206226,0.0000066234297,0.000014730697,0.00001663876,0.00005197791,0.99229276,0.001373982,0.004667549,0.00057480007,0.000009071944],"about_ca_topic_score_codex":0.01573121,"about_ca_topic_score_gemma":0.01278852,"teacher_disagreement_score":0.01573121,"about_ca_system_score_codex":0.0023027116,"about_ca_system_score_gemma":0.00291421,"threshold_uncertainty_score":0.031279325},"labels":[],"label_agreement":null},{"id":"W2169047150","doi":"10.1109/arms.1988.196462","title":"Common-cause failures in repairable systems","year":2003,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Reliability engineering; Reliability (semiconductor); Common cause failure; Computer science; Reliability theory; Mean time between failures; Variance (accounting); Redundancy (engineering); Common cause and special cause; Engineering; Statistics; Failure rate; Mathematics","score_opus":0.008692679838701933,"score_gpt":0.19749812508929754,"score_spread":0.18880544525059562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169047150","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13923654,0.0027379205,0.84524083,0.00049546896,0.000092546885,0.00008479033,0.00028713566,0.00069396803,0.011130714],"genre_scores_gemma":[0.9391165,0.0019545257,0.050180797,0.000079795536,0.00005696316,0.00018770802,0.0003215435,0.00013914626,0.007963088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993907,0.0001762182,0.000019549312,0.000064379026,0.0002714465,0.00007773203],"domain_scores_gemma":[0.9989506,0.00052236515,0.0001388733,0.00014875122,0.00019970122,0.000039753773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008526982,0.0009483881,0.0007725091,0.0012744975,0.0005135814,0.0009438661,0.001169611,0.0008069736,0.0016836702],"category_scores_gemma":[0.002897989,0.0003984884,0.0010598182,0.0007166676,0.0008134193,0.0011722479,0.00064252625,0.00083327165,0.0002984816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003917426,0.000021326829,0.00073750695,0.00006945417,0.00003526661,0.00014703855,0.00014064329,0.92626023,0.0012466855,0.05492977,0.0013460326,0.015026923],"study_design_scores_gemma":[0.0000127914545,0.000051755713,0.00040489048,0.000020260022,0.000029297418,0.000085269574,0.000023658484,0.950289,0.0011432556,0.04522303,0.0026995516,0.00001709759],"about_ca_topic_score_codex":0.006758286,"about_ca_topic_score_gemma":0.0043779695,"teacher_disagreement_score":0.006758286,"about_ca_system_score_codex":0.0012084829,"about_ca_system_score_gemma":0.0009386717,"threshold_uncertainty_score":0.013437867},"labels":[],"label_agreement":null},{"id":"W2169572499","doi":"10.1287/moor.28.2.382.14484","title":"Optimal Replacement Under Partial Observations","year":2003,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematical optimization; Mathematics; Optimal stopping; Bellman equation; Piecewise; State (computer science); Projection (relational algebra); Markov process; Algorithm","score_opus":0.10975972824785399,"score_gpt":0.3453108476573518,"score_spread":0.23555111940949783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169572499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0638251,0.00067306904,0.9320916,0.00052899215,0.000044810433,0.00002979393,0.00010623359,0.00017265628,0.002527803],"genre_scores_gemma":[0.8990273,0.00065237575,0.09451003,0.00009877591,0.000088447974,0.0001225031,0.00026141646,0.00008939709,0.005149694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988368,0.0004688479,0.000052972333,0.00023671215,0.00024974858,0.00015493759],"domain_scores_gemma":[0.99730456,0.0018443834,0.00029790367,0.0002375743,0.00019399388,0.000121534256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021691602,0.00068398163,0.001529677,0.00048298435,0.0003530106,0.001062693,0.0010373708,0.0010736679,0.0022154767],"category_scores_gemma":[0.00855651,0.0006153282,0.0006823416,0.00051364355,0.0012206592,0.0022621427,0.001088403,0.0010202689,0.00026215706],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016194143,0.00005178453,0.0007558794,0.000112390386,0.000036202004,0.00018127692,0.00009423161,0.85975385,0.0013993388,0.10479575,0.0010831762,0.031574085],"study_design_scores_gemma":[0.000023542858,0.00006397171,0.00023104798,0.0000119300175,0.000009457142,0.000044316388,0.000014939667,0.94538945,0.00048592844,0.053024948,0.0006906811,0.000009691824],"about_ca_topic_score_codex":0.001793074,"about_ca_topic_score_gemma":0.0010692939,"teacher_disagreement_score":0.0022154767,"about_ca_system_score_codex":0.0010645295,"about_ca_system_score_gemma":0.0013644327,"threshold_uncertainty_score":0.011471748},"labels":[],"label_agreement":null},{"id":"W2169747735","doi":"10.1007/978-94-007-2521-8_7","title":"An Operator Approach to Viable Attainability of Hybrid Systems [59]","year":2011,"lang":"en","type":"book-chapter","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Controllability; Operator (biology); Constraint (computer-aided design); Interval (graph theory); Mathematics; Mathematical optimization; Hybrid system; Computer science; Control theory (sociology); Applied mathematics; Artificial intelligence; Control (management); Combinatorics","score_opus":0.013597005045168349,"score_gpt":0.18822713244066794,"score_spread":0.1746301273954996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169747735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029709125,0.003535196,0.81630325,0.00091671693,0.00031619557,0.00003235262,0.000099044715,0.00013392215,0.17569241],"genre_scores_gemma":[0.40180013,0.014147886,0.41782737,0.0009759744,0.0010573693,0.0003251953,0.0004138236,0.0005389709,0.16291331],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99970967,0.00008764817,0.000012736866,0.000046299665,0.00011997794,0.00002357597],"domain_scores_gemma":[0.9997285,0.00017496751,0.000012452437,0.000030292093,0.000044497712,0.000009379614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055670325,0.0010216117,0.0005491878,0.00062259356,0.00047973258,0.0013054911,0.0011967357,0.0009628302,0.010723649],"category_scores_gemma":[0.0008553406,0.00041655844,0.0007096958,0.0007636648,0.0018801427,0.0023566945,0.0008784793,0.0024646595,0.0012092866],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000063014354,0.00001038203,0.000022589347,0.00005991843,0.000007857408,0.00003456033,0.000059825765,0.017807648,0.00089909334,0.95608157,0.0045284075,0.020481829],"study_design_scores_gemma":[0.0000030717956,0.000016443917,0.00004035765,0.00002501513,0.0000052749083,0.00005372608,0.000019271623,0.04234695,0.00036814227,0.9380546,0.019058574,0.000008672306],"about_ca_topic_score_codex":0.001371268,"about_ca_topic_score_gemma":0.0016930609,"teacher_disagreement_score":0.010723649,"about_ca_system_score_codex":0.0008424055,"about_ca_system_score_gemma":0.00051335694,"threshold_uncertainty_score":0.035874188},"labels":[],"label_agreement":null},{"id":"W2170282755","doi":"10.1109/tr.2004.824834","title":"A Novel Approach to Determine Minimal Tie-Sets of Complex Network","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Saskatchewan","funders":"","keywords":"Simple (philosophy); Tracing; Reliability (semiconductor); Computer science; Path (computing); Enumeration; Algorithm; Network analysis; Set (abstract data type); Complex network; Mathematical optimization; Mathematics; Discrete mathematics; Engineering","score_opus":0.024168017903296212,"score_gpt":0.22628340290717064,"score_spread":0.20211538500387444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170282755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040254192,0.000048004295,0.9947312,0.00001991609,0.000009460155,0.000024522571,0.000054270287,0.00008471295,0.0010024172],"genre_scores_gemma":[0.09976467,0.00023269898,0.89733577,0.00003151185,0.000034399745,0.00021104339,0.00032806053,0.00012762689,0.0019342094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999632,0.00007030284,0.000020123054,0.00009582336,0.00015116634,0.000030550145],"domain_scores_gemma":[0.998857,0.0005147213,0.00013065423,0.00014378558,0.00031002323,0.00004381296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000634997,0.0008291067,0.0007067992,0.0021812299,0.0007170328,0.0009422887,0.0016446272,0.0005772249,0.003560475],"category_scores_gemma":[0.00342465,0.0004656199,0.0006142597,0.0012129252,0.00055183,0.0020945698,0.0011059511,0.0010550312,0.00069268927],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009265139,0.00010075963,0.0020629107,0.0004693703,0.00010385473,0.0002557821,0.0003065465,0.4080686,0.017969986,0.23062131,0.0036042982,0.33634394],"study_design_scores_gemma":[0.000012753134,0.000051376017,0.00036387006,0.000027740378,0.00002154113,0.00018706292,0.000041150935,0.8994221,0.004969385,0.0893827,0.0054957336,0.000024622195],"about_ca_topic_score_codex":0.0007594237,"about_ca_topic_score_gemma":0.0012569047,"teacher_disagreement_score":0.003560475,"about_ca_system_score_codex":0.0005503461,"about_ca_system_score_gemma":0.00082813995,"threshold_uncertainty_score":0.011910975},"labels":[],"label_agreement":null},{"id":"W2171843606","doi":"10.1109/rams.2011.5754476","title":"Optimal design of a repairable k-out-of-n system considering maintenance","year":2011,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reliability engineering; Redundancy (engineering); Optimal maintenance; Markov decision process; Preventive maintenance; Computer science; Genetic algorithm; Maintenance engineering; Mathematical optimization; Maintenance actions; Markov process; Markov model; Operations research; Engineering; Markov chain; Mathematics; Statistics","score_opus":0.031916838909157565,"score_gpt":0.19434259635330486,"score_spread":0.1624257574441473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171843606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4196561,0.0008115212,0.5642349,0.0005561283,0.00008263634,0.00023580312,0.00024561796,0.0003377521,0.013839511],"genre_scores_gemma":[0.9775204,0.00013634235,0.02035115,0.000030566942,0.000012700743,0.000081553604,0.000047977926,0.00001791056,0.0018014146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995572,0.00012973834,0.000017857279,0.00012382494,0.000076859586,0.00009446141],"domain_scores_gemma":[0.9994734,0.00020772526,0.00017708103,0.000017888116,0.00007945532,0.000044496246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006135172,0.000772232,0.00123283,0.000577919,0.00073010003,0.001158717,0.0008109999,0.0014184801,0.0017124296],"category_scores_gemma":[0.001500622,0.0005989219,0.00060380343,0.0005138647,0.0008397679,0.0007224741,0.0006539698,0.00047768647,0.00021540017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053179163,0.000015094423,0.00029368058,0.00003774288,0.00001317305,0.000079407146,0.000023816989,0.9935888,0.0019711114,0.0010420532,0.00010212643,0.0027797204],"study_design_scores_gemma":[0.000017917746,0.000074015275,0.00029008483,0.000005864671,0.00001457169,0.000021119109,0.000018610071,0.9980888,0.00046141905,0.00085449236,0.00014650707,0.000006658053],"about_ca_topic_score_codex":0.009859073,"about_ca_topic_score_gemma":0.008189161,"teacher_disagreement_score":0.009859073,"about_ca_system_score_codex":0.0013216446,"about_ca_system_score_gemma":0.0014084614,"threshold_uncertainty_score":0.019603372},"labels":[],"label_agreement":null},{"id":"W2172254927","doi":"10.1002/qre.1418","title":"Maximum Likelihood Estimation for a Hidden Semi‐Markov Model with Multivariate Observations","year":2012,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Multivariate statistics; Maximum likelihood; Statistics; Hidden Markov model; Markov model; Markov chain; Estimation; Mathematics; Multivariate analysis; Econometrics; Estimation theory; Expectation–maximization algorithm; Computer science; Artificial intelligence; Engineering","score_opus":0.021745150294988164,"score_gpt":0.2598320851282756,"score_spread":0.23808693483328744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172254927","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006224336,0.00015694006,0.9930368,0.00009946972,0.000008575557,0.000017981663,0.0000577042,0.00016732782,0.00023095179],"genre_scores_gemma":[0.54828095,0.0008667476,0.4440235,0.00012919502,0.000118026706,0.00041712794,0.0011518416,0.00024084015,0.004771699],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986689,0.00070859754,0.00006292653,0.00027705028,0.00018587471,0.000096630116],"domain_scores_gemma":[0.9911255,0.0077468087,0.00048137375,0.0002527512,0.00031151963,0.000081969476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039041222,0.0012219133,0.0018800881,0.00088895875,0.0005688646,0.001159766,0.0019810938,0.001530877,0.0022882388],"category_scores_gemma":[0.014256662,0.0013748372,0.0011905584,0.0011304838,0.0013615731,0.0024522895,0.0014664235,0.0024969059,0.00071246194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110378816,0.000030848387,0.0007025803,0.000096300646,0.000077083874,0.00009296973,0.00007963992,0.9549431,0.00071329763,0.020655813,0.00045040972,0.022047492],"study_design_scores_gemma":[0.000007570066,0.0000073383417,0.00009258517,0.0000053862104,0.0000055111173,0.000008608354,0.0000039761585,0.99222046,0.0001518338,0.007366527,0.00012247961,0.000007793312],"about_ca_topic_score_codex":0.008012141,"about_ca_topic_score_gemma":0.006619483,"teacher_disagreement_score":0.008012141,"about_ca_system_score_codex":0.0013831345,"about_ca_system_score_gemma":0.0019006731,"threshold_uncertainty_score":0.020647228},"labels":[],"label_agreement":null},{"id":"W2172471105","doi":"10.1016/j.ress.2015.11.015","title":"Integrated preventive maintenance and production decisions for imperfect processes","year":2015,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":145,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preventive maintenance; Reliability engineering; Imperfect; Quality (philosophy); Production (economics); Sizing; Context (archaeology); Selection (genetic algorithm); Control (management); Operations research; Point (geometry); Engineering; Computer science; Operations management; Risk analysis (engineering); Mathematics; Economics","score_opus":0.009712888612852624,"score_gpt":0.202891605786752,"score_spread":0.19317871717389937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172471105","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51629657,0.0005724937,0.47654364,0.0005426535,0.00006426272,0.00012959715,0.0002162166,0.0004738286,0.005160804],"genre_scores_gemma":[0.9845025,0.00006908472,0.014046776,0.00002273055,0.000014845431,0.000021761538,0.000059328795,0.00001946243,0.0012435624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904686,0.00020421407,0.000052235464,0.00017768402,0.00028720195,0.00023164488],"domain_scores_gemma":[0.99697256,0.0019345301,0.00048803687,0.00017208241,0.00031366327,0.000119157645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022845424,0.0008854399,0.001217237,0.00077824685,0.00047271562,0.0015319583,0.0010224121,0.0012036609,0.0020334483],"category_scores_gemma":[0.007161514,0.0007997551,0.00063866714,0.00054020307,0.0006644242,0.0014739793,0.00070847466,0.0010762551,0.00020052982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039196436,0.0001505451,0.0019008393,0.00006364433,0.000037814258,0.000107039945,0.000048352023,0.9600309,0.0030602817,0.0038427387,0.00032229134,0.030043468],"study_design_scores_gemma":[0.000022366172,0.00013175262,0.0019096388,0.0000071909076,0.000049910126,0.000023239669,0.000018612456,0.99212545,0.002136312,0.0034404555,0.00012352415,0.000011629683],"about_ca_topic_score_codex":0.0051048314,"about_ca_topic_score_gemma":0.007055091,"teacher_disagreement_score":0.0051048314,"about_ca_system_score_codex":0.0011736327,"about_ca_system_score_gemma":0.0021724799,"threshold_uncertainty_score":0.012081981},"labels":[],"label_agreement":null},{"id":"W2199811318","doi":"10.1016/j.ress.2015.11.001","title":"Kernel estimator of maintenance optimization model for a stochastically degrading system under different operating environments","year":2015,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Estimator; Kernel smoother; Kernel (algebra); Preventive maintenance; Smoothing; Kernel density estimation; Parametric statistics; Mathematical optimization; Computer science; Reliability (semiconductor); Reliability engineering; Mathematics; Statistics; Kernel method; Engineering; Machine learning","score_opus":0.011858594544074829,"score_gpt":0.1984320453932793,"score_spread":0.18657345084920446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2199811318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19610715,0.00023649739,0.802357,0.0002094132,0.000028086351,0.000022292526,0.00009752802,0.00032247647,0.0006194386],"genre_scores_gemma":[0.983082,0.00010010087,0.015385285,0.00002670366,0.000013833398,0.000019851157,0.0001506677,0.00003011228,0.0011914705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953973,0.00012527606,0.000021180513,0.00014655014,0.00008292775,0.00008426838],"domain_scores_gemma":[0.9973688,0.001460687,0.00037936636,0.00024080968,0.00045451298,0.000095778516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015517244,0.00049812073,0.0008254471,0.0004113288,0.00021642371,0.0007185737,0.00093217497,0.0010081658,0.0009995088],"category_scores_gemma":[0.0058685006,0.00029485617,0.00068258744,0.0003000048,0.0006141947,0.0014835946,0.00062614883,0.0011446652,0.00017050788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024019883,0.0000750474,0.0029917317,0.00008518278,0.0001062072,0.00004257963,0.000050141363,0.9689099,0.0050683524,0.009130847,0.00050914875,0.012790604],"study_design_scores_gemma":[0.0000032150947,0.000009997418,0.0006147588,0.0000013606845,0.000008605542,0.00000957126,0.000003000444,0.9981468,0.00035776905,0.0008140868,0.000026324018,0.0000045351303],"about_ca_topic_score_codex":0.005881342,"about_ca_topic_score_gemma":0.0033044303,"teacher_disagreement_score":0.005881342,"about_ca_system_score_codex":0.00095733313,"about_ca_system_score_gemma":0.0009444093,"threshold_uncertainty_score":0.011694193},"labels":[],"label_agreement":null},{"id":"W2211000468","doi":"10.1007/978-1-84800-113-8_2","title":"Modeling and Reliability Evaluation of Multi-state k-out-of-n Systems","year":2008,"lang":"en","type":"book-chapter","venue":"Springer series in reliability engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Redundancy (engineering); Reliability engineering; Computer science; Reliability (semiconductor); State (computer science); Fault tolerance; Distributed computing; Engineering; Algorithm; Physics","score_opus":0.02342789760466311,"score_gpt":0.2274604735732722,"score_spread":0.2040325759686091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2211000468","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19623749,0.0026136425,0.772001,0.0006436369,0.00018685995,0.00012293506,0.00041439498,0.00059161166,0.027188435],"genre_scores_gemma":[0.9694659,0.000978031,0.021943247,0.000037117814,0.00006220013,0.000075469245,0.0001672416,0.000072114,0.007198723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995802,0.00012718183,0.000023092318,0.00007811852,0.00013618331,0.000055199733],"domain_scores_gemma":[0.99915516,0.00049395603,0.00009686377,0.00008404778,0.00014457168,0.000025358373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000961894,0.0007846723,0.001372372,0.00049018825,0.0004924977,0.0013586249,0.0017429464,0.0010869871,0.0021769602],"category_scores_gemma":[0.002284447,0.0005790573,0.00089358096,0.0005860477,0.0008458646,0.002028975,0.0006206497,0.000704784,0.00032353823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030491816,0.000013476985,0.00022873007,0.000033663287,0.000012568775,0.000027001039,0.000021989257,0.98917294,0.0009964386,0.0047901287,0.00022588966,0.004446655],"study_design_scores_gemma":[0.0000011891759,0.00000560721,0.00008069205,0.0000021687122,0.0000027940428,0.000006284031,0.000002185359,0.99751794,0.00014584186,0.0021455025,0.00008754647,0.000002188264],"about_ca_topic_score_codex":0.0055629327,"about_ca_topic_score_gemma":0.006512326,"teacher_disagreement_score":0.0055629327,"about_ca_system_score_codex":0.0011208684,"about_ca_system_score_gemma":0.000835589,"threshold_uncertainty_score":0.011061132},"labels":[],"label_agreement":null},{"id":"W2216042049","doi":"10.1016/j.apm.2015.10.001","title":"A note on a two variable block replacement policy for a system subject to non-homogeneous pure birth shocks","year":2015,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Variable (mathematics); Homogeneous; Function (biology); Renewal theory; Shock (circulatory); Random variable; Catastrophic failure; Failure rate; Interval (graph theory); Mathematics; Computer science; Statistics; Physics; Combinatorics; Thermodynamics; Medicine","score_opus":0.017563890351834817,"score_gpt":0.24272897214651232,"score_spread":0.2251650817946775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2216042049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031713385,0.0047839917,0.89176667,0.011219492,0.0029943709,0.00017492672,0.00039813918,0.00038403829,0.05656495],"genre_scores_gemma":[0.76474243,0.0062494627,0.14714035,0.0026794695,0.0023333167,0.00028231653,0.00020986586,0.0003285092,0.0760343],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997011,0.00012668838,0.000019987267,0.000050549344,0.00007277703,0.000028870074],"domain_scores_gemma":[0.99918383,0.0005338372,0.00005995739,0.00006852405,0.000101027734,0.000052910294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013376481,0.00061238033,0.00088613364,0.00030817467,0.00060817273,0.0011700457,0.00093080197,0.0018244531,0.0068112207],"category_scores_gemma":[0.0031917621,0.00029414444,0.0009069653,0.00042984006,0.0011589018,0.0013962721,0.0011528367,0.0024035836,0.0006466016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030105512,0.00006322692,0.00045408876,0.00052443036,0.00008949172,0.0008938575,0.00022143078,0.30032346,0.011307912,0.6348433,0.01728189,0.033695914],"study_design_scores_gemma":[0.000072040115,0.0001409111,0.0009392644,0.000075545715,0.00006845631,0.00020870859,0.000045173045,0.6942132,0.0014692902,0.28057542,0.022129484,0.0000625189],"about_ca_topic_score_codex":0.005088933,"about_ca_topic_score_gemma":0.0042815655,"teacher_disagreement_score":0.0068112207,"about_ca_system_score_codex":0.0010764331,"about_ca_system_score_gemma":0.0011643908,"threshold_uncertainty_score":0.022785842},"labels":[],"label_agreement":null},{"id":"W2216940447","doi":"10.1016/j.ijpe.2015.12.016","title":"Joint economic design of production, continuous sampling inspection and preventive maintenance of a deteriorating production system","year":2016,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Computer science; Preventive maintenance; Reliability engineering; Reliability (semiconductor); Sampling (signal processing); Quality (philosophy); Production (economics); Continuous production; Mathematical optimization; Constraint (computer-aided design); Acceptance sampling; Operations research; Mathematics; Engineering","score_opus":0.014502361342691917,"score_gpt":0.20875412701312665,"score_spread":0.19425176567043473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2216940447","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16700993,0.00032118775,0.82725376,0.00021708869,0.000028904942,0.0002195465,0.000101088095,0.00020100758,0.004647443],"genre_scores_gemma":[0.95983034,0.00012331163,0.03864704,0.000020971367,0.000008706904,0.0001558074,0.000049210317,0.00001674209,0.0011478694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988846,0.00048815281,0.00003358907,0.0001766801,0.0002646398,0.0001523499],"domain_scores_gemma":[0.99858487,0.000746487,0.00028086454,0.00008575481,0.00018164043,0.00012042968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021022693,0.0008048897,0.0010798455,0.0005317937,0.00028037748,0.0011509336,0.0011948245,0.000857022,0.0017220776],"category_scores_gemma":[0.0030840277,0.0007186644,0.000660494,0.0005014261,0.000916943,0.0008925956,0.00075844565,0.000758947,0.00011536545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011813732,0.000055300352,0.0004693049,0.000049857415,0.000018734067,0.000048057598,0.000017525133,0.9844751,0.0016295917,0.0057386714,0.00013641814,0.0072433827],"study_design_scores_gemma":[0.000014005732,0.0001324539,0.00036412946,0.000003462941,0.000013129466,0.000011774185,0.000007924106,0.997352,0.0004992812,0.0014890481,0.00010886303,0.0000039161646],"about_ca_topic_score_codex":0.003535303,"about_ca_topic_score_gemma":0.0025798907,"teacher_disagreement_score":0.003535303,"about_ca_system_score_codex":0.0015971784,"about_ca_system_score_gemma":0.0022654783,"threshold_uncertainty_score":0.011588395},"labels":[],"label_agreement":null},{"id":"W2254794532","doi":"10.5267/j.msl.2016.2.001","title":"Reliability analysis of two dissimilar parallel unit repairable system with failure during preventive maintenance","year":2016,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preventive maintenance; Reliability engineering; Reliability (semiconductor); Computer science; Unit (ring theory); Mathematics; Engineering","score_opus":0.003826404679866852,"score_gpt":0.19082888681067478,"score_spread":0.18700248213080792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2254794532","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61694336,0.00092544185,0.37474382,0.00031280832,0.00004483104,0.000053608626,0.00012988645,0.00013222206,0.006713949],"genre_scores_gemma":[0.9953479,0.00011139862,0.0034558098,0.0000062894474,0.000013002627,0.000011495381,0.000028411374,0.000006741539,0.0010189259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947745,0.00015235704,0.000017929417,0.00010911959,0.00015172326,0.000091593065],"domain_scores_gemma":[0.99921894,0.00029331836,0.00019698981,0.000074092226,0.00016447253,0.00005215227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010224491,0.0005865336,0.000680118,0.00058786786,0.00033300565,0.00053215446,0.0009096685,0.0005178255,0.0010689955],"category_scores_gemma":[0.0019326922,0.00022766074,0.0007409041,0.00035008564,0.0006140876,0.00057102443,0.000563031,0.00047726685,0.000113093025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016182607,0.0000319586,0.001799748,0.000082267106,0.0000634485,0.00041138378,0.00009349729,0.9726123,0.0075793504,0.010763751,0.00023236535,0.0061680917],"study_design_scores_gemma":[0.0000059645135,0.000058936843,0.0010796564,0.0000028381507,0.000018316225,0.000055957596,0.000020447435,0.9967182,0.00041157243,0.0015085638,0.00011265744,0.0000069094876],"about_ca_topic_score_codex":0.0042806854,"about_ca_topic_score_gemma":0.001471479,"teacher_disagreement_score":0.0042806854,"about_ca_system_score_codex":0.00070247496,"about_ca_system_score_gemma":0.0004476983,"threshold_uncertainty_score":0.008511543},"labels":[],"label_agreement":null},{"id":"W2265194661","doi":"10.1016/j.psep.2015.11.011","title":"A risk-based shutdown inspection and maintenance interval estimation considering human error","year":2015,"lang":"en","type":"article","venue":"Process Safety and Environmental Protection","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Shutdown; Reliability engineering; Interval (graph theory); Criticality; Human error; Engineering; Reliability (semiconductor); Process (computing); Computer science; Power (physics)","score_opus":0.012278640412690675,"score_gpt":0.20759174949505083,"score_spread":0.19531310908236016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2265194661","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050398238,0.00040838387,0.9482502,0.00008456308,0.000025667572,0.000033838085,0.000046153935,0.00019710655,0.00055586087],"genre_scores_gemma":[0.8984146,0.00022460651,0.09988052,0.000037062564,0.000048618356,0.00008452485,0.00013304652,0.000030590283,0.0011464412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988342,0.00029161727,0.00008731365,0.0003062171,0.000366605,0.00011404258],"domain_scores_gemma":[0.99733233,0.0016180025,0.0003519348,0.00011562262,0.0004895221,0.000092715265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016728083,0.0012136613,0.0018401754,0.0011987585,0.0003620003,0.0010739389,0.0012960248,0.001261655,0.0007673458],"category_scores_gemma":[0.0053488193,0.00082186266,0.0010892873,0.0007903501,0.00039051246,0.0012217177,0.000957642,0.00070496876,0.0001263679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018932427,0.00009843028,0.0023645367,0.00009085047,0.000093662085,0.00010014045,0.00006161695,0.9467674,0.002909778,0.0012753687,0.00032907363,0.045719814],"study_design_scores_gemma":[0.0000043771347,0.000026718653,0.00041967494,0.0000031663424,0.000015146439,0.0000125114875,0.000003686986,0.99891627,0.00030795287,0.00024849805,0.00003783002,0.000004177671],"about_ca_topic_score_codex":0.00827154,"about_ca_topic_score_gemma":0.0037282722,"teacher_disagreement_score":0.00827154,"about_ca_system_score_codex":0.00073723815,"about_ca_system_score_gemma":0.0014794968,"threshold_uncertainty_score":0.01644677},"labels":[],"label_agreement":null},{"id":"W2267116782","doi":"10.1016/j.ifacol.2015.06.408","title":"On Reliability Improvement of Second-hand Products","year":2015,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Warranty; Upgrade; Reliability (semiconductor); Reliability engineering; Product (mathematics); Consistency (knowledge bases); Order (exchange); Computer science; Engineering; Mathematics; Business","score_opus":0.011313813178235169,"score_gpt":0.21112235216075179,"score_spread":0.1998085389825166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2267116782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14621553,0.0033981407,0.8242216,0.0008537814,0.00012149576,0.0001740091,0.0003172641,0.00027477246,0.024423415],"genre_scores_gemma":[0.96240956,0.0014906223,0.023323027,0.000066194145,0.00004425063,0.0001293555,0.00012574685,0.00006062568,0.012350513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993581,0.00020931255,0.00001346538,0.00010263992,0.00018243094,0.0001340376],"domain_scores_gemma":[0.99907994,0.0005636127,0.00017221984,0.000041879564,0.000103828825,0.000038438986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010732373,0.0013622658,0.0014501598,0.0009457898,0.0003506184,0.0012312193,0.0012109684,0.001734118,0.0032237435],"category_scores_gemma":[0.0022800246,0.0005598817,0.0010180168,0.00069459435,0.00078952394,0.0010248086,0.00065119873,0.0012351191,0.00040154762],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026526444,0.000016332433,0.00012724717,0.00003653653,0.000008177443,0.00003877431,0.000011010419,0.99282986,0.0007182435,0.0029846053,0.00014113646,0.0030615858],"study_design_scores_gemma":[0.000004795345,0.000053158183,0.00020847272,0.000008223157,0.000008601571,0.000021337059,0.0000056285858,0.9977737,0.00025878003,0.0013398076,0.0003126864,0.0000047555054],"about_ca_topic_score_codex":0.007922996,"about_ca_topic_score_gemma":0.0046021896,"teacher_disagreement_score":0.007922996,"about_ca_system_score_codex":0.0016624944,"about_ca_system_score_gemma":0.0014148052,"threshold_uncertainty_score":0.015753806},"labels":[],"label_agreement":null},{"id":"W2279965377","doi":"10.12792/iciae2015.103","title":"A Single Deteriorating System's Replacement Model with Inspections","year":2015,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Reliability engineering; Failure rate; Catastrophic failure; Computer science; Type (biology); Engineering; Materials science","score_opus":0.020061249478442903,"score_gpt":0.1900871355328911,"score_spread":0.1700258860544482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279965377","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62191,0.0016605825,0.36472058,0.0009707075,0.00013883733,0.00014580086,0.0010832361,0.000533467,0.008836761],"genre_scores_gemma":[0.981448,0.0004904722,0.010021556,0.00006417026,0.00005429677,0.00007766476,0.00025317748,0.00003895175,0.007551718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999005,0.00027454496,0.000050504274,0.00033359983,0.00013563408,0.00020068408],"domain_scores_gemma":[0.998018,0.0007507668,0.00054681627,0.00018630497,0.0002666355,0.00023146415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016878912,0.0015798227,0.0025295576,0.0010508823,0.0005166833,0.0013500494,0.0042962003,0.0025296104,0.0033912607],"category_scores_gemma":[0.0027806289,0.0007962768,0.0015856713,0.0014520917,0.0017767103,0.0018884797,0.0009332147,0.0014586637,0.0005791865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011616971,0.000052831634,0.0010713594,0.0000816992,0.000035520497,0.0004170287,0.000062288294,0.98642945,0.0015114563,0.007459271,0.0003372216,0.002425703],"study_design_scores_gemma":[0.000026544723,0.000078711324,0.00057919737,0.000006088291,0.000030955358,0.000079004625,0.000015449297,0.9965753,0.00013221527,0.0022485075,0.0002113346,0.000016727294],"about_ca_topic_score_codex":0.0126458295,"about_ca_topic_score_gemma":0.0052800514,"teacher_disagreement_score":0.0126458295,"about_ca_system_score_codex":0.0013314905,"about_ca_system_score_gemma":0.0010073348,"threshold_uncertainty_score":0.025144458},"labels":[],"label_agreement":null},{"id":"W2282862148","doi":"10.1177/0954405415616060","title":"Age-dependent production and replacement strategies in failure-prone manufacturing systems","year":2015,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Context (archaeology); Production (economics); Failure rate; Computer science; Time horizon; Imperfect; Function (biology); Mathematical optimization; Product (mathematics); Operations research; Reliability engineering; Engineering; Economics; Mathematics; Microeconomics","score_opus":0.010362180535236817,"score_gpt":0.19564696703351778,"score_spread":0.18528478649828095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2282862148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31110844,0.0025343332,0.68161476,0.00051513355,0.000059932972,0.00009973974,0.00013248598,0.00016730923,0.0037679314],"genre_scores_gemma":[0.98431647,0.0008019315,0.012881681,0.000039548562,0.00002232236,0.000049038274,0.000049405025,0.000011144296,0.0018284952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991216,0.00034884887,0.00006225798,0.00014983835,0.0001790951,0.000138333],"domain_scores_gemma":[0.99770004,0.0012650818,0.0006166063,0.00009045922,0.00019261635,0.00013514014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020463772,0.00096462434,0.0010668692,0.0006932991,0.0004304014,0.0009154337,0.0014849663,0.00119056,0.0013465913],"category_scores_gemma":[0.004313416,0.0005130055,0.00059125706,0.0005330474,0.0008504664,0.0009769013,0.0007226508,0.000636361,0.00018995978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049517465,0.000031651758,0.00096578134,0.00008281132,0.000029620383,0.00028824795,0.00012198564,0.9761041,0.002142605,0.013285119,0.00016097147,0.006737677],"study_design_scores_gemma":[0.000012313215,0.00010999734,0.0007478592,0.000017230583,0.000020764675,0.00008430515,0.000036944315,0.98457515,0.0007893733,0.013161362,0.00042915766,0.000015472375],"about_ca_topic_score_codex":0.0028586944,"about_ca_topic_score_gemma":0.0014524423,"teacher_disagreement_score":0.0028586944,"about_ca_system_score_codex":0.000895614,"about_ca_system_score_gemma":0.00061148306,"threshold_uncertainty_score":0.010822415},"labels":[],"label_agreement":null},{"id":"W2284593537","doi":"10.1016/j.trb.2016.02.001","title":"Measuring reliability of transportation networks using snapshots of movements in the network – An analytical and empirical study","year":2016,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Reliability (semiconductor); Computer science; Empirical research; Reliability engineering; Environmental science; Transport engineering; Statistics; Engineering; Mathematics; Physics","score_opus":0.4685548738092405,"score_gpt":0.4612664940791901,"score_spread":0.007288379730050365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2284593537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9845553,0.00015210568,0.014597935,0.00004822662,0.00000576009,0.000018940395,0.00015975862,0.000021507562,0.0004405805],"genre_scores_gemma":[0.9966673,0.00007828639,0.0030531916,0.0000019188647,0.000004453553,0.0000065919758,0.00012251255,0.000004013804,0.000061762956],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992086,0.00040857823,0.000046857243,0.00013403532,0.00014860315,0.00005330982],"domain_scores_gemma":[0.989087,0.007367077,0.0014663349,0.0010023146,0.00084973313,0.00022752998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001454517,0.00046185407,0.0003280381,0.0016449554,0.00026630796,0.0008783972,0.00074597565,0.00065905566,0.00044216472],"category_scores_gemma":[0.015624804,0.00036027393,0.00038510936,0.0018783539,0.0005641448,0.0024293014,0.00053668086,0.0005561453,0.00009733476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010449842,0.0005108093,0.5463335,0.00023694856,0.00049651845,0.0004809231,0.0017701143,0.35728225,0.008447325,0.009087873,0.00077090925,0.073537864],"study_design_scores_gemma":[0.000023263401,0.0005492826,0.28711727,0.000047592308,0.00019974682,0.00043629593,0.0013951148,0.69942766,0.0042442437,0.0058171204,0.0006791486,0.00006330605],"about_ca_topic_score_codex":0.0057477555,"about_ca_topic_score_gemma":0.0032141658,"teacher_disagreement_score":0.0057477555,"about_ca_system_score_codex":0.000661109,"about_ca_system_score_gemma":0.00030400866,"threshold_uncertainty_score":0.011428595},"labels":[],"label_agreement":null},{"id":"W2293594019","doi":"10.1007/s00170-016-8570-z","title":"Joint optimal maintenance and inspection for a k-out-of-n system","year":2016,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Unavailability; Constraint (computer-aided design); Genetic algorithm; Reliability engineering; Inspection time; Maintenance actions; Preventive maintenance; Integer (computer science); Component (thermodynamics); Mathematical optimization; Computer science; Engineering; Algorithm; Mathematics; Mechanical engineering","score_opus":0.00691725768055543,"score_gpt":0.21114059496935453,"score_spread":0.2042233372887991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293594019","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6843075,0.0006380877,0.3040778,0.0008362832,0.00010540819,0.00013959483,0.00027849918,0.0004971617,0.009119666],"genre_scores_gemma":[0.9912225,0.00004002464,0.0070799887,0.000018872111,0.000011091697,0.000014149124,0.0000354054,0.000010137452,0.0015677979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996007,0.00008042992,0.000020318499,0.00010646071,0.00006586667,0.00012610167],"domain_scores_gemma":[0.9990933,0.00040593932,0.00018514784,0.000049476057,0.00018704362,0.00007906628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080567395,0.00068950676,0.0014714566,0.0005800718,0.00096937787,0.00094800914,0.0010203249,0.0016909699,0.0019490321],"category_scores_gemma":[0.001825552,0.0005248872,0.0006397832,0.00044422114,0.0008604377,0.00064615865,0.0007452108,0.0004836625,0.00020193256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005000222,0.00006895955,0.0016698894,0.00009720729,0.000045520876,0.000337192,0.0000627299,0.98000926,0.005866835,0.0012727812,0.00054148526,0.0095281415],"study_design_scores_gemma":[0.000018273671,0.00006809984,0.0008600446,0.000003232035,0.00002002147,0.00003872224,0.000018883502,0.9979044,0.0004106766,0.00058297766,0.00006690213,0.000007756459],"about_ca_topic_score_codex":0.024847185,"about_ca_topic_score_gemma":0.0203514,"teacher_disagreement_score":0.024847185,"about_ca_system_score_codex":0.0011340907,"about_ca_system_score_gemma":0.001504551,"threshold_uncertainty_score":0.049405098},"labels":[],"label_agreement":null},{"id":"W2293731104","doi":"10.1007/s00170-016-8556-x","title":"Joint optimization of lot-sizing and maintenance policy for a partially observable two-unit system","year":2016,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Production (economics); Sizing; Computer science; Process (computing); Mathematical optimization; Statistic; Markov chain; Turbine; Markov decision process; Unit (ring theory); Reliability engineering; Operations research; Markov process; Engineering; Mathematics; Statistics; Economics","score_opus":0.010908047369551635,"score_gpt":0.23061987001496112,"score_spread":0.21971182264540948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293731104","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4497624,0.0012980682,0.5365751,0.0015271064,0.0001597956,0.00029588616,0.0010779833,0.00089623575,0.008407457],"genre_scores_gemma":[0.9894188,0.00010640636,0.008089513,0.000037171838,0.000024564117,0.00006635178,0.00013551998,0.000030919655,0.002090844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992749,0.00024718043,0.000035738114,0.0001561075,0.000102051046,0.00018397256],"domain_scores_gemma":[0.99709105,0.0018848807,0.0004110402,0.00010384888,0.00029421056,0.00021501884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018277297,0.0016671093,0.0026233767,0.00081626815,0.0005757512,0.0019765124,0.0015847243,0.0022771335,0.0036206327],"category_scores_gemma":[0.0037908037,0.0014431519,0.0008952633,0.00094989524,0.0012578603,0.0012252639,0.0010405877,0.0012561245,0.0003370611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000107222964,0.000023225706,0.00019678073,0.000035295747,0.000025881873,0.000039481103,0.000010343092,0.99732107,0.00036938058,0.0005276526,0.000122001016,0.0012215952],"study_design_scores_gemma":[0.000016603883,0.000028703633,0.00017222739,0.0000017303786,0.000009796178,0.000003976151,0.0000042066195,0.99930763,0.00008355389,0.00034230706,0.000025340556,0.0000038160965],"about_ca_topic_score_codex":0.019696577,"about_ca_topic_score_gemma":0.013064032,"teacher_disagreement_score":0.019696577,"about_ca_system_score_codex":0.0017530316,"about_ca_system_score_gemma":0.002442614,"threshold_uncertainty_score":0.039163828},"labels":[],"label_agreement":null},{"id":"W2297628255","doi":"","title":"Optimal Inspection Interval for a Two-Component System with Failure Dependency","year":2012,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Component (thermodynamics); Interval (graph theory); Failure rate; Reliability engineering; Process (computing); Dependency (UML); Mathematics; Computer science; Engineering; Physics","score_opus":0.00611135233204564,"score_gpt":0.19748279280391334,"score_spread":0.19137144047186772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2297628255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.297934,0.00092032197,0.6953322,0.0002708774,0.000037285943,0.000112833186,0.00012098944,0.0004728452,0.004798549],"genre_scores_gemma":[0.97437817,0.00016345468,0.024403706,0.000015986274,0.000009671895,0.00005022321,0.00005248721,0.00003445532,0.0008918393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939024,0.00016260624,0.000023018983,0.000136868,0.0001457108,0.00014161266],"domain_scores_gemma":[0.998572,0.0007635684,0.0003332025,0.00007072728,0.00016603523,0.000094497445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012602649,0.00099108,0.0009649295,0.00075945124,0.00035064982,0.00067826285,0.00084260857,0.00066933833,0.0016266753],"category_scores_gemma":[0.003161759,0.0005342606,0.00051262253,0.0004929507,0.000533542,0.0006777776,0.00052506384,0.0006354168,0.00016966439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015207486,0.00005013104,0.0005857193,0.00006587363,0.000017882223,0.00008841851,0.000037445607,0.98534006,0.0037692522,0.0017537838,0.0001880408,0.007951347],"study_design_scores_gemma":[0.000016789993,0.00011703885,0.0007931958,0.0000052405017,0.0000170931,0.00003566475,0.000014500737,0.9966018,0.00077400496,0.001477549,0.00013849392,0.000008700853],"about_ca_topic_score_codex":0.0035906588,"about_ca_topic_score_gemma":0.0017946399,"teacher_disagreement_score":0.0035906588,"about_ca_system_score_codex":0.0010141245,"about_ca_system_score_gemma":0.0009595343,"threshold_uncertainty_score":0.007358074},"labels":[],"label_agreement":null},{"id":"W2298109637","doi":"10.1108/jqme-08-2014-0046","title":"An optimal production/maintenance strategy under lease contract with warranty periods","year":2016,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Lease; Warranty; Production (economics); Maintenance actions; Originality; Present value; Order (exchange); Optimal maintenance; Operations research; Reliability engineering; Operations management; Computer science; Business; Engineering; Economics; Finance; Microeconomics","score_opus":0.01393670944188923,"score_gpt":0.2540662062562128,"score_spread":0.24012949681432355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2298109637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32215676,0.0011690914,0.6630193,0.00066410226,0.000055671928,0.0003487123,0.00017991765,0.00022623359,0.012180215],"genre_scores_gemma":[0.96697116,0.00024864118,0.02958211,0.00002113059,0.000013636664,0.000063320505,0.00005894256,0.00002308171,0.003018088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933344,0.00017887667,0.000035098266,0.00017254682,0.00015640754,0.00012352658],"domain_scores_gemma":[0.9989557,0.00039508948,0.0003285272,0.00007892432,0.00014529921,0.00009636224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012668703,0.0007339277,0.0008429314,0.000629272,0.00059820164,0.0017274579,0.0013285966,0.0011950743,0.0032368246],"category_scores_gemma":[0.0027893758,0.000483134,0.0006111186,0.00046549586,0.00056682655,0.0016361146,0.000662069,0.0008273997,0.0002842563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042308425,0.00019974919,0.002664842,0.00028301473,0.000052290834,0.0005066145,0.00021032689,0.9173619,0.016640617,0.0154425185,0.0009973783,0.04521763],"study_design_scores_gemma":[0.000022011793,0.00020832628,0.001385904,0.000017455775,0.00003056796,0.00011730799,0.000094546456,0.99132055,0.0023283537,0.0038420311,0.00061608595,0.00001680663],"about_ca_topic_score_codex":0.0044685346,"about_ca_topic_score_gemma":0.0026056888,"teacher_disagreement_score":0.0044685346,"about_ca_system_score_codex":0.001525965,"about_ca_system_score_gemma":0.0013586294,"threshold_uncertainty_score":0.011071742},"labels":[],"label_agreement":null},{"id":"W2298760341","doi":"10.1108/jqme-12-2014-0060","title":"Joint reliability based design and periodic preventive maintenance policy for systems sold with warranty","year":2016,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Warranty; Preventive maintenance; Reliability engineering; Reliability (semiconductor); Context (archaeology); Constraint (computer-aided design); Total cost; Engineering; Burn-in; Computer science; Mechanical engineering","score_opus":0.01676152537659786,"score_gpt":0.2384288729333553,"score_spread":0.22166734755675743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2298760341","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27200186,0.001804221,0.71384674,0.00054426276,0.00007849123,0.00029038257,0.00015952539,0.00037836234,0.010896167],"genre_scores_gemma":[0.9633979,0.00030001954,0.034393534,0.000027478207,0.000022854176,0.0001163449,0.00007199418,0.000023317782,0.0016464952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992268,0.00022392214,0.000038081373,0.00012625578,0.00027234553,0.00011255175],"domain_scores_gemma":[0.9984798,0.0006636912,0.000446218,0.00007310231,0.000269935,0.000067257715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014345153,0.000782382,0.00078895834,0.0007947834,0.0003330869,0.00092437625,0.0008473328,0.0007068632,0.0022867348],"category_scores_gemma":[0.0035074279,0.0004431384,0.0006741417,0.00040050322,0.00052435417,0.00059944985,0.00045211194,0.0005120641,0.00024884075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001583878,0.00008129859,0.00090521225,0.00022374935,0.000034390323,0.00010329811,0.00006675581,0.9663291,0.004884577,0.0052858433,0.00038472694,0.021542734],"study_design_scores_gemma":[0.000022333077,0.000286977,0.0008217863,0.000020203499,0.00003362855,0.000048055274,0.00002421342,0.99535453,0.0010928737,0.0017572684,0.0005312274,0.000006980238],"about_ca_topic_score_codex":0.003258049,"about_ca_topic_score_gemma":0.0026949483,"teacher_disagreement_score":0.003258049,"about_ca_system_score_codex":0.0009901371,"about_ca_system_score_gemma":0.0016503793,"threshold_uncertainty_score":0.0076498985},"labels":[],"label_agreement":null},{"id":"W2302767502","doi":"","title":"An Adaptive Zero-Variance Importance Sampling Approximation for Static Network Dependability Evaluation","year":2013,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Dependability; Zero (linguistics); Variance (accounting); Computer science; Sampling (signal processing); Adaptive sampling; Algorithm; Mathematical optimization; Statistics; Mathematics; Telecommunications","score_opus":0.01841583334882414,"score_gpt":0.23506531408919049,"score_spread":0.21664948074036636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2302767502","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005828547,0.00009289538,0.99356925,0.00003291714,0.000017198246,0.000013259178,0.000010865382,0.00011150199,0.00032345075],"genre_scores_gemma":[0.535177,0.00030263243,0.46150696,0.00010952334,0.00010960129,0.00015121387,0.00021071924,0.00013575512,0.0022966308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915624,0.00033396168,0.000034624813,0.00011747734,0.0002812274,0.00007647298],"domain_scores_gemma":[0.9958615,0.0029714417,0.00014160795,0.00023416775,0.00067503494,0.00011630146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002501104,0.0006202878,0.0012800918,0.00078988937,0.0003208233,0.0007041385,0.0015849024,0.0009775772,0.0015341421],"category_scores_gemma":[0.010581786,0.0005168617,0.00064384914,0.0007214714,0.000589046,0.0009734561,0.00090178224,0.00134549,0.00027569383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001303079,0.00006793226,0.0006946087,0.000073811534,0.000038198454,0.000037126665,0.00003503636,0.91124934,0.0024649445,0.0070404704,0.0008848207,0.07728336],"study_design_scores_gemma":[0.0000014049893,0.000004263561,0.00003086685,0.000001301022,0.0000015018651,0.000002860752,6.667989e-7,0.999432,0.00009331449,0.00039622272,0.000034751156,8.4915695e-7],"about_ca_topic_score_codex":0.0077452757,"about_ca_topic_score_gemma":0.0064223735,"teacher_disagreement_score":0.0077452757,"about_ca_system_score_codex":0.00083930115,"about_ca_system_score_gemma":0.0011516755,"threshold_uncertainty_score":0.01540035},"labels":[],"label_agreement":null},{"id":"W2317802191","doi":"10.1109/tem.2016.2527684","title":"A Discrete Stress–Strength Interference Theory-Based Dynamic Supplier Selection Model for Maintenance Service Outsourcing","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Engineering Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada; National Natural Science Foundation of China","keywords":"Outsourcing; Service (business); Asset specificity; Operations research; Business; Computer science; Supply chain; Reliability (semiconductor); Process management; Reliability engineering; Industrial organization; Marketing; Engineering; Transaction cost; Finance","score_opus":0.0052788314728405075,"score_gpt":0.19190725503410952,"score_spread":0.18662842356126902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317802191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028460326,0.000702123,0.9580795,0.0009267317,0.00009567718,0.00017657843,0.0003325808,0.000207991,0.011018401],"genre_scores_gemma":[0.93267834,0.0012031636,0.052510355,0.00025010778,0.00011837385,0.00057928456,0.00040295866,0.000059306232,0.012198071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976624,0.00077269686,0.00013761345,0.0005028502,0.00060010236,0.00032434773],"domain_scores_gemma":[0.9971349,0.0016727371,0.0004088907,0.00008025019,0.0005521902,0.00015105415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027186961,0.0014221017,0.001983301,0.0014679912,0.0007639887,0.0022545368,0.0036288367,0.0021092442,0.0068009547],"category_scores_gemma":[0.005172499,0.00088296045,0.0017787497,0.001733301,0.001330747,0.0019090816,0.0016275754,0.0020575467,0.000850451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006427147,0.000060108996,0.0010994789,0.00009149013,0.00007894994,0.00030144144,0.00014869233,0.96178675,0.000685072,0.026794259,0.00087772077,0.008011763],"study_design_scores_gemma":[0.000011241166,0.000024248407,0.00018398066,0.000006689886,0.000018711484,0.00002662349,0.000018921255,0.9947055,0.000052934556,0.0046437373,0.000296268,0.000011121291],"about_ca_topic_score_codex":0.015304227,"about_ca_topic_score_gemma":0.009057083,"teacher_disagreement_score":0.015304227,"about_ca_system_score_codex":0.003115839,"about_ca_system_score_gemma":0.0019669395,"threshold_uncertainty_score":0.030430317},"labels":[],"label_agreement":null},{"id":"W2320828504","doi":"10.1177/1748006x13477008","title":"An efficient method for the estimation of parameters of stochastic gamma process from noisy degradation measurements","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Monte Carlo method; Gamma process; Sizing; Degradation (telecommunications); Computer science; Process (computing); Particle filter; Stochastic process; Noise (video); Algorithm; Filter (signal processing); Mathematics; Statistics; Artificial intelligence","score_opus":0.012497191141599952,"score_gpt":0.24197677737994883,"score_spread":0.22947958623834888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2320828504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043291567,0.000040262225,0.9993082,0.000010760235,0.0000059516565,0.000009989502,0.0000097314705,0.000097889424,0.00008416028],"genre_scores_gemma":[0.032365948,0.00027162395,0.96593815,0.000030008692,0.000030974592,0.0001528013,0.00015852405,0.00010880776,0.00094316754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991535,0.00033942566,0.000038244576,0.00011304056,0.00032563586,0.000030189041],"domain_scores_gemma":[0.9986539,0.00084950466,0.00012452959,0.00012590848,0.00022429116,0.000021867349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015545284,0.0010924006,0.0009246582,0.0013074907,0.0003539783,0.0006207316,0.0010395943,0.0010652513,0.0020535667],"category_scores_gemma":[0.005074899,0.0005714369,0.00074693776,0.0010811244,0.00064994226,0.0011167715,0.0010788537,0.0014829985,0.0011076023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016954895,0.000093714574,0.0014601023,0.00042291865,0.00013183597,0.0003063155,0.00022454036,0.36925155,0.037926536,0.0506402,0.00308819,0.53628457],"study_design_scores_gemma":[0.00001873888,0.000035844958,0.00055480254,0.000022478676,0.000019515588,0.00031886683,0.000013009561,0.9799856,0.005992191,0.0096484,0.0033507317,0.000039814266],"about_ca_topic_score_codex":0.00171606,"about_ca_topic_score_gemma":0.0024550776,"teacher_disagreement_score":0.0020535667,"about_ca_system_score_codex":0.00048506196,"about_ca_system_score_gemma":0.0013098582,"threshold_uncertainty_score":0.008221269},"labels":[],"label_agreement":null},{"id":"W2321994534","doi":"10.1177/1748007810393826","title":"Finite-time maintenance cost analysis of engineering systems affected by stochastic degradation","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University Network of Excellence in Nuclear Engineering","keywords":"Reliability (semiconductor); Time horizon; Gamma process; Optimal maintenance; Mathematical optimization; Preventive maintenance; Minification; Reliability engineering; Computer science; Process (computing); Stochastic process; Variance (accounting); Set (abstract data type); Engineering; Mathematics; Economics","score_opus":0.006534947984490498,"score_gpt":0.17911523458889653,"score_spread":0.17258028660440602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2321994534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48485327,0.0016357771,0.50570995,0.0006867773,0.0000500728,0.000059418417,0.00017207331,0.00015829505,0.006674403],"genre_scores_gemma":[0.9944877,0.00020589012,0.004096233,0.000019342058,0.000015009246,0.000027886716,0.000048307505,0.000023807273,0.0010756933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993048,0.00026724496,0.000029720573,0.000073549374,0.00019813723,0.00012660651],"domain_scores_gemma":[0.9948755,0.0039091236,0.0005509983,0.00012975521,0.00038174857,0.00015280505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025106024,0.0007307028,0.0009158694,0.0008767195,0.0002791529,0.0010158205,0.0011576749,0.00087320997,0.0010914494],"category_scores_gemma":[0.007774328,0.00049971556,0.000707437,0.0005332047,0.00093681156,0.00094035594,0.00055674143,0.00084565626,0.00008405742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022639202,0.000009810007,0.00033032979,0.000015608457,0.000013426785,0.000038361137,0.000010563386,0.9950465,0.00034152615,0.003185632,0.000054463337,0.0009312596],"study_design_scores_gemma":[0.0000016325303,0.000007980935,0.00027084685,0.0000017407054,0.0000046568143,0.0000073601905,0.0000043425584,0.9988362,0.000058998583,0.0007851338,0.000018907085,0.000002209955],"about_ca_topic_score_codex":0.008469543,"about_ca_topic_score_gemma":0.003048529,"teacher_disagreement_score":0.008469543,"about_ca_system_score_codex":0.00181064,"about_ca_system_score_gemma":0.0008684463,"threshold_uncertainty_score":0.016840518},"labels":[],"label_agreement":null},{"id":"W2323692017","doi":"10.3166/jesa.40.703-720","title":"Estimation de la durée de vie des pièces caténaires","year":2006,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Geography","score_opus":0.007318989137163851,"score_gpt":0.23295224339575857,"score_spread":0.2256332542585947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2323692017","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75683695,0.0021252031,0.23778534,0.00008584947,0.000038978018,0.000041189374,0.0011229502,0.0006519928,0.0013115203],"genre_scores_gemma":[0.9705686,0.0006561682,0.026106285,0.0000046429204,0.000021740134,0.000034941557,0.0012006862,0.000041392475,0.0013655175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955124,0.00008482464,0.00003500372,0.00015796932,0.00013243074,0.000038578153],"domain_scores_gemma":[0.9972572,0.001793662,0.00045668054,0.00018455116,0.00021861926,0.000089344445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078399706,0.00063101394,0.0005075817,0.0017974516,0.00017913128,0.0007149629,0.0004765361,0.0008878739,0.0013367739],"category_scores_gemma":[0.0037743803,0.0004707429,0.00057534716,0.00066110934,0.00030208245,0.0010228169,0.00037909587,0.00053152005,0.00049257005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011127463,0.00020055132,0.13696888,0.00069043774,0.0003886571,0.00031828872,0.0005355287,0.5498268,0.07825198,0.002820143,0.0005870696,0.22829902],"study_design_scores_gemma":[0.000019684923,0.00046259488,0.106797345,0.000053369888,0.000059670405,0.00041298792,0.00012970385,0.85830885,0.029924244,0.0013412142,0.0023951756,0.00009511191],"about_ca_topic_score_codex":0.003453957,"about_ca_topic_score_gemma":0.002945436,"teacher_disagreement_score":0.003453957,"about_ca_system_score_codex":0.00043908908,"about_ca_system_score_gemma":0.00018844406,"threshold_uncertainty_score":0.006867707},"labels":[],"label_agreement":null},{"id":"W2328128098","doi":"10.1177/1748006x15598914","title":"Selective maintenance scheduling over a finite planning horizon","year":2015,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preventive maintenance; Shutdown; Scheduling (production processes); Time horizon; Optimal maintenance; Reliability engineering; Planned maintenance; Computer science; Mathematical optimization; Operations research; Engineering; Mathematics","score_opus":0.009654871328553168,"score_gpt":0.21535605835251495,"score_spread":0.20570118702396178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2328128098","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08914686,0.000646498,0.9001115,0.00031822475,0.000063320054,0.00015354194,0.0005703407,0.00041210317,0.008577696],"genre_scores_gemma":[0.93265057,0.0005683699,0.061225016,0.000039895673,0.000032162385,0.00021854408,0.00033518177,0.000029324094,0.004901069],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996227,0.000080309976,0.000019915038,0.00010181753,0.00009894352,0.000076338794],"domain_scores_gemma":[0.99947447,0.00021937421,0.00012370446,0.000048830854,0.00007681988,0.000056884386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005710843,0.00081781705,0.0008254022,0.00045425876,0.0004378051,0.0007571089,0.0018527559,0.00073193916,0.0033486534],"category_scores_gemma":[0.0010508592,0.00049307407,0.0005914893,0.0006717226,0.00050368375,0.00087127934,0.00043770543,0.00072567654,0.00029716583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045353736,0.00003159074,0.0002906887,0.00005674989,0.000019208854,0.0000937889,0.00002293688,0.9808328,0.0010184564,0.007757042,0.0003578275,0.009473518],"study_design_scores_gemma":[0.000014761788,0.000042851087,0.0001703633,0.0000041369176,0.000015382246,0.000024505296,0.000006622361,0.99623233,0.00025987485,0.002705705,0.0005189422,0.0000044869903],"about_ca_topic_score_codex":0.0080484925,"about_ca_topic_score_gemma":0.005844669,"teacher_disagreement_score":0.0080484925,"about_ca_system_score_codex":0.0010121838,"about_ca_system_score_gemma":0.0016915769,"threshold_uncertainty_score":0.01600331},"labels":[],"label_agreement":null},{"id":"W2332013029","doi":"10.1108/ijqrm-05-2015-0069","title":"Supplier selection considering product structure and product life cycle cost","year":2016,"lang":"en","type":"article","venue":"International Journal of Quality & Reliability Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Product (mathematics); Purchasing; Original equipment manufacturer; Product lifecycle; Selection (genetic algorithm); Quality (philosophy); Reliability (semiconductor); Product life-cycle management; Product design specification; Operations research; Ranking (information retrieval); Reliability engineering; New product development; Product design; Computer science; Risk analysis (engineering); Operations management; Business; Engineering; Marketing; Mathematics","score_opus":0.011790393186184106,"score_gpt":0.26388091236709926,"score_spread":0.2520905191809152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2332013029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38145825,0.0013758054,0.5821082,0.000858454,0.00005257865,0.0005271341,0.00043674826,0.00014363111,0.033039175],"genre_scores_gemma":[0.96651846,0.0003524759,0.030195288,0.000034664656,0.0000123901,0.00012989611,0.00013704217,0.000022329592,0.002597525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979108,0.0010311673,0.000055187065,0.00022050376,0.00062301557,0.00015936319],"domain_scores_gemma":[0.99401855,0.004466544,0.00052889425,0.00018165587,0.00066108914,0.00014333958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022335153,0.0008664106,0.00094577984,0.0014781274,0.0008249761,0.0022317627,0.0010167419,0.0011325162,0.0046613426],"category_scores_gemma":[0.009527389,0.0006048627,0.0010206929,0.0020169858,0.00064414897,0.0020707087,0.0008787103,0.00086818525,0.0004068575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006005433,0.00005449389,0.003054869,0.00011506491,0.000036599737,0.00020891076,0.00006125475,0.96520317,0.0010836732,0.011567524,0.00048546764,0.018068805],"study_design_scores_gemma":[0.000016368504,0.0001370628,0.0017741001,0.000032208314,0.000045539065,0.00009018395,0.00007764762,0.98602307,0.0005964466,0.010417139,0.0007676683,0.000022533972],"about_ca_topic_score_codex":0.007720357,"about_ca_topic_score_gemma":0.006309863,"teacher_disagreement_score":0.007720357,"about_ca_system_score_codex":0.002949152,"about_ca_system_score_gemma":0.002514728,"threshold_uncertainty_score":0.02139765},"labels":[],"label_agreement":null},{"id":"W2336477249","doi":"10.1108/jqme-04-2014-0015","title":"Optimizing production while reducing machinery lockout/tagout circumvention possibilities","year":2016,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Production (economics); Reliability engineering; Order (exchange); Risk analysis (engineering); Computer science; Condition-based maintenance; Operations research; Engineering; Operations management; Business; Economics","score_opus":0.015905457621621447,"score_gpt":0.24223969712400603,"score_spread":0.22633423950238457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336477249","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3079199,0.00079476763,0.6758445,0.00054134853,0.000038409496,0.00029764304,0.00010095562,0.0003357175,0.014126861],"genre_scores_gemma":[0.9407917,0.0002091649,0.0576565,0.00003705115,0.0000074547147,0.000051014566,0.000046982346,0.000032812728,0.0011673827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99918956,0.00028953643,0.00004025564,0.00015734408,0.00020196388,0.0001212822],"domain_scores_gemma":[0.99842334,0.000652773,0.0005194,0.000176394,0.00016138393,0.00006670776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013921055,0.00076177355,0.0004697391,0.0005806874,0.00032453611,0.0011892227,0.00084108376,0.00050760515,0.0020618103],"category_scores_gemma":[0.0030335349,0.00032891933,0.00040921525,0.0004891127,0.00054595293,0.00092067185,0.000605985,0.00058657164,0.00031690457],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002686467,0.0003785687,0.0053300615,0.0003735443,0.00006588144,0.00016327006,0.00014732168,0.848444,0.033585556,0.014690899,0.00068972044,0.095862575],"study_design_scores_gemma":[0.000051775798,0.0010118737,0.007821078,0.00008031576,0.00009915365,0.00014842994,0.00021851648,0.94909334,0.023072384,0.015060162,0.003311896,0.000031151085],"about_ca_topic_score_codex":0.0016893005,"about_ca_topic_score_gemma":0.0021471758,"teacher_disagreement_score":0.0020618103,"about_ca_system_score_codex":0.0010074754,"about_ca_system_score_gemma":0.00195797,"threshold_uncertainty_score":0.0073622465},"labels":[],"label_agreement":null},{"id":"W2336675174","doi":"10.1177/1748006x16631202","title":"Reliability estimation considering usage rate profile and warranty claims","year":2016,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Warranty; Reliability (semiconductor); Reliability engineering; Failure rate; Computer science; Population; Field (mathematics); Estimation; Task (project management); Engineering; Mathematics","score_opus":0.006109775821410678,"score_gpt":0.1960387168812009,"score_spread":0.18992894105979022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336675174","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36234596,0.0004050281,0.63449097,0.00018542705,0.000015694268,0.000072499904,0.00056470605,0.0005858064,0.0013338481],"genre_scores_gemma":[0.96871614,0.00017788753,0.029653255,0.000018505943,0.000020997877,0.000043159365,0.00066943915,0.000027040036,0.00067352416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990459,0.000313983,0.00008030223,0.00021342933,0.00026165028,0.000084729385],"domain_scores_gemma":[0.99534744,0.00266463,0.0006918765,0.000560156,0.00064029195,0.00009570463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015122995,0.00076584605,0.0007111426,0.0011569341,0.0001739439,0.00071895996,0.0009033861,0.0010043096,0.00051162473],"category_scores_gemma":[0.0091291955,0.00037471988,0.000776714,0.0010353713,0.00021677998,0.0012422765,0.00042588782,0.0008038285,0.00024970016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009405371,0.000052886648,0.01401695,0.0000575416,0.000051319512,0.00023124843,0.00009061392,0.94404733,0.003771472,0.0013259873,0.00037457855,0.035886034],"study_design_scores_gemma":[0.0000014084607,0.000020014397,0.002302904,0.0000037340485,0.000004926092,0.000045425604,0.000010974141,0.9963207,0.00056752824,0.0006310995,0.000085826454,0.0000053682375],"about_ca_topic_score_codex":0.0042106467,"about_ca_topic_score_gemma":0.0026505764,"teacher_disagreement_score":0.0042106467,"about_ca_system_score_codex":0.00043710333,"about_ca_system_score_gemma":0.00039813356,"threshold_uncertainty_score":0.008372307},"labels":[],"label_agreement":null},{"id":"W2338059164","doi":"10.1109/rams.2016.7448039","title":"Joint maintenance and inspection optimization of a k-out-of-n system","year":2016,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interval (graph theory); Component (thermodynamics); Reliability (semiconductor); Reliability engineering; Function (biology); Computer science; Process (computing); Preventive maintenance; Mathematical optimization; Poisson process; Poisson distribution; Maintenance actions; Optimal maintenance; Mathematics; Power (physics); Engineering; Statistics","score_opus":0.0074513428044287126,"score_gpt":0.17390396864654137,"score_spread":0.16645262584211265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338059164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4679399,0.0009787909,0.52024615,0.000960837,0.00007614243,0.00014712405,0.0003735804,0.00043105197,0.008846434],"genre_scores_gemma":[0.98484993,0.000134295,0.012194289,0.000038814855,0.000013987255,0.000054165783,0.00009155259,0.000023785045,0.0025992002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994785,0.0001237228,0.000025433095,0.0001586072,0.00008806564,0.00012578396],"domain_scores_gemma":[0.99892515,0.00046485767,0.00030388494,0.000058647427,0.00012705156,0.00012035278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094411883,0.0009353315,0.0017267048,0.00059301313,0.00048559165,0.00097087934,0.0013237781,0.0013316764,0.0018732187],"category_scores_gemma":[0.0022202388,0.0006863127,0.00077436166,0.0005879998,0.0009243164,0.000916023,0.00091695,0.00076106103,0.00021065235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087207554,0.000035263813,0.0006322975,0.00004913591,0.000021181413,0.00013210134,0.000028535618,0.99274606,0.0012565046,0.0015604662,0.00021365577,0.0032375439],"study_design_scores_gemma":[0.000010301678,0.00004167833,0.0003696731,0.000002843312,0.0000111077625,0.000019327294,0.000009048064,0.9984079,0.00016552862,0.0009006032,0.00005771654,0.0000043120453],"about_ca_topic_score_codex":0.009509878,"about_ca_topic_score_gemma":0.006067302,"teacher_disagreement_score":0.009509878,"about_ca_system_score_codex":0.0010575983,"about_ca_system_score_gemma":0.0011148077,"threshold_uncertainty_score":0.018909097},"labels":[],"label_agreement":null},{"id":"W2340761181","doi":"10.1109/rams.2016.7448007","title":"Modeling failure and maintenance effects of a system subject to multiple preventive maintenance types","year":2016,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Preventive maintenance; Unavailability; Corrective maintenance; Reliability engineering; Planned maintenance; Reliability (semiconductor); Downtime; Process (computing); Engineering; Proactive maintenance; Risk analysis (engineering); Computer science; Business","score_opus":0.002884141048698975,"score_gpt":0.17036306004073962,"score_spread":0.16747891899204065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2340761181","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48965102,0.0017735533,0.49686044,0.0017782609,0.00015844614,0.00034515138,0.00091642275,0.0006768255,0.007840018],"genre_scores_gemma":[0.98286736,0.0005770877,0.008353494,0.00007176916,0.000078376404,0.00017266216,0.0002375176,0.000041365354,0.00760032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985543,0.00043837208,0.00007922435,0.00035570757,0.00025209592,0.00032027197],"domain_scores_gemma":[0.990983,0.0063266773,0.001419992,0.0002606737,0.00071977574,0.00028978457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00405975,0.0016368857,0.0013600516,0.0018017062,0.0006193798,0.0013676996,0.0027961526,0.0029628896,0.003303364],"category_scores_gemma":[0.009408006,0.0011004285,0.0019403471,0.00085048715,0.0017262273,0.0017813346,0.0015091003,0.0018212992,0.0004501716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005348603,0.000050872917,0.0029872856,0.00004538816,0.0000387689,0.00012899979,0.000070268055,0.9881076,0.0004640767,0.0052763755,0.00018730127,0.0025896193],"study_design_scores_gemma":[0.000009707753,0.000037422025,0.0009683287,0.000006255387,0.000028122477,0.00002363536,0.000016342952,0.99695635,0.00009237165,0.0017431388,0.000109569315,0.000008687686],"about_ca_topic_score_codex":0.05260661,"about_ca_topic_score_gemma":0.02071603,"teacher_disagreement_score":0.05260661,"about_ca_system_score_codex":0.0025316952,"about_ca_system_score_gemma":0.0017474913,"threshold_uncertainty_score":0.10460079},"labels":[],"label_agreement":null},{"id":"W2342105618","doi":"10.1109/rams.2016.7448001","title":"Cutting tool remaining useful life during turning of metal matrix composites","year":2016,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Materials science; Composite material; Matrix (chemical analysis); Metal; Metallurgy","score_opus":0.005989813179882119,"score_gpt":0.19848964128578686,"score_spread":0.19249982810590474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2342105618","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9949309,0.0002477853,0.004334047,0.0000073662804,0.0000045483375,0.000004416909,0.00011615346,0.000071671464,0.0002830909],"genre_scores_gemma":[0.9993912,0.000021871932,0.00039858388,0.0000011627185,0.0000010376502,0.000001751637,0.00010603092,0.000005890115,0.000072429175],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997236,0.000026708136,0.000012345824,0.000056507273,0.00013545672,0.000045266428],"domain_scores_gemma":[0.99906546,0.0004044824,0.00017796765,0.000100919,0.00021553338,0.000035668458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043816044,0.00024692778,0.00033581114,0.0008473607,0.00020899845,0.0002647952,0.00029651378,0.00026603363,0.00050685427],"category_scores_gemma":[0.0014036213,0.00009755759,0.00023669821,0.00038997023,0.00022730285,0.00028832757,0.00016710913,0.00021267879,0.00011069866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033567636,0.00021200634,0.13772102,0.0004817832,0.00012055221,0.0012709679,0.000889416,0.094084725,0.5972779,0.0004107178,0.0004661245,0.16370805],"study_design_scores_gemma":[0.0000161514,0.0022664687,0.50466037,0.000033544893,0.00014264215,0.0009156528,0.0005052246,0.2219831,0.2679056,0.00046591018,0.0010029608,0.00010233389],"about_ca_topic_score_codex":0.0018315798,"about_ca_topic_score_gemma":0.0027778046,"teacher_disagreement_score":0.0018315798,"about_ca_system_score_codex":0.00026505537,"about_ca_system_score_gemma":0.00014794817,"threshold_uncertainty_score":0.0036418438},"labels":[],"label_agreement":null},{"id":"W2343581650","doi":"10.1109/tr.2015.2494689","title":"Selective Maintenance for Multistate Series Systems With S-Dependent Components","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability engineering; Component (thermodynamics); Series (stratigraphy); Computer science; Reliability (semiconductor); Stochastic process; Context (archaeology); Maintenance engineering; Failure rate; Mathematical optimization; Engineering; Mathematics; Statistics","score_opus":0.016492259223131748,"score_gpt":0.2131802180610275,"score_spread":0.19668795883789575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343581650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31832704,0.0006380004,0.67694134,0.0003296594,0.00004813076,0.00006945148,0.0001731656,0.00020963192,0.003263513],"genre_scores_gemma":[0.99043953,0.00014156276,0.0076043033,0.00002471432,0.000018047838,0.000030920077,0.000063567306,0.000018602175,0.0016586508],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963033,0.00007792495,0.000021472057,0.000114084396,0.000076985896,0.000079164776],"domain_scores_gemma":[0.9988877,0.00056870707,0.00026446476,0.00008494006,0.00013467649,0.000059448445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008418979,0.00078978884,0.0010201635,0.0005324957,0.00047058144,0.0007398813,0.001065462,0.0007878942,0.001530796],"category_scores_gemma":[0.001615003,0.0003825927,0.0009088546,0.00051215535,0.0006749768,0.0010185335,0.0007099292,0.00052599155,0.0001293983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012213145,0.000049051807,0.0015942584,0.00011257609,0.00005754674,0.00034433702,0.00006930336,0.9689668,0.005651162,0.009746325,0.00054666656,0.012739881],"study_design_scores_gemma":[0.0000070479578,0.00005080568,0.00042951756,0.0000034249301,0.000018684626,0.00006588659,0.000016928147,0.9950374,0.0005533523,0.0036648125,0.00014702864,0.000005105443],"about_ca_topic_score_codex":0.0046798056,"about_ca_topic_score_gemma":0.0036120587,"teacher_disagreement_score":0.0046798056,"about_ca_system_score_codex":0.000767292,"about_ca_system_score_gemma":0.0004771861,"threshold_uncertainty_score":0.0093051195},"labels":[],"label_agreement":null},{"id":"W2344953494","doi":"10.1177/1748006x16641767","title":"Higher moments and probability distribution of maintenance cost in the delay time model","year":2016,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Kurtosis; Skewness; Variance (accounting); Reliability (semiconductor); Computer science; Random variable; Probability distribution; Reliability engineering; Time horizon; Mathematics; Statistics; Mathematical optimization; Engineering; Economics","score_opus":0.006951164490658533,"score_gpt":0.19376873005203366,"score_spread":0.18681756556137513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344953494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18290654,0.0011533521,0.81016666,0.0007897814,0.00006969408,0.000035636836,0.00028173107,0.00022829458,0.0043684077],"genre_scores_gemma":[0.9740657,0.000875077,0.01941759,0.00005634771,0.00013953456,0.00005177576,0.00018482594,0.000073015806,0.005136167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990595,0.00019984663,0.000044974735,0.0001825602,0.00030613254,0.00020695818],"domain_scores_gemma":[0.9922002,0.0050927266,0.0012572745,0.00048546502,0.0006859293,0.0002784661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025340517,0.0007246272,0.0009627981,0.0016107898,0.0003110055,0.0019040321,0.0013513223,0.0012011377,0.0024640101],"category_scores_gemma":[0.012261737,0.00051334716,0.0007018158,0.0011511687,0.0014221267,0.0031327966,0.0008258283,0.0017517054,0.00031967502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010550874,0.000061846535,0.0026201636,0.000098495795,0.000039928418,0.00035312132,0.00014007608,0.63011414,0.0041270973,0.3528067,0.0008887184,0.008644162],"study_design_scores_gemma":[0.000007214954,0.000024537687,0.00092598645,0.000011966836,0.00001221292,0.00009702946,0.00002051906,0.9472044,0.00048189916,0.05085204,0.0003329269,0.000029254328],"about_ca_topic_score_codex":0.0031477232,"about_ca_topic_score_gemma":0.0013785523,"teacher_disagreement_score":0.0031477232,"about_ca_system_score_codex":0.0018430945,"about_ca_system_score_gemma":0.00085131166,"threshold_uncertainty_score":0.013401508},"labels":[],"label_agreement":null},{"id":"W2346216844","doi":"","title":"Reliability Analysis Approach For Operations Planning Of Hydropower Systems","year":2014,"lang":"en","type":"article","venue":"CUNY Academic Works (City University of New York)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Hydropower; Reliability engineering; Computer science; Engineering; Forensic engineering","score_opus":0.020556152627658416,"score_gpt":0.21893284034071386,"score_spread":0.19837668771305544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2346216844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031893752,0.00055076653,0.9895299,0.00038276863,0.00003284137,0.00006170707,0.0001163535,0.00008339894,0.006052994],"genre_scores_gemma":[0.41604412,0.0029040347,0.56735456,0.0002396312,0.00021099424,0.00081870944,0.00047010157,0.00021488353,0.0117428545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985562,0.00075985084,0.000050813174,0.0001718771,0.0003882778,0.00007296484],"domain_scores_gemma":[0.9987011,0.0008741424,0.00012920797,0.000036769736,0.000224284,0.000034475295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019720148,0.0013859901,0.00088951964,0.0014800993,0.00056676846,0.0018003612,0.0014492439,0.0009900479,0.003141571],"category_scores_gemma":[0.0030874105,0.0006774095,0.001095427,0.0015871042,0.0010693789,0.0011660352,0.0008201919,0.0018219287,0.00048484263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075398702,0.00001652578,0.00023575469,0.00007356052,0.000040975054,0.00008283554,0.00008943128,0.91552895,0.00041415577,0.06742702,0.0010902637,0.014993079],"study_design_scores_gemma":[0.0000043591335,0.000015420816,0.00010494037,0.000027815979,0.00001605041,0.00002156719,0.00004497604,0.9562434,0.00018010399,0.039914068,0.0034177795,0.000009594911],"about_ca_topic_score_codex":0.01766288,"about_ca_topic_score_gemma":0.01748657,"teacher_disagreement_score":0.01766288,"about_ca_system_score_codex":0.003123111,"about_ca_system_score_gemma":0.0042252652,"threshold_uncertainty_score":0.03512013},"labels":[],"label_agreement":null},{"id":"W2367690866","doi":"10.1287/opre.2016.1495","title":"Robust Control of Partially Observable Failing Systems","year":2016,"lang":"en","type":"article","venue":"Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Bellman equation; Computer science; Mathematical optimization; Convexity; Bayesian probability; Dynamic programming; Robust optimization; Optimal decision; Mathematics; Economics; Artificial intelligence; Decision tree","score_opus":0.08018216973207601,"score_gpt":0.29227203387472833,"score_spread":0.21208986414265232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2367690866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03134048,0.000287363,0.96248597,0.00062010146,0.000050226154,0.000044512763,0.00012202145,0.00034718536,0.004702193],"genre_scores_gemma":[0.9815537,0.00018805367,0.016082425,0.00006473704,0.000035134475,0.00007878764,0.0000700814,0.000032455584,0.0018947195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791783,0.0007516829,0.00008533786,0.00048742743,0.00043725496,0.00032049115],"domain_scores_gemma":[0.99440825,0.0035349517,0.0011363657,0.0002996619,0.00046263743,0.0001581636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030262894,0.0015732956,0.0013819003,0.00063654664,0.00041071972,0.0019407073,0.0016923767,0.0014390964,0.0018818384],"category_scores_gemma":[0.01074265,0.00053526857,0.0007777113,0.0005095816,0.0020321028,0.001287847,0.0017022874,0.0017127204,0.00022127606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075712385,0.000021603932,0.00025682172,0.00006632419,0.000036622456,0.00007939072,0.000061759776,0.95303226,0.00169085,0.03811163,0.00034131034,0.0062257852],"study_design_scores_gemma":[0.000010667942,0.000037107162,0.00010755032,0.000006593611,0.000006450884,0.000008839524,0.000006681776,0.98781127,0.00031250002,0.011485971,0.00020027885,0.0000061111746],"about_ca_topic_score_codex":0.006246567,"about_ca_topic_score_gemma":0.0025033536,"teacher_disagreement_score":0.006246567,"about_ca_system_score_codex":0.0017350742,"about_ca_system_score_gemma":0.0012613408,"threshold_uncertainty_score":0.016004741},"labels":[],"label_agreement":null},{"id":"W2399793648","doi":"10.1080/03155986.2001.11732424","title":"A Control-Limit Policy And Software For Condition-Based Maintenance Optimization","year":2001,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":185,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"University of Toronto; Nanyang Technological University","keywords":"Limit (mathematics); Control (management); Software; Computer science; Control limits; Reliability engineering; Engineering; Mathematics; Operating system; Artificial intelligence; Control chart","score_opus":0.020831217428880673,"score_gpt":0.2946814852633072,"score_spread":0.2738502678344265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2399793648","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029499924,0.00008013108,0.99402976,0.00009602937,0.00002311685,0.00004609245,0.000031569427,0.0007251,0.0020183036],"genre_scores_gemma":[0.330137,0.00028883578,0.6593409,0.00017635654,0.00010138989,0.0008163658,0.00016206708,0.000524314,0.008452667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989458,0.00052048167,0.00005340111,0.00013303552,0.0002684653,0.00007892203],"domain_scores_gemma":[0.99659914,0.0027482442,0.00016275291,0.00012821882,0.00031404226,0.00004764536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003396304,0.0009846073,0.0011229496,0.0008753676,0.00037638217,0.0011704776,0.0011274725,0.0012840885,0.009427807],"category_scores_gemma":[0.009025848,0.0005897895,0.0007507742,0.0005761755,0.00088405574,0.0007657015,0.0010128533,0.0016782337,0.0009890571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007849098,0.000073173294,0.00036582528,0.00006956058,0.000026630263,0.00004890918,0.000042688564,0.91167146,0.0007593657,0.039367475,0.0011892997,0.046307232],"study_design_scores_gemma":[0.000009258353,0.000014022425,0.000033388496,0.0000068246186,0.0000039978927,0.0000048589495,0.0000019305828,0.9955806,0.00022819421,0.0035954316,0.000518735,0.0000027048206],"about_ca_topic_score_codex":0.004447801,"about_ca_topic_score_gemma":0.0023606408,"teacher_disagreement_score":0.009427807,"about_ca_system_score_codex":0.0011478799,"about_ca_system_score_gemma":0.0015490154,"threshold_uncertainty_score":0.0315392},"labels":[],"label_agreement":null},{"id":"W2405410966","doi":"10.1002/asmb.2178","title":"Modeling and analysis of a warranty policy using new and reconditioned parts","year":2016,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Warranty; Remanufacturing; Profit (economics); Computer science; Product (mathematics); Operations research; Reliability engineering; Economics; Mathematics; Manufacturing engineering; Engineering; Microeconomics","score_opus":0.025059442939382545,"score_gpt":0.22865071793960431,"score_spread":0.20359127500022178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405410966","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17071582,0.0017846107,0.7914652,0.0017863883,0.0001420349,0.00023410718,0.0006681723,0.00031434602,0.03288929],"genre_scores_gemma":[0.9487285,0.0013693024,0.03151773,0.000083375984,0.00005772521,0.00027730863,0.00023868548,0.000061704966,0.017665638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994216,0.00017987046,0.000029112154,0.00010607744,0.00013121332,0.00013221949],"domain_scores_gemma":[0.9985067,0.00090294523,0.00030048654,0.000038228674,0.00018580271,0.000065830915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016705531,0.0009453358,0.0011156354,0.0011727565,0.0005902495,0.0019064967,0.0015364059,0.0022711763,0.003940534],"category_scores_gemma":[0.0025897892,0.0008208636,0.0012646996,0.00093292346,0.0010180265,0.0013604667,0.0008166093,0.0012657022,0.0003626656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011019628,0.000015082989,0.00018825666,0.000018023298,0.000005420563,0.000040219074,0.000011763817,0.9917053,0.0002228062,0.0063083163,0.00016168627,0.0013121957],"study_design_scores_gemma":[0.0000033245315,0.000010242808,0.00011149888,0.0000030203998,0.00000550004,0.0000064996634,0.000007683494,0.9983266,0.000073235395,0.0012652235,0.0001840007,0.00000316441],"about_ca_topic_score_codex":0.02414081,"about_ca_topic_score_gemma":0.018054755,"teacher_disagreement_score":0.02414081,"about_ca_system_score_codex":0.0032839193,"about_ca_system_score_gemma":0.003151905,"threshold_uncertainty_score":0.048000574},"labels":[],"label_agreement":null},{"id":"W2430768047","doi":"10.1007/s10845-016-1237-7","title":"Optimal preventive maintenance policy based on reinforcement learning of a fleet of military trucks","year":2016,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Truck; Reinforcement learning; Preventive maintenance; Downtime; Markov decision process; Process (computing); Mathematical optimization; Computer science; Monte Carlo method; Component (thermodynamics); Markov process; Operations research; Engineering; Reliability engineering; Mathematics; Artificial intelligence; Statistics","score_opus":0.006745453273948841,"score_gpt":0.21947203294558865,"score_spread":0.2127265796716398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2430768047","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7490256,0.00028687707,0.24758166,0.00047346903,0.0000709512,0.000068361616,0.0001270203,0.00022150305,0.0021444727],"genre_scores_gemma":[0.9951139,0.000026575606,0.0040800595,0.000011965229,0.000008158814,0.00001606375,0.000040390314,0.0000046174696,0.00069828384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972934,0.000061977116,0.000012246478,0.00007006704,0.000039594976,0.00008681806],"domain_scores_gemma":[0.998209,0.0009523926,0.00029100955,0.00009188776,0.00023356195,0.00022202056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009561604,0.00059918454,0.0011127723,0.0005874853,0.00047962696,0.0005256783,0.0013300198,0.0011476825,0.0012385254],"category_scores_gemma":[0.0030903162,0.0005173501,0.000458352,0.00033758738,0.0006166088,0.0006659085,0.0005222373,0.00085980137,0.00011671371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007180973,0.000023921391,0.0006149978,0.000007953523,0.000011263686,0.000043350676,0.000009779296,0.9956494,0.00043295702,0.00048698476,0.000109080494,0.002538391],"study_design_scores_gemma":[0.000003954264,0.000012501443,0.00017656387,7.17624e-7,0.0000026872597,0.000004013444,0.0000022949625,0.9995402,0.00004539853,0.00019734014,0.000012846415,0.0000013681205],"about_ca_topic_score_codex":0.017027,"about_ca_topic_score_gemma":0.008845971,"teacher_disagreement_score":0.017027,"about_ca_system_score_codex":0.0009953699,"about_ca_system_score_gemma":0.0010140158,"threshold_uncertainty_score":0.033855736},"labels":[],"label_agreement":null},{"id":"W24484157","doi":"10.1007/978-1-84882-472-0_13","title":"Inspection Strategies for Randomly Failing Systems","year":2009,"lang":"en","type":"book-chapter","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"ALARM; Reliability engineering; Computer science; State (computer science); Degradation (telecommunications); False alarm; Engineering; Risk analysis (engineering); Artificial intelligence; Business; Electrical engineering; Telecommunications; Algorithm","score_opus":0.010403602472850142,"score_gpt":0.19357376291936615,"score_spread":0.18317016044651602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W24484157","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017815089,0.004618905,0.9315562,0.0004692266,0.00015771402,0.00006304136,0.00006341716,0.0005523307,0.044703968],"genre_scores_gemma":[0.5096018,0.0061835293,0.38825905,0.00024712778,0.0001646791,0.00015440957,0.00026277555,0.0004297946,0.09469688],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997831,0.00005715136,0.000009296822,0.00003599757,0.00009211117,0.000022280723],"domain_scores_gemma":[0.9994825,0.0003205287,0.00003780533,0.00005195046,0.00009250173,0.000014765749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004125025,0.00068849453,0.00047703306,0.0005076247,0.00022833659,0.0005363873,0.0011792965,0.00057553226,0.0059472737],"category_scores_gemma":[0.0017929835,0.00036652488,0.00036612278,0.0004112496,0.00038900858,0.0008800065,0.0003690659,0.0009187449,0.00082256977],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011800182,0.00012668998,0.0004401388,0.0003476572,0.000035106536,0.00014144467,0.0002282711,0.22624649,0.010484406,0.11557202,0.021274474,0.62498534],"study_design_scores_gemma":[0.00002614735,0.00014969359,0.0007586054,0.00012899126,0.00003412502,0.00031629833,0.00008354745,0.81025493,0.0050665587,0.16295427,0.02019016,0.000036671187],"about_ca_topic_score_codex":0.0012886027,"about_ca_topic_score_gemma":0.0020564925,"teacher_disagreement_score":0.0059472737,"about_ca_system_score_codex":0.00062011014,"about_ca_system_score_gemma":0.00045845754,"threshold_uncertainty_score":0.019895554},"labels":[],"label_agreement":null},{"id":"W2463123820","doi":"10.1177/0954405416654184","title":"Predicting the remaining useful life of a cutting tool during turning titanium metal matrix composites","year":2016,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Machining; Aerospace; Reliability (semiconductor); Computer science; Hazard; Reliability engineering; Titanium alloy; Mechanical engineering; Materials science; Engineering; Composite material","score_opus":0.005505999408078238,"score_gpt":0.18471016310821736,"score_spread":0.1792041637001391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2463123820","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98818284,0.00014012557,0.010980661,0.0000087538065,0.000004328847,0.000010161287,0.00013245648,0.000078305166,0.00046241307],"genre_scores_gemma":[0.9982937,0.000038120776,0.0013714668,0.0000012073616,6.3466405e-7,0.0000052027435,0.00013017085,0.000005616732,0.00015401034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997696,0.000034499415,0.000015884683,0.000047894446,0.00009685811,0.00003526618],"domain_scores_gemma":[0.9985763,0.0008129364,0.00022872876,0.00012433229,0.00022758817,0.000030223382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006312134,0.00041024876,0.0002216521,0.000859794,0.0001406883,0.00028848785,0.00035068358,0.00043509377,0.0004680485],"category_scores_gemma":[0.0017919512,0.00014113897,0.0003408049,0.00037304236,0.00018920364,0.00031874317,0.00014816673,0.00023630359,0.00021021115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015222469,0.00024898068,0.10830835,0.00033933055,0.000056637622,0.00045244678,0.00041868855,0.5836143,0.18947102,0.00041433945,0.00046176443,0.11469187],"study_design_scores_gemma":[0.000010798297,0.0017206807,0.11456187,0.000022527056,0.000063367326,0.00022862406,0.00025334096,0.7356406,0.1463999,0.00034444642,0.0006935829,0.00006022232],"about_ca_topic_score_codex":0.00236784,"about_ca_topic_score_gemma":0.0025774166,"teacher_disagreement_score":0.00236784,"about_ca_system_score_codex":0.00024217651,"about_ca_system_score_gemma":0.00019503078,"threshold_uncertainty_score":0.004708171},"labels":[],"label_agreement":null},{"id":"W2469312940","doi":"10.1007/s00170-016-9127-x","title":"Optimal preventive and opportunistic maintenance policy for a two-unit system","year":2016,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Unit (ring theory); Preventive maintenance; Reliability engineering; Power system simulation; Dependency (UML); Engineering; Computer science; Power (physics); Electric power system; Mathematics","score_opus":0.007075006722000177,"score_gpt":0.24252911769353525,"score_spread":0.23545411097153507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2469312940","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5345243,0.0019872347,0.4517256,0.001448833,0.00020423462,0.00023160476,0.0005775753,0.0007366567,0.008563862],"genre_scores_gemma":[0.99092495,0.00013095797,0.00698049,0.000036202626,0.000036301783,0.000032812386,0.0000561985,0.000019724575,0.0017822693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993364,0.00020504148,0.000030383482,0.00014123655,0.000091327995,0.00019549974],"domain_scores_gemma":[0.9969393,0.001845555,0.00037927786,0.00015548026,0.00044741321,0.0002330374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015303727,0.0011803608,0.0021579384,0.00091855106,0.0006547397,0.0015100893,0.0019556559,0.0023716062,0.0033621134],"category_scores_gemma":[0.0034804195,0.0007913524,0.0005590719,0.0007878397,0.00096241065,0.00092904986,0.0008139446,0.0009941818,0.0003027753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038119464,0.00006153613,0.00042309906,0.00007834689,0.00004285034,0.000111585876,0.00003122785,0.990257,0.0014971325,0.0018226982,0.0005523556,0.0047409935],"study_design_scores_gemma":[0.0000232804,0.00003698911,0.00024910417,0.0000029640048,0.000017319195,0.00001487617,0.000008264074,0.99894243,0.00009917886,0.0005599698,0.00004091257,0.000004692513],"about_ca_topic_score_codex":0.011655795,"about_ca_topic_score_gemma":0.007139425,"teacher_disagreement_score":0.011655795,"about_ca_system_score_codex":0.001529578,"about_ca_system_score_gemma":0.0016263996,"threshold_uncertainty_score":0.023175955},"labels":[],"label_agreement":null},{"id":"W2473819074","doi":"10.1017/s089006041600024x","title":"A maintenance-focused approach to complex system design","year":2016,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"King Fahd University of Petroleum and Minerals; National Science Council; Massachusetts Institute of Technology","keywords":"Reliability engineering; Systems engineering; Computer science; Systems design; Reliability (semiconductor); Complex system; Risk analysis (engineering); Engineering; Power (physics); Artificial intelligence","score_opus":0.04493932443122554,"score_gpt":0.22620504008060002,"score_spread":0.1812657156493745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2473819074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043443544,0.00010390354,0.99372935,0.00010354001,0.000007920263,0.00003073993,0.000015832498,0.000060507813,0.0016038658],"genre_scores_gemma":[0.28414586,0.00039571212,0.71211624,0.00015439393,0.00005179337,0.00040405343,0.0000821938,0.00006967154,0.0025800832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993567,0.00026682462,0.00003046813,0.00009028003,0.00021383584,0.000041827214],"domain_scores_gemma":[0.9993117,0.00032126126,0.000079513644,0.000107854896,0.00015147198,0.000028259818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013188537,0.0009858932,0.00063370616,0.000834743,0.00038876352,0.0010029017,0.0011853735,0.0007234225,0.0017894557],"category_scores_gemma":[0.001654276,0.0006027966,0.00093685836,0.00048077843,0.0012019211,0.0009849126,0.0010868204,0.00125877,0.00024642027],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024865967,0.0000554997,0.0004968593,0.00012422354,0.00005372017,0.00009684499,0.00015341988,0.8527036,0.0053602937,0.10657086,0.00050567015,0.033854056],"study_design_scores_gemma":[0.000018107214,0.00006288187,0.00016047865,0.000023940991,0.0000277317,0.000039665185,0.00002045404,0.9392132,0.0011830698,0.05517651,0.0040652007,0.0000088909965],"about_ca_topic_score_codex":0.0017203711,"about_ca_topic_score_gemma":0.0026535806,"teacher_disagreement_score":0.0017894557,"about_ca_system_score_codex":0.0009081829,"about_ca_system_score_gemma":0.0011020524,"threshold_uncertainty_score":0.006974876},"labels":[],"label_agreement":null},{"id":"W2480074868","doi":"10.1016/j.apm.2016.07.019","title":"Optimal maintenance policy for multicomponent systems with periodic and opportunistic inspections and preventive replacements","year":2016,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Context (archaeology); Reliability engineering; Preventive maintenance; Interval (graph theory); Computer science; Function (biology); Mathematical optimization; Base (topology); Corrective maintenance; Type (biology); Engineering; Mathematics","score_opus":0.014427620788357987,"score_gpt":0.21232399751143377,"score_spread":0.19789637672307578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2480074868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3798421,0.0012674589,0.6082841,0.00081906305,0.000114411996,0.00019713175,0.0002913213,0.0002973945,0.008887044],"genre_scores_gemma":[0.98166925,0.00022204877,0.014651272,0.000031597756,0.000023874563,0.000060702736,0.00005279641,0.000023856743,0.0032646558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999542,0.00012382992,0.00002442997,0.00010396088,0.00009213955,0.00011365483],"domain_scores_gemma":[0.9980895,0.0011996367,0.0002940191,0.00008595382,0.00022543872,0.00010546288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012961588,0.0008426652,0.0014160473,0.00084132137,0.0004968532,0.0014578161,0.001460872,0.0013547719,0.0020073222],"category_scores_gemma":[0.003075244,0.000780601,0.00056674005,0.000808995,0.0009129477,0.0009877956,0.0007736805,0.00075021567,0.00022476584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000112601425,0.00004871196,0.00022760499,0.00004928687,0.000022494653,0.000049778137,0.000019929103,0.9904345,0.0010877379,0.0026674194,0.00021913767,0.0050608735],"study_design_scores_gemma":[0.000010927441,0.000023768187,0.00019908928,0.000003060542,0.000009999907,0.000008137763,0.000008408977,0.99791557,0.00014821878,0.0016164642,0.00005275985,0.0000035732837],"about_ca_topic_score_codex":0.010454336,"about_ca_topic_score_gemma":0.007471955,"teacher_disagreement_score":0.010454336,"about_ca_system_score_codex":0.0014594259,"about_ca_system_score_gemma":0.0013508912,"threshold_uncertainty_score":0.020786941},"labels":[],"label_agreement":null},{"id":"W2496838318","doi":"10.1108/jqme-05-2012-0018","title":"Availability analysis of a LNG processing plant using the Markov process","year":2016,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Reliability engineering; Process (computing); Markov chain; Markov process; Interval (graph theory); Reliability (semiconductor); Markov model; State (computer science); Computer science; Engineering; Process state; Algorithm; Mathematics; Machine learning","score_opus":0.018115085283415083,"score_gpt":0.2709274053394155,"score_spread":0.2528123200560004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2496838318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05319488,0.00048246948,0.938775,0.0004318786,0.000051566996,0.00009767562,0.00029319778,0.00027155026,0.0064017633],"genre_scores_gemma":[0.9669489,0.00067707285,0.024839647,0.00007110359,0.00005537954,0.00020274041,0.00035960265,0.00004257511,0.006802994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991284,0.00020248802,0.000039132094,0.00022689004,0.00023821405,0.00016485568],"domain_scores_gemma":[0.9988066,0.00064337865,0.00023465742,0.00004569939,0.00022029481,0.00004934445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010464475,0.00086991803,0.00082551513,0.000960994,0.00070741447,0.0013209557,0.0013094809,0.0008924072,0.0039481847],"category_scores_gemma":[0.0021688866,0.0004983271,0.001556768,0.0006410552,0.0008302729,0.0011114934,0.0009726737,0.001358024,0.00036130098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005810303,0.000035613386,0.0026987738,0.000076005075,0.000044486005,0.00022089593,0.00010357886,0.96068716,0.002417756,0.027244817,0.00046565788,0.0059471997],"study_design_scores_gemma":[0.0000027972342,0.000014768356,0.00033374625,0.0000060490947,0.000011119317,0.000022880375,0.000010953464,0.9961577,0.0002267284,0.0029957946,0.0002107249,0.000006657121],"about_ca_topic_score_codex":0.022463517,"about_ca_topic_score_gemma":0.012761272,"teacher_disagreement_score":0.022463517,"about_ca_system_score_codex":0.0018769866,"about_ca_system_score_gemma":0.001844215,"threshold_uncertainty_score":0.044665575},"labels":[],"label_agreement":null},{"id":"W2500605685","doi":"10.4018/978-1-4666-2095-7.ch008","title":"Modeling Multi-State Equipment Degradation with Non-Homogeneous Continuous-Time Hidden Semi-Markov Process","year":2012,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Process (computing); Markov process; Domain (mathematical analysis); Computer science; State (computer science); Time domain; Degradation (telecommunications); Stochastic process; Condition monitoring; Markov chain; Condition-based maintenance; Engineering; Mathematics; Machine learning; Algorithm; Statistics","score_opus":0.010052626760318054,"score_gpt":0.21237233489430032,"score_spread":0.20231970813398226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2500605685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053043317,0.0014940595,0.9404033,0.0003379975,0.000066936875,0.000025012889,0.00027864048,0.00028544661,0.004065185],"genre_scores_gemma":[0.9428254,0.0018008263,0.046090227,0.00007245469,0.00008621276,0.000080725134,0.0004520432,0.000043945867,0.00854824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997452,0.00007869406,0.000015290278,0.000073788455,0.000051291758,0.000035781606],"domain_scores_gemma":[0.99929905,0.00053634733,0.00007356754,0.000030641437,0.00004302368,0.000017287975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007145733,0.0006709625,0.00077486364,0.00037003966,0.00019626871,0.0009261289,0.0009682471,0.0009684275,0.0014586645],"category_scores_gemma":[0.0011551039,0.00046527447,0.0008969303,0.00049999764,0.00051545404,0.0008137399,0.00057881704,0.0010322536,0.00030273577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035165966,0.000028415976,0.0009645114,0.0000572336,0.00004361363,0.00009911966,0.000056652756,0.96760255,0.0012053312,0.017800225,0.000437374,0.011669915],"study_design_scores_gemma":[0.000001421198,0.0000049662526,0.00016178374,0.0000025264271,0.0000057579714,0.0000067117758,0.0000020748632,0.99646616,0.00008990093,0.0031376614,0.00011880977,0.0000022824604],"about_ca_topic_score_codex":0.006842893,"about_ca_topic_score_gemma":0.0065746033,"teacher_disagreement_score":0.006842893,"about_ca_system_score_codex":0.0007455123,"about_ca_system_score_gemma":0.00055861997,"threshold_uncertainty_score":0.013606131},"labels":[],"label_agreement":null},{"id":"W2512344339","doi":"10.1007/s10489-016-0829-4","title":"Machine learning-based methods for TTF estimation with application to APU prognostics","year":2016,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; National Research Council Canada","funders":"","keywords":"Prognostics; Computer science; Cluster analysis; Machine learning; Support vector machine; Predictive maintenance; Artificial intelligence; Predictive modelling; Data mining; Component (thermodynamics); Predictive analytics; Reliability engineering","score_opus":0.009661680582974275,"score_gpt":0.2736757204206994,"score_spread":0.2640140398377251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2512344339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046312707,0.0004822129,0.9938613,0.00007414995,0.00004562503,0.000017476432,0.00003790938,0.00035458288,0.0004954711],"genre_scores_gemma":[0.45925868,0.0011359982,0.5348932,0.0001129796,0.00028737655,0.00023112529,0.00029542434,0.00019477683,0.0035903596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955875,0.00015164721,0.000042838845,0.00008027146,0.00013790013,0.000028518622],"domain_scores_gemma":[0.99706715,0.0019039622,0.00023476177,0.00017606505,0.00057901425,0.000039076993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014509914,0.0008901636,0.0012191951,0.0013830147,0.0006154713,0.0010683337,0.0011039634,0.0013121808,0.001701717],"category_scores_gemma":[0.0071334047,0.00042192588,0.0006174945,0.0015570823,0.00044918718,0.0012671666,0.0007654378,0.0016937114,0.00064983696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006694537,0.00006156719,0.0007601152,0.00010864679,0.00004473648,0.000036140365,0.000043661337,0.7290287,0.0025984796,0.0033773265,0.0011767462,0.2626969],"study_design_scores_gemma":[0.0000018441948,0.000005162233,0.00011126114,0.0000043037935,0.0000022439706,0.0000063486796,0.0000018320695,0.9983845,0.00032180664,0.0009881187,0.00016844772,0.000004096675],"about_ca_topic_score_codex":0.0052872594,"about_ca_topic_score_gemma":0.0039388197,"teacher_disagreement_score":0.0052872594,"about_ca_system_score_codex":0.0005131679,"about_ca_system_score_gemma":0.00087694835,"threshold_uncertainty_score":0.010512948},"labels":[],"label_agreement":null},{"id":"W2513688333","doi":"10.1109/icphm.2016.7542876","title":"Developing machine learning-based models to estimate time to failure for PHM","year":2016,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; National Research Council Canada","funders":"","keywords":"Prognostics; Machine learning; Artificial intelligence; Predictive modelling; Computer science; Data modeling; Condition monitoring; Engineering; Reliability engineering; Data mining","score_opus":0.012679119682244121,"score_gpt":0.22986637673918844,"score_spread":0.21718725705694433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2513688333","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018774416,0.00032989128,0.9788345,0.00025215535,0.000035034474,0.000046195604,0.00020620556,0.00038733424,0.0011343145],"genre_scores_gemma":[0.8413541,0.000718109,0.15270485,0.00013392571,0.000093744406,0.0003675363,0.00071572117,0.00009401148,0.0038179802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944943,0.00016214202,0.00004661925,0.00013030329,0.00014763916,0.00006391584],"domain_scores_gemma":[0.99731463,0.0017931547,0.00033628047,0.00013176005,0.0003817876,0.000042391723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016516973,0.00095229514,0.00093781255,0.0010511421,0.00036205363,0.0008038381,0.0013359428,0.0012690254,0.0014870769],"category_scores_gemma":[0.0061743413,0.0006075942,0.0009582827,0.0008790215,0.0003875343,0.0011513294,0.00059320865,0.0018723087,0.0004394431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013961199,0.000019186986,0.0009316974,0.000029194756,0.000016028758,0.000020665571,0.000017993916,0.98429585,0.0003355499,0.0019241052,0.0002833732,0.012112356],"study_design_scores_gemma":[7.927267e-7,0.000004082326,0.00012652283,0.0000024982642,0.00000271843,0.0000035134708,0.0000014875482,0.99887866,0.00009261165,0.0007933848,0.00009133153,0.0000023549803],"about_ca_topic_score_codex":0.013805644,"about_ca_topic_score_gemma":0.010994703,"teacher_disagreement_score":0.013805644,"about_ca_system_score_codex":0.000997704,"about_ca_system_score_gemma":0.0010868934,"threshold_uncertainty_score":0.027450621},"labels":[],"label_agreement":null},{"id":"W2523137375","doi":"10.1002/qre.2088","title":"Modeling Failure Process and Quantifying the Effects of Multiple Types of Preventive Maintenance for a Repairable System","year":2016,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Connaught Fund; Sharif University of Technology","keywords":"Preventive maintenance; Reliability engineering; Poisson process; Reliability (semiconductor); Corrective maintenance; Planned maintenance; Truck; Function (biology); Poisson distribution; Process (computing); Engineering; Failure rate; Computer science; Statistics; Power (physics); Mathematics; Automotive engineering","score_opus":0.010317740008198345,"score_gpt":0.24497710677062448,"score_spread":0.23465936676242613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523137375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22227333,0.0005302055,0.7751944,0.000250863,0.000027217535,0.00009354802,0.00010472765,0.00018070896,0.0013450177],"genre_scores_gemma":[0.97348684,0.00034629548,0.02413202,0.000033838696,0.000039106206,0.00009776397,0.0000792213,0.000029868523,0.0017550673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982924,0.00060318,0.00009505787,0.00036697826,0.00041323047,0.00022918814],"domain_scores_gemma":[0.991405,0.005948185,0.001598287,0.0004134018,0.00046418776,0.00017081373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004376517,0.0015955187,0.0012032962,0.001540567,0.00036901628,0.001273788,0.002360323,0.0022504325,0.0014014869],"category_scores_gemma":[0.011799312,0.00088015996,0.0012287146,0.0007498644,0.0012160463,0.0020314772,0.0011544751,0.0011269302,0.00023592314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033874956,0.000042169042,0.0020804547,0.000036703124,0.00003514844,0.00011378629,0.000063603016,0.98710793,0.0016261794,0.004898838,0.000055114735,0.003906316],"study_design_scores_gemma":[0.0000051664074,0.00004029875,0.00082119415,0.000004783179,0.000026755863,0.000035273206,0.000014586043,0.9959287,0.00041762565,0.0026166942,0.00007939644,0.0000094244915],"about_ca_topic_score_codex":0.0071967617,"about_ca_topic_score_gemma":0.0038140302,"teacher_disagreement_score":0.0071967617,"about_ca_system_score_codex":0.0013573627,"about_ca_system_score_gemma":0.0011025813,"threshold_uncertainty_score":0.023145497},"labels":[],"label_agreement":null},{"id":"W2526679493","doi":"10.4236/ajibm.2016.69094","title":"The Application of Reliability Methods for Aircraft Design Project Management","year":2016,"lang":"en","type":"article","venue":"American Journal of Industrial and Business Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université du Québec à Trois-Rivières","funders":"","keywords":"Maintainability; Reliability engineering; Fault tree analysis; Reliability (semiconductor); Process (computing); Computer science; Risk analysis (engineering); Software deployment; Systems engineering; Engineering; Software engineering","score_opus":0.024663741387636964,"score_gpt":0.2825082226568508,"score_spread":0.2578444812692139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2526679493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010292373,0.003344738,0.99055403,0.00035496516,0.00010781419,0.000044914374,0.000049949547,0.00023616682,0.0042781625],"genre_scores_gemma":[0.11442343,0.010922646,0.8686703,0.00021972483,0.0008163394,0.0003662337,0.00017090853,0.00024779313,0.0041627353],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99672806,0.0015676834,0.00016840704,0.00033964342,0.0011348948,0.00006135476],"domain_scores_gemma":[0.9917464,0.0059519927,0.0008149348,0.00059707137,0.000838991,0.000050492927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034566217,0.0013809471,0.0007023236,0.0029287504,0.0004800981,0.0014587985,0.0011508348,0.0009978656,0.0027440824],"category_scores_gemma":[0.013166113,0.0006373251,0.001001722,0.002311689,0.0012615769,0.0016840694,0.0010098014,0.002353664,0.001219741],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032675725,0.000058990616,0.0018808164,0.0006369577,0.00011447665,0.000104245795,0.00035077592,0.1648706,0.0024400733,0.25207084,0.0063388026,0.57110065],"study_design_scores_gemma":[0.000022589502,0.00011396372,0.0014838029,0.0003730634,0.000054183092,0.00032922573,0.00012514055,0.5904495,0.0026585052,0.34026217,0.06405075,0.0000770651],"about_ca_topic_score_codex":0.0020266406,"about_ca_topic_score_gemma":0.0016648655,"teacher_disagreement_score":0.0034566217,"about_ca_system_score_codex":0.0010409758,"about_ca_system_score_gemma":0.0012021009,"threshold_uncertainty_score":0.018280566},"labels":[],"label_agreement":null},{"id":"W2546934367","doi":"10.1109/mnet.2016.1500221nm","title":"Reliability and Criticality Analysis of Communication Networks by Stochastic Computation","year":2016,"lang":"en","type":"article","venue":"IEEE Network","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Criticality; Probabilistic logic; Reliability (semiconductor); Redundancy (engineering); Telecommunications network; Stochastic process; Distributed computing; Computer network; Mathematics","score_opus":0.00693038015841628,"score_gpt":0.22600388553909243,"score_spread":0.21907350538067616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546934367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025408804,0.0005753709,0.96962196,0.00037969614,0.000035570858,0.000033863285,0.00006378966,0.000098933815,0.0037819312],"genre_scores_gemma":[0.93499666,0.0018718378,0.05872834,0.00011750971,0.0001646673,0.00023423437,0.00014385032,0.000109700755,0.0036331713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999258,0.000249286,0.000023411585,0.00009219247,0.000265577,0.00011148547],"domain_scores_gemma":[0.99693775,0.0022751212,0.00030523515,0.0001330595,0.00027812127,0.00007076833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014280941,0.0009469051,0.00077373657,0.0013225526,0.0005643511,0.00089036673,0.00089800346,0.0006817816,0.0013280135],"category_scores_gemma":[0.0065658228,0.00045433562,0.0007491876,0.0007596872,0.0016981346,0.0015955544,0.0009420369,0.0011502665,0.00015225049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000076121555,0.000006224092,0.00025643184,0.00002122478,0.000010258368,0.000028063267,0.000023146576,0.9341391,0.00038261595,0.06280612,0.00021658155,0.0021026807],"study_design_scores_gemma":[0.0000011391376,0.0000036950953,0.00004915699,0.000003178213,0.0000017863305,0.0000061259266,0.0000034976554,0.9819224,0.00006882066,0.017806508,0.00013147818,0.0000021724873],"about_ca_topic_score_codex":0.0053915773,"about_ca_topic_score_gemma":0.0025236572,"teacher_disagreement_score":0.0053915773,"about_ca_system_score_codex":0.0017548501,"about_ca_system_score_gemma":0.0015876626,"threshold_uncertainty_score":0.012732387},"labels":[],"label_agreement":null},{"id":"W2548568551","doi":"","title":"Availability optimization model for stochastically degrading systems under preventive replacement and minimal repair","year":2013,"lang":"en","type":"article","venue":"Industrial Engineering and Systems Management (IESM), Proceedings of 2013 International Conference on","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Preventive maintenance; Optimization problem; Reliability engineering; Mathematical optimization; Condition-based maintenance; Weibull distribution; Stochastic optimization; Computer science; Engineering; Mathematics; Statistics","score_opus":0.03670418675889726,"score_gpt":0.22653997044299226,"score_spread":0.189835783684095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2548568551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08488488,0.0017788358,0.89921033,0.0010926983,0.000100179626,0.000114559836,0.000544655,0.00031355675,0.011960365],"genre_scores_gemma":[0.9640138,0.0008981907,0.024164574,0.00012287007,0.00006288739,0.00022617051,0.0002704489,0.00006221973,0.010178825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989299,0.00033958023,0.000055581688,0.00024041109,0.00023507682,0.00019940629],"domain_scores_gemma":[0.99818546,0.0010072611,0.00040334658,0.00005044316,0.00027165463,0.00008182667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001605719,0.0013922878,0.0015937103,0.00089407875,0.00045729044,0.0015343269,0.001674676,0.0018486134,0.002617823],"category_scores_gemma":[0.0029271161,0.0008000024,0.0011060307,0.00087724876,0.0008865188,0.0009993869,0.0009221613,0.001307299,0.00037921063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028422925,0.000015775431,0.00020169208,0.000039256156,0.000019127343,0.00008344424,0.00002654852,0.9925915,0.00058716926,0.0047979774,0.00019007755,0.001419154],"study_design_scores_gemma":[0.0000057506068,0.000016499893,0.00012664023,0.000003036313,0.0000072347357,0.000012700349,0.0000059519375,0.99836856,0.000067140005,0.0012580231,0.00012482044,0.0000036107626],"about_ca_topic_score_codex":0.008895562,"about_ca_topic_score_gemma":0.004123798,"teacher_disagreement_score":0.008895562,"about_ca_system_score_codex":0.0015562811,"about_ca_system_score_gemma":0.0012090638,"threshold_uncertainty_score":0.017687619},"labels":[],"label_agreement":null},{"id":"W2551310431","doi":"10.1016/j.ress.2016.11.018","title":"Preventive maintenance optimization for a stochastically degrading system with a random initial age","year":2016,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Random variable; Mathematical optimization; Time horizon; Expected value; Mathematics; Preventive maintenance; Variable (mathematics); Computer science; Engineering; Statistics; Reliability engineering","score_opus":0.004181489649392533,"score_gpt":0.18402420661987598,"score_spread":0.17984271697048346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2551310431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46635485,0.0017291763,0.5210476,0.0022841918,0.0001643037,0.00017055593,0.00064475083,0.00075139356,0.0068532936],"genre_scores_gemma":[0.9810678,0.00024219535,0.012576427,0.00009482182,0.000049394068,0.00006691997,0.00013470474,0.00007464223,0.0056932606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994017,0.00018380562,0.000024882196,0.00012001627,0.00009080496,0.00017881604],"domain_scores_gemma":[0.996027,0.0025947478,0.00058575324,0.00014783461,0.0004450013,0.00019973466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020602078,0.0015256192,0.0019491934,0.0013451255,0.00057173404,0.0010593988,0.0014469181,0.0026595185,0.0020078572],"category_scores_gemma":[0.0064088623,0.0012623029,0.001208152,0.0008437504,0.001252196,0.0009504841,0.0010210259,0.0012605139,0.00031026808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008135356,0.000020830314,0.00035184005,0.000037392365,0.00003225582,0.00006869557,0.00001972417,0.9952242,0.0007877339,0.0016938228,0.00022084895,0.0014613194],"study_design_scores_gemma":[0.000011509076,0.00003357809,0.00029986363,0.0000047740127,0.000027111499,0.000021197378,0.0000071271174,0.9983972,0.00018144977,0.0009600162,0.000050464783,0.0000058666733],"about_ca_topic_score_codex":0.010368069,"about_ca_topic_score_gemma":0.004424287,"teacher_disagreement_score":0.010368069,"about_ca_system_score_codex":0.0018580173,"about_ca_system_score_gemma":0.0014167259,"threshold_uncertainty_score":0.020615458},"labels":[],"label_agreement":null},{"id":"W2555143979","doi":"10.1007/s40430-016-0665-9","title":"Quantitative reliability analysis of repairable systems with closed-loop feedback based on GO methodology","year":2016,"lang":"en","type":"article","venue":"Journal of the Brazilian Society of Mechanical Sciences and Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministry of Industry and Information Technology of the People's Republic of China","keywords":"Fault tree analysis; Reliability (semiconductor); Markov process; Computer science; Reliability block diagram; Loop (graph theory); Closed loop; Process (computing); Feedback loop; Reliability engineering; Monte Carlo method; For loop; Operator (biology); Control theory (sociology); Engineering; Control engineering; Mathematics; Control (management)","score_opus":0.02312259834517242,"score_gpt":0.24934508064684335,"score_spread":0.22622248230167094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555143979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06385869,0.00047274365,0.9320228,0.000076890654,0.00002186753,0.000053005268,0.000067423774,0.00023155681,0.0031950225],"genre_scores_gemma":[0.96434903,0.00025243193,0.033977088,0.000026344476,0.000022827506,0.00010914152,0.00007744934,0.00005039811,0.0011352734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945134,0.00022741448,0.000017334116,0.00006705654,0.00017715788,0.000059720147],"domain_scores_gemma":[0.9980507,0.0013325906,0.00017949649,0.00011688885,0.00028074026,0.00003952231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016576706,0.0010516789,0.0009918602,0.0015417602,0.00025125194,0.00075356464,0.00089876406,0.0006620504,0.0013168989],"category_scores_gemma":[0.0036412643,0.00023093128,0.0010194669,0.00054109923,0.00094097387,0.00093825907,0.00072974496,0.00067356596,0.00011143589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010313946,0.000070854,0.0008383707,0.0003898948,0.00008322861,0.00011900466,0.00014865331,0.89872044,0.011193323,0.06690574,0.00029592548,0.021131422],"study_design_scores_gemma":[0.0000040715054,0.000048223897,0.00030974974,0.000009766866,0.00001529815,0.000013213335,0.000011353538,0.9896904,0.0006220798,0.009143968,0.00012709959,0.0000047950916],"about_ca_topic_score_codex":0.0016519221,"about_ca_topic_score_gemma":0.0009929395,"teacher_disagreement_score":0.0016576706,"about_ca_system_score_codex":0.0006349231,"about_ca_system_score_gemma":0.00067371264,"threshold_uncertainty_score":0.008766651},"labels":[],"label_agreement":null},{"id":"W2557045438","doi":"10.1007/s00521-016-2676-y","title":"An evolutionary computation approach to solving repairable multi-state multi-objective redundancy allocation problems","year":2016,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Mathematical optimization; Computer science; Redundancy (engineering); Benchmark (surveying); Sorting; Component (thermodynamics); Computation; Algorithm; Mathematics","score_opus":0.017257597069775404,"score_gpt":0.2512766022432517,"score_spread":0.23401900517347632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557045438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014293243,0.0003387984,0.97925025,0.00023956315,0.000074465024,0.00004572605,0.000021819245,0.000072513765,0.0056636585],"genre_scores_gemma":[0.45387793,0.0005364909,0.53838295,0.00021341759,0.000097677075,0.00035422426,0.00007864188,0.0000697672,0.0063888985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996996,0.00010759128,0.00001642994,0.000038106846,0.000103526865,0.000034774126],"domain_scores_gemma":[0.99946374,0.0003581887,0.00003122001,0.000022432807,0.000105157036,0.000019239744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009801493,0.0006566897,0.0009921734,0.00078740215,0.00052473135,0.0008018872,0.0011711685,0.0013420761,0.0018614419],"category_scores_gemma":[0.0024911927,0.00045482875,0.00074694556,0.0009781837,0.0007138933,0.0006385049,0.0008429033,0.0010154931,0.00015793409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000106115,0.00002247552,0.0001291757,0.000026332862,0.000025357198,0.00003370878,0.00002438313,0.96922326,0.00038094298,0.0114662815,0.00022936707,0.01842805],"study_design_scores_gemma":[0.0000032004582,0.0000075622706,0.000030445422,0.0000030352521,0.000003794029,0.000004701718,0.0000025924774,0.997757,0.000049825627,0.0019987058,0.00013761915,0.0000015401902],"about_ca_topic_score_codex":0.005932844,"about_ca_topic_score_gemma":0.006753483,"teacher_disagreement_score":0.005932844,"about_ca_system_score_codex":0.0007461111,"about_ca_system_score_gemma":0.0009768687,"threshold_uncertainty_score":0.011796594},"labels":[],"label_agreement":null},{"id":"W2562797673","doi":"10.1109/cdc.1999.832745","title":"Production and maintenance control for manufacturing system","year":2003,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Preventive maintenance; Failure rate; State (computer science); Production (economics); Markov process; Reliability engineering; Corrective maintenance; Computer science; Process (computing); Production control; Control (management); Jump; Engineering; Mathematics; Artificial intelligence; Statistics; Algorithm; Economics","score_opus":0.004385965844332355,"score_gpt":0.16997988356320623,"score_spread":0.16559391771887388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562797673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07477037,0.0018257662,0.9158018,0.00051050604,0.00012449142,0.00009899986,0.00014608393,0.0003370513,0.0063849087],"genre_scores_gemma":[0.9815136,0.00054940814,0.013644996,0.00004226278,0.00007942522,0.000087916866,0.000111945345,0.00003601425,0.0039344383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991322,0.00022013599,0.00003200604,0.00023943622,0.00022152849,0.00015461833],"domain_scores_gemma":[0.99902487,0.00046137153,0.00025769396,0.000041902502,0.00017194073,0.000042155978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012414769,0.0010770188,0.0010268282,0.0005327013,0.0004935702,0.0013455702,0.0010703676,0.0008785195,0.0020507358],"category_scores_gemma":[0.0022331786,0.00039959708,0.00052820065,0.0005027398,0.00074728625,0.00077957107,0.0005680502,0.00079672504,0.0002485382],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098541605,0.000069067966,0.0005470794,0.0001720139,0.000043144624,0.00012432259,0.00005486288,0.96474606,0.0031664507,0.010821131,0.0009283141,0.019228948],"study_design_scores_gemma":[0.000017187309,0.0000706153,0.00030293287,0.0000052757914,0.000013127981,0.0000200831,0.000005631185,0.9951481,0.0004867219,0.0035941624,0.0003305838,0.000005466026],"about_ca_topic_score_codex":0.006501503,"about_ca_topic_score_gemma":0.0025591967,"teacher_disagreement_score":0.006501503,"about_ca_system_score_codex":0.0011835526,"about_ca_system_score_gemma":0.00093675527,"threshold_uncertainty_score":0.012927294},"labels":[],"label_agreement":null},{"id":"W2566918304","doi":"10.1109/ieem.2016.7797927","title":"Economic life prediction of repairable multi-component systems based on extension theory","year":2016,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Component (thermodynamics); Reliability engineering; Life extension; Computer science; Maintenance engineering; Extension (predicate logic); Work (physics); Reliability theory; Degradation (telecommunications); Mathematical optimization; Engineering; Failure rate; Mathematics","score_opus":0.010746245735045243,"score_gpt":0.18309707864348915,"score_spread":0.1723508329084439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2566918304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25773132,0.0005877043,0.73674273,0.00022943215,0.00001675073,0.00003188725,0.000080495716,0.00008657767,0.0044930684],"genre_scores_gemma":[0.9878599,0.00018775571,0.011014246,0.000008429221,0.000008877452,0.000027886512,0.000044408516,0.000010049758,0.00083848316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984074,0.00006236522,0.000007525017,0.000025710571,0.00004176303,0.000021996115],"domain_scores_gemma":[0.9993956,0.00040418137,0.00007529544,0.000025868936,0.00006455462,0.000034516645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007118362,0.00041826387,0.00043006832,0.000599383,0.00024095649,0.00044035455,0.00047158997,0.00035162913,0.0007087988],"category_scores_gemma":[0.0016560229,0.00018265723,0.00044491404,0.00033388016,0.0004673118,0.001046783,0.00039422503,0.00050391717,0.000053745865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007929303,0.0000073492292,0.00079431426,0.000008772688,0.0000060861516,0.000028490866,0.000014854718,0.98948914,0.0002770888,0.005980845,0.00008313869,0.0033018796],"study_design_scores_gemma":[4.6939178e-7,0.0000033851177,0.00017419228,0.0000010104625,0.0000013002924,0.0000039307765,0.0000024699862,0.9969215,0.00005344742,0.0027928648,0.00004412536,0.000001419601],"about_ca_topic_score_codex":0.003913762,"about_ca_topic_score_gemma":0.0023505567,"teacher_disagreement_score":0.003913762,"about_ca_system_score_codex":0.0007719694,"about_ca_system_score_gemma":0.00045577055,"threshold_uncertainty_score":0.0077819824},"labels":[],"label_agreement":null},{"id":"W2586116969","doi":"10.1115/imece2016-65384","title":"A New Composite Allocation Method on Balance of Reliability and Maintainability Index With the Goal of Availability","year":2016,"lang":"en","type":"article","venue":"Volume 14: Emerging Technologies; Materials: Genetics to Structures; Safety Engineering and Risk Analysis","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministério da Ciência, Tecnologia e Inovação","keywords":"Analytic hierarchy process; Reliability engineering; Reliability (semiconductor); Index (typography); Computer science; Optimal allocation; Fuzzy logic; Mathematical optimization; Resource allocation; Maintenance engineering; Engineering; Operations research; Mathematics","score_opus":0.002345496640613004,"score_gpt":0.1969346056677928,"score_spread":0.1945891090271798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586116969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051873927,0.0002689067,0.98829854,0.000092951974,0.000051189138,0.00007146743,0.000025329804,0.00015644719,0.005847773],"genre_scores_gemma":[0.34723064,0.0006886194,0.64054096,0.00014769373,0.00013613375,0.0006374658,0.00015747965,0.00019502788,0.010265988],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975667,0.0007152921,0.0001391089,0.00047695558,0.00093420735,0.00016765724],"domain_scores_gemma":[0.99901414,0.0002895604,0.00009163604,0.00008664168,0.00045374813,0.00006432169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020296597,0.0010622354,0.00084326684,0.0018762364,0.0008466274,0.0015137582,0.0013280927,0.00076284254,0.0041828975],"category_scores_gemma":[0.0033183757,0.00038323153,0.00088020856,0.0015652002,0.0006591081,0.0026059973,0.0013585194,0.00088992954,0.00058040227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002300756,0.00013643429,0.0021184636,0.0005582393,0.00018092025,0.00013372257,0.0006498809,0.3195745,0.01649486,0.08970354,0.005942384,0.56427705],"study_design_scores_gemma":[0.0000709845,0.0001602152,0.0012058696,0.000070591064,0.000084955616,0.00019593656,0.00019044524,0.9347474,0.0052085994,0.042885445,0.015117274,0.000062235886],"about_ca_topic_score_codex":0.003437877,"about_ca_topic_score_gemma":0.0025007653,"teacher_disagreement_score":0.0041828975,"about_ca_system_score_codex":0.0010517286,"about_ca_system_score_gemma":0.001809099,"threshold_uncertainty_score":0.013993204},"labels":[],"label_agreement":null},{"id":"W2586354084","doi":"10.1115/imece2016-65383","title":"Reliability Optimization Allocation Method for Multifunction Systems Based on Goal Oriented Methodology","year":2016,"lang":"en","type":"article","venue":"Volume 14: Emerging Technologies; Materials: Genetics to Structures; Safety Engineering and Risk Analysis","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministry of Industry and Information Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Reliability (semiconductor); Reliability engineering; Mathematical optimization; Constraint (computer-aided design); Computer science; Function (biology); Optimization problem; Process (computing); Genetic algorithm; Minification; Power (physics); Engineering; Mathematics","score_opus":0.00770786937912056,"score_gpt":0.23601020762621627,"score_spread":0.22830233824709573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586354084","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022288186,0.00016233568,0.9953181,0.00004274934,0.000012111585,0.000022487824,0.0000070257697,0.00007732922,0.0021289634],"genre_scores_gemma":[0.41928336,0.0010609004,0.5735111,0.00016390682,0.00006538942,0.00040509162,0.0001096469,0.00017689135,0.0052237604],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932384,0.00019726335,0.000029897621,0.000109503206,0.0002769455,0.00006257503],"domain_scores_gemma":[0.99968266,0.00012661924,0.000047406927,0.000023058947,0.00010572525,0.000014593568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010949377,0.0010166764,0.0007168265,0.00097174593,0.00043232142,0.00080064416,0.0009656887,0.00063897157,0.0018859493],"category_scores_gemma":[0.0010262673,0.00036273923,0.0009340771,0.0006242894,0.0005758727,0.0011924583,0.0006569824,0.00084172474,0.00034029648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046915615,0.000057927806,0.00057218183,0.00024941456,0.00006545733,0.00009288256,0.00014662172,0.82178354,0.010011075,0.06899988,0.0012178005,0.09675625],"study_design_scores_gemma":[0.00001290822,0.000052170166,0.0001469074,0.000018444309,0.000018729299,0.000032830998,0.000023301656,0.9839993,0.0013723003,0.011806425,0.0025050528,0.000011543232],"about_ca_topic_score_codex":0.0025102454,"about_ca_topic_score_gemma":0.0018008779,"teacher_disagreement_score":0.0025102454,"about_ca_system_score_codex":0.0009000148,"about_ca_system_score_gemma":0.0012275494,"threshold_uncertainty_score":0.006530106},"labels":[],"label_agreement":null},{"id":"W2589126456","doi":"10.1007/s00170-017-0100-0","title":"Optimal multi-level condition-based maintenance policy for multi-unit systems under economic dependence","year":2017,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Preventive maintenance; Reliability engineering; Condition-based maintenance; Limit (mathematics); Unit (ring theory); Control limits; Process (computing); Engineering; Failure mode and effects analysis; Control (management); Mathematical optimization; Computer science; Mathematics; Control chart; Artificial intelligence","score_opus":0.03207794986343367,"score_gpt":0.3051586580936337,"score_spread":0.27308070823020003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589126456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28560385,0.0011084132,0.70433915,0.0011840219,0.0001569073,0.00019365989,0.00037364895,0.00072776346,0.0063126124],"genre_scores_gemma":[0.9912956,0.00008026752,0.0076495735,0.000039623483,0.000022157637,0.00003851487,0.000058930753,0.000020192043,0.00079513964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992792,0.00020177137,0.00003458323,0.00015957135,0.00011593674,0.00020902474],"domain_scores_gemma":[0.9969549,0.0018055282,0.0004438093,0.00013440284,0.00044921294,0.00021208574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001659628,0.001148611,0.0021769663,0.00088588597,0.00055868516,0.0015738814,0.0016591193,0.0018130117,0.0030564622],"category_scores_gemma":[0.004409751,0.0008492732,0.00058351783,0.0006284644,0.00088740746,0.0014505445,0.0010441327,0.0012583085,0.00026991937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019487523,0.000052594012,0.00028641662,0.00005305016,0.000032898624,0.00004985184,0.000023490607,0.99127686,0.001108485,0.0015744416,0.00048398884,0.004863006],"study_design_scores_gemma":[0.00001428775,0.000028652794,0.00023083374,0.0000027987653,0.000009467486,0.000007027196,0.000005433553,0.9988686,0.00011585783,0.00068171625,0.000031770847,0.0000036225067],"about_ca_topic_score_codex":0.008806263,"about_ca_topic_score_gemma":0.005696276,"teacher_disagreement_score":0.008806263,"about_ca_system_score_codex":0.0017042293,"about_ca_system_score_gemma":0.0015335848,"threshold_uncertainty_score":0.017509997},"labels":[],"label_agreement":null},{"id":"W2589443969","doi":"10.1108/jqme-07-2015-0030","title":"Optimal CBM policy with two sampling intervals","year":2017,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Reliability engineering; Weibull distribution; Condition-based maintenance; Reliability (semiconductor); Residual; Interval (graph theory); Sampling (signal processing); Importance sampling; Statistics; Hazard; Preventive maintenance; Sensitivity (control systems); Computer science; Engineering; Mathematical optimization; Mathematics; Monte Carlo method; Algorithm","score_opus":0.026692761526110474,"score_gpt":0.3153177090883588,"score_spread":0.2886249475622483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589443969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053940095,0.0003818622,0.9426404,0.00027630982,0.000054160915,0.00013763922,0.00006025951,0.00033321162,0.00217609],"genre_scores_gemma":[0.9440932,0.0001236815,0.054474335,0.00005213896,0.000028236998,0.00009991295,0.00004921531,0.000019800422,0.0010595169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867415,0.00036584743,0.00006407063,0.00035091158,0.0003543904,0.000190614],"domain_scores_gemma":[0.99752945,0.0012654798,0.00047185487,0.00020014968,0.00035030313,0.00018276137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001643167,0.00070118206,0.0010558672,0.0005526359,0.00037816027,0.0008145715,0.0015578023,0.00085879915,0.0023108919],"category_scores_gemma":[0.005520577,0.00033198082,0.0005430153,0.00035437336,0.00073243986,0.0009144961,0.0008144439,0.0012108951,0.00020172723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005354171,0.00016525519,0.002411653,0.00016796711,0.000041631753,0.00016081183,0.00010812108,0.89844966,0.0054992577,0.02323759,0.0011371013,0.06808555],"study_design_scores_gemma":[0.000027439226,0.00009140453,0.00043033372,0.000010961845,0.000011795209,0.00004027175,0.000013698793,0.99496907,0.0008654652,0.0031933924,0.0003375928,0.00000859671],"about_ca_topic_score_codex":0.0051481714,"about_ca_topic_score_gemma":0.0020032886,"teacher_disagreement_score":0.0051481714,"about_ca_system_score_codex":0.0012747196,"about_ca_system_score_gemma":0.001726819,"threshold_uncertainty_score":0.010236442},"labels":[],"label_agreement":null},{"id":"W2589626016","doi":"10.1002/qre.2142","title":"Pattern‐based prognostic methodology for condition‐based maintenance using selected and weighted survival curves","year":2017,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prognostics; Survival analysis; Estimator; Reliability (semiconductor); Set (abstract data type); Covariate; Statistics; Computer science; Data mining; Reliability engineering; Mathematics; Engineering","score_opus":0.05793070159335487,"score_gpt":0.3303299445134436,"score_spread":0.2723992429200887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589626016","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037522737,0.00034926197,0.9598164,0.00014183906,0.000027692708,0.00012323968,0.00086433766,0.00073907914,0.0004154451],"genre_scores_gemma":[0.738802,0.0003378102,0.25626138,0.00006514838,0.00007777626,0.0003885191,0.002775295,0.00011951281,0.0011725205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987208,0.0003908658,0.00011892538,0.00036572258,0.0003114222,0.0000922062],"domain_scores_gemma":[0.9936493,0.0035623314,0.0008456753,0.00065539393,0.0011276369,0.00015967514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003920279,0.00081293425,0.00097208266,0.003322188,0.00028981498,0.0009907023,0.0015857108,0.00083140266,0.0020236077],"category_scores_gemma":[0.013738269,0.00033865904,0.0010979851,0.0019364357,0.00034144035,0.0013540282,0.0010496511,0.0008791772,0.00046447245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023915501,0.00012969028,0.04079968,0.00023724617,0.0002781178,0.00016901194,0.00014955502,0.6460082,0.0021306195,0.0089412555,0.0023284962,0.298589],"study_design_scores_gemma":[0.000008540964,0.000042139964,0.0022979034,0.000013495769,0.000017599736,0.0000429991,0.000013672378,0.9901398,0.00043702903,0.0064048194,0.00057249685,0.000009472883],"about_ca_topic_score_codex":0.0033344976,"about_ca_topic_score_gemma":0.0024328944,"teacher_disagreement_score":0.003920279,"about_ca_system_score_codex":0.0006154543,"about_ca_system_score_gemma":0.00083110324,"threshold_uncertainty_score":0.020732641},"labels":[],"label_agreement":null},{"id":"W2589721556","doi":"10.1080/00207543.2017.1290295","title":"Selective maintenance optimisation for series-parallel systems alternating missions and scheduled breaks with stochastic durations","year":2017,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Reliability (semiconductor); Maintenance actions; Component (thermodynamics); Reliability engineering; Series (stratigraphy); Mathematical optimization; Stochastic process; Imperfect; Stochastic modelling; Computer science; Duration (music); Engineering; Operations research; Mathematics; Statistics","score_opus":0.04950199809601195,"score_gpt":0.3453118151378958,"score_spread":0.2958098170418838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589721556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38288423,0.0006230786,0.61098915,0.0002543669,0.000028577659,0.00009449007,0.00019944973,0.00016189451,0.00476482],"genre_scores_gemma":[0.98059607,0.00016719772,0.01700256,0.000017371483,0.0000115857065,0.000079537494,0.00008230714,0.00002643984,0.002016873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996643,0.000113413196,0.000017820861,0.00006936527,0.000067751695,0.000067230096],"domain_scores_gemma":[0.9987884,0.0008162736,0.00021625095,0.000043367694,0.000072764495,0.00006303449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010124891,0.0007700256,0.000842552,0.00048555856,0.00027150178,0.0005593968,0.0007252037,0.00064074,0.0015403559],"category_scores_gemma":[0.0019124409,0.00046067903,0.00078556116,0.0005258512,0.00062482664,0.0004893764,0.00058341667,0.00059481943,0.00010990328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030589472,0.000012930316,0.00016085204,0.000019473911,0.000011497402,0.0000252552,0.000009971431,0.9954906,0.00054298283,0.0013159548,0.000056675748,0.0023231772],"study_design_scores_gemma":[0.000011752886,0.000060170725,0.00028104516,0.0000024063975,0.000007081752,0.000013535954,0.000009182341,0.9974438,0.00021181935,0.0018613999,0.000095449876,0.0000024918295],"about_ca_topic_score_codex":0.004004907,"about_ca_topic_score_gemma":0.0023744917,"teacher_disagreement_score":0.004004907,"about_ca_system_score_codex":0.00073401234,"about_ca_system_score_gemma":0.0006560992,"threshold_uncertainty_score":0.007963181},"labels":[],"label_agreement":null},{"id":"W2594428476","doi":"10.1016/j.jmsy.2017.02.005","title":"Optimizing upgrade and imperfect preventive maintenance in failure-prone second-hand systems","year":2017,"lang":"en","type":"article","venue":"Journal of Manufacturing Systems","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Upgrade; Preventive maintenance; Reliability engineering; Imperfect; Reliability (semiconductor); Engineering; Computer science; Risk analysis (engineering); Operations research; Business","score_opus":0.007477820561850925,"score_gpt":0.20944413519885313,"score_spread":0.2019663146370022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594428476","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77297425,0.000781123,0.22076131,0.0003161,0.000062424886,0.00008245343,0.00014141876,0.0004560158,0.004424937],"genre_scores_gemma":[0.9939937,0.000047705886,0.005200691,0.000012378207,0.000008566018,0.000010268885,0.000030049014,0.000016734111,0.0006799064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995241,0.00013510408,0.000021516673,0.00007127502,0.0000953205,0.00015267513],"domain_scores_gemma":[0.9985422,0.00076685456,0.00025887415,0.00013699962,0.00019742681,0.00009760659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010788379,0.00088156166,0.0013112637,0.00071496464,0.0004116134,0.00082082994,0.00094845495,0.0008390081,0.0012933121],"category_scores_gemma":[0.003523343,0.00051374955,0.00039378105,0.00043047706,0.000426457,0.0007001194,0.00060249853,0.00060102023,0.00015882435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001541715,0.000064553635,0.0008333861,0.00003971311,0.000024811998,0.00005306114,0.000015297632,0.9846298,0.0024576688,0.0005475201,0.00020891556,0.010971053],"study_design_scores_gemma":[0.000013030628,0.00012708644,0.0011590555,0.000004038364,0.000021637781,0.000029825613,0.000011462503,0.99718225,0.00077421026,0.0005875566,0.00008487263,0.0000050223316],"about_ca_topic_score_codex":0.0050898846,"about_ca_topic_score_gemma":0.0051650573,"teacher_disagreement_score":0.0050898846,"about_ca_system_score_codex":0.00079017726,"about_ca_system_score_gemma":0.0010136425,"threshold_uncertainty_score":0.010120511},"labels":[],"label_agreement":null},{"id":"W2596419481","doi":"","title":"Network reliability simulation with extension to Marshall-Olkin copula-based dependent failures","year":2014,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal","funders":"","keywords":"Estimator; Computer science; Importance sampling; Bounded function; Copula (linguistics); Mathematical optimization; Monte Carlo method; Algorithm; Approximation algorithm; Mathematics; Statistics","score_opus":0.00864678301463268,"score_gpt":0.20762596298704683,"score_spread":0.19897917997241416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596419481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15449108,0.0002842651,0.8291987,0.0004189252,0.00013679963,0.00016880599,0.0007224715,0.001153914,0.013424962],"genre_scores_gemma":[0.9177903,0.00020791568,0.07511226,0.000094875526,0.00004811975,0.00028540328,0.00037795008,0.00038604144,0.0056970203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995414,0.00022823391,0.000021114476,0.00007290934,0.00007032402,0.00006609374],"domain_scores_gemma":[0.9972493,0.0018708232,0.0001830889,0.00023597041,0.00034098906,0.000119809665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010933096,0.0007621693,0.0011314043,0.0006104364,0.0005201949,0.0006872893,0.0016329709,0.0012188746,0.0052273413],"category_scores_gemma":[0.0060306974,0.0006435016,0.0011485333,0.00075450307,0.0004717415,0.0010401352,0.001010999,0.0017978256,0.0005501985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011738633,0.000014843593,0.00013519739,0.000008595714,0.00000817672,0.000015709147,0.000011413912,0.99688894,0.00010878987,0.0016597589,0.00014282455,0.0009939142],"study_design_scores_gemma":[0.000001589583,0.0000025153145,0.000017018068,6.800958e-7,0.0000013295961,0.0000012462533,0.000001124025,0.99956197,0.000043505588,0.00031834183,0.000049836515,8.249975e-7],"about_ca_topic_score_codex":0.018394625,"about_ca_topic_score_gemma":0.007957134,"teacher_disagreement_score":0.018394625,"about_ca_system_score_codex":0.00086552807,"about_ca_system_score_gemma":0.0011857123,"threshold_uncertainty_score":0.03657508},"labels":[],"label_agreement":null},{"id":"W2598217660","doi":"10.1155/2017/4682409","title":"Optimal Cost‐Effective Maintenance Policy for a Helicopter Gearbox Early Fault Detection under Varying Load","year":2017,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Division of Graduate Education; University of Toronto; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Graduate Research and Innovation Projects of Jiangsu Province; China Scholarship Council","keywords":"Unobservable; Control limits; Bayesian probability; Multivariate statistics; Fault (geology); Fault detection and isolation; Hidden Markov model; Control chart; Engineering; Hidden semi-Markov model; Computer science; Optimal maintenance; Markov chain; Partially observable Markov decision process; Minification; Mathematical optimization; Reliability engineering; Markov model; Process (computing); Artificial intelligence; Econometrics; Machine learning; Variable-order Markov model; Mathematics","score_opus":0.010743039190355106,"score_gpt":0.23897825268764486,"score_spread":0.22823521349728976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2598217660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17677265,0.00032263732,0.8194135,0.00030477287,0.00002822294,0.00005628636,0.00007356411,0.0004216412,0.002606703],"genre_scores_gemma":[0.9897254,0.00006219616,0.009589599,0.000015360705,0.0000060601537,0.000024541048,0.000020644085,0.000008602691,0.0005477392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997193,0.00005352004,0.000015930576,0.000071721886,0.00008344159,0.000056031422],"domain_scores_gemma":[0.99922645,0.00037085064,0.00017601103,0.000030278672,0.0001554359,0.00004089385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069156266,0.00045465838,0.00059182936,0.00039998544,0.00029533898,0.0006386817,0.00062203186,0.00058364886,0.0010447223],"category_scores_gemma":[0.0021289364,0.00022003162,0.00024396772,0.00021989089,0.00038920145,0.00057497877,0.0003808231,0.0005292058,0.000087233064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012540704,0.000049500373,0.00069135195,0.00004879163,0.000012161285,0.00004162439,0.000044762022,0.96321625,0.0047910106,0.0044256207,0.0004631396,0.026090436],"study_design_scores_gemma":[0.0000049535056,0.000017288641,0.00023650263,0.0000017630007,0.0000042397387,0.0000045913293,0.000003935691,0.99858105,0.0005530567,0.0005432859,0.000046555255,0.000002704837],"about_ca_topic_score_codex":0.009053313,"about_ca_topic_score_gemma":0.004679452,"teacher_disagreement_score":0.009053313,"about_ca_system_score_codex":0.00096587965,"about_ca_system_score_gemma":0.0010789093,"threshold_uncertainty_score":0.018001258},"labels":[],"label_agreement":null},{"id":"W259825525","doi":"10.1016/j.mcm.2006.01.018","title":"On a conjecture of optimal repair-replacement strategies for warranted products","year":2006,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Warranty; Limit (mathematics); Conjecture; Control limits; Product (mathematics); Mathematics; Time limit; Mathematical optimization; Computer science; Reliability engineering; Economics; Combinatorics; Engineering; Law","score_opus":0.010304931785741196,"score_gpt":0.1910634425280413,"score_spread":0.18075851074230012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W259825525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12371861,0.0027272317,0.78173554,0.02237701,0.00056739,0.00013526293,0.00078413705,0.00036845042,0.0675864],"genre_scores_gemma":[0.8824457,0.0030415005,0.095597215,0.002711171,0.0009730925,0.00029760448,0.00041677195,0.0002490105,0.014267887],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985026,0.0006261894,0.00009652396,0.00032023704,0.00022842959,0.00022607211],"domain_scores_gemma":[0.9701616,0.025327401,0.0013447084,0.0014832751,0.0011636603,0.0005193115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054109246,0.0012172346,0.0018030213,0.0010223292,0.0011013456,0.003243044,0.0023129915,0.0048767044,0.011217132],"category_scores_gemma":[0.04453139,0.0010248305,0.0016115392,0.0013694227,0.004626529,0.008636981,0.0020754011,0.004932913,0.0007630639],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001631969,0.000061419916,0.00041558337,0.00022557091,0.0000332308,0.000050148887,0.00020723074,0.06731845,0.0010561061,0.91208726,0.005754953,0.012626886],"study_design_scores_gemma":[0.000034695007,0.00005098552,0.0001737628,0.00003379073,0.00001419046,0.000032104897,0.000042238295,0.08544219,0.0002959249,0.91268593,0.0011811882,0.0000129723685],"about_ca_topic_score_codex":0.0018782761,"about_ca_topic_score_gemma":0.0012599935,"teacher_disagreement_score":0.011217132,"about_ca_system_score_codex":0.0021106096,"about_ca_system_score_gemma":0.0017364903,"threshold_uncertainty_score":0.037525117},"labels":[],"label_agreement":null},{"id":"W2600605616","doi":"","title":"Single Sampling Plan for Truncated Life Tests Based on Rayleigh Distribution","year":2013,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Rayleigh distribution; Mathematics; Sampling (signal processing); Plan (archaeology); Statistics; Acceptance sampling; Percentile; Selection (genetic algorithm); Distribution (mathematics); Product (mathematics); Sample (material); Rayleigh scattering; Sample size determination; Computer science; Mathematical analysis; Probability density function; Filter (signal processing); Geometry; Geography; Artificial intelligence","score_opus":0.12401117181265862,"score_gpt":0.3385998698884467,"score_spread":0.21458869807578806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600605616","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02152322,0.00009605993,0.9766,0.00004949252,0.000014894003,0.00020285604,0.00007300554,0.00031095944,0.001129492],"genre_scores_gemma":[0.6562479,0.00016135452,0.34038335,0.00009226853,0.000045536184,0.0008170022,0.00048664308,0.00011014614,0.0016558336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99452364,0.0023227164,0.00025683205,0.00067287334,0.0018071263,0.0004167641],"domain_scores_gemma":[0.98704576,0.008036452,0.0010551998,0.00089188723,0.0026205452,0.0003500941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049374793,0.00085814233,0.0008285058,0.0010156865,0.0004144613,0.0009038442,0.0015433684,0.00069277437,0.003146899],"category_scores_gemma":[0.0145946015,0.0003712481,0.0007624268,0.00060247927,0.0010037452,0.0011369793,0.0010030752,0.0012512673,0.0005307232],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018789223,0.00022427265,0.008402977,0.00032567463,0.00010866401,0.00065066665,0.00048252926,0.67237866,0.03633373,0.06504032,0.0034719352,0.21070169],"study_design_scores_gemma":[0.000064635475,0.00048300033,0.0015583457,0.00002601343,0.000025169666,0.0001789877,0.000038538044,0.97794193,0.0076339575,0.010935953,0.0010747305,0.0000386325],"about_ca_topic_score_codex":0.0020883388,"about_ca_topic_score_gemma":0.0013492075,"teacher_disagreement_score":0.0049374793,"about_ca_system_score_codex":0.0009271159,"about_ca_system_score_gemma":0.0015297346,"threshold_uncertainty_score":0.026112199},"labels":[],"label_agreement":null},{"id":"W2601490499","doi":"10.5267/j.dsl.2017.2.004","title":"Sensitivity analysis of repairable redundant system with switching failure and geometric reneging","year":2017,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sensitivity (control systems); Computer science; Reliability engineering; Mathematical optimization; Mathematics; Engineering","score_opus":0.007124138999549476,"score_gpt":0.22140360888150648,"score_spread":0.214279469881957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2601490499","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5704921,0.0020789378,0.41349182,0.00058227265,0.000081367616,0.00020009877,0.00045035282,0.00035984168,0.012263248],"genre_scores_gemma":[0.99591345,0.00023878894,0.0030167606,0.000023788305,0.000010428201,0.00003308085,0.000060268412,0.000017341303,0.0006860575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824417,0.00093281706,0.00004809483,0.00018066455,0.0002978064,0.00029646492],"domain_scores_gemma":[0.9930195,0.005599965,0.00058554375,0.00021342744,0.00045799432,0.00012355401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004400146,0.0013764093,0.0013338622,0.0017735974,0.00038908143,0.0011122767,0.0008727795,0.0010604801,0.0011994244],"category_scores_gemma":[0.007305948,0.0005294156,0.0014728045,0.0007813903,0.00094502594,0.0009767658,0.0010121989,0.0008328898,0.0000803553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003612974,0.000011915279,0.00032428166,0.000036654736,0.000038525584,0.00012216136,0.000019868128,0.99572855,0.0007596961,0.0019866368,0.00006919134,0.00086639466],"study_design_scores_gemma":[0.0000033669157,0.000037957503,0.00041356147,0.000005943761,0.000023864115,0.000027096365,0.000020031634,0.9978389,0.00039836523,0.001149979,0.00007202017,0.000008933873],"about_ca_topic_score_codex":0.0063649667,"about_ca_topic_score_gemma":0.0017314489,"teacher_disagreement_score":0.0063649667,"about_ca_system_score_codex":0.0017554848,"about_ca_system_score_gemma":0.0007025138,"threshold_uncertainty_score":0.023270488},"labels":[],"label_agreement":null},{"id":"W2603240292","doi":"10.1109/ram.2017.7889710","title":"Joint optimization of maintenance and production scheduling","year":2017,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Tardiness; Preventive maintenance; Scheduling (production processes); Computer science; Reliability engineering; Shutdown; Job shop scheduling; Real-time computing; Engineering; Operations management; Schedule","score_opus":0.01283708414475449,"score_gpt":0.20711642496194285,"score_spread":0.19427934081718837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2603240292","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10815751,0.0010759941,0.8782735,0.000528101,0.00012038187,0.00016665205,0.00036008377,0.0006812501,0.010636472],"genre_scores_gemma":[0.92369,0.000424226,0.06740405,0.00006487854,0.000067985384,0.00019932435,0.0003530769,0.00014063435,0.0076559167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885535,0.00030909982,0.000047063302,0.00024614765,0.0002519796,0.0002904336],"domain_scores_gemma":[0.99881893,0.00055221724,0.00021393999,0.00009668741,0.00017387592,0.00014435152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014741591,0.0014105855,0.0019170829,0.0010232189,0.00044994228,0.0015947334,0.001313453,0.0011416026,0.0030492914],"category_scores_gemma":[0.0030984357,0.0007596192,0.00084927026,0.0012087517,0.0007491208,0.0015069211,0.0010248803,0.0009845392,0.00044590543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009939061,0.000041831736,0.0004109516,0.00006260469,0.00003179418,0.000057716512,0.000017408254,0.981121,0.0012786902,0.0037123554,0.0006165544,0.012549624],"study_design_scores_gemma":[0.000013910279,0.000060061953,0.00040200283,0.0000035649064,0.000014024501,0.000021079568,0.000011597328,0.99544793,0.00043086862,0.0031249858,0.0004635186,0.000006447853],"about_ca_topic_score_codex":0.004619419,"about_ca_topic_score_gemma":0.0023945463,"teacher_disagreement_score":0.004619419,"about_ca_system_score_codex":0.001237592,"about_ca_system_score_gemma":0.0020660884,"threshold_uncertainty_score":0.010200858},"labels":[],"label_agreement":null},{"id":"W2605400039","doi":"10.5539/ijsp.v6n3p183","title":"Characterizations Based on Cumulative Entropy of the Last-order Statistics","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Cumulative distribution function; Maximum entropy probability distribution; Weibull distribution; Statistics; Entropy (arrow of time); Maximum entropy thermodynamics; Order statistic; Residual entropy; Principle of maximum entropy; Min entropy; Entropy rate; Statistical physics; Binary entropy function; Probability density function; Thermodynamics; Configuration entropy; Physics","score_opus":0.011770716739005102,"score_gpt":0.2540617800784587,"score_spread":0.2422910633394536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605400039","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08116151,0.0034835362,0.90562123,0.00032356175,0.00012022866,0.00004103859,0.00042567073,0.00020465397,0.008618661],"genre_scores_gemma":[0.95937216,0.002484349,0.03455138,0.00013661991,0.00045254276,0.000105212566,0.00048194494,0.00009327898,0.0023225506],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985361,0.00029490964,0.00011308562,0.00029220805,0.0005769091,0.0001867711],"domain_scores_gemma":[0.9906941,0.0062747356,0.0010390169,0.0006791524,0.0010357678,0.00027725828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022225915,0.0007393968,0.0009024755,0.0037191177,0.00055150036,0.0018299355,0.0010216662,0.0007798442,0.001517052],"category_scores_gemma":[0.009888788,0.00025208984,0.00083510816,0.0017752049,0.0020585486,0.0036185172,0.0011383557,0.0013578273,0.00020837087],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001382958,0.000062914834,0.0065178857,0.0002456054,0.00012111225,0.0003761694,0.0002591522,0.20434853,0.010884023,0.73932844,0.0012722885,0.036445595],"study_design_scores_gemma":[0.00001055681,0.00009602552,0.004502304,0.00006622662,0.00004350295,0.00041952427,0.00007215529,0.60366434,0.0038382707,0.38466096,0.0025258565,0.000100326855],"about_ca_topic_score_codex":0.0009123586,"about_ca_topic_score_gemma":0.00033772193,"teacher_disagreement_score":0.0037191177,"about_ca_system_score_codex":0.0011180674,"about_ca_system_score_gemma":0.000704059,"threshold_uncertainty_score":0.011754334},"labels":[],"label_agreement":null},{"id":"W2606440416","doi":"10.14248/jkosse.2016.12.2.047","title":"Review of Studies on V-METRIC Related Models","year":2016,"lang":"en","type":"article","venue":"Journal of the Korea Society of Systems Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metric (unit); Operations research; Computer science; Inventory theory; Operations management; Reliability engineering; Inventory control; Engineering","score_opus":0.01671215736666305,"score_gpt":0.21804066286813534,"score_spread":0.2013285055014723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606440416","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00668915,0.67413056,0.2483551,0.0036501877,0.0016998303,0.000095462834,0.0010292518,0.00045126423,0.06389919],"genre_scores_gemma":[0.12663813,0.7872696,0.068735614,0.0010490922,0.002227971,0.0001943218,0.0017411829,0.00025649206,0.011887539],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99928004,0.00024178435,0.00006476791,0.0001232786,0.00023620458,0.0000538922],"domain_scores_gemma":[0.99726176,0.0018806449,0.00019312905,0.00012630297,0.0004934207,0.000044710465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014325414,0.0014477136,0.0013583396,0.0023209895,0.0004994796,0.002424763,0.002647879,0.0012519177,0.00709076],"category_scores_gemma":[0.0050999876,0.0006577088,0.0014810615,0.0050021755,0.00056310295,0.002836001,0.0008927517,0.0013884195,0.0020830035],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008670173,0.00017533902,0.0022231967,0.005621558,0.0002960202,0.0003009986,0.00016189214,0.18514384,0.000618446,0.28169405,0.05601052,0.46766734],"study_design_scores_gemma":[0.00002494974,0.00016721344,0.0017819514,0.003722327,0.00031406627,0.0006632406,0.00033603545,0.34708497,0.0006698202,0.19283915,0.45226133,0.00013506223],"about_ca_topic_score_codex":0.007632344,"about_ca_topic_score_gemma":0.0044382275,"teacher_disagreement_score":0.007632344,"about_ca_system_score_codex":0.0014012707,"about_ca_system_score_gemma":0.0017252621,"threshold_uncertainty_score":0.02372092},"labels":[],"label_agreement":null},{"id":"W2606918641","doi":"10.1115/1.4036428","title":"Risk-Based Maintenance Planning for Deteriorating Pressure Vessels With Multiple Defects","year":2017,"lang":"en","type":"article","venue":"Journal of Pressure Vessel Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Pressure vessel; Leak; Reliability engineering; Process (computing); Planned maintenance; Oil refinery; Computer science; Risk analysis (engineering); Engineering; Business; Mechanical engineering","score_opus":0.008365015107557975,"score_gpt":0.23212943329128755,"score_spread":0.22376441818372958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606918641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13181065,0.0006978923,0.8623173,0.0006186395,0.000035872825,0.0001501032,0.00017866211,0.00027030715,0.003920575],"genre_scores_gemma":[0.9208228,0.0003653334,0.07582205,0.000055065328,0.000025266369,0.00014340648,0.00016472208,0.000054729266,0.0025466965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993592,0.00024866822,0.00002206073,0.00011764079,0.00012988957,0.00012265696],"domain_scores_gemma":[0.99858826,0.000913721,0.00019994944,0.000030132482,0.00014939574,0.00011851811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015901073,0.0010687644,0.0011815847,0.00090949,0.00044584784,0.0010920527,0.0011593711,0.0010835134,0.002045946],"category_scores_gemma":[0.0027653056,0.00089201366,0.00076577335,0.0005642489,0.00074424705,0.00089257775,0.00093298394,0.0010237527,0.00013810572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027615524,0.00001682579,0.00031457402,0.000017758648,0.000010676778,0.000048902584,0.000019751573,0.9938619,0.00034123575,0.0017444206,0.0001487844,0.0034475587],"study_design_scores_gemma":[0.0000047622484,0.000021203907,0.00013190028,0.0000029300384,0.0000059364434,0.000008044227,0.000010475264,0.99843794,0.00011802432,0.0011581235,0.00009709915,0.0000035612873],"about_ca_topic_score_codex":0.014807887,"about_ca_topic_score_gemma":0.010840807,"teacher_disagreement_score":0.014807887,"about_ca_system_score_codex":0.0018326001,"about_ca_system_score_gemma":0.001961415,"threshold_uncertainty_score":0.029443383},"labels":[],"label_agreement":null},{"id":"W2612617210","doi":"","title":"Engineering Systems Reliability, Safety, and Maintenance: An Integrated Approach","year":2017,"lang":"en","type":"book","venue":"CERN Document Server (European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Reliability engineering; Reliability (semiconductor); Computer science; Engineering; Physics","score_opus":0.01621570769655114,"score_gpt":0.22583521193732653,"score_spread":0.20961950424077538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612617210","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015895711,0.15720835,0.31079948,0.012178985,0.004428739,0.0002277104,0.00035766457,0.002262497,0.5109469],"genre_scores_gemma":[0.033847585,0.20031412,0.23128587,0.0045897597,0.0053923125,0.00041217273,0.0008924863,0.0016903076,0.5215754],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982987,0.00018023969,0.000052759773,0.00013163434,0.001274502,0.00006222077],"domain_scores_gemma":[0.99931705,0.00020218334,0.000038207956,0.00008855112,0.00029409703,0.000059877726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009100409,0.001692213,0.001070487,0.0022007779,0.00065646356,0.0045781694,0.0017762352,0.0018038077,0.016541189],"category_scores_gemma":[0.0013962422,0.00085716613,0.00061101874,0.003172407,0.0013019677,0.0060973885,0.0023214435,0.0034632408,0.011652089],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025086776,0.00012808629,0.0001499989,0.00093749043,0.000046565085,0.000097269665,0.0002876357,0.010047001,0.0023739138,0.21322864,0.23462322,0.53805506],"study_design_scores_gemma":[0.000009148923,0.0000485983,0.00027394053,0.00047846293,0.000029180137,0.00034846965,0.000105709405,0.0070532616,0.0007531134,0.13713455,0.8537391,0.000026429998],"about_ca_topic_score_codex":0.0017655746,"about_ca_topic_score_gemma":0.0026410136,"teacher_disagreement_score":0.016541189,"about_ca_system_score_codex":0.0020074788,"about_ca_system_score_gemma":0.0018837658,"threshold_uncertainty_score":0.05533576},"labels":[],"label_agreement":null},{"id":"W2612871222","doi":"10.3166/jesa.49.559-578","title":"Considération d’un indicateur d’efficacité énergétique pour la prise de décision en maintenance. De sa définition au fondement de son pronostic","year":2016,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Welfare economics; Economics; Philosophy","score_opus":0.009294637538150876,"score_gpt":0.22760659241475276,"score_spread":0.21831195487660188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612871222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19039778,0.005620466,0.7707526,0.0025006116,0.00046142327,0.00013877553,0.00037446962,0.00041429143,0.029339511],"genre_scores_gemma":[0.94347525,0.0016732253,0.04805619,0.00016386923,0.00023299313,0.00008516517,0.00019978645,0.0001144402,0.0059990343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972276,0.001089006,0.00015446657,0.00039799535,0.001018358,0.0001126398],"domain_scores_gemma":[0.9874669,0.010095385,0.00070432655,0.0003717607,0.0012674711,0.000094247735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004309277,0.0018050075,0.0011287186,0.0016925676,0.00040937934,0.0033943807,0.0010837265,0.0022501799,0.0047822385],"category_scores_gemma":[0.01866951,0.00043787112,0.00078686996,0.00092265697,0.0012056642,0.002782446,0.0007833369,0.001480012,0.00069544086],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016035682,0.0004153451,0.014224485,0.0013203694,0.0005392874,0.00042272714,0.00041137578,0.57790405,0.10475498,0.038891956,0.002272779,0.25723916],"study_design_scores_gemma":[0.000070329785,0.0013865302,0.014329835,0.000305762,0.00024947154,0.0005779805,0.0002626004,0.90101373,0.05249939,0.020971961,0.008193037,0.00013931793],"about_ca_topic_score_codex":0.0017267253,"about_ca_topic_score_gemma":0.0014793742,"teacher_disagreement_score":0.0047822385,"about_ca_system_score_codex":0.00080099446,"about_ca_system_score_gemma":0.00048806597,"threshold_uncertainty_score":0.022789955},"labels":[],"label_agreement":null},{"id":"W2614002522","doi":"","title":"Benders Decomposition for Production Routing under Demand Uncertainty","year":2012,"lang":"fr","type":"article","venue":"Les Cahiers du GERAD","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Mathematical optimization; Production (economics); Generalization; Routing (electronic design automation); Pareto principle; Upper and lower bounds; Product (mathematics); Time horizon; Branch and bound; Decomposition; Branch and cut; Computer science; Mathematics; Operations research; Linear programming; Economics","score_opus":0.01087848835570577,"score_gpt":0.23200012419225466,"score_spread":0.2211216358365489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2614002522","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052377535,0.00032642108,0.98750883,0.0002568545,0.000037847727,0.00006445998,0.0002273076,0.00011894702,0.0062215473],"genre_scores_gemma":[0.26330578,0.002162861,0.7082476,0.00030293738,0.00022375728,0.00079741556,0.0014263982,0.00032239966,0.023210712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999067,0.00040626308,0.00004128625,0.00012801177,0.00023548742,0.00012199986],"domain_scores_gemma":[0.9992386,0.0004521824,0.000104965955,0.00005197812,0.00010477383,0.000047486705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021370142,0.0019519709,0.0012444136,0.0011763026,0.0005640249,0.0014585576,0.00085084984,0.0014232539,0.008068828],"category_scores_gemma":[0.0026253772,0.0011550395,0.0016457081,0.0013489332,0.0007855956,0.001632032,0.0010187819,0.0024699876,0.0011370465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032365708,0.000026132377,0.00013506305,0.00006074505,0.000026652011,0.000045459255,0.000037929345,0.90690035,0.00082672574,0.07756744,0.001322625,0.013018495],"study_design_scores_gemma":[0.000011211476,0.000014632383,0.00006323472,0.00001303539,0.0000078151315,0.000010235109,0.00001094817,0.9251826,0.00020514132,0.0728291,0.0016454228,0.0000066729035],"about_ca_topic_score_codex":0.0047471165,"about_ca_topic_score_gemma":0.004068462,"teacher_disagreement_score":0.008068828,"about_ca_system_score_codex":0.0019963908,"about_ca_system_score_gemma":0.0017651074,"threshold_uncertainty_score":0.026992917},"labels":[],"label_agreement":null},{"id":"W2617293968","doi":"10.1109/tr.2017.2703111","title":"Model Mis-Specification Analyses of Weibull and Gamma Models Based on One-Shot Device Test Data","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Weibull distribution; Reliability (semiconductor); Specification; Computer science; Akaike information criterion; Reliability engineering; Inference; Context (archaeology); Statistics; Mathematics; Engineering; Artificial intelligence; Machine learning; Power (physics)","score_opus":0.21600004372168258,"score_gpt":0.33482666624991475,"score_spread":0.11882662252823217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617293968","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30876437,0.0011537619,0.6869152,0.00027175894,0.000061988816,0.00014084988,0.000432527,0.00056060683,0.0016988954],"genre_scores_gemma":[0.969625,0.00025712905,0.028427653,0.00010040935,0.000025375402,0.000111141315,0.0007806982,0.00007423581,0.00059832603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9905014,0.004127407,0.00055925467,0.0018545076,0.0024493444,0.00050812395],"domain_scores_gemma":[0.8587689,0.11614189,0.008799315,0.011103769,0.0047114165,0.00047475784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017947465,0.0012385189,0.0014483421,0.0018815723,0.0004958504,0.0012338503,0.0021804557,0.0013804586,0.0010695073],"category_scores_gemma":[0.0763983,0.000531571,0.0021395884,0.0013647595,0.001458129,0.0029358116,0.0015958939,0.002200577,0.00015912145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007874401,0.00023628687,0.093516976,0.00062596775,0.0010873483,0.0016179925,0.0011735345,0.75465536,0.009768764,0.046632618,0.0015396398,0.08835807],"study_design_scores_gemma":[0.000033383567,0.00040291488,0.033971623,0.00008419178,0.00034407293,0.0008975243,0.0003809349,0.92622644,0.010827846,0.025462499,0.0012147005,0.00015383791],"about_ca_topic_score_codex":0.003959674,"about_ca_topic_score_gemma":0.0032142298,"teacher_disagreement_score":0.017947465,"about_ca_system_score_codex":0.0010704257,"about_ca_system_score_gemma":0.0011001958,"threshold_uncertainty_score":0.0949164},"labels":[],"label_agreement":null},{"id":"W2624372375","doi":"10.1007/s00170-017-0580-y","title":"Joint optimization of production and maintenance planning with an environmental impact study","year":2017,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Optimal maintenance; Production (economics); Reliability engineering; Robustness (evolution); Failure rate; Production planning; Corrective maintenance; Context (archaeology); Maintenance actions; Production manager; Degradation (telecommunications); Production rate; Sensitivity (control systems); Preventive maintenance; Engineering; Computer science; Operations research; Manufacturing engineering","score_opus":0.007917802637552786,"score_gpt":0.24016655260289246,"score_spread":0.23224874996533967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2624372375","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5082348,0.0013231456,0.46953025,0.0007455829,0.00014052671,0.0002284912,0.00058619335,0.00034149046,0.018869465],"genre_scores_gemma":[0.9651088,0.00022039053,0.03118339,0.000043818,0.000031359185,0.00009117827,0.0001928562,0.0000760393,0.0030520586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991511,0.00039204909,0.00003142211,0.00010501132,0.00017554787,0.00014476989],"domain_scores_gemma":[0.9974584,0.0018933928,0.00018911777,0.000096427975,0.00027482564,0.00008800389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002119838,0.001333664,0.0017007703,0.0015283354,0.0005382462,0.0015316841,0.0009251297,0.0015820904,0.002417887],"category_scores_gemma":[0.004833408,0.0012369368,0.0015661214,0.0014595733,0.0007400573,0.0012570824,0.0008529762,0.00085806294,0.00015231119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018165374,0.00001707394,0.00019151771,0.00000722721,0.000015830981,0.000015166163,0.0000028558024,0.998412,0.000085560096,0.00022572438,0.000031760264,0.0009771321],"study_design_scores_gemma":[0.000005311251,0.000027627675,0.000384344,0.0000013688206,0.00001596896,0.000004454681,0.000005957101,0.99910504,0.00013952154,0.00024468443,0.000062042156,0.0000036044362],"about_ca_topic_score_codex":0.017368453,"about_ca_topic_score_gemma":0.013079547,"teacher_disagreement_score":0.017368453,"about_ca_system_score_codex":0.0010396556,"about_ca_system_score_gemma":0.0018940504,"threshold_uncertainty_score":0.034534752},"labels":[],"label_agreement":null},{"id":"W2624804828","doi":"","title":"Availability based maintenance scheduling in Domestic Hot water of HVAC system","year":2016,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mean time between failures; Reliability engineering; Schedule; Maintenance engineering; HVAC; Reliability (semiconductor); Scheduling (production processes); Engineering; Failure rate; Preventive maintenance; Computer science; Power (physics); Air conditioning; Operations management","score_opus":0.01167392972720342,"score_gpt":0.234687919072667,"score_spread":0.2230139893454636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2624804828","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67484564,0.0005355093,0.31979147,0.00018065573,0.000042954325,0.00013068778,0.00022880611,0.00033362463,0.003910682],"genre_scores_gemma":[0.98809195,0.00005163323,0.011462202,0.0000060930647,0.0000058587593,0.000018606816,0.00004353593,0.000009034324,0.00031113354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968016,0.0000775009,0.000020315485,0.00008367692,0.00007828289,0.000060073737],"domain_scores_gemma":[0.9992649,0.0003626499,0.00014232013,0.000036261546,0.00014280481,0.000051005136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052802596,0.0002579764,0.00034132646,0.00042344758,0.00040087808,0.0005333532,0.00049388147,0.0002501579,0.000893782],"category_scores_gemma":[0.001600328,0.00017711359,0.000267531,0.00033925055,0.00019825943,0.0004436289,0.00029822276,0.0002889743,0.000060113194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017788191,0.0000611416,0.0061689964,0.000097710334,0.000029606437,0.0001548975,0.0001470411,0.9247661,0.012414394,0.004413697,0.00056709105,0.0510014],"study_design_scores_gemma":[0.000007429434,0.00006981042,0.003038698,0.000004412297,0.000011779373,0.000032033768,0.00004561465,0.9930072,0.0021048568,0.00130752,0.00036405155,0.0000065890413],"about_ca_topic_score_codex":0.009277292,"about_ca_topic_score_gemma":0.0070159333,"teacher_disagreement_score":0.009277292,"about_ca_system_score_codex":0.00083143194,"about_ca_system_score_gemma":0.0007841569,"threshold_uncertainty_score":0.018446565},"labels":[],"label_agreement":null},{"id":"W2691602913","doi":"10.1115/1.4037123","title":"Reliability Optimization Allocation Method for Multifunction Systems With Multistate Units Based on Goal-Oriented Methodology","year":2017,"lang":"en","type":"article","venue":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministry of Education of the People's Republic of China; Ministry of Industry and Information Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Reliability (semiconductor); Optimal allocation; Reliability engineering; Computer science; Process (computing); Mathematical optimization; Optimization problem; Power (physics); Engineering; Mathematics; Algorithm","score_opus":0.016578979974203188,"score_gpt":0.2516002759133315,"score_spread":0.23502129593912832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2691602913","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021602595,0.00007712315,0.99632,0.00003380413,0.000008253872,0.000018628985,0.0000065701,0.000064061176,0.0013113223],"genre_scores_gemma":[0.46635166,0.0005601759,0.5283928,0.00012176181,0.000047771005,0.00037095224,0.000099663645,0.00015779978,0.0038973535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938047,0.00019590088,0.000029969031,0.00010302517,0.00022764353,0.00006290306],"domain_scores_gemma":[0.9997415,0.00009615427,0.00003931266,0.000016992999,0.000091209535,0.000014845889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011027436,0.0009850463,0.0006683118,0.0008389857,0.00046877228,0.0007662245,0.0008518651,0.0005243119,0.0019361066],"category_scores_gemma":[0.00089423003,0.00038363284,0.0008888943,0.0005590999,0.0004957788,0.001156365,0.00069277803,0.0007487469,0.0002670458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036740144,0.000046255547,0.00046189805,0.0001444442,0.000046834815,0.000052785155,0.000108203836,0.88345003,0.0052474276,0.040828403,0.0010165692,0.068560384],"study_design_scores_gemma":[0.0000090998465,0.000033746117,0.000114969276,0.000010371707,0.000013613317,0.00002004856,0.000016306934,0.9891192,0.000891527,0.008463123,0.0012994738,0.000008483081],"about_ca_topic_score_codex":0.0030470716,"about_ca_topic_score_gemma":0.0023362092,"teacher_disagreement_score":0.0030470716,"about_ca_system_score_codex":0.00089640747,"about_ca_system_score_gemma":0.0013176479,"threshold_uncertainty_score":0.0065039396},"labels":[],"label_agreement":null},{"id":"W2707849037","doi":"10.1016/j.neucom.2016.11.096","title":"Fluctuation analysis of instantaneous availability under specific distribution","year":2017,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Distribution (mathematics); Computer science; Unit (ring theory); Applied mathematics; Differential (mechanical device); Differential equation; Mathematics; Mathematical optimization; Mathematical analysis; Physics; Thermodynamics","score_opus":0.013917740020790801,"score_gpt":0.22639682759256377,"score_spread":0.21247908757177297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2707849037","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64379233,0.0003295639,0.35372505,0.00015426255,0.000028434863,0.000012762053,0.00014269186,0.00017312211,0.0016417257],"genre_scores_gemma":[0.9985084,0.000040207255,0.00127579,0.0000033715532,0.0000048545176,0.0000027109954,0.000028899665,0.000006273771,0.0001294309],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983215,0.000041361785,0.00000754806,0.00004078894,0.00004353956,0.000034633897],"domain_scores_gemma":[0.99925405,0.00045693168,0.00009157897,0.00004852913,0.000119136836,0.000029699568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053974916,0.0002237374,0.0002888459,0.00041030173,0.00017228005,0.0003224909,0.00036012713,0.0002232158,0.00040599832],"category_scores_gemma":[0.0023130819,0.000103313134,0.00023517259,0.00051087816,0.0003358851,0.0005722318,0.00019546099,0.0002836469,0.000037988022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030557555,0.00003577605,0.009398509,0.00008611209,0.00006526487,0.00024351224,0.00010231986,0.939558,0.012931945,0.013429876,0.0006625248,0.023180483],"study_design_scores_gemma":[7.111204e-7,0.000009942534,0.0020434929,8.818073e-7,0.00000400997,0.000020306516,0.000008206405,0.99663216,0.00039565229,0.00084879855,0.00003320882,0.0000026198722],"about_ca_topic_score_codex":0.0024475583,"about_ca_topic_score_gemma":0.0015963004,"teacher_disagreement_score":0.0024475583,"about_ca_system_score_codex":0.00045879153,"about_ca_system_score_gemma":0.00020039295,"threshold_uncertainty_score":0.0048666},"labels":[],"label_agreement":null},{"id":"W2724744744","doi":"10.1016/j.ejor.2017.06.062","title":"Production and replacement policies for a deteriorating manufacturing system under random demand and quality","year":2017,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Hamilton–Jacobi–Bellman equation; Markov decision process; Production (economics); Mathematical optimization; Time horizon; Computer science; Sensitivity (control systems); Production planning; Quality (philosophy); Markov process; Order (exchange); Bellman equation; Operations research; Mathematics; Economics; Engineering; Microeconomics","score_opus":0.09305910125818627,"score_gpt":0.35965701011906903,"score_spread":0.26659790886088275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2724744744","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8660342,0.0011739228,0.12519936,0.0015240187,0.00008915867,0.0001943071,0.0009400626,0.0005402569,0.0043047103],"genre_scores_gemma":[0.9919607,0.00019819665,0.006114568,0.00005100792,0.000023363085,0.000031207797,0.00014579359,0.000037546768,0.0014375884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880946,0.00045430567,0.00007607858,0.00019243006,0.00012608529,0.00034165315],"domain_scores_gemma":[0.98691815,0.008904895,0.0020358881,0.0005026417,0.0009921929,0.00064617826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003951224,0.00094911625,0.0015385784,0.0011453851,0.00070867187,0.0017509608,0.0022351916,0.0022995195,0.0033756515],"category_scores_gemma":[0.009079927,0.0009456691,0.00076202775,0.001109653,0.0013947118,0.0012608586,0.0008468739,0.0012637813,0.000395483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048688438,0.00005578503,0.0009433438,0.00007825623,0.000027782435,0.0001638341,0.00005618927,0.98947525,0.0023084662,0.0029074443,0.00051511085,0.002981628],"study_design_scores_gemma":[0.000048055503,0.00014061129,0.0015685383,0.00001249896,0.000042463467,0.000050978517,0.00004377035,0.9946637,0.0005712289,0.0027203741,0.0001220582,0.000015755222],"about_ca_topic_score_codex":0.005828486,"about_ca_topic_score_gemma":0.0029543992,"teacher_disagreement_score":0.005828486,"about_ca_system_score_codex":0.0017940011,"about_ca_system_score_gemma":0.0011356614,"threshold_uncertainty_score":0.020896316},"labels":[],"label_agreement":null},{"id":"W2730354864","doi":"10.1109/tr.2017.2715172","title":"Combined Redundancy Allocation and Maintenance Planning Using a Two-Stage Stochastic Programming Model for Multiple Component Systems","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Polytechnique Montréal; National Science Foundation","keywords":"Redundancy (engineering); Component (thermodynamics); Computer science; Mathematical optimization; Reliability engineering; Stochastic programming; Preventive maintenance; Stochastic modelling; Optimal maintenance; Maintenance actions; Reliability (semiconductor); Selection (genetic algorithm); Maintenance engineering; Engineering; Mathematics; Machine learning","score_opus":0.035286667558733845,"score_gpt":0.27629863950074685,"score_spread":0.24101197194201301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2730354864","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010768156,0.00023732918,0.98648167,0.00014188165,0.000025727477,0.000044987988,0.000069143505,0.000102342834,0.0021288267],"genre_scores_gemma":[0.7747386,0.001109414,0.21138039,0.00011382847,0.00009688748,0.00073086296,0.00027831027,0.00011226375,0.01143942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932444,0.00025132307,0.000025733294,0.0001048324,0.00020030075,0.000093440154],"domain_scores_gemma":[0.9994696,0.00033133992,0.00007102922,0.000020156658,0.00007636743,0.00003154543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011434632,0.0014542588,0.0015225753,0.0006955373,0.0004343915,0.0012396342,0.0015618927,0.0012603962,0.002237241],"category_scores_gemma":[0.0012510909,0.0011893122,0.0018291118,0.0010328807,0.0006597778,0.0010360152,0.00087603513,0.0013933085,0.0002529484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009247754,0.000010758072,0.000054198543,0.000013427701,0.000008409703,0.000016428883,0.0000079305155,0.9957618,0.00019914635,0.0022865634,0.00007036914,0.0015616901],"study_design_scores_gemma":[0.0000028699887,0.0000091980855,0.00001776591,0.000001334455,0.0000036218523,0.0000030678812,0.0000014811244,0.99911493,0.000051115705,0.000711464,0.00008082086,0.0000023182108],"about_ca_topic_score_codex":0.009883196,"about_ca_topic_score_gemma":0.009049083,"teacher_disagreement_score":0.009883196,"about_ca_system_score_codex":0.0011820681,"about_ca_system_score_gemma":0.0021987192,"threshold_uncertainty_score":0.019651353},"labels":[],"label_agreement":null},{"id":"W2731618922","doi":"10.1109/tr.2017.2711621","title":"Two-Phase Degradation Process Model With Abrupt Jump at Change Point Governed by Wiener Process","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Degradation (telecommunications); Wiener process; Unobservable; Jump; Process (computing); Point process; Maximization; Computer science; Stochastic process; Gamma process; Jump process; Mathematics; Control theory (sociology); Mathematical optimization; Applied mathematics; Statistics; Econometrics; Artificial intelligence; Physics","score_opus":0.01802597597072788,"score_gpt":0.27362189588719993,"score_spread":0.25559591991647207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2731618922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065870024,0.0010131915,0.9255887,0.00044427105,0.00013466849,0.000114181195,0.00045066068,0.0004699621,0.0059143454],"genre_scores_gemma":[0.9615416,0.0010988119,0.020287976,0.00010095297,0.000081767925,0.00026554806,0.0004716449,0.00004814922,0.01610362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891603,0.00015623026,0.00007768666,0.00037816042,0.00031252654,0.0001593279],"domain_scores_gemma":[0.99865305,0.00061085675,0.0002788499,0.00007049361,0.00034291047,0.00004390878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015378593,0.0014043013,0.0018372228,0.0010732261,0.0004956699,0.0017220065,0.0024781737,0.0024511453,0.002724984],"category_scores_gemma":[0.0029810867,0.00065489416,0.0014930996,0.0012136624,0.0010051032,0.0018152802,0.00096990017,0.0020723566,0.0006316162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011769126,0.000074731244,0.00260292,0.00020191546,0.00008203475,0.00050414016,0.00017864034,0.9483891,0.0065211575,0.02985661,0.00078725157,0.010683738],"study_design_scores_gemma":[0.000008255393,0.000026856002,0.0004149557,0.0000046686187,0.000019841846,0.000048835824,0.000008298444,0.99619967,0.00043062627,0.0025909613,0.00023182131,0.000015200306],"about_ca_topic_score_codex":0.0076498003,"about_ca_topic_score_gemma":0.0037401076,"teacher_disagreement_score":0.0076498003,"about_ca_system_score_codex":0.0009723359,"about_ca_system_score_gemma":0.00076421915,"threshold_uncertainty_score":0.015210569},"labels":[],"label_agreement":null},{"id":"W2734425079","doi":"10.1049/iet-net.2017.0033","title":"Approximate reliability of multi‐state two‐terminal networks by stochastic analysis","year":2017,"lang":"en","type":"article","venue":"IET Networks","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Science Foundation","keywords":"Reliability (semiconductor); Scalability; Bernoulli's principle; Computer science; Monte Carlo method; Stochastic modelling; State (computer science); Mathematical optimization; Algorithm; Mathematics; Statistics","score_opus":0.007234336464950456,"score_gpt":0.23133020278048766,"score_spread":0.2240958663155372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734425079","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041141946,0.0002796488,0.9565705,0.000104729486,0.000012744749,0.000023624718,0.000060419727,0.00013317495,0.0016730897],"genre_scores_gemma":[0.9435754,0.00048243257,0.053999983,0.000037702805,0.000023979122,0.0001252988,0.00015458046,0.000048287508,0.0015523386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993735,0.0002605183,0.000023325745,0.000088915775,0.00018342186,0.00007029987],"domain_scores_gemma":[0.9976555,0.0017092361,0.00027786614,0.000106002335,0.00020991906,0.000041519903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015032922,0.00085026625,0.0008429443,0.0010158385,0.0004128542,0.00080982456,0.0009033095,0.00093651563,0.0009934041],"category_scores_gemma":[0.0044270097,0.0005622014,0.0009863253,0.0007807319,0.0009600556,0.0010182575,0.00061776116,0.0008566113,0.00015969007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045328065,0.0000026261655,0.00009596276,0.000004955492,0.0000030025408,0.0000074651994,0.0000049787695,0.9967033,0.00010267191,0.0022726932,0.000028154542,0.0007695867],"study_design_scores_gemma":[4.3098672e-7,0.0000019194363,0.000023810044,9.4699107e-7,7.811578e-7,0.000001946126,9.559013e-7,0.9990658,0.000032673375,0.0008515885,0.000018265859,7.8067e-7],"about_ca_topic_score_codex":0.007466214,"about_ca_topic_score_gemma":0.0044814507,"teacher_disagreement_score":0.007466214,"about_ca_system_score_codex":0.0014542138,"about_ca_system_score_gemma":0.00090066716,"threshold_uncertainty_score":0.01484555},"labels":[],"label_agreement":null},{"id":"W2734896324","doi":"10.1080/00207543.2017.1349953","title":"Periodic replacement strategies: optimality conditions and numerical performance comparisons","year":2017,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Function (biology); Principal (computer security); Statement (logic); Distribution (mathematics); Computer science; Basis (linear algebra); Unit (ring theory); Mathematics; Operations research","score_opus":0.07374362534788195,"score_gpt":0.3975140257979437,"score_spread":0.3237704004500618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734896324","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5667696,0.009386817,0.36810753,0.0016109262,0.00018666178,0.00037487553,0.0006892153,0.00041239944,0.052461896],"genre_scores_gemma":[0.9662035,0.0011254968,0.03102465,0.00006598694,0.00003809152,0.00015714948,0.0001843292,0.00003619553,0.001164561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992374,0.0003908398,0.000046941248,0.000061127714,0.00014710153,0.000116619405],"domain_scores_gemma":[0.98562074,0.01267859,0.0006775221,0.00027345103,0.0006026469,0.00014712941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036397302,0.00079160207,0.0011362983,0.0014357571,0.00038933134,0.0010302558,0.0008893933,0.0018114152,0.004550075],"category_scores_gemma":[0.018425805,0.0002854665,0.0005765653,0.0010378392,0.0007585265,0.0009361919,0.0008317814,0.0008787761,0.000259797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014945066,0.00013454107,0.0007890803,0.00016744129,0.000024085104,0.000047487596,0.000051491235,0.96893096,0.00041563628,0.017198237,0.0005337843,0.011557846],"study_design_scores_gemma":[0.000024673172,0.00012286815,0.0002514929,0.000033330474,0.000012413113,0.000022829337,0.000042583186,0.99341255,0.0003052438,0.005557058,0.00020626861,0.000008762883],"about_ca_topic_score_codex":0.00393044,"about_ca_topic_score_gemma":0.0021509062,"teacher_disagreement_score":0.004550075,"about_ca_system_score_codex":0.0009628164,"about_ca_system_score_gemma":0.00089413073,"threshold_uncertainty_score":0.019248962},"labels":[],"label_agreement":null},{"id":"W2739581391","doi":"","title":"Multi-objective optimisation of asset maintenance management","year":2002,"lang":"en","type":"article","venue":"NPARC","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Asset management; Maintenance actions; Risk analysis (engineering); Life cycle costing; Preventive maintenance; Optimal maintenance; Minimisation (clinical trials); Computer science; Operations research; Reliability engineering; Stochastic programming; Compromise; Operations management; Engineering; Business; Mathematical optimization; Mathematics; Finance","score_opus":0.012079366799586202,"score_gpt":0.20260108111052494,"score_spread":0.19052171431093873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739581391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014565635,0.0005142869,0.98206824,0.00012854973,0.00002552637,0.000040223124,0.00003929979,0.00007759742,0.0025406564],"genre_scores_gemma":[0.7389851,0.00091488654,0.2569436,0.00006977818,0.000054133292,0.00020283334,0.00011462772,0.0000597847,0.0026551944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990927,0.00047755812,0.000029992474,0.00008921164,0.00025135415,0.000059173446],"domain_scores_gemma":[0.99905676,0.00063261174,0.00013923128,0.000044427885,0.000100944875,0.000026029384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015133828,0.00070125563,0.00077755237,0.0006015174,0.00025529566,0.0007092867,0.0007178779,0.0007205156,0.0010172854],"category_scores_gemma":[0.0026733023,0.00039215595,0.00056056277,0.00057338865,0.00046051282,0.0007686994,0.0006172586,0.0005928334,0.00015949772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010662871,0.000017815852,0.000126587,0.00004225629,0.000026585978,0.000013358303,0.000010573234,0.9805546,0.0004756418,0.004506111,0.0001286354,0.014087179],"study_design_scores_gemma":[0.000004793126,0.00002062848,0.00007699127,0.0000045845463,0.0000056284607,0.00000582876,0.00000315922,0.99535745,0.00022092572,0.0039743558,0.0003227274,0.0000029343892],"about_ca_topic_score_codex":0.0018629798,"about_ca_topic_score_gemma":0.002138652,"teacher_disagreement_score":0.0018629798,"about_ca_system_score_codex":0.00068502175,"about_ca_system_score_gemma":0.0009873883,"threshold_uncertainty_score":0.0080035925},"labels":[],"label_agreement":null},{"id":"W2742418309","doi":"10.5539/mas.v11n9p20","title":"An Optimization Approach to the Preventive Maintenance Planning Process","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Mathematical optimization; Schedule; Scheduling (production processes); Preventive maintenance; Solver; Job shop scheduling; Task (project management); Operations research; Reliability engineering; Mathematics","score_opus":0.013379008238699634,"score_gpt":0.2574609788941941,"score_spread":0.24408197065549447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742418309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017701731,0.00015392991,0.995105,0.00013361323,0.000021853133,0.000037004145,0.000035305868,0.00006592897,0.0026771252],"genre_scores_gemma":[0.19795202,0.0010626449,0.7933098,0.00016077954,0.000105139756,0.0005724653,0.000217357,0.00012764324,0.0064920993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941134,0.00021460897,0.00002442864,0.00011162674,0.00018140783,0.00005650945],"domain_scores_gemma":[0.99948657,0.00035488556,0.000054614822,0.000019717536,0.00006973575,0.000014579871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009827206,0.0010702854,0.0008404211,0.00079140975,0.0004405311,0.0011843087,0.0011153617,0.00096434593,0.003770712],"category_scores_gemma":[0.001621651,0.0006613091,0.0010148591,0.00093087903,0.0007687012,0.0007953073,0.00068341277,0.0013426809,0.00051770825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012367983,0.000016886692,0.00008358462,0.000077748,0.000020449,0.000033007214,0.000026340855,0.9653979,0.0005920953,0.018427791,0.00044926512,0.014862452],"study_design_scores_gemma":[0.0000075424823,0.000025258405,0.000065905544,0.0000112336575,0.000010974079,0.000019422925,0.000008838674,0.9860721,0.00031613547,0.011760906,0.0016956334,0.000006076429],"about_ca_topic_score_codex":0.0058379374,"about_ca_topic_score_gemma":0.004058597,"teacher_disagreement_score":0.0058379374,"about_ca_system_score_codex":0.0013249278,"about_ca_system_score_gemma":0.0024314506,"threshold_uncertainty_score":0.01261425},"labels":[],"label_agreement":null},{"id":"W2745729819","doi":"10.21595/jve.2017.17864","title":"Reliability sequential compliance method for a partially observable gear system subject to vibration monitoring","year":2017,"lang":"en","type":"article","venue":"Journal of Vibroengineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Division of Graduate Education; Graduate Research and Innovation Projects of Jiangsu Province; China Scholarship Council; University of Toronto","keywords":"Weibull distribution; Reliability (semiconductor); Residual; Sequential probability ratio test; Computer science; Reliability engineering; Statistics; Mathematics; Algorithm; Engineering","score_opus":0.04017801668266697,"score_gpt":0.29808085098791776,"score_spread":0.2579028343052508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2745729819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014915024,0.00004958012,0.98405343,0.000029810368,0.000009523159,0.000026618678,0.000025991494,0.0002834669,0.0006065433],"genre_scores_gemma":[0.7905951,0.00018642319,0.20615783,0.000053184544,0.000050445913,0.00020967319,0.00024572006,0.00009403009,0.0024076283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936897,0.00013857419,0.000041980304,0.0001451543,0.00026342116,0.00004203675],"domain_scores_gemma":[0.9985801,0.0007569268,0.00022150745,0.000101070545,0.0003000393,0.000040349354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010274515,0.0005598032,0.0005173728,0.0007106771,0.00022509096,0.0003636821,0.00072640064,0.00046190625,0.002549725],"category_scores_gemma":[0.0038865933,0.00021301264,0.0005923804,0.000459179,0.00032101056,0.0006529649,0.0005074752,0.0005565237,0.00028139912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023345464,0.00008941335,0.004605782,0.00027908132,0.00006589269,0.00023916503,0.00018044948,0.78684443,0.015719416,0.013607656,0.00088325865,0.17725208],"study_design_scores_gemma":[0.000003461361,0.00003478206,0.00041206615,0.000003141367,0.000004104408,0.00003677907,0.000005789651,0.9975903,0.000695553,0.0010489094,0.00016096978,0.0000041060525],"about_ca_topic_score_codex":0.0032945902,"about_ca_topic_score_gemma":0.0021520036,"teacher_disagreement_score":0.0032945902,"about_ca_system_score_codex":0.00036574813,"about_ca_system_score_gemma":0.0010568822,"threshold_uncertainty_score":0.008529723},"labels":[],"label_agreement":null},{"id":"W2746245043","doi":"10.1007/s10845-017-1353-z","title":"Multi-phase sequential preventive maintenance scheduling for deteriorating repairable systems","year":2017,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Preventive maintenance; Reliability engineering; Operability; Scheduling (production processes); Optimal maintenance; Schedule; Engineering; Predictive maintenance; Computer science; Operations research; Mathematical optimization; Operations management; Mathematics","score_opus":0.034582221793743984,"score_gpt":0.3021643902928102,"score_spread":0.26758216849906624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2746245043","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28214633,0.00070005545,0.7144865,0.00021081307,0.00013788605,0.00015609925,0.00014654534,0.00029998578,0.001715757],"genre_scores_gemma":[0.9727893,0.00009703578,0.026338495,0.000021667121,0.000024847708,0.000046717596,0.00005559482,0.000016752843,0.00060966774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948055,0.00014030757,0.000031859665,0.00010001917,0.00011914577,0.00012815725],"domain_scores_gemma":[0.9985417,0.00074365165,0.00027407816,0.000084723615,0.00021570161,0.000140269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014167926,0.00083733647,0.0015306744,0.00070224307,0.0005948605,0.0006421761,0.0015880557,0.00054295553,0.0015738411],"category_scores_gemma":[0.0022845524,0.00062637293,0.00050475844,0.00064047653,0.00038183446,0.0006080446,0.00048722068,0.00060416176,0.00013275098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049024256,0.00015823584,0.0005133094,0.000114478455,0.00004920357,0.00007507057,0.00004033269,0.9670588,0.0040512895,0.0017180294,0.0003986852,0.025332395],"study_design_scores_gemma":[0.000024491972,0.00014872108,0.00028169114,0.0000029740484,0.000015860993,0.000016642067,0.00000703092,0.99804074,0.0005064904,0.00085626135,0.000095331285,0.0000037956543],"about_ca_topic_score_codex":0.004625062,"about_ca_topic_score_gemma":0.0047367015,"teacher_disagreement_score":0.004625062,"about_ca_system_score_codex":0.0007162461,"about_ca_system_score_gemma":0.0013141355,"threshold_uncertainty_score":0.009196281},"labels":[],"label_agreement":null},{"id":"W2757172030","doi":"","title":"Reliability modeling and analysis of complex hierarchical systems: Editorial","year":2005,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Computer science; Engineering; Physics","score_opus":0.010818171566282167,"score_gpt":0.2221039186199728,"score_spread":0.21128574705369063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757172030","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018326666,0.015239829,0.001956718,0.026010022,0.95557296,0.0000120569375,0.00008465663,0.00008050284,0.00086004304],"genre_scores_gemma":[0.002337577,0.011473483,0.00064455863,0.0068623843,0.97516394,0.000012892574,0.000047866422,0.0000643504,0.0033930533],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976647,0.00036220712,0.00035626278,0.00036306155,0.001112302,0.00014140602],"domain_scores_gemma":[0.9735681,0.011109755,0.0012666474,0.00082605786,0.011551596,0.0016778198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038980744,0.002348796,0.0031529854,0.0025053762,0.0011894177,0.0037351644,0.0038002895,0.008906222,0.0037712636],"category_scores_gemma":[0.018295666,0.0010799114,0.001952703,0.0017081463,0.0017808321,0.003553728,0.00088873383,0.010754337,0.0027366192],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059887207,0.000024373514,0.000046637,0.00034798187,0.000050023133,0.000094615316,0.000010773629,0.0004010835,0.00014322979,0.00075363513,0.9888905,0.009177239],"study_design_scores_gemma":[0.00015541707,0.000117015945,0.0011815364,0.0010266232,0.00029237464,0.0007382356,0.000049567614,0.0055391416,0.0007209917,0.008099469,0.9820092,0.00007056208],"about_ca_topic_score_codex":0.0014042498,"about_ca_topic_score_gemma":0.002356644,"teacher_disagreement_score":0.008906222,"about_ca_system_score_codex":0.0011889798,"about_ca_system_score_gemma":0.0015616688,"threshold_uncertainty_score":0.02061522},"labels":[],"label_agreement":null},{"id":"W2759313934","doi":"10.1016/j.ress.2017.09.010","title":"General model for the risk priority number in failure mode and effects analysis","year":2017,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":105,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Konkuk University","keywords":"Failure mode and effects analysis; Reliability engineering; Mode (computer interface); Computer science; Risk analysis (engineering); Econometrics; Mathematics; Engineering; Business","score_opus":0.0034921556363257623,"score_gpt":0.21058657706983644,"score_spread":0.20709442143351067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2759313934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005263826,0.0004328658,0.9822037,0.0005401712,0.00008924134,0.000059174232,0.00038291462,0.0002244948,0.010803703],"genre_scores_gemma":[0.58673537,0.002301036,0.32038763,0.0008992095,0.0007034142,0.001066599,0.0015077938,0.00077552564,0.0856234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992322,0.0002771074,0.000029655363,0.0001404514,0.00019442555,0.0001262697],"domain_scores_gemma":[0.9986172,0.00079849403,0.000110908506,0.0001415243,0.0002705335,0.000061321494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002080944,0.001270139,0.001475858,0.001306918,0.00071347674,0.0016225537,0.004290894,0.0028799758,0.01485641],"category_scores_gemma":[0.005383503,0.0008832104,0.001787296,0.0013473296,0.0010613322,0.0031704938,0.0011376033,0.0023897681,0.002516373],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024425743,0.000037410413,0.00028451902,0.0000671029,0.000034950943,0.00009966106,0.00004757074,0.79876435,0.0010100692,0.19097821,0.002243957,0.0064077256],"study_design_scores_gemma":[0.000009914593,0.00000610099,0.0000913528,0.000008093968,0.000011511334,0.000028207356,0.00000466143,0.9527079,0.00008984735,0.046083625,0.00094928173,0.000009465137],"about_ca_topic_score_codex":0.011124347,"about_ca_topic_score_gemma":0.010763806,"teacher_disagreement_score":0.01485641,"about_ca_system_score_codex":0.0019050274,"about_ca_system_score_gemma":0.0018952298,"threshold_uncertainty_score":0.049699605},"labels":[],"label_agreement":null},{"id":"W2763991617","doi":"10.1108/jqme-06-2016-0023","title":"Maintenance strategies: Decision Making Grid vs Jack-Knife Diagram","year":2018,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Downtime; Pareto analysis; Reliability engineering; Ranking (information retrieval); Predictive maintenance; Computer science; Preventive maintenance; Operations research; Scope (computer science); Pareto principle; Engineering; Operations management; Artificial intelligence","score_opus":0.012230045370374027,"score_gpt":0.2785204771279729,"score_spread":0.26629043175759887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2763991617","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.100664884,0.0013583268,0.83614576,0.0019107419,0.00026290285,0.0017452592,0.0027912124,0.0015089836,0.05361193],"genre_scores_gemma":[0.48379442,0.00082537864,0.509514,0.00021242636,0.000043556392,0.0013961806,0.0011587257,0.00014078061,0.0029145603],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9931809,0.0043112827,0.0003676674,0.00067827775,0.001308256,0.00015365059],"domain_scores_gemma":[0.97487426,0.020644374,0.001717929,0.0008986046,0.0015216212,0.0003432869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073005036,0.0009910795,0.000686592,0.002886044,0.00045787817,0.004290717,0.0015051614,0.0009861036,0.01111408],"category_scores_gemma":[0.02911621,0.00034881337,0.00085010583,0.0029029509,0.0012822308,0.003377373,0.0012855798,0.0012176556,0.001185102],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011881792,0.00043559138,0.013926601,0.0030277704,0.00022561468,0.0003208017,0.0029860726,0.23399301,0.001634762,0.26023367,0.0115735335,0.47045448],"study_design_scores_gemma":[0.00044817146,0.0009065308,0.010190738,0.0014917607,0.0001868,0.000333737,0.0043294467,0.69048953,0.0031541837,0.21864012,0.06963289,0.00019612856],"about_ca_topic_score_codex":0.0049970965,"about_ca_topic_score_gemma":0.004136839,"teacher_disagreement_score":0.01111408,"about_ca_system_score_codex":0.002785267,"about_ca_system_score_gemma":0.002508122,"threshold_uncertainty_score":0.038609266},"labels":[],"label_agreement":null},{"id":"W2765934269","doi":"10.1115/icone25-66757","title":"Transition From Time–Based Preventive Maintenance to Condition–Based Maintenance","year":2017,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"CANDU Owners Group","keywords":"Preventive maintenance; Scope (computer science); Condition-based maintenance; Reliability engineering; Predictive maintenance; Condition monitoring; Reliability (semiconductor); Maintenance engineering; Engineering; Risk analysis (engineering); Computer science; Power (physics); Business","score_opus":0.005167955036223714,"score_gpt":0.21035473060304563,"score_spread":0.2051867755668219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765934269","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.452707,0.0016233138,0.50979906,0.001874623,0.00031846945,0.00078073813,0.00031318775,0.005847383,0.02673622],"genre_scores_gemma":[0.9247317,0.0002922418,0.07097056,0.00027844348,0.000045259436,0.00007235645,0.00017965665,0.000085811014,0.0033438643],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995291,0.0000663768,0.00002528145,0.00009942468,0.00021459741,0.00006525954],"domain_scores_gemma":[0.99905044,0.00017756336,0.0001768008,0.00024496455,0.00027543126,0.00007471723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007961914,0.00031829742,0.0003355137,0.00068777514,0.00029379802,0.0009295794,0.0010704456,0.0005161131,0.0020189313],"category_scores_gemma":[0.0014000649,0.00015757204,0.0003418651,0.00034032163,0.00034511846,0.00079646613,0.00055303395,0.00078397914,0.0005185483],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006119884,0.001477765,0.010983175,0.00037614285,0.00006960558,0.00016564268,0.0004757168,0.019759238,0.12129179,0.017556421,0.00480287,0.82242966],"study_design_scores_gemma":[0.0005356702,0.015377896,0.15537985,0.0005130917,0.00026573316,0.002481154,0.0012330693,0.30855167,0.29613635,0.044188194,0.17502365,0.00031375856],"about_ca_topic_score_codex":0.0015277672,"about_ca_topic_score_gemma":0.0011936567,"teacher_disagreement_score":0.0020189313,"about_ca_system_score_codex":0.0005067876,"about_ca_system_score_gemma":0.00056559045,"threshold_uncertainty_score":0.006753981},"labels":[],"label_agreement":null},{"id":"W2766330528","doi":"10.3390/en10111740","title":"Approximate Analysis of Multi-State Weighted k-Out-of-n Systems Applied to Transmission Lines","year":2017,"lang":"en","type":"article","venue":"Energies","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Reliability (semiconductor); State (computer science); Computer science; Fuzzy logic; Stochastic ordering; Transmission (telecommunications); Electric power transmission; Electric power system; Reliability engineering; Mathematics; Mathematical optimization; Algorithm; Power (physics); Engineering; Applied mathematics; Artificial intelligence","score_opus":0.01614770592882449,"score_gpt":0.24361648103471584,"score_spread":0.22746877510589134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766330528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10394907,0.00030961592,0.8931647,0.00012154082,0.00001804062,0.000030625564,0.000058280173,0.00013219466,0.0022159372],"genre_scores_gemma":[0.9792387,0.00016297007,0.01938591,0.000023265475,0.000009870858,0.000041367985,0.000069430265,0.000026638816,0.0010418777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964905,0.00011805067,0.000014154366,0.00006746015,0.00009104103,0.000060197573],"domain_scores_gemma":[0.99875176,0.000843765,0.00014850563,0.000059141683,0.00016434107,0.000032601078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009307049,0.00071425404,0.0007864397,0.0005075444,0.0003703474,0.0005911554,0.0007958592,0.00073376024,0.001278883],"category_scores_gemma":[0.0030626608,0.00037675677,0.0008604418,0.00044384485,0.00064230344,0.00086313556,0.00052120315,0.0006273994,0.00011629999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011488513,0.0000047903904,0.0003091561,0.000013786362,0.000011912683,0.000024421151,0.000016176204,0.99445266,0.00032825838,0.002567868,0.000042009055,0.0022174795],"study_design_scores_gemma":[3.0770255e-7,0.000002279,0.00003720252,6.40032e-7,9.1167084e-7,0.0000021967903,0.0000015574918,0.9993735,0.0000426033,0.0005250984,0.000013130045,6.2043085e-7],"about_ca_topic_score_codex":0.009454358,"about_ca_topic_score_gemma":0.0063184584,"teacher_disagreement_score":0.009454358,"about_ca_system_score_codex":0.0009527779,"about_ca_system_score_gemma":0.0007117562,"threshold_uncertainty_score":0.01879865},"labels":[],"label_agreement":null},{"id":"W2767756074","doi":"10.2495/safe-v7-n4-568-576","title":"Approaches for evaluating failure probability of emergency power supply systems in hospitals","year":2017,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Reliability engineering; Computer science; Medical emergency; Risk analysis (engineering); Engineering; Medicine","score_opus":0.017251602317574485,"score_gpt":0.2532131268114315,"score_spread":0.235961524493857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767756074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20055094,0.00041206685,0.7956012,0.00007186127,0.000009917683,0.00020041064,0.0002240684,0.0003002345,0.0026292833],"genre_scores_gemma":[0.9178992,0.00018421088,0.081052065,0.000012982571,0.000012555545,0.00014941857,0.00021916637,0.00001338268,0.00045701466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982798,0.00061530416,0.00014941228,0.00022620021,0.0006149047,0.00011428099],"domain_scores_gemma":[0.9955226,0.0027980343,0.00071963575,0.00012151928,0.00073322904,0.000104989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022081072,0.0010210333,0.0006165213,0.006564162,0.0003754128,0.0010150239,0.0011980103,0.0007469812,0.001469657],"category_scores_gemma":[0.0083509,0.00033729928,0.00078236,0.0018906172,0.00040458812,0.0011042714,0.0007865592,0.00036828336,0.0001778867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014609231,0.00012801238,0.040927418,0.00019159695,0.00015543126,0.00030016818,0.00018079932,0.8671705,0.0043429164,0.0059084524,0.0003215242,0.08022708],"study_design_scores_gemma":[0.000004982404,0.000075923075,0.0076173292,0.0000147742185,0.0000313623,0.00010076906,0.00008993238,0.9881343,0.0010469136,0.0026867113,0.00018040683,0.000016653723],"about_ca_topic_score_codex":0.003692541,"about_ca_topic_score_gemma":0.0032443732,"teacher_disagreement_score":0.006564162,"about_ca_system_score_codex":0.0011198822,"about_ca_system_score_gemma":0.000840778,"threshold_uncertainty_score":0.011677742},"labels":[],"label_agreement":null},{"id":"W2768239481","doi":"","title":"Cross-layer reliability modeling and optimization for embedded systems under process variations","year":2013,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reliability (semiconductor); Computer science; Layer (electronics); Process (computing); Reliability engineering; Engineering; Materials science","score_opus":0.014666918550065773,"score_gpt":0.2526464501479963,"score_spread":0.23797953159793053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768239481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10465179,0.0011597988,0.88898504,0.00025237064,0.000053109386,0.000037468464,0.000120442746,0.00029406318,0.0044458467],"genre_scores_gemma":[0.95410526,0.00066500925,0.040445853,0.000058007976,0.000033541175,0.00007714871,0.00015474783,0.00014675969,0.004313702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996288,0.00013414283,0.000017576005,0.000057170662,0.00009735107,0.000065068416],"domain_scores_gemma":[0.9993486,0.00034263806,0.0001035865,0.000064223066,0.00011908801,0.000021743701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014977718,0.0010658477,0.00082877703,0.0005815848,0.0003240195,0.00097299245,0.0011137266,0.0008319471,0.0012913807],"category_scores_gemma":[0.00257301,0.00073841313,0.0009382555,0.0006983521,0.0005140542,0.0014735837,0.0009209505,0.0008245143,0.00020159282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009910888,0.0000042828597,0.00007678569,0.0000067741717,0.0000099490335,0.0000068718387,0.0000050417275,0.9975624,0.0003334162,0.0008215761,0.000035574805,0.0011273808],"study_design_scores_gemma":[5.8902145e-7,0.0000035278383,0.00003306983,5.4916137e-7,0.0000025686554,0.0000012975964,0.000001270767,0.99942577,0.000090461916,0.00041353138,0.000026551304,8.520776e-7],"about_ca_topic_score_codex":0.006966932,"about_ca_topic_score_gemma":0.0047071436,"teacher_disagreement_score":0.006966932,"about_ca_system_score_codex":0.00084923673,"about_ca_system_score_gemma":0.0009455145,"threshold_uncertainty_score":0.0138527155},"labels":[],"label_agreement":null},{"id":"W2768986317","doi":"10.1007/s00170-017-1419-2","title":"Optimal failure mode-based preventive maintenance scheduling for a complex mechanical device","year":2017,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Preventive maintenance; Scheduling (production processes); Failure mode and effects analysis; Failure rate; Reliability engineering; Optimal maintenance; Computer science; Engineering; Operations management","score_opus":0.01276844845674327,"score_gpt":0.27246081098076563,"score_spread":0.2596923625240224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768986317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37195668,0.0006994904,0.6231252,0.00039848278,0.000118826254,0.00013763791,0.00021442275,0.00053009746,0.0028191376],"genre_scores_gemma":[0.98174703,0.00006336144,0.017341105,0.000021557973,0.00001973637,0.00002793941,0.000037693088,0.000019786443,0.00072191784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978,0.00004399604,0.000009139559,0.000059673053,0.000048740945,0.000058415993],"domain_scores_gemma":[0.9990683,0.000535496,0.00014845355,0.000049588944,0.00012065402,0.0000775537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005865026,0.0007457422,0.0010080491,0.000548771,0.0004571928,0.0006426579,0.0010363271,0.00085556554,0.0015273091],"category_scores_gemma":[0.0016135435,0.00046583806,0.00044909448,0.0003905065,0.000450512,0.00038176912,0.00041046707,0.00056691706,0.00013639456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015824691,0.00005038387,0.00037166403,0.000042540596,0.000019698722,0.00004552546,0.000022483004,0.9846414,0.0029762357,0.0007601965,0.00030611476,0.010605484],"study_design_scores_gemma":[0.000009141123,0.000033863194,0.0002362201,0.0000014441497,0.000008560479,0.000008456204,0.000005035782,0.99899787,0.00024245046,0.00041593536,0.000038565882,0.0000025059387],"about_ca_topic_score_codex":0.0098068165,"about_ca_topic_score_gemma":0.0073980712,"teacher_disagreement_score":0.0098068165,"about_ca_system_score_codex":0.0008255572,"about_ca_system_score_gemma":0.0014383175,"threshold_uncertainty_score":0.01949948},"labels":[],"label_agreement":null},{"id":"W2786680887","doi":"10.1109/icsrs.2017.8272806","title":"Modeling and analysis of cluster of failures in redundant systems","year":2017,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Redundancy (engineering); Computer science; Cluster analysis; Event (particle physics); Reliability (semiconductor); Reliability engineering; Cluster (spacecraft); Electric power system; Process (computing); Data mining; Power (physics); Engineering; Machine learning","score_opus":0.009650000768100492,"score_gpt":0.2209426208714278,"score_spread":0.2112926201033273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786680887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29795334,0.00019268268,0.69897157,0.00010321561,0.000013319559,0.00007610076,0.00018157104,0.00021312515,0.0022951255],"genre_scores_gemma":[0.98023,0.00012396762,0.018270858,0.000012441062,0.00001643071,0.000075138625,0.00011062403,0.000027283568,0.0011332125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993382,0.00022929962,0.000030047318,0.00013762734,0.0001672201,0.000097538614],"domain_scores_gemma":[0.9980373,0.0008905823,0.0005675074,0.00021771232,0.00021656035,0.00007045223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012131857,0.0007917099,0.0009031606,0.0009678671,0.00029255333,0.0005622807,0.0014931955,0.0007192426,0.0008138721],"category_scores_gemma":[0.00425132,0.00034616698,0.0006911281,0.0007828499,0.0009069182,0.000752994,0.0008615588,0.0005084533,0.00015014762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011662508,0.000010671687,0.0007227584,0.000011025742,0.000011767853,0.000038554816,0.000024264833,0.99149877,0.0006262718,0.0058367243,0.00006064367,0.0011468802],"study_design_scores_gemma":[0.0000015074775,0.000011769399,0.0002856941,9.3540285e-7,0.0000028042302,0.0000071808145,0.0000070352526,0.9969267,0.000120390796,0.0025850825,0.000048381666,0.000002404946],"about_ca_topic_score_codex":0.006013261,"about_ca_topic_score_gemma":0.0030874426,"teacher_disagreement_score":0.006013261,"about_ca_system_score_codex":0.0007110646,"about_ca_system_score_gemma":0.0005875472,"threshold_uncertainty_score":0.0119565725},"labels":[],"label_agreement":null},{"id":"W2790716556","doi":"10.1080/00207543.2018.1436789","title":"Selective maintenance scheduling under stochastic maintenance quality with multiple maintenance actions","year":2018,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":268,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Fundo para o Desenvolvimento das Ciências e da Tecnologia; China Scholarship Council; National Natural Science Foundation of China","keywords":"Maintenance actions; Scheduling (production processes); Optimal maintenance; Reliability engineering; Computer science; Predictive maintenance; Preventive maintenance; Simulated annealing; Condition-based maintenance; Mathematical optimization; Component (thermodynamics); Corrective maintenance; Operations research; Engineering; Mathematics; Algorithm","score_opus":0.07131587596696437,"score_gpt":0.3766434791121299,"score_spread":0.30532760314516555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790716556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22282086,0.0004299874,0.7738059,0.00017558098,0.00003723206,0.00012153235,0.0001470505,0.0001930724,0.0022687938],"genre_scores_gemma":[0.9824378,0.000102823265,0.016595973,0.000017166407,0.000012030564,0.000051621584,0.000064993525,0.000015524109,0.00070194213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991973,0.00020306688,0.00004089683,0.00014999925,0.00021365905,0.0001950902],"domain_scores_gemma":[0.9982326,0.0008410253,0.0004398355,0.00013117988,0.0002184986,0.0001368305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013828429,0.00080989365,0.001091817,0.0004933341,0.00035848876,0.0007699626,0.001624336,0.0007926694,0.001147696],"category_scores_gemma":[0.0027680802,0.0005732892,0.0008649086,0.0005642145,0.00058730756,0.00076845585,0.00062390836,0.0006722808,0.00012792509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043544438,0.000017082017,0.00031614018,0.000024738316,0.000016252061,0.000053711265,0.000017637003,0.99332523,0.0009855917,0.001639052,0.00006845454,0.0034925367],"study_design_scores_gemma":[0.000011881222,0.000046472876,0.00027974232,0.0000017190548,0.00001033747,0.0000171559,0.000005375887,0.9983468,0.00021460866,0.0009897705,0.00007352794,0.0000027387703],"about_ca_topic_score_codex":0.0074471286,"about_ca_topic_score_gemma":0.004459029,"teacher_disagreement_score":0.0074471286,"about_ca_system_score_codex":0.0011369246,"about_ca_system_score_gemma":0.0010261651,"threshold_uncertainty_score":0.014807582},"labels":[],"label_agreement":null},{"id":"W2791102065","doi":"10.1016/j.cie.2018.03.026","title":"Optimal Bayesian control policy for gear shaft fault detection using hidden semi-Markov model","year":2018,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; Graduate Research and Innovation Projects of Jiangsu Province; China Scholarship Council; Central University Basic Research Fund of China; National Natural Science Foundation of China","keywords":"Hidden Markov model; Unobservable; Hidden semi-Markov model; Bayesian probability; Fault detection and isolation; Markov process; Engineering; Computer science; Mathematical optimization; Markov chain; Markov model; Control theory (sociology); Variable-order Markov model; Mathematics; Artificial intelligence; Control (management); Econometrics; Statistics; Machine learning","score_opus":0.016141990934944558,"score_gpt":0.22734887615418328,"score_spread":0.21120688521923872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791102065","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05092087,0.0003701574,0.9455642,0.00049064716,0.00007151145,0.00005087978,0.00008530304,0.0003652338,0.0020812203],"genre_scores_gemma":[0.9727419,0.00018051037,0.024406847,0.00011276686,0.000041154235,0.000073285366,0.00010382706,0.000035908713,0.0023037426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999006,0.00022634574,0.000054432763,0.00025235862,0.00023854313,0.00022232714],"domain_scores_gemma":[0.9953074,0.0035012153,0.00037778053,0.00012874337,0.00053858827,0.00014629985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019114356,0.0008802405,0.002208909,0.00078293314,0.0005946471,0.001171645,0.0015014282,0.0015232046,0.0028519984],"category_scores_gemma":[0.0071789245,0.00086722453,0.00069153,0.00051740685,0.0012729112,0.0014372051,0.0011680569,0.001744496,0.0004362294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002837224,0.00008169522,0.00046576886,0.000074019066,0.000038811388,0.000048477053,0.000051455198,0.96820843,0.001231964,0.007587811,0.0005956116,0.021332199],"study_design_scores_gemma":[0.000011727822,0.000013838732,0.00010683851,0.0000049085197,0.000005949808,0.000004118903,0.0000022775002,0.99768496,0.00018334313,0.0019375304,0.000039243612,0.0000051515804],"about_ca_topic_score_codex":0.01755006,"about_ca_topic_score_gemma":0.011949804,"teacher_disagreement_score":0.01755006,"about_ca_system_score_codex":0.0016743746,"about_ca_system_score_gemma":0.0026839694,"threshold_uncertainty_score":0.034895778},"labels":[],"label_agreement":null},{"id":"W2791865852","doi":"10.1080/03610926.2014.988266","title":"On the mean residual life of a generalized <font><i>k</i></font>-out-of-<font><i>n</i></font> system","year":2018,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Isfahan University of Technology","keywords":"Font; Residual; Arithmetic; Mathematics; Computer science; Artificial intelligence; Algorithm","score_opus":0.030360820253357082,"score_gpt":0.33150786284206984,"score_spread":0.3011470425887128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791865852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48664534,0.0027514752,0.50423425,0.00072814594,0.00007783596,0.000032247113,0.00021677569,0.00014607652,0.0051678945],"genre_scores_gemma":[0.98572505,0.0008123913,0.011467927,0.0000623032,0.00006884934,0.00003268069,0.00019425564,0.00005522438,0.001581278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99953437,0.00022570459,0.000017162587,0.0000711073,0.000072154646,0.00007950755],"domain_scores_gemma":[0.99558204,0.0028756128,0.00052448094,0.00023912918,0.00057940756,0.00019936709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020731033,0.0007587413,0.0006721945,0.0010784001,0.00029290444,0.0004995101,0.0008149034,0.0006859962,0.0012324889],"category_scores_gemma":[0.007158294,0.00020283084,0.00055775006,0.00059705816,0.0013162587,0.0014460221,0.0006568963,0.0005838233,0.00016738806],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014647614,0.000044164048,0.004267418,0.00018117293,0.000069298214,0.00017801744,0.00015064204,0.9105137,0.004138169,0.066588126,0.0016244814,0.012098417],"study_design_scores_gemma":[0.0000034815614,0.0000385464,0.0012014504,0.000014647958,0.000012850545,0.000048837468,0.00003295869,0.9826418,0.00035574482,0.0153719,0.00026528366,0.00001236235],"about_ca_topic_score_codex":0.0025800918,"about_ca_topic_score_gemma":0.0013723002,"teacher_disagreement_score":0.0025800918,"about_ca_system_score_codex":0.00084044837,"about_ca_system_score_gemma":0.0005054799,"threshold_uncertainty_score":0.010963798},"labels":[],"label_agreement":null},{"id":"W2792162769","doi":"10.1016/j.apm.2018.02.020","title":"Reliability modelling and assessment of a heterogeneously repaired system with partially relevant recurrence data","year":2018,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Estimator; Reliability (semiconductor); Reliability engineering; Computer science; Bayesian probability; Importance sampling; Preventive maintenance; Process (computing); Bayesian inference; Statistical model; Sampling (signal processing); Data mining; Algorithm; Statistics; Engineering; Machine learning; Monte Carlo method; Artificial intelligence; Mathematics","score_opus":0.038048997037890255,"score_gpt":0.2504365910240899,"score_spread":0.21238759398619964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792162769","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7072339,0.0005029889,0.2891832,0.00030933993,0.000028591809,0.0000535686,0.00033988216,0.00023865656,0.0021099446],"genre_scores_gemma":[0.9954697,0.00007368199,0.0035071233,0.000006604682,0.000009071665,0.000017049259,0.0000816738,0.000014190614,0.0008209789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995136,0.00015810943,0.00003274371,0.00012258423,0.000100550984,0.00007240027],"domain_scores_gemma":[0.99789304,0.0011811041,0.00046029975,0.00015898132,0.00023880879,0.000067816436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014409566,0.0008736723,0.0011857909,0.0008364023,0.0003575377,0.0010933304,0.0016405149,0.0013895419,0.000788666],"category_scores_gemma":[0.0045425203,0.0005179595,0.0009082011,0.0008738526,0.0010238937,0.001161749,0.00057037413,0.0005931638,0.00013656206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004125613,0.000010398932,0.0004884554,0.000013451458,0.000014237885,0.00012194492,0.000018312152,0.9965071,0.00080931577,0.00093054405,0.000036142064,0.0010088801],"study_design_scores_gemma":[0.0000026792154,0.00001699994,0.0003509476,0.0000013277784,0.000012031712,0.000017314003,0.000005996241,0.99889636,0.00020914443,0.00046544292,0.000017554219,0.000004152912],"about_ca_topic_score_codex":0.009193034,"about_ca_topic_score_gemma":0.0049850103,"teacher_disagreement_score":0.009193034,"about_ca_system_score_codex":0.00084126566,"about_ca_system_score_gemma":0.0007238949,"threshold_uncertainty_score":0.018279076},"labels":[],"label_agreement":null},{"id":"W2793538941","doi":"10.3390/safety4010007","title":"Failure Rates for Aging Aircraft","year":2018,"lang":"en","type":"article","venue":"Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Failure rate; Degradation (telecommunications); Accelerated aging; Reliability engineering; Aeronautics; Forensic engineering; Process (computing); Life span; Engineering; Environmental science; Computer science; Gerontology; Medicine; Telecommunications","score_opus":0.005830413840286996,"score_gpt":0.23492157530385982,"score_spread":0.22909116146357283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793538941","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8950738,0.01074595,0.06564612,0.00023193515,0.00017959345,0.00018325407,0.012795015,0.0008205276,0.014323924],"genre_scores_gemma":[0.9819265,0.00144832,0.0053577623,0.000029278148,0.000049776885,0.00013827639,0.0055986186,0.00007181789,0.005379773],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986369,0.00018359859,0.00012051718,0.00026864556,0.0006412512,0.00014906118],"domain_scores_gemma":[0.9888764,0.0059109936,0.001890728,0.0010154843,0.0020783942,0.00022793692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023069826,0.00047189553,0.00054245326,0.0046812557,0.00031173872,0.00047363914,0.00086709816,0.0006199512,0.0059944373],"category_scores_gemma":[0.010919103,0.00016062269,0.0007495211,0.0017569044,0.00034331915,0.0007601014,0.0003333881,0.00055065705,0.0016690865],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010785038,0.00022139317,0.3148484,0.0013256331,0.00051512773,0.00069207244,0.0012995945,0.36167,0.023952201,0.017864348,0.007983748,0.26854894],"study_design_scores_gemma":[0.00005947576,0.0023383817,0.5919578,0.0003267806,0.0003711957,0.005525794,0.0007632394,0.3038561,0.02977706,0.023737263,0.04080432,0.0004825963],"about_ca_topic_score_codex":0.0023707005,"about_ca_topic_score_gemma":0.0018225402,"teacher_disagreement_score":0.0059944373,"about_ca_system_score_codex":0.0005239307,"about_ca_system_score_gemma":0.00023119213,"threshold_uncertainty_score":0.020053387},"labels":[],"label_agreement":null},{"id":"W2794455003","doi":"10.1177/1475921718758517","title":"A probabilistic approach to remaining useful life prediction of rolling element bearings","year":2018,"lang":"en","type":"article","venue":"Structural Health Monitoring","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prior probability; Fault (geology); Computer science; Unobservable; Degradation (telecommunications); Probabilistic logic; Bearing (navigation); Vibration; Bayesian probability; Fault detection and isolation; Reliability engineering; Data mining; Engineering; Artificial intelligence; Mathematics; Econometrics; Geology","score_opus":0.0279576354798446,"score_gpt":0.26534274623788606,"score_spread":0.23738511075804147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794455003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020495025,0.00026927885,0.97832835,0.000114918825,0.000015459394,0.000015365764,0.00008014253,0.00015688273,0.0005246098],"genre_scores_gemma":[0.9292794,0.0005574883,0.068051286,0.000034656507,0.000067271736,0.00007965058,0.0002670222,0.000041951775,0.0016212908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949515,0.00014558705,0.000025108913,0.000114867515,0.00016866968,0.000050523224],"domain_scores_gemma":[0.9980944,0.0012205761,0.00027178947,0.00008987359,0.0002632403,0.000060202397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011971621,0.0007648807,0.00086770195,0.0009105626,0.00028411677,0.0008233744,0.0012016533,0.00089619745,0.0010391849],"category_scores_gemma":[0.0056148265,0.00060376147,0.0006028986,0.0006628992,0.00062554935,0.0011291116,0.00068388745,0.0009906352,0.00020889544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023636023,0.000011519893,0.0005349467,0.000020949663,0.0000101747855,0.00003087485,0.000017837849,0.98633087,0.00088801177,0.0031934418,0.00012821602,0.008809529],"study_design_scores_gemma":[8.489925e-7,0.000008706179,0.00013463506,0.0000016241528,0.0000018512268,0.000008480534,0.0000016035814,0.99835557,0.00013340844,0.0012943018,0.00005595536,0.0000029244036],"about_ca_topic_score_codex":0.0038625454,"about_ca_topic_score_gemma":0.0025046815,"teacher_disagreement_score":0.0038625454,"about_ca_system_score_codex":0.00053693185,"about_ca_system_score_gemma":0.00053314655,"threshold_uncertainty_score":0.0076801777},"labels":[],"label_agreement":null},{"id":"W2801301220","doi":"10.1139/tcsme-2016-0051","title":"STATISTICAL ANALYSIS OF CONSTANT-STRESS ACCELERATED DEGRADATION TESTING WITH MULTIPLE PERFORMANCE PARAMETERS","year":2016,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Principal component analysis; Reliability (semiconductor); Degradation (telecommunications); Process (computing); Computer science; Support vector machine; Accelerated life testing; Constant (computer programming); Statistical analysis; Reliability engineering; Data mining; Machine learning; Artificial intelligence; Mathematics; Engineering; Statistics","score_opus":0.01606399438177228,"score_gpt":0.19160810067544226,"score_spread":0.17554410629366998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801301220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29927218,0.0007566565,0.69649893,0.000114292634,0.000074331365,0.00014452118,0.0005793354,0.0010401359,0.0015196089],"genre_scores_gemma":[0.95166326,0.0002043347,0.046638057,0.000027085121,0.000051117648,0.00013002346,0.0006780673,0.00006554703,0.00054234604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967722,0.0008600128,0.00019430793,0.0005779108,0.0014784529,0.000117178366],"domain_scores_gemma":[0.9911151,0.0047092484,0.0013121746,0.0009270076,0.0018264222,0.000110044246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033658703,0.00073485676,0.0005462494,0.0022086282,0.00024424703,0.0005289251,0.0005951608,0.00031420033,0.0006480258],"category_scores_gemma":[0.008056343,0.00017817495,0.0008205657,0.0018263924,0.00062550703,0.0006587122,0.00034177903,0.00052581483,0.0001918525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00095906504,0.00047602403,0.12688702,0.0007209882,0.0009605621,0.0008542145,0.00039371868,0.1331197,0.08462641,0.0076408293,0.002658216,0.64070314],"study_design_scores_gemma":[0.000024403898,0.0010481463,0.15798214,0.00003345299,0.0002501308,0.00083016825,0.0001906844,0.7978636,0.03209987,0.0059546297,0.0036131246,0.00010964672],"about_ca_topic_score_codex":0.0009844078,"about_ca_topic_score_gemma":0.0007675085,"teacher_disagreement_score":0.0033658703,"about_ca_system_score_codex":0.00031297846,"about_ca_system_score_gemma":0.0005842714,"threshold_uncertainty_score":0.01780063},"labels":[],"label_agreement":null},{"id":"W2801598878","doi":"10.1139/tcsme-2016-0057","title":"NUMERICAL SIMULATION ON THE EXISTENCE OF FLUCTUATION OF INSTANTANEOUS AVAILABILITY","year":2016,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Applied mathematics; Computer simulation; Failure rate; Mathematical analysis; Control theory (sociology); Computer science; Statistics","score_opus":0.01254792966769394,"score_gpt":0.19727280876274125,"score_spread":0.1847248790950473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801598878","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67240775,0.00055803166,0.3120521,0.00042312656,0.00009563202,0.00004550553,0.00021366257,0.0003761546,0.013828008],"genre_scores_gemma":[0.9905096,0.00009058947,0.008895685,0.000012959862,0.0000065325507,0.000020967394,0.000047763657,0.000013706054,0.00040222702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962854,0.00011973436,0.000023749662,0.000051855583,0.00011576787,0.000060381648],"domain_scores_gemma":[0.9980276,0.0012314824,0.0001955618,0.00017346103,0.00030943242,0.00006239412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008470357,0.00026720294,0.00038460727,0.00049779797,0.0003318688,0.00045547783,0.0004708269,0.0005963873,0.0008151251],"category_scores_gemma":[0.0041561667,0.00014888021,0.0003397041,0.0005971037,0.0007297474,0.0007278534,0.0004516569,0.000496852,0.000060512804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068813395,0.000020721654,0.0023153943,0.000051038573,0.000011965675,0.00010603705,0.000082805316,0.9799226,0.0026907627,0.010495162,0.00023370405,0.0040009897],"study_design_scores_gemma":[0.0000034912623,0.000008926387,0.00021949007,0.0000028598058,0.0000020927757,0.0000114295835,0.0000090398635,0.9982834,0.00048293782,0.00086604647,0.00010657879,0.0000037467744],"about_ca_topic_score_codex":0.0039696842,"about_ca_topic_score_gemma":0.0012223073,"teacher_disagreement_score":0.0039696842,"about_ca_system_score_codex":0.00045131988,"about_ca_system_score_gemma":0.0004558722,"threshold_uncertainty_score":0.007893145},"labels":[],"label_agreement":null},{"id":"W2807140878","doi":"10.1002/asmb.2343","title":"Nonparametric evaluation of the first passage time of degradation processes","year":2018,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Nonparametric statistics; Laplace transform; Mathematics; Applied mathematics; Empirical distribution function; Interval (graph theory); Kolmogorov–Smirnov test; Goodness of fit; Statistics; Algorithm; Mathematical optimization; Statistical hypothesis testing; Mathematical analysis","score_opus":0.020862634895961144,"score_gpt":0.21931936498873916,"score_spread":0.19845673009277803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807140878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08160848,0.00019836704,0.916631,0.00006129479,0.00002854651,0.000037091468,0.000062965475,0.00021238733,0.0011599422],"genre_scores_gemma":[0.94893736,0.00012135343,0.049815014,0.000021116943,0.000033395056,0.00006144036,0.00013074942,0.00003575448,0.0008437725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984653,0.00072366314,0.00007012508,0.00022575041,0.000417395,0.00009774838],"domain_scores_gemma":[0.982041,0.013628761,0.0015341461,0.0011869601,0.0013794128,0.00022968146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005497624,0.00055750133,0.0008172932,0.0014577911,0.00033271557,0.0010040513,0.0010738996,0.0010568827,0.001302792],"category_scores_gemma":[0.03049513,0.00025419396,0.0007332101,0.0008812013,0.0009746003,0.0017182947,0.00083809835,0.0011124827,0.00016102508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020199918,0.00008933099,0.0059136404,0.00015944909,0.00008706937,0.0002865787,0.00014352807,0.902037,0.0047575794,0.041902013,0.00037962542,0.04404213],"study_design_scores_gemma":[0.0000028794614,0.000038374554,0.0012020101,0.0000068670874,0.0000062594754,0.00007244349,0.000013197431,0.99338126,0.0009711508,0.0040924386,0.00020116859,0.000011953363],"about_ca_topic_score_codex":0.0018753329,"about_ca_topic_score_gemma":0.0008008319,"teacher_disagreement_score":0.005497624,"about_ca_system_score_codex":0.00078678294,"about_ca_system_score_gemma":0.00075665815,"threshold_uncertainty_score":0.02907455},"labels":[],"label_agreement":null},{"id":"W2807289112","doi":"10.1139/tcsme-2017-0094","title":"Availability models of series mechanical systems considering failure and maintenance dependencies","year":2018,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Series (stratigraphy); Monte Carlo method; Computer science; Reliability engineering; Path (computing); Mechanical system; Degradation (telecommunications); Mathematical optimization; Engineering; Mathematics","score_opus":0.010472830496936309,"score_gpt":0.1786534994008806,"score_spread":0.16818066890394429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807289112","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08738125,0.00077487057,0.90518206,0.00028181478,0.000047846206,0.0000631001,0.00036333286,0.00037166363,0.005534055],"genre_scores_gemma":[0.98229283,0.00064908643,0.012327217,0.000037150912,0.000058692527,0.00014150451,0.00023745913,0.00004656326,0.0042095697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992681,0.0001833679,0.000053305266,0.00017552907,0.00021289414,0.00010685528],"domain_scores_gemma":[0.9974406,0.0012817817,0.0007035725,0.00010694719,0.00039627336,0.0000708563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013740356,0.0012401586,0.0011801303,0.001294704,0.00042486476,0.0009938745,0.0020244804,0.0012255762,0.0021048698],"category_scores_gemma":[0.003641576,0.0006632506,0.0010987652,0.0011097504,0.0008854184,0.0015176067,0.0009339022,0.0011479121,0.00036669808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001421484,0.000009785541,0.00032160472,0.00002324508,0.000011744497,0.000045795572,0.000034789067,0.992207,0.00044335212,0.0048073423,0.00011815063,0.0019629197],"study_design_scores_gemma":[0.0000011780342,0.0000062748877,0.00010574594,0.000001477166,0.000004001187,0.000008967325,0.0000036681326,0.9985738,0.000040608084,0.0012002651,0.000051515966,0.0000024070523],"about_ca_topic_score_codex":0.009893252,"about_ca_topic_score_gemma":0.0059986548,"teacher_disagreement_score":0.009893252,"about_ca_system_score_codex":0.0011169156,"about_ca_system_score_gemma":0.0007734298,"threshold_uncertainty_score":0.01967132},"labels":[],"label_agreement":null},{"id":"W2808226066","doi":"10.4236/wjet.2018.63032","title":"Multiple-Objective Optimization and Design of Series-Parallel Systems Using Novel Hybrid Genetic Algorithm Meta-Heuristic Approach","year":2018,"lang":"en","type":"article","venue":"World Journal of Engineering and Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Heuristics; Computer science; Genetic algorithm; Mathematical optimization; Meta heuristic; Redundancy (engineering); Series (stratigraphy); Heuristic; Series and parallel circuits; Reliability (semiconductor); Process (computing); Algorithm; Mathematics; Engineering; Artificial intelligence; Machine learning; Power (physics)","score_opus":0.014408454481458395,"score_gpt":0.1941593114206499,"score_spread":0.17975085693919152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808226066","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034722764,0.0006837914,0.9590501,0.00013371093,0.000043490298,0.00007059273,0.000030436062,0.00017904634,0.005086091],"genre_scores_gemma":[0.63583016,0.0006464527,0.36003685,0.00008888627,0.000033931283,0.00036543232,0.00007960991,0.00005787543,0.0028607075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975103,0.000093369206,0.000010651872,0.000039958373,0.0000784902,0.000026550753],"domain_scores_gemma":[0.99974936,0.00013524051,0.000044893823,0.000016013217,0.00004115593,0.000013333434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006550658,0.0009251138,0.00068950147,0.00088370783,0.00032364993,0.0006596354,0.0008089276,0.00090282416,0.0008935844],"category_scores_gemma":[0.0007771412,0.0005303318,0.0009776573,0.00071049464,0.00043722708,0.00039325116,0.0004561778,0.00054541515,0.00012441646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008230054,0.000010763771,0.00010266324,0.000020776124,0.000022906912,0.000025037714,0.000009218603,0.9913174,0.0007606548,0.0016519676,0.000071591516,0.0059987255],"study_design_scores_gemma":[0.000005392573,0.000017200598,0.000031915813,0.000003172649,0.0000064492347,0.000007278102,0.000003612969,0.9986167,0.0002116286,0.0008491203,0.00024577128,0.0000018655347],"about_ca_topic_score_codex":0.0030849106,"about_ca_topic_score_gemma":0.0030550226,"teacher_disagreement_score":0.0030849106,"about_ca_system_score_codex":0.0006119454,"about_ca_system_score_gemma":0.000945842,"threshold_uncertainty_score":0.0061338544},"labels":[],"label_agreement":null},{"id":"W2809886006","doi":"10.1109/tr.2018.2846649","title":"Reliability Modeling and Analysis of Load-Sharing Systems With Continuously Degrading Components","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Reliability engineering; Reliability (semiconductor); Reliability theory; Computer science; Load sharing; Distributed computing; Engineering; Failure rate","score_opus":0.01348183206013888,"score_gpt":0.21413293522599253,"score_spread":0.20065110316585366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809886006","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16742894,0.000947489,0.8292169,0.00023007761,0.00001775456,0.000038819413,0.0000810552,0.00022010361,0.0018188204],"genre_scores_gemma":[0.9882438,0.0002900969,0.010366314,0.000011514859,0.000019221077,0.00003012197,0.000047128102,0.000021539972,0.00097022834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999556,0.00014711362,0.000020777283,0.00007513822,0.00014324095,0.000057726458],"domain_scores_gemma":[0.99911267,0.0004622597,0.00020147856,0.000066906905,0.00013188146,0.00002483873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011818103,0.000883287,0.0007005562,0.0006897969,0.00031998576,0.0006375597,0.0008230437,0.0006791558,0.00061117107],"category_scores_gemma":[0.002570472,0.00028774346,0.0006077054,0.0005481105,0.00060771994,0.0008896595,0.0005294785,0.0005606167,0.00010498464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013726863,0.0000065779977,0.00040139077,0.000013717363,0.000007999863,0.000035246056,0.00003813196,0.9934145,0.0012958543,0.0018371867,0.00005500352,0.0028805812],"study_design_scores_gemma":[4.4786597e-7,0.000005939762,0.00017805392,8.1804444e-7,0.0000022556214,0.000007979366,0.0000046280406,0.99891984,0.00011966305,0.0007270495,0.00003185965,0.0000015147598],"about_ca_topic_score_codex":0.0073984815,"about_ca_topic_score_gemma":0.003047196,"teacher_disagreement_score":0.0073984815,"about_ca_system_score_codex":0.0008073743,"about_ca_system_score_gemma":0.0006579373,"threshold_uncertainty_score":0.014710844},"labels":[],"label_agreement":null},{"id":"W2810514261","doi":"10.1139/tcsme-2017-0130","title":"Reliability modeling for competing failure systems with instant-shift hard failure threshold","year":2018,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Reliability (semiconductor); Reliability engineering; Physics of failure; Instant; Computer science; Shock (circulatory); Catastrophic failure; Failure rate; Sensitivity (control systems); Engineering; Materials science; Physics","score_opus":0.00987630057402586,"score_gpt":0.18486285930208926,"score_spread":0.1749865587280634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810514261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2039737,0.0017180195,0.78086597,0.0010235525,0.00011582667,0.00013900234,0.00048542916,0.00042918493,0.0112493215],"genre_scores_gemma":[0.98111075,0.00049500307,0.012051127,0.00007197068,0.000043176788,0.00011027641,0.00013709874,0.00004653637,0.005934126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989594,0.0003001175,0.000050945317,0.00020437194,0.00028985317,0.00019525256],"domain_scores_gemma":[0.99782556,0.0012721958,0.00034190726,0.000102213126,0.0003449078,0.00011318095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002293985,0.001178503,0.0013880342,0.0012650941,0.00053432223,0.0011997706,0.002429416,0.0017821582,0.0019332747],"category_scores_gemma":[0.0045535844,0.0006075456,0.0014061722,0.0009124416,0.0011713805,0.0014491797,0.0011293794,0.0016180004,0.0002668259],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002418021,0.00001762017,0.000451941,0.00003443689,0.000017800283,0.000079027945,0.000057004974,0.9822666,0.0006966431,0.014713383,0.00020885645,0.0014325659],"study_design_scores_gemma":[0.000002756631,0.000013427464,0.0001576636,0.0000024907147,0.000005859433,0.000015060709,0.000008239856,0.99686736,0.00006356071,0.0027709312,0.00008740656,0.0000052543105],"about_ca_topic_score_codex":0.012288447,"about_ca_topic_score_gemma":0.004554159,"teacher_disagreement_score":0.012288447,"about_ca_system_score_codex":0.0018338409,"about_ca_system_score_gemma":0.0010616549,"threshold_uncertainty_score":0.024433851},"labels":[],"label_agreement":null},{"id":"W2810996379","doi":"10.1007/978-3-319-94767-9_12","title":"Optimizing Combination Warranty Policies Using Remanufactured Replacement Products from the Seller and Buyer’s Perspectives","year":2018,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Warranty; Sustainability; Point (geometry); Computer science; Business; Legislation; Forcing (mathematics); Remanufacturing; Risk analysis (engineering); Operations research; Process management; Manufacturing engineering; Engineering; Mathematics","score_opus":0.028858255170756134,"score_gpt":0.2569140219840273,"score_spread":0.22805576681327117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810996379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12789074,0.009648268,0.8264976,0.0021178653,0.0003455784,0.00017836994,0.00032054976,0.0007978871,0.03220311],"genre_scores_gemma":[0.90081644,0.0030625414,0.07916633,0.00013520455,0.00024806173,0.000102601414,0.00024591613,0.00027403593,0.015948912],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989961,0.0003698131,0.000052823725,0.0001705665,0.0002540537,0.00015660425],"domain_scores_gemma":[0.9971765,0.0020665629,0.00025333476,0.00018741668,0.00021171331,0.00010445575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023368485,0.0018797436,0.0027401366,0.00081922923,0.0005781661,0.0027580382,0.0021340062,0.0021989369,0.0070869736],"category_scores_gemma":[0.0056251152,0.0012701566,0.0011065454,0.0009979326,0.0007356773,0.003940227,0.0010093667,0.00213621,0.00061143615],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036497024,0.00013020837,0.00035046865,0.0003195466,0.00008357037,0.00017458168,0.00008395964,0.9014371,0.003955068,0.03338585,0.0033720573,0.056342497],"study_design_scores_gemma":[0.000019682799,0.000102776045,0.00023089463,0.000032999284,0.000042394637,0.000052783926,0.000025884545,0.98001546,0.00082132354,0.017295782,0.0013448526,0.000015174495],"about_ca_topic_score_codex":0.002388827,"about_ca_topic_score_gemma":0.0019184776,"teacher_disagreement_score":0.0070869736,"about_ca_system_score_codex":0.002207604,"about_ca_system_score_gemma":0.0013323893,"threshold_uncertainty_score":0.023708224},"labels":[],"label_agreement":null},{"id":"W2811221022","doi":"10.4050/f-0074-2018-12849","title":"Estimating On-condition Direct Maintenance Cost (DMC)","year":2018,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Reliability engineering; Computer science; Environmental science; Process engineering; Engineering","score_opus":0.00762928704802726,"score_gpt":0.22658091125077898,"score_spread":0.21895162420275172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811221022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24654546,0.007040511,0.69532007,0.0012559383,0.0002808647,0.00055140705,0.004881554,0.00060734915,0.04351693],"genre_scores_gemma":[0.83090717,0.0032862788,0.15366873,0.000111324625,0.00009673655,0.00013320598,0.0043112333,0.0001969376,0.007288336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856925,0.00028748353,0.00007155355,0.0001773929,0.0008029863,0.00009125083],"domain_scores_gemma":[0.9954644,0.0025117015,0.0004619726,0.00036994572,0.0011240231,0.000067975976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015190858,0.0011107266,0.0006207724,0.0033093889,0.0002543421,0.0012239174,0.0012997766,0.00086996076,0.0020967347],"category_scores_gemma":[0.009744437,0.00036103494,0.00076424866,0.001964239,0.00026982982,0.0017014545,0.00052236667,0.00074278744,0.0003890093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000092005444,0.000111919224,0.033515647,0.00058678066,0.00020268446,0.00018719024,0.00007296341,0.6736183,0.004200319,0.017036306,0.008225213,0.2621506],"study_design_scores_gemma":[0.000011961871,0.00018355131,0.04659366,0.0002644276,0.00014110177,0.00047077407,0.00015056333,0.9140188,0.00837982,0.010086321,0.019618718,0.00008025993],"about_ca_topic_score_codex":0.021768583,"about_ca_topic_score_gemma":0.020927174,"teacher_disagreement_score":0.021768583,"about_ca_system_score_codex":0.0020463567,"about_ca_system_score_gemma":0.0012860757,"threshold_uncertainty_score":0.0432837},"labels":[],"label_agreement":null},{"id":"W2882978227","doi":"10.1016/j.ress.2018.07.022","title":"Reliability allocation model and algorithm for phased mission systems with uncertain component parameters based on importance measure","year":2018,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Component (thermodynamics); Reliability (semiconductor); Crossover; Mathematical optimization; Particle swarm optimization; Genetic algorithm; Computer science; Measure (data warehouse); Heuristic; Variance (accounting); Roulette; Algorithm; Reliability engineering; Data mining; Engineering; Mathematics; Artificial intelligence","score_opus":0.010059070276920126,"score_gpt":0.2046224699767779,"score_spread":0.19456339969985778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2882978227","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069793295,0.00011150256,0.9915987,0.00007580117,0.00001554742,0.00003418895,0.000017802524,0.000106359454,0.0010606456],"genre_scores_gemma":[0.62541986,0.00033019614,0.36865363,0.00010718886,0.000062488914,0.00046810292,0.00015794663,0.0001262631,0.00467438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959844,0.00012548118,0.00001776995,0.000093877505,0.00010410068,0.00006025483],"domain_scores_gemma":[0.9992648,0.00042217207,0.000069158574,0.000035708723,0.00017163383,0.000036473142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011414422,0.0007213551,0.0015731121,0.0006766727,0.0005583812,0.00089657144,0.0017338184,0.0011136609,0.002768612],"category_scores_gemma":[0.0019955272,0.0007357929,0.00076562894,0.00075579045,0.000532469,0.0013044744,0.0011338806,0.0013228303,0.00034738056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002556703,0.000015611562,0.00011728307,0.000031243868,0.00001152304,0.000012730331,0.000023649694,0.98170483,0.00046897327,0.0041681044,0.00029505158,0.013125403],"study_design_scores_gemma":[0.000003103957,0.000006003312,0.000023514589,0.0000014508964,0.0000026782418,0.0000036343135,0.0000016675955,0.99874,0.0000629573,0.0010940233,0.00005937161,0.0000015294648],"about_ca_topic_score_codex":0.0063516535,"about_ca_topic_score_gemma":0.0042359135,"teacher_disagreement_score":0.0063516535,"about_ca_system_score_codex":0.0012274296,"about_ca_system_score_gemma":0.0014320613,"threshold_uncertainty_score":0.01262933},"labels":[],"label_agreement":null},{"id":"W2883181832","doi":"10.1080/00207543.2018.1478150","title":"Quality issue in forecasting problem of production and maintenance policy for production unit","year":2018,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Production (economics); Preventive maintenance; Maintenance actions; Quality (philosophy); Operations research; Reliability engineering; Production planning; Failure rate; Time horizon; Sensitivity (control systems); Optimal maintenance; Production rate; Engineering; Computer science; Operations management; Mathematical optimization; Economics; Industrial engineering; Mathematics; Microeconomics","score_opus":0.11265970920784535,"score_gpt":0.4138725533156686,"score_spread":0.30121284410782323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883181832","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11091513,0.0030847213,0.8771788,0.0023782882,0.00023325357,0.00016310223,0.00070964417,0.00025497537,0.005082139],"genre_scores_gemma":[0.97248983,0.0012157391,0.022309173,0.000120455545,0.0001809563,0.00009826411,0.0005219002,0.000049068865,0.0030145294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985934,0.0003812966,0.0000853161,0.00043835831,0.00025066113,0.00025094362],"domain_scores_gemma":[0.9954026,0.003113467,0.00073659746,0.00012276969,0.00048695292,0.00013751273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037656266,0.0011218874,0.001987131,0.00093331374,0.00061699306,0.0021996985,0.002161424,0.0029867995,0.0031377175],"category_scores_gemma":[0.011757353,0.0008189416,0.0011981478,0.0013113536,0.0008264187,0.0025020419,0.0007153616,0.001848705,0.00020969508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064336804,0.000021481861,0.0016158419,0.0001249375,0.00004810265,0.000115696064,0.00004044007,0.98096025,0.00038030074,0.0084596425,0.0007422726,0.007426659],"study_design_scores_gemma":[0.000009906082,0.000026421309,0.0006240876,0.000009298496,0.000018341516,0.000024620555,0.00001328363,0.9939797,0.00014051299,0.004905445,0.00024082456,0.0000075188773],"about_ca_topic_score_codex":0.012698772,"about_ca_topic_score_gemma":0.004982258,"teacher_disagreement_score":0.012698772,"about_ca_system_score_codex":0.0022173822,"about_ca_system_score_gemma":0.0015092554,"threshold_uncertainty_score":0.02524972},"labels":[],"label_agreement":null},{"id":"W2886737844","doi":"10.1177/1748006x18765521","title":"Condition-based selective maintenance for stochastically degrading multi-component systems under periodic inspection and imperfect maintenance","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Downtime; Component (thermodynamics); Reliability engineering; Maintenance actions; Reliability (semiconductor); Imperfect; Optimal maintenance; Computer science; Preventive maintenance; Condition-based maintenance; Mathematical optimization; Predictive maintenance; Engineering; Mathematics; Power (physics)","score_opus":0.00722911458293761,"score_gpt":0.2149677174833689,"score_spread":0.20773860290043128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886737844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09784372,0.0005005381,0.8979112,0.00024573063,0.0000316677,0.0000678736,0.00012072486,0.00025797726,0.0030205199],"genre_scores_gemma":[0.98041725,0.00020908563,0.01694815,0.000024757828,0.000018760975,0.000072498406,0.000077365454,0.00002618686,0.0022058808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993186,0.000150375,0.00002610617,0.00016918694,0.00022323862,0.00011248376],"domain_scores_gemma":[0.99907684,0.00048238505,0.00021651029,0.000061555605,0.0001143658,0.000048288235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009463888,0.0010168174,0.0011735081,0.0005067854,0.00035327167,0.00087564037,0.0014826371,0.0011937,0.0013395917],"category_scores_gemma":[0.0019809494,0.00049751834,0.0008017153,0.00046867193,0.00087990507,0.0011177209,0.0008066565,0.00076751143,0.00015495859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032236327,0.000014446458,0.00021238292,0.000021214557,0.0000083872355,0.00004506838,0.000016312446,0.9941695,0.00090663164,0.0020197418,0.00008741308,0.0024667212],"study_design_scores_gemma":[0.0000036438316,0.000016393204,0.000117476084,0.0000010304674,0.0000036644901,0.000009331003,0.0000024807052,0.9991722,0.00009038585,0.0005365447,0.0000450619,0.0000017418441],"about_ca_topic_score_codex":0.008350987,"about_ca_topic_score_gemma":0.0046757627,"teacher_disagreement_score":0.008350987,"about_ca_system_score_codex":0.0012640463,"about_ca_system_score_gemma":0.00090187247,"threshold_uncertainty_score":0.016604781},"labels":[],"label_agreement":null},{"id":"W2886789461","doi":"10.1007/s11009-018-9657-9","title":"First Passage Time of a Lévy Degradation Model with Random Effects","year":2018,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Convolution (computer science); Laplace transform; Gamma process; Applied mathematics; Percentile; Parameterized complexity; Function (biology); Reliability (semiconductor); Algorithm; Mathematical optimization; Statistics; Computer science; Power (physics); Mathematical analysis; Artificial intelligence","score_opus":0.022276226960706445,"score_gpt":0.23537057214952656,"score_spread":0.21309434518882012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886789461","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.099628024,0.0018462437,0.8863992,0.001688328,0.00039103866,0.00011578752,0.0003529955,0.00030839298,0.009269993],"genre_scores_gemma":[0.9122611,0.0018815321,0.042931158,0.0005343659,0.0004682691,0.00032824485,0.00052524457,0.0003270305,0.040743012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99834716,0.00063276687,0.00008226617,0.00030366218,0.00033390548,0.00030024207],"domain_scores_gemma":[0.98660904,0.009497304,0.0014671305,0.0004085289,0.001156362,0.0008615961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053301547,0.0017594411,0.0030325903,0.0031854631,0.0010026724,0.0028286104,0.0038099762,0.004148314,0.005854455],"category_scores_gemma":[0.016234972,0.001491775,0.0022984375,0.0017687129,0.003563364,0.004179484,0.0022836437,0.0030633744,0.0006357045],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098853205,0.00008077325,0.0014757277,0.0002428015,0.00015570819,0.00036841418,0.0002362315,0.6715307,0.0014087803,0.32055682,0.001213703,0.0026313972],"study_design_scores_gemma":[0.000014795149,0.000017598919,0.00015621226,0.000013542601,0.00003733922,0.000054092914,0.000016286307,0.97648007,0.00010915134,0.022828614,0.0002542612,0.000018035846],"about_ca_topic_score_codex":0.009489868,"about_ca_topic_score_gemma":0.003561084,"teacher_disagreement_score":0.009489868,"about_ca_system_score_codex":0.0024993892,"about_ca_system_score_gemma":0.0020982577,"threshold_uncertainty_score":0.028188944},"labels":[],"label_agreement":null},{"id":"W2888454927","doi":"10.1037/met0000176","title":"A cautionary note on the finite sample behavior of maximal reliability.","year":2018,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Safety Canada","funders":"Aalto-Yliopisto; Academy of Finland","keywords":"Reliability (semiconductor); Sample (material); Statistics; Mathematics; Psychology; Econometrics; Applied mathematics; Physics; Thermodynamics","score_opus":0.056199717784466405,"score_gpt":0.38422726487056996,"score_spread":0.32802754708610354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888454927","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014798485,0.031099075,0.2870837,0.5910544,0.038748596,0.00047812727,0.0020880415,0.002204279,0.032445394],"genre_scores_gemma":[0.24595408,0.009911564,0.34478068,0.34181845,0.028271606,0.0020998556,0.00048935047,0.0013562769,0.02531808],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9012196,0.057953358,0.008918181,0.012886186,0.018111574,0.0009110235],"domain_scores_gemma":[0.55040866,0.3915319,0.008944341,0.023502985,0.024127632,0.0014844898],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.11036038,0.0016746034,0.0023432516,0.0024215162,0.0032296802,0.005442529,0.008788242,0.005244973,0.004645648],"category_scores_gemma":[0.41949606,0.00080005766,0.002185091,0.0038837106,0.015580269,0.007876874,0.0037400688,0.030800791,0.003149368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004725113,0.00011614779,0.011995209,0.0016835985,0.00053242786,0.0016483658,0.010631501,0.0023544438,0.0015318466,0.371765,0.48155272,0.11571614],"study_design_scores_gemma":[0.00023197503,0.0003297259,0.015769992,0.0032028263,0.00031083636,0.0027755648,0.002921083,0.013360972,0.004752382,0.5926485,0.3631836,0.00051245635],"about_ca_topic_score_codex":0.010138965,"about_ca_topic_score_gemma":0.013518292,"teacher_disagreement_score":0.8896396,"about_ca_system_score_codex":0.0026600016,"about_ca_system_score_gemma":0.0035743758,"threshold_uncertainty_score":0.58364844},"labels":[],"label_agreement":null},{"id":"W2889021254","doi":"","title":"Maintenance, Replacement, and Reliability: Theory and Applications, Second Edition","year":2013,"lang":"en","type":"book","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Reliability engineering; Reliability (semiconductor); Computer science; Engineering; Physics; Thermodynamics","score_opus":0.002798135177617753,"score_gpt":0.17682011110577847,"score_spread":0.1740219759281607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889021254","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014872898,0.31770846,0.07256429,0.004468693,0.011569607,0.00011559788,0.0037678394,0.0026966815,0.5856217],"genre_scores_gemma":[0.008927242,0.12148827,0.026885098,0.0013419384,0.0030275185,0.0001492273,0.0019631963,0.000947834,0.83526975],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99958426,0.000023292714,0.000019969462,0.000068948575,0.00028367052,0.000019711719],"domain_scores_gemma":[0.999522,0.00018762762,0.000031408243,0.00004978322,0.00017789859,0.00003123434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033707134,0.0017541386,0.0018228439,0.0022073279,0.000512473,0.0028990887,0.0016528284,0.0010098587,0.07521311],"category_scores_gemma":[0.0010620559,0.00075554714,0.0006821504,0.0029792902,0.000732414,0.0033166225,0.0008254345,0.0024793104,0.042760026],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033550343,0.00006429933,0.000101677775,0.0007278506,0.000023673017,0.000041183644,0.00010459929,0.0026523222,0.001077872,0.043042514,0.6325854,0.31954515],"study_design_scores_gemma":[0.0000065038325,0.000024814508,0.00033102487,0.00027866557,0.000014975421,0.00026689537,0.000042191707,0.0015717205,0.00030126853,0.031785738,0.9653571,0.000019048455],"about_ca_topic_score_codex":0.0022159333,"about_ca_topic_score_gemma":0.0052919183,"teacher_disagreement_score":0.07521311,"about_ca_system_score_codex":0.0009387826,"about_ca_system_score_gemma":0.0013620309,"threshold_uncertainty_score":0.2516129},"labels":[],"label_agreement":null},{"id":"W2889906917","doi":"10.1080/00207543.2018.1521021","title":"Integrated production quality and condition-based maintenance optimisation for a stochastically deteriorating manufacturing system","year":2018,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Preventive maintenance; Reliability engineering; Production (economics); Condition-based maintenance; Quality (philosophy); Time horizon; Degradation (telecommunications); Planned maintenance; Interval (graph theory); Product (mathematics); Computer science; Engineering; Operations research; Mathematical optimization; Mathematics; Economics","score_opus":0.06958917730880548,"score_gpt":0.3773577705898289,"score_spread":0.3077685932810234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889906917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20050658,0.0006467106,0.7951959,0.0002215324,0.000027549873,0.00009155536,0.00015271448,0.0002647083,0.0028927722],"genre_scores_gemma":[0.9777193,0.00016521172,0.020529382,0.000020989515,0.000011270474,0.000059619782,0.00007483261,0.000023624456,0.0013957226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922156,0.00020641653,0.000029709936,0.00018097462,0.00020122265,0.00016023175],"domain_scores_gemma":[0.9986671,0.0007880906,0.00030143358,0.00004606733,0.00013987596,0.000057403224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014792433,0.0010460683,0.0018015819,0.0007276599,0.00040535192,0.0012008053,0.0010433358,0.0014078055,0.0011612688],"category_scores_gemma":[0.0029514676,0.00072786136,0.0009775562,0.00073144794,0.0007986732,0.00081584125,0.00070199574,0.00085862156,0.0001301735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022446093,0.0000132586,0.00016605835,0.000015417752,0.000009537305,0.000017976321,0.000009374806,0.99712163,0.0005595988,0.0003981398,0.000027794731,0.0016387346],"study_design_scores_gemma":[0.000007977756,0.000041598665,0.00023640318,0.00000178136,0.000008980562,0.000008250613,0.0000036012614,0.999084,0.00019243268,0.00036754413,0.00004441033,0.0000030334047],"about_ca_topic_score_codex":0.009674464,"about_ca_topic_score_gemma":0.005669846,"teacher_disagreement_score":0.009674464,"about_ca_system_score_codex":0.0014994551,"about_ca_system_score_gemma":0.00136848,"threshold_uncertainty_score":0.019236267},"labels":[],"label_agreement":null},{"id":"W2891444883","doi":"","title":"Branch-and-Cut for Nonlinear Power Systems Problems","year":2015,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Defense; National Science Foundation","keywords":"Mathematical optimization; Conic optimization; Mathematics; Nonlinear programming; Quadratically constrained quadratic program; Semidefinite programming; Convex optimization; Quadratic programming; Nonlinear system; Regular polygon; Convex combination","score_opus":0.013623358066024627,"score_gpt":0.1950233141653595,"score_spread":0.18139995609933487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891444883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015984008,0.0008113073,0.9913805,0.00031661816,0.000054791846,0.00009744925,0.0000868325,0.00013033063,0.005523781],"genre_scores_gemma":[0.14602545,0.004151129,0.8377312,0.00040159677,0.00036309264,0.0010574306,0.00078271027,0.00037495245,0.009112422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983437,0.00082915655,0.000073155315,0.0002489862,0.00037303576,0.0001319642],"domain_scores_gemma":[0.99648005,0.002926723,0.00014534635,0.000117898744,0.00025901722,0.00007099915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032305291,0.0023806966,0.0020719732,0.0010762694,0.0010440054,0.0026435456,0.0014082392,0.0016730926,0.00864301],"category_scores_gemma":[0.00811983,0.0011039949,0.0010572432,0.002561358,0.0017500018,0.0019520238,0.001865878,0.0051164855,0.0015498033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007159312,0.00007007386,0.00027228848,0.00045451816,0.000052391286,0.00010082018,0.000109173976,0.74842083,0.00065919495,0.1654563,0.007707883,0.076624945],"study_design_scores_gemma":[0.000024711047,0.000022577085,0.000041756182,0.000040346313,0.000008493378,0.000024428395,0.000018375707,0.90200686,0.00028300425,0.09306874,0.0044551403,0.00000562121],"about_ca_topic_score_codex":0.0038088884,"about_ca_topic_score_gemma":0.0035352176,"teacher_disagreement_score":0.00864301,"about_ca_system_score_codex":0.0022670967,"about_ca_system_score_gemma":0.002098205,"threshold_uncertainty_score":0.028913736},"labels":[],"label_agreement":null},{"id":"W2891565222","doi":"10.1007/s12351-018-0424-z","title":"Effects of non-normal quality data on the integrated model of imperfect maintenance, early replacement, and economic design of $${\\bar{X}}$$-control charts","year":2018,"lang":"en","type":"article","venue":"Operational Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Control chart; Normality; Reliability engineering; Computer science; Quality (philosophy); Reliability (semiconductor); Imperfect; \\bar x and R chart; Sample (material); Control (management); Control limits; Process (computing); Statistics; Mathematics; Engineering; Power (physics)","score_opus":0.05567758386509606,"score_gpt":0.32665989470964635,"score_spread":0.27098231084455027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891565222","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66942626,0.00057905144,0.32005244,0.0015667465,0.00017130135,0.00011714335,0.0005371319,0.00069855194,0.0068513155],"genre_scores_gemma":[0.9945803,0.00007215775,0.004445847,0.000029500616,0.000011070425,0.000023626706,0.00006250357,0.000029856417,0.00074508553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976961,0.0012280995,0.00006682555,0.0002972854,0.00030796358,0.00040370825],"domain_scores_gemma":[0.96577424,0.028700013,0.0022098883,0.0008566265,0.0019057617,0.0005534432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009775504,0.0011762207,0.0016753896,0.0008194868,0.00057035754,0.0029980324,0.001730395,0.0013926717,0.0025555294],"category_scores_gemma":[0.03851848,0.0010336722,0.00074674457,0.0005206532,0.0018884067,0.0024307973,0.0010466743,0.002516725,0.00014286004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013500421,0.000033903932,0.0004002252,0.000013536603,0.000010072183,0.000016573882,0.000011700952,0.99492455,0.00022180517,0.0033222977,0.00010196204,0.00080836844],"study_design_scores_gemma":[0.000016073842,0.000039106606,0.00022577945,0.000002547786,0.0000111551435,0.0000025482789,0.000004494826,0.99847,0.00024829915,0.00094314176,0.000029664128,0.0000071221953],"about_ca_topic_score_codex":0.020157266,"about_ca_topic_score_gemma":0.011354207,"teacher_disagreement_score":0.020157266,"about_ca_system_score_codex":0.0028703613,"about_ca_system_score_gemma":0.0019268455,"threshold_uncertainty_score":0.051698446},"labels":[],"label_agreement":null},{"id":"W2892022033","doi":"10.1504/ijpqm.2018.10016019","title":"Maintenance policy selection using fuzzy failure modes and effective analysis and key performance indicators","year":2018,"lang":"en","type":"article","venue":"International Journal of Productivity and Quality Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Reliability engineering; Key (lock); Fuzzy logic; Analytic hierarchy process; Failure mode, effects, and criticality analysis; Criticality; Selection (genetic algorithm); Computer science; Failure mode and effects analysis; Process (computing); Condition-based maintenance; Operations research; Risk analysis (engineering); Engineering; Machine learning; Artificial intelligence","score_opus":0.008530499353165055,"score_gpt":0.26642558400306304,"score_spread":0.25789508464989797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892022033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1053546,0.000250373,0.8914684,0.00010077959,0.000018921102,0.00011549048,0.000083973944,0.00017017806,0.002437363],"genre_scores_gemma":[0.9122845,0.00016887469,0.0865274,0.00001584794,0.00001776829,0.000111605514,0.00009887427,0.000012521756,0.00076271576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884593,0.00024782453,0.0000854376,0.00019071224,0.00049472845,0.00013523393],"domain_scores_gemma":[0.9985858,0.0007399054,0.00021460518,0.000048279235,0.00035098864,0.00006043411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019045542,0.0010182395,0.0007768684,0.00343218,0.0005064075,0.0012586778,0.00077606854,0.00061514875,0.0009173803],"category_scores_gemma":[0.004279795,0.0003064262,0.0011072472,0.0011996626,0.00036030466,0.0011339205,0.00060240494,0.000447088,0.00010186618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033681444,0.00021070977,0.01217002,0.00030152404,0.00019601091,0.0003045967,0.00044434858,0.7299472,0.0132539235,0.01476409,0.0010597792,0.22701097],"study_design_scores_gemma":[0.000009477696,0.00007334192,0.0021217554,0.000017525379,0.000046783756,0.000047707712,0.000063779364,0.99137336,0.0015634621,0.004373493,0.00028998303,0.000019352947],"about_ca_topic_score_codex":0.0046779,"about_ca_topic_score_gemma":0.003115469,"teacher_disagreement_score":0.0046779,"about_ca_system_score_codex":0.001247331,"about_ca_system_score_gemma":0.0013037372,"threshold_uncertainty_score":0.0100723505},"labels":[],"label_agreement":null},{"id":"W2896324903","doi":"10.1108/ijqrm-09-2017-0187","title":"Optimizing replacement time for mining shovel teeth using reliability analysis and Markov chain Monte Carlo simulation","year":2018,"lang":"en","type":"article","venue":"International Journal of Quality & Reliability Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Shovel; Monte Carlo method; Reliability (semiconductor); Reliability engineering; Computer science; Interval (graph theory); Markov chain Monte Carlo; Engineering; Statistics; Mathematics","score_opus":0.020208027828625023,"score_gpt":0.31265690970788745,"score_spread":0.29244888187926243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896324903","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12210978,0.0003506051,0.873943,0.00019628694,0.000017496368,0.00008849852,0.000057836085,0.0002157586,0.0030207483],"genre_scores_gemma":[0.9323757,0.00015579844,0.06669256,0.000020315052,0.0000066014986,0.00006548024,0.000041820716,0.00002706999,0.0006146476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918395,0.0003431998,0.000043385244,0.000108508684,0.00022893891,0.00009208643],"domain_scores_gemma":[0.996372,0.0026446786,0.0004378723,0.00013877914,0.00033941655,0.00006724092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018906873,0.00045179704,0.0006953915,0.00089373643,0.00030348016,0.00079375936,0.0007925002,0.00057987444,0.0012935601],"category_scores_gemma":[0.0066402983,0.00044242296,0.00068253995,0.00049587735,0.00043431492,0.00062458037,0.00047711012,0.00056658586,0.00014465574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003200438,0.000015144681,0.0009776646,0.000025613297,0.000011186846,0.000021974816,0.000016329986,0.98923534,0.00076017104,0.0020913698,0.00007406379,0.006739211],"study_design_scores_gemma":[0.0000026600983,0.000018621191,0.00023512635,0.000004310011,0.0000059262197,0.00000948836,0.0000067709766,0.9983718,0.0002923478,0.000984025,0.00006587301,0.0000030605222],"about_ca_topic_score_codex":0.0061510764,"about_ca_topic_score_gemma":0.0056407237,"teacher_disagreement_score":0.0061510764,"about_ca_system_score_codex":0.0011067938,"about_ca_system_score_gemma":0.0016645747,"threshold_uncertainty_score":0.012230575},"labels":[],"label_agreement":null},{"id":"W2899892964","doi":"10.1155/2018/4329053","title":"Performance Analysis of Switched Control Systems Under Common‐source Digital Upsets Modeled by MDHMM","year":2018,"lang":"en","type":"article","venue":"Complexity","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; Civil Aviation University of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Reliability (semiconductor); Markov chain; Control (management); Control system; Complex system; Markov process; Reliability engineering; Digital control; Control engineering; Electronic engineering; Engineering; Artificial intelligence; Machine learning; Mathematics","score_opus":0.016795008014973214,"score_gpt":0.21622299443499404,"score_spread":0.19942798642002083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899892964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3758819,0.00096179947,0.6111516,0.0006230446,0.000055700762,0.000058408143,0.00016558138,0.000467788,0.010634191],"genre_scores_gemma":[0.99776936,0.00009668682,0.0013651459,0.00001566806,0.000006344272,0.000016402673,0.000020382004,0.000008458049,0.0007015783],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940586,0.00012730596,0.000021882957,0.00009945946,0.0002140269,0.00013151142],"domain_scores_gemma":[0.9986558,0.0006797645,0.000274133,0.00009536066,0.00025689986,0.000037989877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009135886,0.0006154921,0.00060052495,0.0005986126,0.0003974315,0.0007205805,0.0006099642,0.0006841257,0.0013152193],"category_scores_gemma":[0.0023345193,0.00022416894,0.00055898214,0.00027346134,0.00084003,0.0006483912,0.0005600693,0.00061275606,0.00013372736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046425437,0.000011011412,0.0006229434,0.000030390845,0.000020654079,0.00005346624,0.00006626611,0.9873586,0.0019484608,0.007142184,0.00012535202,0.0025741314],"study_design_scores_gemma":[0.0000013094752,0.000013333142,0.00014159021,0.0000014035604,0.000003900939,0.000006234504,0.0000066699445,0.99889743,0.00024642437,0.0006510926,0.000028921046,0.0000016846161],"about_ca_topic_score_codex":0.008221574,"about_ca_topic_score_gemma":0.002797453,"teacher_disagreement_score":0.008221574,"about_ca_system_score_codex":0.0017053823,"about_ca_system_score_gemma":0.0008362465,"threshold_uncertainty_score":0.016347408},"labels":[],"label_agreement":null},{"id":"W2900842568","doi":"10.1109/tr.2018.2877643","title":"FBM-Based Remaining Useful Life Prediction for Degradation Processes With Long-Range Dependence and Multiple Modes","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Degradation (telecommunications); Range (aeronautics); Markov process; Markov chain; Applied mathematics; Stochastic process; Computer science; Cluster analysis; Statistical physics; Mathematical optimization; Convergence (economics); Mathematics; Control theory (sociology); Engineering; Physics; Statistics","score_opus":0.015411244662720001,"score_gpt":0.2131413521121657,"score_spread":0.1977301074494457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900842568","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064439625,0.00049663684,0.93377095,0.00008749715,0.000025361407,0.000020421712,0.00006324273,0.00046595215,0.0006303121],"genre_scores_gemma":[0.92070603,0.00036906375,0.077186495,0.00005164401,0.000025875708,0.000062160565,0.0001664333,0.000051428884,0.0013807993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999765,0.000040335828,0.0000139770145,0.00007037257,0.000078012796,0.000032250107],"domain_scores_gemma":[0.9993637,0.0003268376,0.00012660227,0.000042767137,0.00011275154,0.000027334368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008065842,0.00094528846,0.000670263,0.0010050854,0.00031391706,0.00042169206,0.0008015694,0.00088775886,0.00059176475],"category_scores_gemma":[0.0019168768,0.00031247636,0.0006016802,0.0006300996,0.00037719592,0.0008043756,0.00035199051,0.00079485914,0.00021787405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001386506,0.00006481124,0.0037356005,0.000086958,0.000034615146,0.00016388718,0.00010431409,0.9082045,0.0116427,0.0026076983,0.00056709425,0.07264927],"study_design_scores_gemma":[8.7093963e-7,0.0000058184455,0.00026021028,0.0000019034685,0.0000024811281,0.000010861448,0.000002413037,0.9986356,0.0005740878,0.00043236974,0.00007009726,0.0000033891597],"about_ca_topic_score_codex":0.007799488,"about_ca_topic_score_gemma":0.00369734,"teacher_disagreement_score":0.007799488,"about_ca_system_score_codex":0.0006727948,"about_ca_system_score_gemma":0.0005868722,"threshold_uncertainty_score":0.015508175},"labels":[],"label_agreement":null},{"id":"W2903017557","doi":"10.1007/s42452-018-0063-2","title":"Failure interaction models for multicomponent systems: a comparative study","year":2018,"lang":"en","type":"article","venue":"SN Applied Sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Failure mode and effects analysis; Component (thermodynamics); Dependency (UML); Copula (linguistics); Computer science; Interaction model; Reliability (semiconductor); Cascading failure; Statistical physics; Reliability engineering; Mathematics; Econometrics; Engineering; Physics; Artificial intelligence","score_opus":0.05392225955405584,"score_gpt":0.2933471669365086,"score_spread":0.23942490738245276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903017557","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6206439,0.008070195,0.33753318,0.0009271015,0.00016398118,0.00022644925,0.0005030857,0.00044617048,0.031485967],"genre_scores_gemma":[0.98030245,0.0022187817,0.0117954975,0.00006423623,0.000072464,0.00009898138,0.00016970554,0.000100772035,0.005177164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993242,0.0003523086,0.000028025624,0.000074353025,0.00013814562,0.000082921964],"domain_scores_gemma":[0.9908613,0.0077729346,0.00045612897,0.0002568657,0.00047183444,0.00018080622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027136186,0.0011109419,0.002079646,0.0021379367,0.0006791286,0.0017535498,0.0024187556,0.0015047623,0.004665884],"category_scores_gemma":[0.00459047,0.0004907828,0.0021374687,0.0016955473,0.0006611522,0.0022679986,0.00080128526,0.0012103866,0.00051829434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009646808,0.000100582896,0.0008826938,0.00007689074,0.00006916377,0.000027881786,0.000056423884,0.98615927,0.00012107265,0.00406207,0.00023376902,0.008113649],"study_design_scores_gemma":[0.000006094993,0.000056846508,0.00059763447,0.000009017983,0.00003169941,0.000011790553,0.000026818096,0.996555,0.000036837486,0.0024812364,0.00017896123,0.000008102979],"about_ca_topic_score_codex":0.012602536,"about_ca_topic_score_gemma":0.008811922,"teacher_disagreement_score":0.012602536,"about_ca_system_score_codex":0.0019232753,"about_ca_system_score_gemma":0.0012553995,"threshold_uncertainty_score":0.025058389},"labels":[],"label_agreement":null},{"id":"W2903043057","doi":"10.5539/ijsp.v8n1p44","title":"Characteristics and Application of the NHPP Log-Logistic Reliability Model","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Mathematics; Statistics; Poisson distribution; Maximum likelihood; Poisson process; Flexibility (engineering); Applied mathematics","score_opus":0.011084206579270065,"score_gpt":0.24379221780913035,"score_spread":0.2327080112298603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903043057","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08915372,0.00084521575,0.8953037,0.00088101893,0.000056817145,0.00012219063,0.00050729443,0.00043639788,0.012693589],"genre_scores_gemma":[0.9569014,0.00085869577,0.03659629,0.00009608938,0.000078852514,0.0001898135,0.0005537359,0.00008615811,0.004639054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978903,0.000903627,0.00009827004,0.00031864797,0.0006333634,0.00015583562],"domain_scores_gemma":[0.9941216,0.0037367367,0.00080226007,0.00041447373,0.00081679225,0.00010810409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029062487,0.0004776305,0.0006021116,0.0013526317,0.00041197726,0.0009961411,0.0015409264,0.0010293178,0.0022334207],"category_scores_gemma":[0.015310524,0.00036495292,0.000789023,0.0016587299,0.00076642603,0.0016666315,0.0010654498,0.0012018679,0.0004926966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008662726,0.00008237905,0.014160277,0.00022770153,0.000056548408,0.0011600628,0.00043714477,0.82905537,0.0027839611,0.112063386,0.0023816172,0.037504885],"study_design_scores_gemma":[0.0000074141913,0.00005439665,0.0027715305,0.00001634636,0.000016729615,0.00075682654,0.000085707485,0.95696634,0.0004992132,0.036625903,0.002172306,0.000027232622],"about_ca_topic_score_codex":0.0052617155,"about_ca_topic_score_gemma":0.0019244774,"teacher_disagreement_score":0.0052617155,"about_ca_system_score_codex":0.00090312836,"about_ca_system_score_gemma":0.0010112426,"threshold_uncertainty_score":0.015369892},"labels":[],"label_agreement":null},{"id":"W2903255379","doi":"10.1002/qre.2428","title":"Optimal Bayesian maintenance policy for a gearbox subject to two dependent failure modes","year":2018,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Qinglan Project of Jiangsu Province of China; China Scholarship Council; Nanjing Institute of Technology; Nanjing University of Aeronautics and Astronautics; University of Toronto; National Natural Science Foundation of China","keywords":"Unobservable; Control chart; Bayesian probability; Residual; Hidden Markov model; Failure mode and effects analysis; Computer science; Fault (geology); Preventive maintenance; Condition-based maintenance; Bayesian inference; Process (computing); Markov process; Engineering; Reliability engineering; Artificial intelligence; Econometrics; Mathematics; Statistics; Algorithm","score_opus":0.00982033292679253,"score_gpt":0.2760010790639216,"score_spread":0.26618074613712905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903255379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20965146,0.00035823905,0.7858857,0.00045650068,0.00003767011,0.0001078372,0.00010813132,0.00049714395,0.0028974446],"genre_scores_gemma":[0.9841828,0.00007437677,0.01459809,0.000033586795,0.000010578286,0.00004545364,0.000042813266,0.000015182694,0.0009971856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993285,0.00016271502,0.00003554187,0.0001623116,0.0001796468,0.00013137625],"domain_scores_gemma":[0.9981616,0.00096869294,0.0003499086,0.00007063122,0.00033584464,0.000113326765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001552867,0.0006733811,0.0009610686,0.00063428713,0.00031687558,0.00066691486,0.00096102705,0.0010156484,0.0017379565],"category_scores_gemma":[0.0044267364,0.00037817922,0.00037741172,0.00025111853,0.00063750194,0.0007538914,0.0006234561,0.0007973368,0.00017078628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019492862,0.00007473864,0.0008428883,0.00006198598,0.000023787397,0.00006119317,0.00005695655,0.97032845,0.0038889516,0.0054339,0.0004931965,0.018539019],"study_design_scores_gemma":[0.0000132989635,0.00003565136,0.00030282277,0.0000039589504,0.0000071800982,0.000007531427,0.000004325318,0.9981134,0.00046473226,0.0009844914,0.000057487723,0.0000050441035],"about_ca_topic_score_codex":0.010807315,"about_ca_topic_score_gemma":0.0041282927,"teacher_disagreement_score":0.010807315,"about_ca_system_score_codex":0.0011752616,"about_ca_system_score_gemma":0.0012679616,"threshold_uncertainty_score":0.021488786},"labels":[],"label_agreement":null},{"id":"W2903293455","doi":"10.1002/nav.21811","title":"A new computation method for signature: Markov process method","year":2018,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; China Scholarship Council; McMaster University","keywords":"Signature (topology); Computation; Independent and identically distributed random variables; Markov process; Computer science; Markov chain; Algorithm; Reliability (semiconductor); Process (computing); Component (thermodynamics); Measure (data warehouse); Field (mathematics); Mathematics; Applied mathematics; Theoretical computer science; Data mining; Statistics; Random variable; Physics; Pure mathematics","score_opus":0.07392465770975362,"score_gpt":0.44939679587568815,"score_spread":0.3754721381659345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903293455","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009592661,0.000040510855,0.9982266,0.00003726615,0.000022277583,0.000019226321,0.000021705955,0.0001505471,0.00052272977],"genre_scores_gemma":[0.15285803,0.00025131012,0.8430764,0.00013682626,0.0001384333,0.00024193167,0.00020521139,0.0003052705,0.002786533],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99850094,0.0005097657,0.00009730571,0.00023338776,0.0005336886,0.00012489536],"domain_scores_gemma":[0.997692,0.00096904923,0.00016230237,0.00036358568,0.00070093235,0.000112123984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016440431,0.0007064164,0.0010846949,0.0020709608,0.00076669594,0.0011389556,0.0018462472,0.00084797415,0.005806489],"category_scores_gemma":[0.0059446525,0.0004206454,0.0012310774,0.0016007441,0.0011636313,0.002312271,0.0018034074,0.0019744996,0.0012796475],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013474216,0.00009775974,0.001266009,0.00022640429,0.000086476786,0.00019291486,0.00016439984,0.20035921,0.008914225,0.51879627,0.005350974,0.2644106],"study_design_scores_gemma":[0.00001155327,0.000016094722,0.00006961277,0.000012094821,0.000007940363,0.000044607397,0.000008035034,0.9367129,0.0012678608,0.059998434,0.0018364262,0.000014328096],"about_ca_topic_score_codex":0.002866082,"about_ca_topic_score_gemma":0.0021180192,"teacher_disagreement_score":0.005806489,"about_ca_system_score_codex":0.0011017969,"about_ca_system_score_gemma":0.002256332,"threshold_uncertainty_score":0.019424617},"labels":[],"label_agreement":null},{"id":"W2905653303","doi":"10.1016/j.ymssp.2018.11.040","title":"Optimal Bayesian early fault detection for CNC equipment using hidden semi-Markov process","year":2018,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council","keywords":"Unobservable; Hidden Markov model; Markov chain; Control chart; Hidden semi-Markov model; Autoregressive model; Multivariate statistics; Computer science; Bayesian probability; Partially observable Markov decision process; Fault detection and isolation; Prognostics; Markov model; Engineering; Process (computing); Data mining; Artificial intelligence; Machine learning; Markov property; Mathematics; Statistics; Econometrics","score_opus":0.013542365703999882,"score_gpt":0.2426400449127578,"score_spread":0.22909767920875793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905653303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038226474,0.00021735049,0.96014905,0.00021352865,0.000025452528,0.000024154662,0.00006818032,0.00029644585,0.00077931763],"genre_scores_gemma":[0.91353357,0.00024112647,0.082588166,0.00008352147,0.000044968754,0.00006937359,0.00026942222,0.000053239197,0.0031165364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993585,0.00014794173,0.0000347604,0.00016201304,0.0001750683,0.00012166498],"domain_scores_gemma":[0.9961202,0.0030235471,0.00027457761,0.00010370364,0.00038417568,0.00009388172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013168048,0.0010642178,0.0017462948,0.00083088945,0.00043174817,0.0008930699,0.0012493083,0.0012841217,0.0018547587],"category_scores_gemma":[0.0053883255,0.0008698174,0.0007755177,0.0005541653,0.00079856935,0.0014738706,0.0009863677,0.0015995358,0.00037638145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030662116,0.00008015835,0.0010313601,0.00009276821,0.0000379805,0.000057456582,0.000041270752,0.95506305,0.0023282093,0.00592566,0.0006163748,0.034419037],"study_design_scores_gemma":[0.0000054189104,0.000009358387,0.00017956813,0.0000036018805,0.0000043473706,0.0000054845805,0.00000172892,0.997677,0.0003485143,0.0017271023,0.000033698594,0.0000040882283],"about_ca_topic_score_codex":0.0108336955,"about_ca_topic_score_gemma":0.011602403,"teacher_disagreement_score":0.0108336955,"about_ca_system_score_codex":0.0011699777,"about_ca_system_score_gemma":0.0021976596,"threshold_uncertainty_score":0.021541238},"labels":[],"label_agreement":null},{"id":"W2906209034","doi":"10.1002/qre.2442","title":"Parameter estimation for load‐sharing system subject to Wiener degradation process using the expectation‐maximization algorithm","year":2018,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Research Grants Council, University Grants Committee; National Natural Science Foundation of China","keywords":"Estimator; Expectation–maximization algorithm; Maximization; Computer science; Degradation (telecommunications); Reliability (semiconductor); Component (thermodynamics); Function (biology); Estimation theory; Load sharing; Process (computing); Interdependence; Mathematical optimization; Maximum likelihood; Algorithm; Mathematics; Statistics; Distributed computing","score_opus":0.021603777674966854,"score_gpt":0.292014791287289,"score_spread":0.27041101361232217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906209034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009885396,0.00017076121,0.98927104,0.00009528389,0.00000643701,0.000016283377,0.000017803995,0.0000922049,0.00044479122],"genre_scores_gemma":[0.7976645,0.00061658805,0.19742516,0.00013644093,0.00004984692,0.00022345954,0.00032649015,0.000107624684,0.0034498523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992698,0.00025969453,0.000047982438,0.00017570071,0.00016954273,0.00007736129],"domain_scores_gemma":[0.99784577,0.0015411795,0.00020641304,0.00007487086,0.00028505598,0.000046746496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021566346,0.0012269474,0.0015225657,0.00067802763,0.00040256002,0.0010618238,0.0009021298,0.0011815835,0.0013551983],"category_scores_gemma":[0.005989827,0.00063604966,0.00089683715,0.0006126495,0.00087049184,0.001646139,0.0012828148,0.0012841467,0.0003054817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000657519,0.000023984629,0.0010143139,0.00010058576,0.000047963058,0.00007206744,0.000066535395,0.9657773,0.0017819734,0.006254391,0.0004164157,0.024378706],"study_design_scores_gemma":[0.0000028780855,0.000007608475,0.00011149513,0.0000037887974,0.0000035513117,0.00000964937,0.0000039114757,0.9984291,0.00022185108,0.0011363325,0.00006484452,0.0000049812825],"about_ca_topic_score_codex":0.005640491,"about_ca_topic_score_gemma":0.0025800131,"teacher_disagreement_score":0.005640491,"about_ca_system_score_codex":0.0007725459,"about_ca_system_score_gemma":0.0012026294,"threshold_uncertainty_score":0.011405468},"labels":[],"label_agreement":null},{"id":"W2909183615","doi":"10.1108/jqme-04-2017-0027","title":"A decision support tool for bi-objective risk-based maintenance scheduling of an LNG gas sweetening unit","year":2019,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Scheduling (production processes); Engineering; Multi-objective optimization; Pareto principle; Reliability engineering; Schedule; Shutdown; Operations research; Computer science; Operations management","score_opus":0.012253743581122224,"score_gpt":0.26686274072369576,"score_spread":0.2546089971425735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909183615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046399806,0.00013990952,0.9438681,0.00022295197,0.000041753872,0.0002125869,0.00044038778,0.0041353116,0.0045391917],"genre_scores_gemma":[0.5022851,0.00019237447,0.4928783,0.00014111308,0.000023572404,0.0004899447,0.00056231295,0.00022205563,0.003205171],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996506,0.00010063384,0.000027254222,0.00006337812,0.00011808723,0.000040047933],"domain_scores_gemma":[0.99859816,0.00091172935,0.00015076439,0.000060094273,0.00021864887,0.00006061368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010132293,0.0009489354,0.00081238634,0.0008425424,0.00042899698,0.001090392,0.0011588235,0.0011004631,0.007527911],"category_scores_gemma":[0.0025787987,0.00053405284,0.00084813486,0.00039908144,0.00021778018,0.0006200912,0.0006972134,0.0010666258,0.0007709963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000112697555,0.000117982985,0.0011791572,0.00012239272,0.000044885142,0.00016541677,0.00006299474,0.93741417,0.0029826632,0.0032853945,0.0012419837,0.053270236],"study_design_scores_gemma":[0.000017867775,0.000022599614,0.000087841734,0.000011014897,0.000008942238,0.000014789954,0.000009644781,0.99762696,0.00073494116,0.0006842267,0.00077642733,0.0000047097164],"about_ca_topic_score_codex":0.0056665596,"about_ca_topic_score_gemma":0.004866863,"teacher_disagreement_score":0.007527911,"about_ca_system_score_codex":0.0007605492,"about_ca_system_score_gemma":0.0014148215,"threshold_uncertainty_score":0.02518338},"labels":[],"label_agreement":null},{"id":"W2910651238","doi":"10.1109/ieem.2018.8607501","title":"Condition-based Selective Maintenance for Multicomponent Systems Under Environmental and Energy Considerations","year":2018,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Reliability engineering; Reliability (semiconductor); Component (thermodynamics); Work (physics); Energy consumption; Computer science; Maintenance engineering; Energy (signal processing); Optimal maintenance; Quality (philosophy); Mathematical optimization; Engineering; Mathematics; Mechanical engineering","score_opus":0.006794257490902724,"score_gpt":0.19422099037874624,"score_spread":0.1874267328878435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910651238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06991941,0.0003751625,0.9255012,0.00016727729,0.000024558605,0.00006174804,0.00009759571,0.00017896295,0.0036740531],"genre_scores_gemma":[0.9338584,0.00024263337,0.062364712,0.0000374283,0.000019701483,0.00012493496,0.000112753994,0.000057221874,0.0031822645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997079,0.000069829395,0.000010054325,0.000057644338,0.00010349595,0.000051112496],"domain_scores_gemma":[0.9996561,0.00020075073,0.00005558391,0.000024783649,0.000043711603,0.00001911417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005762818,0.0008524445,0.00079592445,0.00040495215,0.0003102787,0.0005876824,0.0009247657,0.0007959195,0.0016019813],"category_scores_gemma":[0.0008920362,0.0003860577,0.0006642754,0.00037853594,0.00051384995,0.00079108967,0.0006457794,0.0006584913,0.0001419267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002238314,0.00001633432,0.00018613496,0.000025560008,0.0000085181055,0.00002456083,0.000011573369,0.99143326,0.0012524084,0.002287198,0.00013011106,0.004601943],"study_design_scores_gemma":[0.0000034712002,0.000019440025,0.000096027135,0.0000013159959,0.0000030503363,0.0000069250923,0.0000035322346,0.9987949,0.0001889193,0.00076608476,0.00011481714,0.0000014619463],"about_ca_topic_score_codex":0.00590666,"about_ca_topic_score_gemma":0.0051150927,"teacher_disagreement_score":0.00590666,"about_ca_system_score_codex":0.0006715782,"about_ca_system_score_gemma":0.00094525475,"threshold_uncertainty_score":0.011744559},"labels":[],"label_agreement":null},{"id":"W2913636270","doi":"10.1016/j.apm.2019.01.036","title":"Group maintenance scheduling for two-component systems with failure interaction","year":2019,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Component (thermodynamics); Scheduling (production processes); Group (periodic table); Computer science; Reliability engineering; Engineering; Operations management; Physics","score_opus":0.010011770922015068,"score_gpt":0.19965562384917512,"score_spread":0.18964385292716004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913636270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3564182,0.00036769023,0.6389597,0.0003070682,0.00011039452,0.00014971533,0.00011899714,0.0002554771,0.003312853],"genre_scores_gemma":[0.9771339,0.000067283,0.02095296,0.00002058417,0.000029284756,0.00006280934,0.000067436726,0.000039982573,0.0016257677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995596,0.00014341479,0.000017768358,0.000088584435,0.00007236962,0.00011836669],"domain_scores_gemma":[0.9985172,0.0008413582,0.0002225231,0.00010833343,0.00014500071,0.00016565983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011698591,0.0008012364,0.0013518314,0.0005577546,0.0005992151,0.00075913395,0.0017348207,0.0008548728,0.0024987308],"category_scores_gemma":[0.0024582867,0.00044621414,0.0006803598,0.00062990596,0.0005521336,0.0008517782,0.0007170803,0.0007171008,0.00023665128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003488756,0.00010413479,0.0005023511,0.00007080892,0.000045009834,0.00010410213,0.000078162455,0.97853506,0.002001517,0.005073552,0.0007743443,0.01236198],"study_design_scores_gemma":[0.000018242661,0.000063820204,0.0002469978,0.0000016670269,0.000011757664,0.000012197574,0.000013375325,0.99604475,0.0002470447,0.0032101683,0.00012591423,0.0000040187288],"about_ca_topic_score_codex":0.0045591467,"about_ca_topic_score_gemma":0.0031650898,"teacher_disagreement_score":0.0045591467,"about_ca_system_score_codex":0.0009581105,"about_ca_system_score_gemma":0.0007677419,"threshold_uncertainty_score":0.00906527},"labels":[],"label_agreement":null},{"id":"W2917659601","doi":"","title":"A rollingstock door system’s dynamic maintenance strategies based on a sensitivity analysis through bayesian networks","year":2013,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Obsolescence; Dynamic Bayesian network; Computer science; Markov process; Bayesian network; Process industry; Reliability engineering; Formalism (music); Bayesian probability; Process (computing); System dynamics; Maintenance engineering; Component (thermodynamics); Sensitivity (control systems); Engineering; Artificial intelligence; Mathematics","score_opus":0.005231312528657204,"score_gpt":0.19587457161890043,"score_spread":0.1906432590902432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917659601","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22552347,0.0007703345,0.76739913,0.00046064713,0.000023697126,0.00013108211,0.0003101239,0.00038918862,0.0049923537],"genre_scores_gemma":[0.9824738,0.00022054256,0.015683841,0.00002814799,0.000007829441,0.000056858822,0.00007780007,0.000018026727,0.0014330321],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935776,0.00021871275,0.000026078453,0.00017451642,0.00013841783,0.00008446277],"domain_scores_gemma":[0.9977629,0.0016375366,0.00027673875,0.000052995376,0.00019097295,0.000078711084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016938525,0.0010284123,0.001007261,0.0015424107,0.00037795235,0.0011527187,0.00096890883,0.00117458,0.001964882],"category_scores_gemma":[0.0052299155,0.00074964715,0.0011017249,0.00063266815,0.00078952697,0.0012034825,0.0007378781,0.00087664515,0.00011905173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042964533,0.000014352802,0.0004990561,0.000026941654,0.000029504885,0.00004962914,0.000027197482,0.99187005,0.0008158028,0.0033037004,0.00007788238,0.0032429295],"study_design_scores_gemma":[0.000002854931,0.000013639313,0.00024488504,0.000004104457,0.000013402204,0.000010581189,0.0000044320714,0.9979741,0.00018004297,0.0014956859,0.00005076239,0.000005528831],"about_ca_topic_score_codex":0.013821495,"about_ca_topic_score_gemma":0.006719497,"teacher_disagreement_score":0.013821495,"about_ca_system_score_codex":0.0017622882,"about_ca_system_score_gemma":0.0007983662,"threshold_uncertainty_score":0.027482092},"labels":[],"label_agreement":null},{"id":"W2921878706","doi":"10.33889/ijmems.2016.1.2-008","title":"Reliability Calculation for Dormant k-out-of-n Systems with Periodic Maintenance","year":2016,"lang":"en","type":"article","venue":"International Journal of Mathematical Engineering and Management Sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Mean time between failures; Reliability engineering; Maintainability; Redundancy (engineering); Reliability (semiconductor); Intra-rater reliability; Computer science; Failure rate; Interval (graph theory); Engineering; Mathematics; Statistics; Confidence interval","score_opus":0.009315239850712503,"score_gpt":0.22604630974242726,"score_spread":0.21673106989171476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921878706","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06690061,0.00049922516,0.9270247,0.00008261742,0.00004117399,0.000038303526,0.000059667756,0.00020276077,0.0051509375],"genre_scores_gemma":[0.877989,0.0005212263,0.118422076,0.000022468765,0.000033658274,0.00006978142,0.000116502466,0.00006316262,0.0027620639],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976474,0.000047558085,0.000015083782,0.000044793156,0.00010831321,0.000019476929],"domain_scores_gemma":[0.9995633,0.00017718549,0.00007861103,0.00004610247,0.00012264107,0.0000122001275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004119162,0.00036414163,0.0002711404,0.00061633124,0.0002768449,0.00031032073,0.0006284272,0.00026034375,0.0013655378],"category_scores_gemma":[0.0016819936,0.00014299646,0.0004769724,0.00030541793,0.00022923683,0.00057521637,0.0002621241,0.0003300026,0.00023649204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075145785,0.000027615866,0.0020371494,0.00022922968,0.000033071097,0.00030824848,0.00014146519,0.87820166,0.01817974,0.020460024,0.00099032,0.0793163],"study_design_scores_gemma":[0.0000027220942,0.00004752878,0.00072690536,0.000010063949,0.00001102524,0.0001292797,0.0000134538295,0.99264854,0.0019546172,0.0036359464,0.0008138047,0.0000060124676],"about_ca_topic_score_codex":0.0023812843,"about_ca_topic_score_gemma":0.001929347,"teacher_disagreement_score":0.0023812843,"about_ca_system_score_codex":0.0003710526,"about_ca_system_score_gemma":0.00049760984,"threshold_uncertainty_score":0.004734814},"labels":[],"label_agreement":null},{"id":"W2922088561","doi":"10.33889/ijmems.2019.4.1-008","title":"System Reliability Growth Analysis during Warranty","year":2019,"lang":"en","type":"article","venue":"International Journal of Mathematical Engineering and Management Sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Warranty; Field (mathematics); Computer science; Growth model; Engineering; Mathematics","score_opus":0.003394414914767086,"score_gpt":0.1933171880142001,"score_spread":0.189922773099433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922088561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83244336,0.0003820714,0.1551194,0.0002999073,0.000030122366,0.000094785726,0.0008531797,0.0006010694,0.0101760775],"genre_scores_gemma":[0.98941886,0.00011055588,0.007911702,0.000012676835,0.000009060261,0.00003354361,0.0005326644,0.000042489726,0.0019284341],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992016,0.00015142598,0.00002631941,0.00010295312,0.00043901638,0.00007871821],"domain_scores_gemma":[0.9967078,0.0014243437,0.00037647437,0.00029540542,0.0011499331,0.000046037716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014841302,0.00035888594,0.00026290398,0.000886341,0.0002607534,0.00027876542,0.0004512289,0.00027120634,0.0010500932],"category_scores_gemma":[0.0049796416,0.00012552984,0.00038539123,0.00043722833,0.00029260383,0.00074177096,0.0002752996,0.0007774356,0.0002485908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002750389,0.000106721025,0.017123943,0.00013418727,0.000044492906,0.0002760635,0.0004075383,0.85543424,0.028117696,0.011086834,0.001773286,0.08521994],"study_design_scores_gemma":[0.0000047669155,0.00024583912,0.011742205,0.0000074999466,0.000011868898,0.00010481915,0.00006161259,0.9753278,0.0087731825,0.0020269258,0.0016790517,0.00001451045],"about_ca_topic_score_codex":0.007873398,"about_ca_topic_score_gemma":0.0044091754,"teacher_disagreement_score":0.007873398,"about_ca_system_score_codex":0.0007573539,"about_ca_system_score_gemma":0.000578851,"threshold_uncertainty_score":0.0156551},"labels":[],"label_agreement":null},{"id":"W2922601328","doi":"10.1002/0471667196.ess1145.pub3","title":"Repairable Systems in Reliability","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Statistical Sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ASTER","funders":"","keywords":"Encyclopedia; Citation; Reliability (semiconductor); Computer science; Library science; Physics","score_opus":0.005923039002681355,"score_gpt":0.22992407166880982,"score_spread":0.22400103266612847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922601328","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019060002,0.111342855,0.06977353,0.004965351,0.00605457,0.000083153456,0.0018215594,0.0015639883,0.80248904],"genre_scores_gemma":[0.023879426,0.111526206,0.036109626,0.0010962229,0.00455586,0.00012954071,0.0023727964,0.00096742983,0.81936276],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99953187,0.00009702911,0.000020459232,0.00007700673,0.00023978912,0.00003385271],"domain_scores_gemma":[0.9992231,0.00029899867,0.000057168178,0.00011893506,0.00021451637,0.00008728586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006017476,0.0014234126,0.0013236123,0.0031407496,0.00048738858,0.0028591745,0.0013339389,0.0010290238,0.1118174],"category_scores_gemma":[0.0019381294,0.0003757184,0.00048392103,0.0062543005,0.0008161036,0.0024856853,0.0010840337,0.0018301558,0.042730603],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017448856,0.00005671433,0.00017000537,0.0004712793,0.0000131932875,0.000054875443,0.0000939959,0.0022456949,0.0003100027,0.15552896,0.5419064,0.29913142],"study_design_scores_gemma":[0.000009699466,0.00003099363,0.00083633844,0.00040863236,0.00002126268,0.00026942644,0.00007647338,0.004619253,0.00037786245,0.09323615,0.90009767,0.00001620201],"about_ca_topic_score_codex":0.00354006,"about_ca_topic_score_gemma":0.00661576,"teacher_disagreement_score":0.1118174,"about_ca_system_score_codex":0.001070948,"about_ca_system_score_gemma":0.0011776828,"threshold_uncertainty_score":0.3740664},"labels":[],"label_agreement":null},{"id":"W2925520559","doi":"10.1145/3522588","title":"Network Design for <i>s</i> - <i>t</i> Effective Resistance","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Algorithms","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Google (Canada)","funders":"","keywords":"Randomized rounding; Rounding; Approximation algorithm; Mathematics; Shortest path problem; Linear programming relaxation; Steiner tree problem; Combinatorics; Discrete mathematics; Mathematical optimization; Graph; Linear programming; Computer science","score_opus":0.011926888137200577,"score_gpt":0.21249345874963238,"score_spread":0.2005665706124318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2925520559","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07482471,0.00018427592,0.9171687,0.00054908806,0.000038981627,0.00007653786,0.00012778732,0.00032978525,0.0067001283],"genre_scores_gemma":[0.79401755,0.000355551,0.19889674,0.00019002709,0.000055053242,0.00022119253,0.00019999745,0.00016564217,0.0058981664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993686,0.00015160808,0.000026447868,0.0002195607,0.00012634105,0.000107557404],"domain_scores_gemma":[0.99896264,0.00047882655,0.0002033133,0.00015802753,0.00011381689,0.000083400555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008120272,0.00087112404,0.00088102004,0.00055476325,0.0005576467,0.0011346218,0.0011974536,0.000794775,0.0038321537],"category_scores_gemma":[0.003155317,0.0003868979,0.00057064224,0.00072202645,0.0009683689,0.002641447,0.0009122294,0.00073638256,0.00042889928],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021597296,0.000073318835,0.00060812326,0.0001499988,0.000036391288,0.00014845529,0.00011390498,0.8737831,0.015968122,0.06506,0.0026406667,0.04120189],"study_design_scores_gemma":[0.000029089362,0.000143722,0.00019775465,0.0000136617755,0.000023356733,0.00011100692,0.000047735695,0.95332986,0.005481254,0.037959363,0.0026512905,0.000011958818],"about_ca_topic_score_codex":0.0011555826,"about_ca_topic_score_gemma":0.001445099,"teacher_disagreement_score":0.0038321537,"about_ca_system_score_codex":0.0011451767,"about_ca_system_score_gemma":0.00062941026,"threshold_uncertainty_score":0.012819886},"labels":[],"label_agreement":null},{"id":"W2937788857","doi":"10.1007/s10479-019-03197-z","title":"Preface: reliability and quality management in stochastic systems","year":2019,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Theory of computation; Robustness (evolution); Reliability (semiconductor); Operations research; Quality (philosophy); Annals; Task (project management); Risk analysis (engineering); Systems engineering; Engineering","score_opus":0.12200030967543281,"score_gpt":0.407875745290656,"score_spread":0.2858754356152232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937788857","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000893706,0.05439571,0.017119631,0.082549796,0.7965455,0.00010885999,0.0012675585,0.00030364984,0.04681548],"genre_scores_gemma":[0.018433737,0.04529979,0.0044643213,0.019114112,0.7722335,0.0001653644,0.0014326356,0.0006230956,0.13823354],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998841,0.00029700476,0.0001291317,0.00023092117,0.0004442526,0.000057819772],"domain_scores_gemma":[0.9846219,0.006137446,0.00045306032,0.0007634118,0.006908984,0.0011152682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022094492,0.0022526146,0.0015611834,0.003797878,0.002257597,0.0031803695,0.0016308748,0.0029055302,0.041890394],"category_scores_gemma":[0.02041253,0.00055145274,0.0011640027,0.003678196,0.0011310009,0.0034063011,0.0013106025,0.008440976,0.020396061],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017566215,0.00003132156,0.000080116304,0.00020427417,0.000008717529,0.00006383738,0.000044932516,0.0008691365,0.00017837017,0.007812791,0.97092676,0.019762252],"study_design_scores_gemma":[0.000026468726,0.000118742755,0.0020190687,0.0006864162,0.000027717275,0.0002217347,0.0001321745,0.0030882014,0.00039591492,0.04046929,0.9527585,0.00005570698],"about_ca_topic_score_codex":0.005744598,"about_ca_topic_score_gemma":0.0051194504,"teacher_disagreement_score":0.041890394,"about_ca_system_score_codex":0.002650537,"about_ca_system_score_gemma":0.0018719736,"threshold_uncertainty_score":0.14013731},"labels":[],"label_agreement":null},{"id":"W2940098564","doi":"10.1016/j.apm.2019.04.015","title":"Optimization issues in k-out-of-n systems","year":2019,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Schedule; Idle; Component (thermodynamics); Economic shortage; Poisson process; Type (biology); Mathematical optimization; Term (time); Shock (circulatory); Process (computing); Computer science; Heuristics; Reliability engineering; Mathematics; Poisson distribution; Statistics; Engineering; Physics","score_opus":0.013075344861318255,"score_gpt":0.21042190364193802,"score_spread":0.19734655878061977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940098564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15555939,0.0060250927,0.7730274,0.0044690496,0.0005690858,0.00009803969,0.00040102538,0.00015991168,0.05969103],"genre_scores_gemma":[0.9693662,0.0012639281,0.017922806,0.00018637057,0.00015485536,0.00004820727,0.00009333012,0.000060841558,0.010903589],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934655,0.00025962957,0.000040496325,0.00014837287,0.00011044342,0.0000944952],"domain_scores_gemma":[0.99829644,0.0011714236,0.00021219178,0.000069640424,0.00019887373,0.000051499694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015273053,0.0006127727,0.0017141503,0.0004571749,0.0010329536,0.002203112,0.0014462894,0.0017935631,0.004601257],"category_scores_gemma":[0.0057271505,0.0006492841,0.0006916806,0.0008921164,0.0012002456,0.0024331952,0.0010515262,0.0009327026,0.00038062892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009101565,0.000037387104,0.00046939886,0.0001492549,0.000036622456,0.00014995041,0.00006117545,0.9291264,0.0006150565,0.058414053,0.0015333471,0.009316297],"study_design_scores_gemma":[0.000010248782,0.000021461492,0.00034498158,0.000014011511,0.000009040499,0.000042878448,0.00002074214,0.95594656,0.0001593877,0.042679247,0.00074028136,0.000011132632],"about_ca_topic_score_codex":0.008712476,"about_ca_topic_score_gemma":0.007132922,"teacher_disagreement_score":0.008712476,"about_ca_system_score_codex":0.0012928316,"about_ca_system_score_gemma":0.00081560685,"threshold_uncertainty_score":0.017323494},"labels":[],"label_agreement":null},{"id":"W2943418631","doi":"10.1108/jqme-05-2018-0041","title":"Non-linear threshold algorithm based solution for the redundancy allocation problem considering multiple redundancy strategies","year":2019,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Redundancy (engineering); Mathematical optimization; Simulated annealing; Computer science; Triple modular redundancy; Particle swarm optimization; Computational complexity theory; Algorithm; Mathematics","score_opus":0.01871479499429573,"score_gpt":0.26048179076073097,"score_spread":0.24176699576643523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943418631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011329539,0.00012046323,0.9849192,0.00008660627,0.000030805673,0.00006225078,0.000013826562,0.00017671369,0.0032605915],"genre_scores_gemma":[0.39660755,0.00024670194,0.59768707,0.00011443169,0.000029470975,0.00035764562,0.00010085606,0.000080201,0.0047760177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996344,0.00010290217,0.00002553214,0.000068836394,0.00011435304,0.000054010317],"domain_scores_gemma":[0.9994265,0.00032869895,0.000076131815,0.000026558035,0.00011672103,0.000025374307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000516368,0.0006052589,0.000622227,0.0004966245,0.00041892604,0.00094188255,0.001186883,0.0008465181,0.003250422],"category_scores_gemma":[0.0016089564,0.0002731351,0.0006995218,0.0005421175,0.00037412805,0.00066657906,0.00068315887,0.00093600445,0.0004199004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008970098,0.00008281536,0.0005801821,0.00016584338,0.00003968824,0.000088188935,0.00008858613,0.88872665,0.0048429803,0.018710962,0.0011466921,0.0854377],"study_design_scores_gemma":[0.000008992271,0.000044306533,0.000050575185,0.000006410296,0.000005385007,0.000025661684,0.000011148581,0.996842,0.0006808979,0.0018291511,0.0004925772,0.0000030084307],"about_ca_topic_score_codex":0.0028466526,"about_ca_topic_score_gemma":0.0023060585,"teacher_disagreement_score":0.003250422,"about_ca_system_score_codex":0.00060006493,"about_ca_system_score_gemma":0.0017458874,"threshold_uncertainty_score":0.010873735},"labels":[],"label_agreement":null},{"id":"W2945967979","doi":"10.1016/j.cie.2019.05.020","title":"Optimal condition-based and age-based opportunistic maintenance policy for a two-unit series system","year":2019,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Jilin Office of Philosophy and Social Science; China Scholarship Council; National Natural Science Foundation of China","keywords":"Preventive maintenance; Unit (ring theory); Reliability engineering; Condition-based maintenance; Maintenance actions; Corrective maintenance; Planned maintenance; Optimal maintenance; Engineering; Computer science; Predictive maintenance; Mathematics","score_opus":0.01723645798155501,"score_gpt":0.21464352288972632,"score_spread":0.1974070649081713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945967979","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64250445,0.0008504299,0.34853223,0.00079753663,0.00010866676,0.00016488958,0.00029359965,0.0007178236,0.0060303495],"genre_scores_gemma":[0.9955903,0.000036965215,0.0036555156,0.000019118594,0.000011529717,0.00001294937,0.000023730476,0.0000071616955,0.0006428314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995653,0.000096812815,0.000024041918,0.00010210582,0.0000732825,0.00013840699],"domain_scores_gemma":[0.9983481,0.00078820746,0.00025976228,0.000091370115,0.0003568177,0.00015567562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010256921,0.00067722815,0.0012731701,0.00061862025,0.0005076136,0.0008406114,0.0010706119,0.0012189593,0.0018668176],"category_scores_gemma":[0.002197369,0.00035304393,0.0003293156,0.00038474807,0.0006332087,0.0006762462,0.0005215975,0.0005353765,0.00017977602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031392786,0.00006838311,0.0007303289,0.00004366584,0.000021023974,0.000079033714,0.000035192552,0.98408985,0.0028691979,0.0011475757,0.00064839603,0.00995349],"study_design_scores_gemma":[0.000014656638,0.000045758403,0.0004924481,0.0000021398428,0.0000111178915,0.000017520631,0.000008743361,0.99862194,0.00023099012,0.0005065972,0.000043522414,0.000004547547],"about_ca_topic_score_codex":0.010419425,"about_ca_topic_score_gemma":0.009314755,"teacher_disagreement_score":0.010419425,"about_ca_system_score_codex":0.0011490111,"about_ca_system_score_gemma":0.0012746425,"threshold_uncertainty_score":0.020717561},"labels":[],"label_agreement":null},{"id":"W2948416843","doi":"10.48550/arxiv.1906.02359","title":"All Terminal Reliability Roots of Smallest Modulus","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Terminal (telecommunication); Reliability (semiconductor); Modulus; Mathematics; Reliability engineering; Computer science; Engineering; Physics; Computer network; Geometry; Thermodynamics","score_opus":0.03317728080286578,"score_gpt":0.16517395769789694,"score_spread":0.13199667689503117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948416843","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7044901,0.0008615916,0.26858723,0.0015368741,0.00011466474,0.00008121895,0.0011250072,0.00068173243,0.0225216],"genre_scores_gemma":[0.9791347,0.000337514,0.015780322,0.00008109975,0.00006924766,0.000051969415,0.00026124774,0.000114065944,0.0041698986],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994912,0.000068570305,0.000019314231,0.00017589692,0.00015168932,0.00009334014],"domain_scores_gemma":[0.99656373,0.0016844844,0.00074711227,0.00040014856,0.00039474777,0.00020980276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004895131,0.0004918723,0.0005907988,0.0008313274,0.0006523202,0.00089641643,0.00066473644,0.0007400235,0.004251356],"category_scores_gemma":[0.0093828915,0.00029258514,0.00042720686,0.0006333686,0.0012592449,0.0020143595,0.0010528698,0.0013009977,0.0005611794],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004901666,0.0001025298,0.011185954,0.0006361065,0.000080357306,0.0010612209,0.0009739824,0.09692243,0.040886026,0.7109569,0.0102769155,0.12642743],"study_design_scores_gemma":[0.00006123206,0.00022609642,0.010110906,0.00009554554,0.00004901546,0.0013721271,0.0003793738,0.16685328,0.016218826,0.79398066,0.010582203,0.00007070488],"about_ca_topic_score_codex":0.00064365094,"about_ca_topic_score_gemma":0.0006888808,"teacher_disagreement_score":0.004251356,"about_ca_system_score_codex":0.000749128,"about_ca_system_score_gemma":0.00056003913,"threshold_uncertainty_score":0.014222205},"labels":[],"label_agreement":null},{"id":"W2949116816","doi":"10.1186/s40488-019-0095-1","title":"A new class of survival distribution for degradation processes subject to shocks","year":2019,"lang":"en","type":"article","venue":"Journal of Statistical Distributions and Applications","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Ottawa Hospital","funders":"National Eye Institute; National Institutes of Health","keywords":"Shock (circulatory); Degradation (telecommunications); Wiener process; Gamma process; Computer science; Phase-type distribution; Hitting time; Stochastic process; Lévy process; Process (computing); Statistical physics; Mathematics; Event (particle physics); Applied mathematics; Mathematical analysis; Statistics; Physics; Markov process","score_opus":0.007780989496004339,"score_gpt":0.24699827213442396,"score_spread":0.23921728263841963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949116816","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07541206,0.0010259526,0.9149224,0.0011040422,0.0001598423,0.00014224644,0.000382848,0.00047671335,0.006373967],"genre_scores_gemma":[0.9234608,0.0032245826,0.049356017,0.00069562194,0.0006703596,0.0004838682,0.0008964099,0.00027081463,0.020941626],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990808,0.00020294506,0.00005806538,0.00021688458,0.00027028017,0.00017102242],"domain_scores_gemma":[0.9936924,0.0032236013,0.0010600768,0.00048368526,0.001085313,0.0004549164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041105226,0.0012456077,0.00109055,0.0023323847,0.0009023181,0.0019113916,0.0019853588,0.0020586387,0.0067596496],"category_scores_gemma":[0.014672284,0.0004649763,0.001391739,0.0012875446,0.0023845,0.0033510972,0.0017119813,0.0032990181,0.0010824064],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083305385,0.00007483402,0.008208155,0.00019993608,0.000070001945,0.0006547304,0.00078909803,0.103311464,0.006454188,0.8456871,0.00517251,0.029294852],"study_design_scores_gemma":[0.00004627969,0.0001452004,0.00410998,0.00010661616,0.00004298596,0.0010933309,0.00030749408,0.69332814,0.0015130077,0.29102185,0.008191284,0.00009370588],"about_ca_topic_score_codex":0.0026730911,"about_ca_topic_score_gemma":0.0012805436,"teacher_disagreement_score":0.0067596496,"about_ca_system_score_codex":0.0015110724,"about_ca_system_score_gemma":0.0009846212,"threshold_uncertainty_score":0.022613287},"labels":[],"label_agreement":null},{"id":"W2951567270","doi":"10.1287/ijoc.2018.0863","title":"Group Maintenance: A Restless Bandits Approach","year":2019,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematical optimization; Lagrangian relaxation; Markov decision process; Heuristics; Computer science; Time horizon; Curse of dimensionality; Linear programming relaxation; Linear programming; Scheduling (production processes); Dynamic programming; Benchmark (surveying); Stochastic programming; Mathematics; Markov process","score_opus":0.006555777813665351,"score_gpt":0.19829966750556455,"score_spread":0.1917438896918992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951567270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037041247,0.00029845515,0.9540567,0.00045245865,0.000055865617,0.00008874501,0.00010349629,0.00030967494,0.0075933603],"genre_scores_gemma":[0.8360869,0.00028078526,0.15622152,0.00026711816,0.0000823199,0.00025313188,0.00018401639,0.00017486907,0.0064494475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992822,0.00030010397,0.000022887005,0.00012312965,0.00013023589,0.00014158843],"domain_scores_gemma":[0.9984478,0.0009994436,0.00022200317,0.00010610109,0.00013454865,0.00009016077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014493046,0.0010224973,0.0014371626,0.00071233907,0.00059072673,0.0013582697,0.0017892366,0.0013155438,0.00460087],"category_scores_gemma":[0.0040922863,0.0006375712,0.0006568212,0.00072423683,0.0012295458,0.0017185318,0.0010164159,0.0014643541,0.00041407172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065426924,0.000040100625,0.00017611995,0.000031302705,0.000016579887,0.000032083404,0.000035508554,0.9764608,0.00028859594,0.014761669,0.0005278459,0.0075640557],"study_design_scores_gemma":[0.000008767838,0.000015926018,0.000026350579,0.0000038379612,0.000003829139,0.0000036821523,0.0000080874315,0.99350893,0.00009294929,0.0060986364,0.00022665231,0.0000022697743],"about_ca_topic_score_codex":0.0070297825,"about_ca_topic_score_gemma":0.0048234,"teacher_disagreement_score":0.0070297825,"about_ca_system_score_codex":0.0017222851,"about_ca_system_score_gemma":0.001401262,"threshold_uncertainty_score":0.015391469},"labels":[],"label_agreement":null},{"id":"W2953046944","doi":"","title":"Approximately Optimal Monitoring of Plan Preconditions","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Precondition; Plan (archaeology); Computer science; Predicate transformer semantics; Point (geometry); Markov process; Risk analysis (engineering); Mathematics; Business; Semantics (computer science); Statistics","score_opus":0.0532551568953811,"score_gpt":0.16483499944827631,"score_spread":0.11157984255289521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953046944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047808595,0.0002075662,0.9478818,0.00044902548,0.00002423613,0.000051560644,0.00021178529,0.0004405241,0.00292488],"genre_scores_gemma":[0.836757,0.00020937373,0.16078588,0.00009334056,0.000027049442,0.0001306605,0.00029228753,0.00009328307,0.001611133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899524,0.00033703676,0.00004530209,0.00025135028,0.00020793876,0.00016314205],"domain_scores_gemma":[0.99575126,0.0030797168,0.00052800967,0.00027257195,0.00022938022,0.00013912628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014437449,0.0006022789,0.0009013081,0.00041477216,0.00031096785,0.0010329693,0.00078580563,0.0007803106,0.0020230552],"category_scores_gemma":[0.009735185,0.00061643845,0.00055297493,0.0004000537,0.00095284934,0.0016374157,0.00090337836,0.0013348655,0.0001901798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007419881,0.000017549362,0.00058978074,0.0000488433,0.00001754675,0.000029044952,0.000048251153,0.9647182,0.0007743536,0.021768587,0.0007319627,0.011181633],"study_design_scores_gemma":[0.000008463423,0.000010655107,0.00012101744,0.0000066227244,0.0000044020276,0.0000050492604,0.000009873891,0.98513633,0.00038497703,0.01409101,0.00021839594,0.0000032926362],"about_ca_topic_score_codex":0.008740231,"about_ca_topic_score_gemma":0.008656072,"teacher_disagreement_score":0.008740231,"about_ca_system_score_codex":0.0016946387,"about_ca_system_score_gemma":0.0027201949,"threshold_uncertainty_score":0.017378747},"labels":[],"label_agreement":null},{"id":"W2956059284","doi":"10.1108/jqme-01-2018-0004","title":"Intersection of corrosion prevention strategy and practice","year":2019,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Armed Forces; Royal Military College of Canada","funders":"","keywords":"Originality; Engineering; Preventive maintenance; Corrosion prevention; Corrosion; Order (exchange); Risk analysis (engineering); Transport engineering; Intersection (aeronautics); Operations research; Operations management; Computer science; Business; Reliability engineering; Finance","score_opus":0.014525414493720459,"score_gpt":0.2766741700682861,"score_spread":0.26214875557456563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956059284","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16325985,0.012038389,0.07217251,0.06869957,0.00036328248,0.00032239666,0.00010512584,0.00026368588,0.68277526],"genre_scores_gemma":[0.9768742,0.0024740396,0.010927357,0.0010940725,0.00007923878,0.00007124512,0.000024156716,0.00003525238,0.00842043],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9875206,0.0051742736,0.0005021761,0.001493983,0.00412914,0.001179911],"domain_scores_gemma":[0.9848135,0.004313847,0.0028095725,0.0015827677,0.0049637207,0.001516514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008817528,0.000464588,0.00036458988,0.0028199472,0.0019626636,0.008855508,0.0016083391,0.0020968944,0.007161941],"category_scores_gemma":[0.017466215,0.00023452504,0.00033747812,0.0015856366,0.010190434,0.0034397482,0.0041958676,0.0015991445,0.00093468104],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074280986,0.00040094278,0.02390022,0.0007109269,0.000064447384,0.00032416813,0.005901742,0.0036126906,0.0009468225,0.6684936,0.012481144,0.2830889],"study_design_scores_gemma":[0.00004795191,0.00070248696,0.045687735,0.0020811174,0.000057533023,0.0013678949,0.031308897,0.009862562,0.00282005,0.5526638,0.35330397,0.00009597103],"about_ca_topic_score_codex":0.010243622,"about_ca_topic_score_gemma":0.010164806,"teacher_disagreement_score":0.011144875,"about_ca_system_score_codex":0.011144875,"about_ca_system_score_gemma":0.01496855,"threshold_uncertainty_score":0.080862105},"labels":[],"label_agreement":null},{"id":"W2964929082","doi":"10.1109/rams.2019.8768911","title":"A Methodology for Aircraft Reliability, Maintainability, Availability, and Cost Management","year":2019,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Maintainability; Reliability engineering; Reliability (semiconductor); Computer science; Aircraft maintenance; Engineering; Aeronautics","score_opus":0.017661375116657944,"score_gpt":0.2548030817882515,"score_spread":0.23714170667159357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964929082","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005863152,0.0004294989,0.99469995,0.0002242929,0.00006398861,0.00023149926,0.00018333596,0.00019548347,0.0033857694],"genre_scores_gemma":[0.014869474,0.000460316,0.9825378,0.000052510808,0.000029961417,0.0003881236,0.00017533141,0.000028001945,0.0014584452],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99495345,0.001544777,0.00040348462,0.0005036173,0.002445107,0.00014953376],"domain_scores_gemma":[0.99717414,0.0011724295,0.0003511025,0.00027746815,0.000937033,0.00008771659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006460936,0.001797752,0.00094496383,0.0058599026,0.001396056,0.0029998275,0.0019075812,0.0012203394,0.003990252],"category_scores_gemma":[0.0065752133,0.0005337394,0.0018410867,0.004419157,0.0010281076,0.0020742854,0.0013649202,0.0015507229,0.0011005176],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025937954,0.00015138532,0.0010539076,0.0013735803,0.0002461251,0.00024132595,0.0003780072,0.11245956,0.0055067614,0.38358486,0.010175321,0.48480308],"study_design_scores_gemma":[0.000050229002,0.0003824581,0.001618071,0.0010544077,0.00019776206,0.000805314,0.0006039296,0.44988558,0.008159783,0.33797905,0.19911265,0.00015072676],"about_ca_topic_score_codex":0.004549859,"about_ca_topic_score_gemma":0.0066012596,"teacher_disagreement_score":0.006460936,"about_ca_system_score_codex":0.0028196832,"about_ca_system_score_gemma":0.006551423,"threshold_uncertainty_score":0.034169078},"labels":[],"label_agreement":null},{"id":"W2966114666","doi":"10.1109/rams.2019.8769273","title":"Maintenance Effectiveness Estimation with Applications to Railway Industry","year":2019,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Estimation; Computer science; Maintenance engineering; Reliability engineering; Engineering; Systems engineering","score_opus":0.0038939898242173338,"score_gpt":0.20652926417351772,"score_spread":0.2026352743493004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966114666","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031352196,0.00046327696,0.9668151,0.00018179372,0.0000090873455,0.000042278036,0.00011056188,0.00019640062,0.00082928967],"genre_scores_gemma":[0.7553576,0.0007240061,0.24062102,0.000059710062,0.00008173307,0.00016066707,0.00047717732,0.00006728727,0.0024507914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989422,0.0006349102,0.0000421324,0.00015469673,0.00018567646,0.000040385352],"domain_scores_gemma":[0.99411726,0.004758147,0.00041691095,0.00024759964,0.00040979436,0.00005028618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028396836,0.0005604615,0.0007441179,0.0014234241,0.00017248552,0.0005042895,0.00084799714,0.0007103582,0.0018290379],"category_scores_gemma":[0.014057308,0.0003152472,0.00059707556,0.001096305,0.00033068034,0.0004629481,0.00059363316,0.00057472044,0.00017033741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006242667,0.00006579355,0.008283921,0.000118417775,0.00012506887,0.000064627464,0.000060854487,0.8563204,0.001284318,0.009855444,0.0006137306,0.12314486],"study_design_scores_gemma":[0.000007736357,0.000034031826,0.0030273646,0.000009497931,0.000017544533,0.000024329036,0.000011655168,0.9913378,0.0004022951,0.00462021,0.00049937324,0.000008167156],"about_ca_topic_score_codex":0.0053860084,"about_ca_topic_score_gemma":0.0035805558,"teacher_disagreement_score":0.0053860084,"about_ca_system_score_codex":0.00066454004,"about_ca_system_score_gemma":0.00069976354,"threshold_uncertainty_score":0.015017867},"labels":[],"label_agreement":null},{"id":"W2977529085","doi":"10.1016/j.ress.2019.106668","title":"Multi-distribution multi-commodity multistate flow network model and its reliability evaluation algorithm","year":2019,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science and Technology, Taiwan; National Natural Science Foundation of China","keywords":"Correctness; Reliability (semiconductor); Path (computing); Computer science; Complement (music); Flow network; Commodity; Flow (mathematics); State (computer science); Component (thermodynamics); Algorithm; Distribution (mathematics); Mathematical optimization; Mathematics","score_opus":0.009777381483399473,"score_gpt":0.21238388904058175,"score_spread":0.2026065075571823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977529085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02337292,0.0003887383,0.9699478,0.000367235,0.000047308768,0.00010523161,0.00028636743,0.00024259412,0.005241792],"genre_scores_gemma":[0.72669464,0.0007477296,0.25642088,0.00010332855,0.00005862768,0.00045183045,0.000728533,0.00015892187,0.014635601],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996362,0.00014042623,0.000012223029,0.00008797425,0.000072064686,0.000051100786],"domain_scores_gemma":[0.99937075,0.00034588017,0.00006274403,0.00003469949,0.00014643624,0.00003946905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014137009,0.00092846574,0.0013629989,0.001065929,0.00069398136,0.0012692636,0.0022211485,0.0013609806,0.0041738776],"category_scores_gemma":[0.0022145882,0.0007165263,0.0009327275,0.0015144468,0.0006412862,0.0019584442,0.0010109706,0.0011320238,0.0003721407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018737188,0.000011182342,0.00011413398,0.000017477425,0.000006978417,0.00001144783,0.0000069749176,0.99148387,0.000074281146,0.004121399,0.0004011553,0.003732335],"study_design_scores_gemma":[0.000002129444,0.0000029390196,0.000022244723,0.0000015377869,0.0000023384716,0.0000025215722,0.0000015811546,0.9985324,0.00002295678,0.0013458258,0.000062227264,0.000001291594],"about_ca_topic_score_codex":0.01639408,"about_ca_topic_score_gemma":0.009769049,"teacher_disagreement_score":0.01639408,"about_ca_system_score_codex":0.0020527744,"about_ca_system_score_gemma":0.0017782603,"threshold_uncertainty_score":0.032597303},"labels":[],"label_agreement":null},{"id":"W2979848724","doi":"10.1007/s10479-019-03371-3","title":"Joint maintenance and just-in-time spare parts provisioning policy for a multi-unit production system","year":2019,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Spare part; Production (economics); Computer science; Reliability engineering; Interval (graph theory); Provisioning; Process (computing); Sensitivity (control systems); Holding cost; Theory of computation; Minification; Operations research; Total cost; Mathematical optimization; Operations management; Engineering; Mathematics; Economics; Algorithm","score_opus":0.1834285068207878,"score_gpt":0.4011294589257651,"score_spread":0.21770095210497734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979848724","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7291849,0.0011374861,0.26157728,0.0014279529,0.0001856538,0.00026646245,0.0004858917,0.0005908648,0.0051435023],"genre_scores_gemma":[0.9944999,0.00007775923,0.0045651672,0.000021207143,0.000026909107,0.00001958241,0.000050829836,0.0000125806555,0.00072602887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998565,0.00039580447,0.000084582774,0.00027281453,0.00019939886,0.00048238147],"domain_scores_gemma":[0.9964365,0.001687731,0.00070553983,0.00022122427,0.00050229335,0.00044665908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025161826,0.0010854825,0.0022591378,0.0009190619,0.0008129656,0.0015648132,0.002228326,0.0017075002,0.0027156363],"category_scores_gemma":[0.003749966,0.00076105125,0.0006311607,0.00081610173,0.00088740024,0.001724964,0.000996896,0.0009875359,0.0002661714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009634744,0.00015567671,0.0010332718,0.00013190004,0.000060331822,0.0002756867,0.00006124078,0.977517,0.0046467464,0.003108553,0.0012053467,0.010840876],"study_design_scores_gemma":[0.000023629258,0.00011353222,0.00075173273,0.0000046149685,0.000025944386,0.00004841769,0.000025270258,0.9974815,0.000385703,0.0010597778,0.00007079964,0.00000914989],"about_ca_topic_score_codex":0.005099163,"about_ca_topic_score_gemma":0.0041884026,"teacher_disagreement_score":0.005099163,"about_ca_system_score_codex":0.0014633441,"about_ca_system_score_gemma":0.0016925265,"threshold_uncertainty_score":0.013306975},"labels":[],"label_agreement":null},{"id":"W2996776319","doi":"10.1016/j.enconman.2019.112162","title":"Operations management of wind farms integrating multiple impacts of wind conditions and resource constraints","year":2020,"lang":"en","type":"article","venue":"Energy Conversion and Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Downtime; Revenue; Wind power; Resource (disambiguation); Operations research; Scheduling (production processes); Offshore wind power; Reliability engineering; Environmental economics; Engineering; Computer science; Operations management; Business; Economics","score_opus":0.006680381834218742,"score_gpt":0.191135232679019,"score_spread":0.18445485084480026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996776319","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69267905,0.00060656026,0.29664958,0.00055378466,0.00007787828,0.00014621031,0.0002658322,0.0001906669,0.008830469],"genre_scores_gemma":[0.9920259,0.00010931188,0.0069236266,0.000010559681,0.000013106227,0.000024768071,0.00004718825,0.000015220375,0.000830268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996344,0.00013830834,0.000016252758,0.00005577663,0.000072360686,0.000082948405],"domain_scores_gemma":[0.9995907,0.00020102899,0.000082704115,0.000020858493,0.000058299087,0.000046473484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007634112,0.0006230664,0.0008030732,0.00048246188,0.0005248997,0.0012894345,0.0006471876,0.00063672895,0.0009896768],"category_scores_gemma":[0.0010604393,0.00049472065,0.00041155508,0.00056951126,0.00038508972,0.0011669847,0.0004415426,0.0005593675,0.00009937309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059776332,0.000046130725,0.00088167994,0.000020908099,0.00003741,0.00010845887,0.000021204689,0.98624575,0.0016051709,0.00074973237,0.00027444845,0.009949364],"study_design_scores_gemma":[0.000008806468,0.000059558377,0.0009825208,0.0000035594755,0.000013717277,0.000018727795,0.00003342509,0.99719656,0.00042243747,0.0011334473,0.00012178632,0.0000054887396],"about_ca_topic_score_codex":0.004788704,"about_ca_topic_score_gemma":0.0064346567,"teacher_disagreement_score":0.004788704,"about_ca_system_score_codex":0.0005463991,"about_ca_system_score_gemma":0.00077092065,"threshold_uncertainty_score":0.009521663},"labels":[],"label_agreement":null},{"id":"W2997379470","doi":"10.1007/s12206-019-1141-0","title":"Reliability-based design optimization of time-dependent systems with stochastic degradation","year":2019,"lang":"en","type":"article","venue":"Journal of Mechanical Science and Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Computer science; Monte Carlo method; Moving least squares; Mathematical optimization; Inefficiency; Systems design; Engineering; Mathematics; Power (physics)","score_opus":0.005164306769744498,"score_gpt":0.1870830822697045,"score_spread":0.18191877549996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997379470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11896735,0.0013602872,0.87039673,0.00065286807,0.000109710745,0.00014389031,0.0001760664,0.00033429012,0.007858678],"genre_scores_gemma":[0.973639,0.00027479758,0.023035508,0.00006703263,0.00003594996,0.00014091148,0.00009633019,0.00007015466,0.0026401484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990528,0.00038962445,0.000034614295,0.00013691682,0.0002338372,0.00015220098],"domain_scores_gemma":[0.9976144,0.0015749829,0.00029436636,0.00006736137,0.0003689471,0.00008005191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021106487,0.0014967638,0.0020860536,0.0011447301,0.000470725,0.0011875863,0.0010460169,0.0016254311,0.0015679902],"category_scores_gemma":[0.0052385237,0.0012518044,0.0010404676,0.00069680816,0.00096421177,0.0007746838,0.0011155067,0.001056796,0.00021095002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017077597,0.000007466002,0.000056394,0.000015487954,0.00001202694,0.000010387773,0.000006575046,0.9980737,0.00024848361,0.00048776722,0.00004711729,0.001017474],"study_design_scores_gemma":[0.0000030224512,0.000014421826,0.000037742888,0.0000015017642,0.000004227617,0.0000021549517,0.0000017180873,0.99954695,0.000060332975,0.000300168,0.000026558551,0.0000012226175],"about_ca_topic_score_codex":0.005636994,"about_ca_topic_score_gemma":0.0038793858,"teacher_disagreement_score":0.005636994,"about_ca_system_score_codex":0.0014116276,"about_ca_system_score_gemma":0.0015260858,"threshold_uncertainty_score":0.011208355},"labels":[],"label_agreement":null},{"id":"W2998380289","doi":"10.1016/j.ifacol.2019.11.339","title":"Developing a bi-objective imperfect selective maintenance optimization model for multicomponent systems","year":2019,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Imperfect; Reliability (semiconductor); Component (thermodynamics); Reliability engineering; Computer science; Decision maker; Preference; Optimal maintenance; Operations research; Maintenance actions; System optimization; Mathematical optimization; Engineering; Mathematics; Statistics","score_opus":0.011401655092648927,"score_gpt":0.22478823349398122,"score_spread":0.2133865784013323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998380289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027976925,0.0006358719,0.96270674,0.00024539678,0.000043603482,0.000078395824,0.000102490325,0.00010561341,0.008104989],"genre_scores_gemma":[0.8943894,0.0009990786,0.09132805,0.00012234227,0.00004206948,0.00045644635,0.00022518978,0.000062642765,0.012374636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966407,0.00009010254,0.000015081918,0.00006536627,0.00010661123,0.000058702626],"domain_scores_gemma":[0.99966764,0.00017205447,0.000055410535,0.000015758,0.00006821536,0.000020932875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087128265,0.001022512,0.0011842918,0.0005195918,0.0004315723,0.0014778416,0.0014458349,0.0015227869,0.0021954963],"category_scores_gemma":[0.0009908307,0.0007067598,0.0010159556,0.0006966501,0.0005982618,0.00092429656,0.0010466374,0.0012797829,0.0002948829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000088271,0.000009910729,0.00008706122,0.000024616067,0.00001075861,0.000025480313,0.000011687653,0.99570054,0.00041269627,0.0019728495,0.00010017589,0.0016353078],"study_design_scores_gemma":[0.000002333383,0.000008058148,0.00003310956,0.000002024925,0.0000029073838,0.0000030116153,0.000003405052,0.9992543,0.0000683846,0.0004948533,0.0001258462,0.0000018302134],"about_ca_topic_score_codex":0.008613886,"about_ca_topic_score_gemma":0.0050790827,"teacher_disagreement_score":0.008613886,"about_ca_system_score_codex":0.0009176641,"about_ca_system_score_gemma":0.0012489817,"threshold_uncertainty_score":0.017127514},"labels":[],"label_agreement":null},{"id":"W2998897999","doi":"10.1016/j.cie.2020.106273","title":"Joint optimization of production and maintenance strategies considering a dynamic sampling strategy for a deteriorating system","year":2020,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":85,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flexibility (engineering); Preventive maintenance; Quality (philosophy); Production (economics); Reliability engineering; Sampling (signal processing); Control (management); Constraint (computer-aided design); Production planning; Computer science; Engineering; Production manager; Operations research; Risk analysis (engineering); Industrial engineering; Mathematics","score_opus":0.04787367796418796,"score_gpt":0.22125648100380402,"score_spread":0.17338280303961606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998897999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36551642,0.00085890805,0.62704825,0.0004581188,0.000067123125,0.00016973059,0.00012942233,0.00027178647,0.0054801702],"genre_scores_gemma":[0.98608,0.000110112,0.012573254,0.00002608631,0.00001604774,0.000053251155,0.00003804418,0.000017177597,0.0010858906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962866,0.00011608285,0.000017989167,0.00006683099,0.000077612436,0.00009285237],"domain_scores_gemma":[0.9988405,0.00070332777,0.00015711895,0.000039702336,0.00018456568,0.00007474143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012306097,0.0011091998,0.0014218303,0.0006886041,0.00045514686,0.0011364926,0.0008562065,0.0014926291,0.0014039617],"category_scores_gemma":[0.0024961887,0.0007469719,0.00065193675,0.00074394635,0.0006519215,0.0007214332,0.0006074958,0.0006645809,0.00014458528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095847216,0.000039251234,0.00023920492,0.00003596304,0.00002257541,0.000035955603,0.000015272433,0.9930957,0.0012582046,0.0005481051,0.00009615264,0.0045178086],"study_design_scores_gemma":[0.000007260616,0.00004021659,0.00016176463,0.0000017510604,0.000010671234,0.0000041634253,0.0000036729102,0.999411,0.0001916896,0.0001415632,0.000024033094,0.0000021247101],"about_ca_topic_score_codex":0.010724098,"about_ca_topic_score_gemma":0.0051228446,"teacher_disagreement_score":0.010724098,"about_ca_system_score_codex":0.0010548115,"about_ca_system_score_gemma":0.001294661,"threshold_uncertainty_score":0.021323323},"labels":[],"label_agreement":null},{"id":"W2999977700","doi":"10.1061/9780784482445.002","title":"Data-Driven Remaining Useful Life Prediction to Plan Operations Shutdown and Maintenance of an Industrial Plant","year":2019,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Shutdown; Plan (archaeology); Reliability engineering; Maintenance engineering; Engineering; Computer science","score_opus":0.03979642672874525,"score_gpt":0.224381900393315,"score_spread":0.18458547366456973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999977700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47479802,0.00053531263,0.51953137,0.0003823642,0.00004243523,0.00009828451,0.0014924804,0.0013005927,0.0018191474],"genre_scores_gemma":[0.98367834,0.00007629207,0.015377061,0.000018433759,0.000009409836,0.000033978704,0.0005102528,0.000013841882,0.00028236993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985147,0.00003060226,0.000011181118,0.00003911264,0.00004537509,0.000022146407],"domain_scores_gemma":[0.99879265,0.0007133692,0.00019311588,0.00007046179,0.00017451656,0.000055773206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046237168,0.00057670905,0.00041594854,0.00066112884,0.00016356184,0.0003839623,0.00046259278,0.00042598974,0.0005314638],"category_scores_gemma":[0.0020833258,0.00020131373,0.0002356983,0.0005082754,0.00017767634,0.00048436126,0.0002503817,0.00051205675,0.00016095907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007839111,0.00008172175,0.0067785326,0.0000385812,0.000017734214,0.000058026708,0.000018628525,0.9626313,0.0025201486,0.00040732426,0.0005494998,0.026820185],"study_design_scores_gemma":[0.000001445202,0.00002463651,0.0010522149,0.0000021452709,0.0000025240586,0.000006509996,0.0000052011474,0.99783033,0.0006979752,0.00029727354,0.00007729157,0.0000024850406],"about_ca_topic_score_codex":0.004306277,"about_ca_topic_score_gemma":0.005907727,"teacher_disagreement_score":0.004306277,"about_ca_system_score_codex":0.00040509502,"about_ca_system_score_gemma":0.000525802,"threshold_uncertainty_score":0.008562446},"labels":[],"label_agreement":null},{"id":"W2999978373","doi":"10.1109/iesm45758.2019.8948140","title":"An Optimum Comparative Analysis for Minimal Repair and Replacement on Failures","year":2019,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Weibull distribution; Reliability engineering; Unit (ring theory); Preventive maintenance; Computer science; Engineering; Mathematics; Statistics","score_opus":0.01192495536727821,"score_gpt":0.25348947383063375,"score_spread":0.24156451846335553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999978373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35592332,0.003424239,0.59501016,0.0006186367,0.00009883492,0.00018119001,0.00022472099,0.00024178247,0.044277128],"genre_scores_gemma":[0.96340287,0.0006694637,0.03138525,0.000050705672,0.000029328427,0.000101080885,0.000063278094,0.000039556144,0.004258565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99940646,0.00028104818,0.0000110912515,0.00005914694,0.00014243765,0.000099727265],"domain_scores_gemma":[0.9984743,0.0011311211,0.0001285271,0.00008809948,0.00010744916,0.000070448004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017258864,0.0004934495,0.0007313319,0.0007712124,0.0002856568,0.0007413133,0.000856466,0.00077097287,0.0060144784],"category_scores_gemma":[0.00623717,0.00036568815,0.00076216314,0.000360616,0.0006481984,0.0010315115,0.00044221384,0.0004945332,0.00022667824],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004532513,0.000087864515,0.0003987379,0.00017482312,0.000041421656,0.000055456057,0.00006257138,0.8533602,0.0033975749,0.11284438,0.0010512726,0.028072506],"study_design_scores_gemma":[0.000055177217,0.00058179465,0.00093760353,0.000038510098,0.00006955664,0.00008414147,0.00004421302,0.96254385,0.0015477668,0.030928757,0.0031475122,0.000021210239],"about_ca_topic_score_codex":0.0013538678,"about_ca_topic_score_gemma":0.000984609,"teacher_disagreement_score":0.0060144784,"about_ca_system_score_codex":0.0015277715,"about_ca_system_score_gemma":0.0008876912,"threshold_uncertainty_score":0.020120382},"labels":[],"label_agreement":null},{"id":"W3004460695","doi":"","title":"A multi-agent system for the reactive fleet maintenance support planning of a fleet of mobile cyber-physical systems : application to rail transport industry","year":2019,"lang":"en","type":"preprint","venue":"theses.fr (ABES)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Fleet management; Train; Work (physics); Reliability (semiconductor); Cyber-physical system; Transport engineering; Operational planning; Operations research; Engineering; Computer science; Business","score_opus":0.01984038807268441,"score_gpt":0.26400534168908135,"score_spread":0.24416495361639695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004460695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040835842,0.00048389746,0.9516897,0.0003095359,0.00010536945,0.0002109282,0.000122067504,0.0014378706,0.004804739],"genre_scores_gemma":[0.6741936,0.00034958572,0.31919226,0.00007869817,0.000029024228,0.00038574616,0.00016014346,0.0000645969,0.005546323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998142,0.000058907455,0.000013967189,0.00004446666,0.000045631336,0.000022873914],"domain_scores_gemma":[0.9997439,0.0000978759,0.000033207012,0.000022666065,0.00007078712,0.000031577358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004436934,0.00058994273,0.00045669512,0.0003322157,0.0006103165,0.00085738825,0.0007582414,0.0009572333,0.0030462127],"category_scores_gemma":[0.0008659942,0.00025442382,0.0005016056,0.00025095555,0.0002934019,0.0005272276,0.000684068,0.00082826224,0.00044401598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013543341,0.000077944904,0.0010619458,0.00020064284,0.00006201829,0.00027672556,0.00018091254,0.9101524,0.01111081,0.0064157234,0.001259906,0.06906549],"study_design_scores_gemma":[0.000015795737,0.000057393758,0.00015394208,0.000009703233,0.000012688986,0.00002413669,0.00002500883,0.9954124,0.0012280546,0.00062474573,0.002429206,0.000006882507],"about_ca_topic_score_codex":0.006412325,"about_ca_topic_score_gemma":0.0070187515,"teacher_disagreement_score":0.006412325,"about_ca_system_score_codex":0.00053153903,"about_ca_system_score_gemma":0.0011510783,"threshold_uncertainty_score":0.01275003},"labels":[],"label_agreement":null},{"id":"W3005517811","doi":"10.1002/qre.2636","title":"Remaining useful life prediction for multivariable stochastic degradation systems with non‐Markovian diffusion processes","year":2020,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Multivariable calculus; Estimator; Wiener process; Markov process; Univariate; Computer science; Stochastic process; Degradation (telecommunications); Brownian motion; Mathematical optimization; Control theory (sociology); Mathematics; Engineering; Applied mathematics; Statistics; Multivariate statistics; Control engineering; Artificial intelligence; Machine learning","score_opus":0.01747506278217821,"score_gpt":0.2226593193288464,"score_spread":0.20518425654666816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005517811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2221723,0.00030594142,0.7760757,0.00021071572,0.000023346123,0.000031150033,0.00007655651,0.00018531774,0.00091900805],"genre_scores_gemma":[0.9954656,0.000059215632,0.004061423,0.0000086892005,0.0000062827926,0.000013001499,0.000030816205,0.0000040182995,0.00035086944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996741,0.00007943264,0.00001681152,0.000089236826,0.00008365693,0.000056760204],"domain_scores_gemma":[0.99886227,0.0007295767,0.00019089604,0.000030490135,0.00015145297,0.000035328114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011838384,0.00071020593,0.00067116943,0.000516738,0.00025138876,0.00060694513,0.00050852454,0.0006287211,0.00045394388],"category_scores_gemma":[0.0021934458,0.00030583228,0.00052408106,0.00037137323,0.0004217182,0.0005107623,0.0005053231,0.0007272322,0.000048859456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030091303,0.000015425507,0.0010200354,0.000020881578,0.000010913918,0.00003479193,0.000018465222,0.9924892,0.0010362084,0.0011557414,0.00007378268,0.004094486],"study_design_scores_gemma":[5.3008114e-7,0.0000029828143,0.000117970965,3.449475e-7,6.972511e-7,0.0000013276766,0.0000010246974,0.9996984,0.000045703222,0.00012254984,0.00000741515,9.0799085e-7],"about_ca_topic_score_codex":0.010307576,"about_ca_topic_score_gemma":0.004280285,"teacher_disagreement_score":0.010307576,"about_ca_system_score_codex":0.0007213033,"about_ca_system_score_gemma":0.0005635534,"threshold_uncertainty_score":0.020495176},"labels":[],"label_agreement":null},{"id":"W3015867196","doi":"10.1002/net.21938","title":"Rational roots of all‐terminal reliability","year":2020,"lang":"en","type":"article","venue":"Networks","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Combinatorics; Terminal (telecommunication); Spanning tree; Rational number; Moduli; Class (philosophy); Reliability (semiconductor); Discrete mathematics; Graph; Order (exchange); Computer science","score_opus":0.009746665294790957,"score_gpt":0.20200839948166663,"score_spread":0.19226173418687567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015867196","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7523295,0.0004360506,0.23335576,0.00086717535,0.000070004135,0.00002710101,0.00022897372,0.00026241015,0.012423029],"genre_scores_gemma":[0.9960317,0.000082334314,0.003248431,0.000016521477,0.000022048107,0.0000069815305,0.000020275014,0.000015514055,0.00055612007],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99959236,0.00008361197,0.000015679989,0.00010960912,0.0001216373,0.00007709215],"domain_scores_gemma":[0.9950771,0.0026400327,0.0010954592,0.00044502402,0.00047307232,0.00026933622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070857053,0.00032802994,0.0004784874,0.0009791147,0.00039683084,0.0009424274,0.0005304693,0.0004490902,0.0024054023],"category_scores_gemma":[0.010263156,0.00019999077,0.0002790257,0.00041187558,0.0018816458,0.0015340106,0.00069694396,0.00086208264,0.00022801699],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031202773,0.000056921777,0.009723943,0.00020407485,0.000045460096,0.00045286366,0.0004932598,0.1585356,0.019346347,0.7782602,0.002569809,0.029999463],"study_design_scores_gemma":[0.00003768543,0.00011189702,0.005861872,0.000045417302,0.00002517893,0.00047874675,0.00020920372,0.26360396,0.006423429,0.72086763,0.0022870821,0.000047969414],"about_ca_topic_score_codex":0.00060995744,"about_ca_topic_score_gemma":0.00032743972,"teacher_disagreement_score":0.0024054023,"about_ca_system_score_codex":0.00073138135,"about_ca_system_score_gemma":0.00040254643,"threshold_uncertainty_score":0.008046865},"labels":[],"label_agreement":null},{"id":"W3018718829","doi":"10.1109/tste.2020.2986586","title":"Operations &amp; Maintenance Optimization of Wind Turbines Integrating Wind and Aging Information","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Sustainable Energy","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":147,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Wind power; Offshore wind power; Turbine; Reliability engineering; Revenue; Optimal maintenance; Production (economics); Maintenance engineering; Reliability (semiconductor); Renewable energy; Engineering; Computer science; Marine engineering; Power (physics); Business","score_opus":0.005592530214258211,"score_gpt":0.18853014103600388,"score_spread":0.18293761082174567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018718829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39659667,0.0006571196,0.59264284,0.00048831006,0.00004761478,0.00016916454,0.0003295961,0.00020111291,0.008867575],"genre_scores_gemma":[0.9829161,0.00015505613,0.015869921,0.000015002687,0.000008624507,0.000029471592,0.00007229467,0.000016709942,0.0009168767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953735,0.00019211923,0.000019117111,0.00007980709,0.00007466416,0.0000969844],"domain_scores_gemma":[0.9990606,0.0005343606,0.00019429524,0.000059963833,0.00007085912,0.00008001879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010757056,0.00073293346,0.0007471606,0.00037941837,0.00030575355,0.0007564955,0.00070748525,0.0006080478,0.0011628257],"category_scores_gemma":[0.0023778793,0.00032398273,0.00036358816,0.0005769842,0.0003442283,0.0009958275,0.000613198,0.00055811275,0.00011158497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060249386,0.000051917617,0.00084369484,0.00002559156,0.000012787625,0.000049491406,0.00001414762,0.9794956,0.0008979068,0.0029853357,0.0003266654,0.015236498],"study_design_scores_gemma":[0.0000044687267,0.000034876357,0.0002703166,0.0000021065957,0.000004424696,0.000012131515,0.000009805689,0.99807847,0.00021484745,0.0012452769,0.00012146084,0.0000018546123],"about_ca_topic_score_codex":0.004516933,"about_ca_topic_score_gemma":0.00413118,"teacher_disagreement_score":0.004516933,"about_ca_system_score_codex":0.00077411823,"about_ca_system_score_gemma":0.00122114,"threshold_uncertainty_score":0.0089812875},"labels":[],"label_agreement":null},{"id":"W3026711441","doi":"10.1109/tmech.2020.2995757","title":"Prognostics of Health Measures for Machines With Aging and Dynamic Cumulative Damage","year":2020,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Prognostics; Reliability engineering; Reliability (semiconductor); Residual; Computer science; Process (computing); Condition-based maintenance; Condition monitoring; Engineering; Algorithm","score_opus":0.014659465537370056,"score_gpt":0.23918348344111465,"score_spread":0.2245240179037446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3026711441","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03278135,0.0003725159,0.9654997,0.00013256111,0.00003578387,0.000023363307,0.00007026195,0.0003396899,0.0007447505],"genre_scores_gemma":[0.9615868,0.00034638928,0.03688297,0.000034102373,0.000034114633,0.000038536364,0.00009739482,0.000017962448,0.00096178934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998068,0.000045481196,0.000012311414,0.00004757315,0.00006194669,0.000025934925],"domain_scores_gemma":[0.99955934,0.00021381292,0.00008670186,0.000043239757,0.00007720496,0.000019592331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058385864,0.0006233315,0.0005144062,0.00047141474,0.00022195898,0.00042751164,0.0005836772,0.0005993627,0.001013628],"category_scores_gemma":[0.0020543125,0.00019074423,0.00038134298,0.00028817504,0.00033938672,0.00087845215,0.0006674913,0.000712845,0.00013678164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007946063,0.00003285146,0.0027009519,0.000090341055,0.00002029651,0.00007619779,0.00007742475,0.91697425,0.005297674,0.005892752,0.0005394742,0.06821827],"study_design_scores_gemma":[0.0000021787873,0.00002836856,0.0005011175,0.000004487847,0.0000055271944,0.000021130878,0.000008878273,0.996283,0.000864995,0.0020378046,0.00023811401,0.0000043731548],"about_ca_topic_score_codex":0.0016134559,"about_ca_topic_score_gemma":0.0014907974,"teacher_disagreement_score":0.0016134559,"about_ca_system_score_codex":0.00036260425,"about_ca_system_score_gemma":0.0005394537,"threshold_uncertainty_score":0.0033909678},"labels":[],"label_agreement":null},{"id":"W3031198926","doi":"10.3390/s20113071","title":"Disjoint Spanning Tree Based Reliability Evaluation of Wireless Sensor Network","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Deanship of Scientific Research, Princess Nourah Bint Abdulrahman University; Alfaisal University; Princess Nourah Bint Abdulrahman University","keywords":"Wireless sensor network; Computer science; Disjoint sets; Reliability (semiconductor); Spanning tree; Distributed computing; Computer network; Wireless; Key (lock); Key distribution in wireless sensor networks; Wireless network; Computer security; Mathematics","score_opus":0.023091904566464608,"score_gpt":0.22796485352687731,"score_spread":0.2048729489604127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3031198926","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10322398,0.0014741987,0.8912357,0.00013309089,0.00007406486,0.00009693146,0.00018078674,0.00037143403,0.003209724],"genre_scores_gemma":[0.87052286,0.00087562465,0.1271582,0.00003310168,0.00003861098,0.00011820312,0.0003214525,0.00004728898,0.000884819],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998569,0.0006070586,0.00009962156,0.0001708784,0.0004904845,0.00006294142],"domain_scores_gemma":[0.9977865,0.0010917309,0.00022508632,0.00012761298,0.0007160607,0.000052987787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013460001,0.0008105414,0.00071467645,0.0014765384,0.00028928608,0.00061597564,0.00058958994,0.0004581183,0.000683055],"category_scores_gemma":[0.0076822517,0.0001757707,0.0005033027,0.0010547291,0.0003346794,0.0011700701,0.00052760914,0.00032286468,0.00016451847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019189327,0.00004804323,0.0036647175,0.0003117377,0.00008969017,0.00014524924,0.00011992489,0.869662,0.011568721,0.010181175,0.0008055135,0.1032114],"study_design_scores_gemma":[0.0000025343968,0.00008149872,0.0006431239,0.000012828036,0.000014811788,0.00006493897,0.00002341864,0.99369377,0.0022626393,0.0028504846,0.0003418509,0.000007974396],"about_ca_topic_score_codex":0.0010652255,"about_ca_topic_score_gemma":0.0008019583,"teacher_disagreement_score":0.0014765384,"about_ca_system_score_codex":0.0006082159,"about_ca_system_score_gemma":0.00044898703,"threshold_uncertainty_score":0.0071184635},"labels":[],"label_agreement":null},{"id":"W3033721775","doi":"10.1109/tr.2020.2995277","title":"State-Based Opportunistic Maintenance With Multifunctional Maintenance Windows","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":114,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Fok Ying Tung Education Foundation; National Natural Science Foundation of China","keywords":"Reliability engineering; Initialization; Spare part; Scheduling (production processes); Maintenance engineering; Computer science; Probabilistic logic; Maintenance actions; Preventive maintenance; Interval (graph theory); Software maintenance; Corrective maintenance; Engineering; Real-time computing; Operations management; Mathematics; Artificial intelligence","score_opus":0.01348958919451938,"score_gpt":0.1960511003331629,"score_spread":0.18256151113864352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033721775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23413493,0.0003952121,0.76143146,0.00017464743,0.000058765727,0.00009905803,0.00012210694,0.00088965264,0.0026942065],"genre_scores_gemma":[0.98772484,0.00004174873,0.011560222,0.000017383458,0.000009639624,0.000023093116,0.000029018149,0.000012523448,0.000581486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944466,0.00009232215,0.0000419038,0.00018031344,0.00012626406,0.000114517716],"domain_scores_gemma":[0.99844366,0.0006038781,0.0003756264,0.00026882393,0.00017201023,0.00013602717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007187562,0.0005476494,0.00057575636,0.0003955073,0.0002741113,0.00060421595,0.0011227599,0.00047981678,0.001144112],"category_scores_gemma":[0.0022336917,0.00023886809,0.00031390632,0.00029264903,0.0004125029,0.0008469764,0.0006577486,0.00045689003,0.00015036468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054228643,0.00023707827,0.0040286994,0.000096204276,0.00005685571,0.00018389113,0.00016025279,0.84754217,0.01965053,0.013351828,0.0010878707,0.11306241],"study_design_scores_gemma":[0.000017212364,0.00014695278,0.0009956034,0.0000061133182,0.000021221656,0.00006522396,0.000014520993,0.99265176,0.0023789243,0.0031976574,0.00049494463,0.000009824814],"about_ca_topic_score_codex":0.0017637989,"about_ca_topic_score_gemma":0.0019144991,"teacher_disagreement_score":0.0017637989,"about_ca_system_score_codex":0.0005018366,"about_ca_system_score_gemma":0.00065577537,"threshold_uncertainty_score":0.0038274527},"labels":[],"label_agreement":null},{"id":"W3045761394","doi":"10.33262/concienciadigital.v3i3.1266","title":"Estudio de fiabilidad, mantenibilidad y disponibilidad aplicado a grupos electrógenos prime","year":2020,"lang":"es","type":"article","venue":"ConcienciaDigital","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy","score_opus":0.010366639886850035,"score_gpt":0.21543378989692552,"score_spread":0.20506715001007547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045761394","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98701185,0.0009504793,0.008996697,0.000045548924,0.000020241823,0.00007648347,0.00021107716,0.000103233935,0.002584346],"genre_scores_gemma":[0.9878841,0.0007097961,0.006418666,0.00004302217,0.000008035533,0.000106354746,0.00024893065,0.00003661293,0.00454439],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99955326,0.00006543431,0.000029115683,0.00010738191,0.00019520697,0.00004960644],"domain_scores_gemma":[0.9985933,0.00052811997,0.00020283402,0.00016117342,0.00046740376,0.00004717618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089227123,0.00047647158,0.00052572513,0.0008036424,0.00035426035,0.0007102516,0.0006487308,0.0005330891,0.0032090873],"category_scores_gemma":[0.0016490074,0.00023908065,0.00040552198,0.0006446473,0.00040182812,0.00087547774,0.00052491174,0.00037920414,0.0005269249],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010933453,0.00026966076,0.053739034,0.00094577763,0.00015606187,0.0004347692,0.0014666942,0.0053429357,0.8065658,0.00064780173,0.00049735757,0.12884082],"study_design_scores_gemma":[0.000054738965,0.009545066,0.25644922,0.00015162637,0.0004548466,0.00083154155,0.003731197,0.013706031,0.6970429,0.001470751,0.016484741,0.000077275625],"about_ca_topic_score_codex":0.0011077115,"about_ca_topic_score_gemma":0.0024917487,"teacher_disagreement_score":0.0032090873,"about_ca_system_score_codex":0.0003208879,"about_ca_system_score_gemma":0.00021810664,"threshold_uncertainty_score":0.010735452},"labels":[],"label_agreement":null},{"id":"W3046731328","doi":"10.1109/rams48030.2020.9153629","title":"Joint optimizing the Production Sequence and Maintenance Plan for a Single-Machine Multi-Failure System","year":2020,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Reliability engineering; Computer science; Imperfect; Quality (philosophy); Production (economics); Production line; Product (mathematics); Weibull distribution; Engineering; Mechanical engineering","score_opus":0.04305002787785761,"score_gpt":0.20504749271678835,"score_spread":0.16199746483893074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046731328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21828178,0.001199837,0.77523106,0.0005614651,0.00006286519,0.000340407,0.00038793279,0.0006863042,0.003248422],"genre_scores_gemma":[0.94477427,0.00025043992,0.05204915,0.0000503646,0.000025719353,0.00022475702,0.0002544903,0.00006395734,0.0023069133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919766,0.00020934941,0.000039986826,0.00019820729,0.00015446603,0.00020030055],"domain_scores_gemma":[0.9984647,0.0007392331,0.00032205143,0.00008212746,0.00023439528,0.00015752667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018597557,0.0015424032,0.0021759816,0.0012103566,0.0005777387,0.0012894652,0.0013891316,0.0013983506,0.0026384362],"category_scores_gemma":[0.002672992,0.0008679976,0.0009044774,0.000794233,0.0007762622,0.0011062446,0.0008384442,0.0009218999,0.00036433138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043692835,0.000020861957,0.00040692327,0.000035250865,0.00001705858,0.000042575928,0.000012773679,0.9944289,0.0005009831,0.00051054073,0.00009869064,0.0038818228],"study_design_scores_gemma":[0.0000097649745,0.0000569033,0.0003106618,0.000003971754,0.00001180956,0.000011595188,0.00001022741,0.9985337,0.0002241812,0.00074265775,0.00008006946,0.000004523211],"about_ca_topic_score_codex":0.010196419,"about_ca_topic_score_gemma":0.006822175,"teacher_disagreement_score":0.010196419,"about_ca_system_score_codex":0.0014096214,"about_ca_system_score_gemma":0.0024994237,"threshold_uncertainty_score":0.020274162},"labels":[],"label_agreement":null},{"id":"W3046948313","doi":"10.1002/nav.21932","title":"Application of Markov renewal theory and <scp>semi‐Markov</scp> decision processes in maintenance modeling and optimization of multi‐unit systems","year":2020,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partially observable Markov decision process; Markov decision process; Markov chain; Mathematical optimization; Renewal theory; Markov process; Markov renewal process; Dynamic programming; Markov model; Expression (computer science); Computer science; Production (economics); Mathematics; Variable-order Markov model; Statistics; Economics","score_opus":0.04714117010832025,"score_gpt":0.30967470172001044,"score_spread":0.2625335316116902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046948313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028498074,0.00032325764,0.9682843,0.00032417546,0.000041049414,0.00004200244,0.00004351946,0.00007939891,0.0023641973],"genre_scores_gemma":[0.9557148,0.00047641763,0.04118827,0.00007061381,0.000053197484,0.0001708552,0.00006708113,0.000027245294,0.002231582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988943,0.00047305843,0.000041545776,0.00012210655,0.00031485103,0.00015416002],"domain_scores_gemma":[0.9968015,0.0024704572,0.00027839135,0.00006642421,0.00030686017,0.00007633773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023482833,0.00065702526,0.001283784,0.0008009856,0.0005533132,0.0012258728,0.0011933123,0.00101558,0.0016829803],"category_scores_gemma":[0.0038963065,0.00058703753,0.0012165629,0.000769474,0.0015029944,0.0011755998,0.0007267278,0.0014582645,0.00014636958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010630254,0.000014546154,0.00020020628,0.000015890777,0.000014439191,0.000028373257,0.000014301515,0.9779654,0.00017427781,0.01985681,0.00010261809,0.0016024692],"study_design_scores_gemma":[0.0000020092632,0.0000048517613,0.000030998563,0.00000192649,0.0000018364647,0.0000022841753,0.0000016362667,0.99688464,0.000040614585,0.002987438,0.00003985958,0.0000018771271],"about_ca_topic_score_codex":0.014575934,"about_ca_topic_score_gemma":0.0064416323,"teacher_disagreement_score":0.014575934,"about_ca_system_score_codex":0.0019747438,"about_ca_system_score_gemma":0.0022338065,"threshold_uncertainty_score":0.028982162},"labels":[],"label_agreement":null},{"id":"W3047447214","doi":"10.1007/s11668-020-00949-z","title":"Optimal Inspection and Preventive Maintenance Scheduling of Mining Equipment","year":2020,"lang":"en","type":"article","venue":"Journal of Failure Analysis and Prevention","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Downtime; Preventive maintenance; Excavator; Truck; Open-pit mining; Heavy equipment; Reliability engineering; Planned maintenance; Reliability (semiconductor); Mining industry; Scheduling (production processes); Engineering; Corrective maintenance; Computer science; Risk analysis (engineering); Operations management; Automotive engineering; Civil engineering; Mining engineering","score_opus":0.007956099778440093,"score_gpt":0.21990746430567779,"score_spread":0.2119513645272377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047447214","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6279245,0.0006169922,0.36712933,0.00037083717,0.00010248047,0.00012901262,0.00016619159,0.00031381368,0.0032468461],"genre_scores_gemma":[0.98459375,0.000066455694,0.01448456,0.000013262725,0.000019246314,0.000019522795,0.000047624726,0.000015874828,0.00073970825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959284,0.00012113714,0.000019580808,0.00008785541,0.00008725672,0.000091369555],"domain_scores_gemma":[0.9976802,0.0014583344,0.00036171137,0.000106984,0.00022948597,0.00016321859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010663484,0.00054458505,0.0011843387,0.00070825766,0.0003645939,0.00063746946,0.00092876615,0.0006642029,0.0013470621],"category_scores_gemma":[0.004834743,0.0006641691,0.0003536486,0.0004334887,0.0004226632,0.00059948355,0.00033674776,0.0005460849,0.00012913623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072886876,0.00028005434,0.0020526608,0.000084638916,0.000042192456,0.000082186925,0.00005506777,0.95367527,0.0067936163,0.0024726156,0.00063621555,0.03309656],"study_design_scores_gemma":[0.000028421477,0.00013178894,0.0018124222,0.0000041866424,0.000015615205,0.000017451383,0.000015248085,0.9955337,0.0008316797,0.0015182694,0.00008574476,0.000005373566],"about_ca_topic_score_codex":0.0068146926,"about_ca_topic_score_gemma":0.0048182462,"teacher_disagreement_score":0.0068146926,"about_ca_system_score_codex":0.0008369456,"about_ca_system_score_gemma":0.0016155705,"threshold_uncertainty_score":0.013550043},"labels":[],"label_agreement":null},{"id":"W3084389888","doi":"10.1109/icphm49022.2020.9187022","title":"Semi-Supervised Learning Approach for Optimizing Condition-based-Maintenance (CBM) Decisions","year":2020,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Reliability (semiconductor); Predictive maintenance; Prognostics; Condition monitoring; Preventive maintenance; Machine learning; Set (abstract data type); Fault (geology); Artificial intelligence; Big data; Condition-based maintenance; Fault detection and isolation; Reliability engineering; Data mining; Engineering","score_opus":0.020762617673452572,"score_gpt":0.22343554994281337,"score_spread":0.20267293226936078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084389888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037739992,0.00035779626,0.95971996,0.00027119066,0.000037901205,0.00007028441,0.00015214391,0.0008012349,0.00084956916],"genre_scores_gemma":[0.89574254,0.00015238892,0.10167472,0.00018822869,0.00010680395,0.00020575634,0.00043716343,0.00005941174,0.0014331256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929273,0.00023498532,0.000050714756,0.0002045466,0.00014171826,0.00007519074],"domain_scores_gemma":[0.9971084,0.0018861587,0.00034242746,0.000144951,0.0004307308,0.00008729522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016685375,0.0009415761,0.0013695615,0.0007992442,0.000324667,0.0007254291,0.0017176822,0.001114743,0.0013030507],"category_scores_gemma":[0.004027758,0.00061439315,0.0007264796,0.00058895163,0.00062735396,0.00086085516,0.00077967765,0.0011905489,0.00034415495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010772248,0.00014165418,0.0012468279,0.000079612495,0.00006837222,0.000039683648,0.000047407364,0.93157804,0.00083035737,0.0013783259,0.0008091308,0.06367285],"study_design_scores_gemma":[0.000004827508,0.000019866457,0.00010050414,0.0000022907896,0.000003709209,0.0000041367734,0.0000026977507,0.9989594,0.00016357614,0.0006733725,0.00006367053,0.0000018444089],"about_ca_topic_score_codex":0.0060186475,"about_ca_topic_score_gemma":0.0050808974,"teacher_disagreement_score":0.0060186475,"about_ca_system_score_codex":0.00077611697,"about_ca_system_score_gemma":0.0015487795,"threshold_uncertainty_score":0.011967242},"labels":[],"label_agreement":null},{"id":"W3087146381","doi":"10.1002/qre.2762","title":"Inverse Gaussian process model with frailty term in reliability analysis","year":2020,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Inverse Gaussian distribution; Estimator; Covariate; Context (archaeology); Reliability (semiconductor); Gaussian; Computer science; Gaussian process; Process (computing); Algorithm; Statistics; Gamma process; Inverse; Applied mathematics; Data mining; Mathematics; Distribution (mathematics)","score_opus":0.01881183577315044,"score_gpt":0.2590735520717327,"score_spread":0.24026171629858228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087146381","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07389018,0.000848872,0.9223229,0.0004494215,0.00009047543,0.00006762417,0.00018252537,0.00018073518,0.0019672804],"genre_scores_gemma":[0.94128704,0.0010833313,0.05021193,0.00016191149,0.00011407769,0.0001860443,0.00036585945,0.000049357568,0.0065404465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99776983,0.0008387388,0.00008055087,0.00055738864,0.00049329916,0.00026032657],"domain_scores_gemma":[0.994422,0.0034737326,0.0006556228,0.0004898559,0.0008159832,0.00014284728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060258717,0.0013930459,0.0016084716,0.0017321694,0.00047163895,0.0014728339,0.0028244965,0.002565008,0.001917572],"category_scores_gemma":[0.012959976,0.0006478359,0.0021143819,0.0017041106,0.0024247563,0.0021482725,0.0016819383,0.0030048916,0.00042536747],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011188077,0.000071166825,0.004163473,0.00013041287,0.00012338826,0.0003896017,0.00026186445,0.8674085,0.002463359,0.110370666,0.00072823593,0.013777413],"study_design_scores_gemma":[0.000013569143,0.00005443094,0.00088107394,0.000017893975,0.000035343473,0.00007968696,0.000023955841,0.97398305,0.00029955755,0.02411292,0.00047402718,0.000024506167],"about_ca_topic_score_codex":0.01174924,"about_ca_topic_score_gemma":0.0047375066,"teacher_disagreement_score":0.01174924,"about_ca_system_score_codex":0.0014570956,"about_ca_system_score_gemma":0.0012828857,"threshold_uncertainty_score":0.03186822},"labels":[],"label_agreement":null},{"id":"W3093631795","doi":"10.1080/00207543.2020.1832275","title":"Maintenance policies with minimal repair and replacement on failures: analysis and comparison","year":2020,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability engineering; Preventive maintenance; Corrective maintenance; Weibull distribution; Function (biology); Maintenance actions; Component (thermodynamics); Condition-based maintenance; Sensitivity (control systems); Operations research; Maintenance engineering; Computer science; Engineering; Risk analysis (engineering); Mathematics; Business; Statistics","score_opus":0.04236819338401998,"score_gpt":0.3376296996380919,"score_spread":0.29526150625407194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093631795","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70891994,0.008276233,0.27193746,0.00035678546,0.00009030469,0.00025571752,0.0002671193,0.0003776043,0.0095187565],"genre_scores_gemma":[0.9868515,0.0007837254,0.011597351,0.000016978007,0.000024749119,0.00005723802,0.00007267105,0.000017178876,0.0005785468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912196,0.00033038584,0.000040954004,0.000081953374,0.00028537045,0.0001393703],"domain_scores_gemma":[0.9916562,0.006968962,0.0005568782,0.00026654918,0.00042945895,0.00012191669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023259216,0.00051232014,0.0009926131,0.0014494391,0.000207043,0.0006116428,0.00083174335,0.00077967654,0.0014161464],"category_scores_gemma":[0.009653563,0.00016212849,0.0005450654,0.00066784763,0.000326246,0.0007112995,0.00040558327,0.00036129405,0.000106657964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006553533,0.00020846826,0.001919921,0.00027809676,0.000072329225,0.00004674694,0.000045550394,0.9345909,0.0017892262,0.00844088,0.00048243106,0.051470075],"study_design_scores_gemma":[0.00003389947,0.0005256988,0.0030940764,0.000025978516,0.00006393211,0.0000696838,0.00003133978,0.99198735,0.00090852065,0.0027993764,0.0004493497,0.000010769608],"about_ca_topic_score_codex":0.00204554,"about_ca_topic_score_gemma":0.00092419406,"teacher_disagreement_score":0.0023259216,"about_ca_system_score_codex":0.0011108213,"about_ca_system_score_gemma":0.00084639434,"threshold_uncertainty_score":0.012300789},"labels":[],"label_agreement":null},{"id":"W3093692113","doi":"10.1049/iet-cps.2019.0063","title":"Multi‐agent system for the reactive fleet maintenance support planning of a fleet of mobile cyber–physical systems","year":2020,"lang":"en","type":"article","venue":"IET Cyber-Physical Systems Theory & Applications","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"European Regional Development Fund; Région Hauts-de-France; European Commission","keywords":"Cyber-physical system; Context (archaeology); Train; Reliability (semiconductor); Fleet management; Computer science; Multi-agent system; Decision support system; Operations research; Systems engineering; Engineering; Transport engineering","score_opus":0.01400463729459605,"score_gpt":0.24908224479343452,"score_spread":0.23507760749883846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093692113","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010848107,0.0002608306,0.98403084,0.00024085163,0.00007276034,0.00011544302,0.000060061844,0.00030080797,0.0040702755],"genre_scores_gemma":[0.793753,0.00045647883,0.19675305,0.00011718048,0.000060475206,0.00058693,0.0001643617,0.000050098613,0.008058345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958545,0.00018081914,0.000023767196,0.00007919641,0.000088495195,0.00004231751],"domain_scores_gemma":[0.9996137,0.00016944524,0.0000677254,0.000032087704,0.00008405316,0.000033054162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008025293,0.00068367796,0.0006721943,0.00041270052,0.00061138166,0.001205108,0.001053636,0.0011690196,0.0033256144],"category_scores_gemma":[0.0012122663,0.00032414615,0.0007315654,0.00029940018,0.00052747387,0.00082711305,0.0009323166,0.0012979296,0.00047350215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006210236,0.000032735446,0.00034580304,0.00007250033,0.000044978926,0.00013065487,0.00007528675,0.9648646,0.0020589281,0.01841715,0.00072674535,0.013168427],"study_design_scores_gemma":[0.000007770995,0.000022953203,0.000042970572,0.000004629214,0.0000062826853,0.000009874423,0.0000074163586,0.99721307,0.0001769345,0.0013916221,0.0011131087,0.0000033094923],"about_ca_topic_score_codex":0.005035915,"about_ca_topic_score_gemma":0.005408282,"teacher_disagreement_score":0.005035915,"about_ca_system_score_codex":0.00077403,"about_ca_system_score_gemma":0.0012450205,"threshold_uncertainty_score":0.011125267},"labels":[],"label_agreement":null},{"id":"W3096059542","doi":"10.33889/ijmems.2021.6.1.026","title":"Non-Linear Threshold Algorithm for the Redundancy Optimization of Multi-State Systems","year":2020,"lang":"en","type":"article","venue":"International Journal of Mathematical Engineering and Management Sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université de Moncton","funders":"","keywords":"Redundancy (engineering); Mathematical optimization; Computer science; Tabu search; Metaheuristic; Optimization problem; Genetic algorithm; Algorithm; Mathematics","score_opus":0.02183075227278545,"score_gpt":0.25311996870517806,"score_spread":0.2312892164323926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096059542","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023710683,0.0003404094,0.9718208,0.000112321715,0.000029859895,0.000050205646,0.000024557508,0.00034566392,0.0035655743],"genre_scores_gemma":[0.7261842,0.00026722145,0.26908484,0.00009911429,0.00001933598,0.00022791557,0.0001194113,0.000103368584,0.0038946401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997775,0.00006504297,0.000012223125,0.000036765443,0.00007032957,0.000038116574],"domain_scores_gemma":[0.9996854,0.00017286443,0.000049887847,0.000013898878,0.00006165876,0.000016357244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044386977,0.000535741,0.0006354337,0.00061283895,0.00041377736,0.00064453675,0.0007521961,0.0006407156,0.002426719],"category_scores_gemma":[0.0010868426,0.0002563772,0.0005810947,0.00060935924,0.00039748466,0.0005338577,0.0005084014,0.00075946643,0.0002607626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004630125,0.00003392127,0.00030789225,0.000059114234,0.000028953924,0.000029044577,0.00004742213,0.9468961,0.001969956,0.009303807,0.0005488534,0.040728673],"study_design_scores_gemma":[0.000005336364,0.000023632967,0.0000446551,0.000003819252,0.0000035527714,0.0000075793832,0.000005233763,0.9980568,0.000263469,0.0013255933,0.00025821835,0.0000020017014],"about_ca_topic_score_codex":0.0034935127,"about_ca_topic_score_gemma":0.002479684,"teacher_disagreement_score":0.0034935127,"about_ca_system_score_codex":0.00084502395,"about_ca_system_score_gemma":0.0011286755,"threshold_uncertainty_score":0.008118153},"labels":[],"label_agreement":null},{"id":"W3101528348","doi":"10.1115/1.4048787","title":"Toward a Big Data-Based Approach: A Review on Degradation Models for Prognosis of Critical Infrastructure","year":2020,"lang":"en","type":"review","venue":"Journal of Nondestructive Evaluation Diagnostics and Prognostics of Engineering Systems","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Reliability (semiconductor); Big data; Computer science; Degradation (telecommunications); Data science; Reliability engineering; Risk analysis (engineering); Physics of failure; The Internet; Engineering; Power (physics); Data mining; Telecommunications; World Wide Web; Business","score_opus":0.16437380430109907,"score_gpt":0.33741126040444025,"score_spread":0.17303745610334118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101528348","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003220604,0.99152184,0.00626843,0.00056772947,0.00025794283,0.00001750814,0.0000869527,0.000037971357,0.00091965386],"genre_scores_gemma":[0.0040339017,0.9904287,0.004342701,0.0002670691,0.0004016334,0.000029460518,0.00014564578,0.000011456265,0.0003394614],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993286,0.00016393306,0.00009431892,0.00014515841,0.00023271161,0.00003532379],"domain_scores_gemma":[0.9960634,0.0027974474,0.00023414732,0.00010704436,0.00073029136,0.00006774059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022681293,0.0013588786,0.0018101989,0.0035685909,0.00025953335,0.0016953754,0.0018998537,0.0013692172,0.0018346241],"category_scores_gemma":[0.0042338506,0.0005110094,0.0013729838,0.0043870057,0.0005541885,0.0026851133,0.0007492055,0.0014400992,0.0009903348],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006854396,0.00011483815,0.000989939,0.0269063,0.00039787218,0.00019207045,0.00014556384,0.006872006,0.0011143788,0.019149054,0.024566643,0.9194828],"study_design_scores_gemma":[0.000038471884,0.00032405416,0.0035726088,0.02660127,0.0013454781,0.0012817575,0.0003465017,0.017909074,0.0017183489,0.034669068,0.9119781,0.00021520811],"about_ca_topic_score_codex":0.0023375433,"about_ca_topic_score_gemma":0.0021281252,"teacher_disagreement_score":0.0035685909,"about_ca_system_score_codex":0.00074500305,"about_ca_system_score_gemma":0.001492769,"threshold_uncertainty_score":0.011995196},"labels":[],"label_agreement":null},{"id":"W3104607828","doi":"10.1007/s00170-020-06325-3","title":"Joint production preventive maintenance and dynamic inspection for a degrading manufacturing system","year":2020,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Preventive maintenance; Reliability engineering; Production (economics); Quality (philosophy); Product (mathematics); Failure rate; Engineering; Constraint (computer-aided design); Computer science; Operations research; Mathematics","score_opus":0.0067486211932219955,"score_gpt":0.20741181729587968,"score_spread":0.20066319610265768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104607828","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6321458,0.00033650253,0.3645801,0.00019746125,0.00003702538,0.000049043265,0.000046842157,0.0003740704,0.0022332452],"genre_scores_gemma":[0.98958606,0.000026399084,0.0098724635,0.000006168583,0.0000039783977,0.000005082012,0.0000125594015,0.000006217189,0.00048103346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970764,0.000043024196,0.000012192236,0.000058077876,0.000106061765,0.000072966606],"domain_scores_gemma":[0.999199,0.0003965482,0.00014194816,0.00008038763,0.00013876682,0.000043314783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054891437,0.0004278192,0.0005231851,0.0004588799,0.00044312194,0.0004391383,0.0005228018,0.0006266318,0.0007934127],"category_scores_gemma":[0.0020699282,0.00021258066,0.0003644054,0.00029649795,0.00039587304,0.0003329812,0.0004137174,0.00037956453,0.00007126594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00112261,0.00018869055,0.0055039823,0.00017188172,0.00004579853,0.0006657876,0.00016851624,0.86685205,0.047965657,0.0030326373,0.00056885055,0.07371351],"study_design_scores_gemma":[0.0000087578765,0.00020105233,0.0027547048,0.000002930633,0.000023001343,0.00015779062,0.000022707023,0.9927267,0.0034244198,0.00058387686,0.00008773815,0.0000063208167],"about_ca_topic_score_codex":0.0053086183,"about_ca_topic_score_gemma":0.003963119,"teacher_disagreement_score":0.0053086183,"about_ca_system_score_codex":0.000513907,"about_ca_system_score_gemma":0.0009111662,"threshold_uncertainty_score":0.010555446},"labels":[],"label_agreement":null},{"id":"W3107105815","doi":"10.1155/2020/8861942","title":"Generative Adversarial Network-based Missing Data Handling and Remaining Useful Life Estimation for Smart Train Control and Monitoring Systems","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Education; Korea Railroad Research Institute; National Research Foundation","keywords":"ALARM; Train; Predictive maintenance; Computer science; Artificial neural network; Fault (geology); Data mining; Condition monitoring; Safety monitoring; False alarm; Missing data; Generator (circuit theory); Machine learning; Reliability engineering; Artificial intelligence; Real-time computing; Engineering","score_opus":0.025891436450487044,"score_gpt":0.24431928919312107,"score_spread":0.21842785274263402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107105815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06295441,0.000589274,0.9337054,0.00031118194,0.000050451057,0.00003883497,0.00013068024,0.0005598306,0.0016600145],"genre_scores_gemma":[0.98203,0.00014322146,0.01630406,0.000081009,0.000025403151,0.000039615734,0.00014194369,0.000023475215,0.0012113441],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995629,0.00013270929,0.000021874588,0.00011221357,0.00009905099,0.00007116924],"domain_scores_gemma":[0.99885786,0.0007218422,0.00014893385,0.00007509597,0.00015365331,0.00004263845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001389139,0.0007973238,0.0007416529,0.00036773237,0.00020576752,0.0004571318,0.001062482,0.0006579558,0.0009424521],"category_scores_gemma":[0.0024240748,0.0003603007,0.0005544783,0.0003598025,0.0005945213,0.00070109015,0.0007861587,0.0013063467,0.00011944038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005068621,0.00001730848,0.00054867537,0.000021017073,0.000018462795,0.000038207847,0.000018908233,0.9843562,0.0004782463,0.0012385722,0.00027991302,0.0129337935],"study_design_scores_gemma":[0.0000010207787,0.000007633437,0.00009269669,0.0000011705786,0.00000241866,0.0000042741076,0.0000012292807,0.9993136,0.00012313623,0.00042008617,0.000031335825,0.0000014865308],"about_ca_topic_score_codex":0.0071625314,"about_ca_topic_score_gemma":0.004795376,"teacher_disagreement_score":0.0071625314,"about_ca_system_score_codex":0.0008251718,"about_ca_system_score_gemma":0.00060477597,"threshold_uncertainty_score":0.014241695},"labels":[],"label_agreement":null},{"id":"W3108278835","doi":"10.1080/24725854.2020.1836434","title":"Optimal structure screening for large-scale multi-state series-parallel systems based on structure ordinal optimization","year":2020,"lang":"en","type":"article","venue":"IISE Transactions","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Ordinal optimization; Reliability (semiconductor); Computer science; Mathematical optimization; Series (stratigraphy); Complex system; Fuzzy logic; Process (computing); State (computer science); Constraint (computer-aided design); Algorithm; Ordinal data; Mathematics; Artificial intelligence; Machine learning","score_opus":0.013627730461462189,"score_gpt":0.21727158859914067,"score_spread":0.2036438581376785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108278835","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023696888,0.0001369228,0.97418845,0.00007106348,0.000011443978,0.000043416378,0.000021456724,0.00010508383,0.0017252328],"genre_scores_gemma":[0.80850524,0.0002554722,0.18881878,0.00005273229,0.000018765624,0.00022534572,0.0001263617,0.000060073544,0.0019373057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999501,0.0001992134,0.00002162339,0.00006683673,0.00015560394,0.000055825647],"domain_scores_gemma":[0.9991666,0.0005293295,0.000100152276,0.000038985374,0.00013227327,0.000032563465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011905761,0.0008917799,0.0009906188,0.00107997,0.00054143125,0.0007279446,0.00064843346,0.0005890294,0.0018580917],"category_scores_gemma":[0.002364831,0.00042126296,0.00086944655,0.0006687651,0.00067707093,0.0009142486,0.00080787996,0.0007083677,0.00013922511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026441228,0.000027779415,0.00049111695,0.000045992314,0.000019441415,0.000039588762,0.00003129042,0.9722286,0.0010603832,0.009892864,0.0002892794,0.01584725],"study_design_scores_gemma":[0.00000257252,0.0000105263,0.000059862738,0.0000018170972,0.0000026368932,0.000003928503,0.0000039865636,0.997575,0.00013665915,0.002127426,0.00007327383,0.0000023249208],"about_ca_topic_score_codex":0.0034270755,"about_ca_topic_score_gemma":0.0027632064,"teacher_disagreement_score":0.0034270755,"about_ca_system_score_codex":0.0008318059,"about_ca_system_score_gemma":0.0010975713,"threshold_uncertainty_score":0.006814301},"labels":[],"label_agreement":null},{"id":"W3110752823","doi":"10.1109/tr.2020.3032157","title":"Probabilistic Analysis for Remaining Useful Life Prediction and Reliability Assessment","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Department of National Defence; National Research Council Canada; Okanagan University College; Government of Canada; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Reliability (semiconductor); Probabilistic logic; Computer science; Reliability engineering; Bayesian probability; Workload; Inference; Posterior probability; Data mining; Predictive inference; Bayesian inference; Machine learning; Grid; Set (abstract data type); Artificial intelligence; Engineering; Frequentist inference; Mathematics","score_opus":0.01834430008340569,"score_gpt":0.23532952783607136,"score_spread":0.21698522775266568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110752823","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040587545,0.00023333631,0.9948613,0.000069888294,0.000008807782,0.00001076259,0.000039976967,0.000116124065,0.0006010105],"genre_scores_gemma":[0.8673981,0.0012286995,0.12882562,0.00009758455,0.00015445634,0.00015816727,0.00038368453,0.00008368935,0.0016700465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886644,0.00037332255,0.000056017856,0.00023153966,0.00038052618,0.000092166454],"domain_scores_gemma":[0.9975666,0.0016878252,0.00025690367,0.00015943985,0.00029135583,0.000037984148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002505026,0.0009539438,0.0010832533,0.001613211,0.0003998682,0.0009845907,0.0014958071,0.0009002369,0.0015454037],"category_scores_gemma":[0.007014193,0.0005426813,0.0010897513,0.0011761895,0.0007925078,0.0015747373,0.0008680368,0.0011380109,0.00026554835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022296548,0.00001534733,0.0008058912,0.000052253752,0.000033347464,0.000050094954,0.000025901501,0.95744824,0.0008152939,0.017968815,0.00037893996,0.022383556],"study_design_scores_gemma":[9.951274e-7,0.000005499409,0.000119308526,0.0000027353835,0.0000047666686,0.0000099665895,0.0000021347178,0.9937232,0.0001212772,0.005865969,0.00014045008,0.0000037201567],"about_ca_topic_score_codex":0.006135117,"about_ca_topic_score_gemma":0.0031567735,"teacher_disagreement_score":0.006135117,"about_ca_system_score_codex":0.00082520803,"about_ca_system_score_gemma":0.0009825601,"threshold_uncertainty_score":0.013247967},"labels":[],"label_agreement":null},{"id":"W3111328003","doi":"10.1177/1748006x20978099","title":"A nonlinear Wiener degradation model integrating degradation data under accelerated stresses and real operating environment","year":2020,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Degradation (telecommunications); Acceleration; Precondition; Nonlinear system; Test data; Computer science; Structural engineering; Reliability engineering; Engineering; Physics","score_opus":0.0262943912961832,"score_gpt":0.22756266050764667,"score_spread":0.20126826921146349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111328003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060609125,0.0005026506,0.933811,0.00020028105,0.000057310717,0.00005989912,0.00027833862,0.00053580943,0.003945634],"genre_scores_gemma":[0.9739983,0.00059415906,0.016932396,0.0000548641,0.00003496439,0.00013615783,0.00030481204,0.000039619084,0.007904715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936134,0.00009776905,0.00004398575,0.00023159606,0.00018427875,0.00008108075],"domain_scores_gemma":[0.9995602,0.00015356456,0.00009706267,0.000035814963,0.0001386056,0.000014768079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087554357,0.001108623,0.00086819567,0.0007276848,0.00033901096,0.0010390957,0.0013969057,0.0014030728,0.0011670903],"category_scores_gemma":[0.0015510957,0.000439105,0.00087175646,0.00070952164,0.0006510684,0.0016084373,0.00057443045,0.0010393878,0.00039749226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049352413,0.00003113826,0.0015495386,0.00006653296,0.000030807703,0.00014531678,0.000074852265,0.9760064,0.00442603,0.0030603951,0.00029551247,0.014264233],"study_design_scores_gemma":[0.000002224832,0.000016024063,0.00033978908,0.000002364309,0.000008994764,0.000026078373,0.0000047090534,0.99845254,0.0003682443,0.00067134487,0.00010124072,0.000006465058],"about_ca_topic_score_codex":0.008684409,"about_ca_topic_score_gemma":0.005222105,"teacher_disagreement_score":0.008684409,"about_ca_system_score_codex":0.00070585107,"about_ca_system_score_gemma":0.00075448153,"threshold_uncertainty_score":0.017267704},"labels":[],"label_agreement":null},{"id":"W3120079950","doi":"10.1002/asmb.2601","title":"Optimal burn‐in policy based on a set of cutoff points using mixture inverse Gaussian degradation process and copulas","year":2021,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Cutoff; Gamma process; Burn-in; Gaussian; Inverse; Wiener process; Copula (linguistics); Mathematical optimization; Reliability (semiconductor); Applied mathematics; Monotone polygon; Computer science; Mathematics; Process (computing); Gaussian process; Set (abstract data type); Econometrics; Statistics; Reliability engineering; Engineering; Physics","score_opus":0.0182264073315623,"score_gpt":0.24181386103878205,"score_spread":0.22358745370721975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120079950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14934379,0.00044915735,0.8473467,0.0004919277,0.0000427812,0.00022519323,0.00010834781,0.00041286825,0.0015792173],"genre_scores_gemma":[0.90628475,0.00017127387,0.09219242,0.00014865906,0.000023495366,0.00021565464,0.00014018643,0.00006300465,0.00076059566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99618226,0.0018089149,0.00026854395,0.00060566276,0.0005356252,0.0005990263],"domain_scores_gemma":[0.95791215,0.030944362,0.003861006,0.0016213028,0.0036519412,0.002009199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012328927,0.001088264,0.0024769986,0.0022311395,0.00081733445,0.001857431,0.0024555228,0.0021390426,0.0021004316],"category_scores_gemma":[0.044632576,0.0008025224,0.0009991513,0.00092347316,0.0025178625,0.0029297546,0.0016958984,0.0029280277,0.00023492966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085511885,0.00047206137,0.008868972,0.00019311508,0.00011045684,0.00029674967,0.00030421917,0.8914344,0.0032783446,0.04264326,0.0014589756,0.050084382],"study_design_scores_gemma":[0.000019817508,0.00010768433,0.0008694178,0.000044170134,0.000020100659,0.000023222281,0.00005674171,0.9837235,0.0015226647,0.01342813,0.00016558156,0.000019023282],"about_ca_topic_score_codex":0.0036087001,"about_ca_topic_score_gemma":0.002258105,"teacher_disagreement_score":0.012328927,"about_ca_system_score_codex":0.0020562997,"about_ca_system_score_gemma":0.0022425351,"threshold_uncertainty_score":0.065202355},"labels":[],"label_agreement":null},{"id":"W3138990266","doi":"10.36001/ijphm.2017.v8i1.2532","title":"Condition Based Maintenance of Low Speed Rolling Element Bearings using Hidden Markov Model","year":2020,"lang":"en","type":"article","venue":"International Journal of Prognostics and Health Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hidden Markov model; Bearing (navigation); Residual; Vibration; Fault (geology); Condition-based maintenance; Reliability engineering; Condition monitoring; Failure rate; Engineering; Computer science; Hidden semi-Markov model; Markov chain; Markov model; Pattern recognition (psychology); Artificial intelligence; Machine learning; Algorithm; Markov property","score_opus":0.02755443777433166,"score_gpt":0.28032153845819313,"score_spread":0.2527671006838615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138990266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1448554,0.0004288106,0.85184944,0.00013158955,0.000041747604,0.000041942585,0.00021410975,0.0011227395,0.0013142652],"genre_scores_gemma":[0.9728017,0.00013565249,0.025838464,0.000016240205,0.000014714122,0.000027741818,0.00020689743,0.00002325985,0.00093525974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999783,0.000037159174,0.000014397551,0.00006662797,0.00006759406,0.000031214742],"domain_scores_gemma":[0.99932015,0.00044378717,0.0000857166,0.0000341355,0.00009401163,0.00002213977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048144843,0.00042043248,0.00065984257,0.0004365039,0.00021322476,0.00044883988,0.00066041824,0.00041252293,0.0010559412],"category_scores_gemma":[0.0015263784,0.0003161643,0.0005296544,0.00022910596,0.00025011282,0.0005605408,0.00026347986,0.00052737864,0.00021729607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001385306,0.000045616154,0.0033906598,0.000052323227,0.00003656256,0.000076782715,0.000052979794,0.9543179,0.0041957,0.0016685959,0.00028412952,0.035740215],"study_design_scores_gemma":[0.0000024688818,0.000015465177,0.0005376323,0.0000018718014,0.0000062393974,0.0000069000107,0.0000020436717,0.998519,0.0004068581,0.00044919443,0.000049265644,0.000003002084],"about_ca_topic_score_codex":0.012132627,"about_ca_topic_score_gemma":0.01224872,"teacher_disagreement_score":0.012132627,"about_ca_system_score_codex":0.0006172871,"about_ca_system_score_gemma":0.00063094357,"threshold_uncertainty_score":0.024123967},"labels":[],"label_agreement":null},{"id":"W3139482639","doi":"10.1287/opre.2020.2086","title":"Optimal Control of Partially Observable Semi-Markovian Failing Systems: An Analysis Using a Phase Methodology","year":2021,"lang":"en","type":"article","venue":"Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Toronto","funders":"","keywords":"Observable; Markov decision process; Limit (mathematics); Computer science; Optimal control; Mathematical optimization; Markov process; Partially observable Markov decision process; Sequence (biology); Control (management); Reliability (semiconductor); Class (philosophy); Markov chain; Control limits; Phase (matter); Process (computing); Mathematics; Artificial intelligence; Statistics","score_opus":0.16210525793678052,"score_gpt":0.41887687022670583,"score_spread":0.2567716122899253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139482639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0110882595,0.00042823984,0.98456305,0.00043207177,0.00003709164,0.00006146588,0.000032650383,0.000046480673,0.003310706],"genre_scores_gemma":[0.8258232,0.0021798443,0.16564143,0.00037199925,0.00015812094,0.00046115497,0.00011593932,0.000106762265,0.005141647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994042,0.00027549412,0.00002034316,0.000062866886,0.0001508829,0.00008620307],"domain_scores_gemma":[0.9951324,0.0040905047,0.00026147277,0.00009290649,0.00033972666,0.00008294951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031234806,0.0010955308,0.0009479477,0.00084989646,0.0003763843,0.000979913,0.0010293087,0.0012735509,0.0042512165],"category_scores_gemma":[0.007419416,0.0006905056,0.0013012207,0.00067471596,0.0014251474,0.0019858642,0.0010717558,0.0016823411,0.00017293394],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004656011,0.000059797476,0.00036532435,0.00013425172,0.000046053843,0.00005293208,0.000075785625,0.8395661,0.0010705083,0.1493604,0.00079189596,0.008430408],"study_design_scores_gemma":[0.0000051379598,0.000026825595,0.000053166732,0.000009316739,0.0000056011622,0.000005708291,0.000007908311,0.9837977,0.0001371632,0.015725449,0.00022241587,0.0000036150268],"about_ca_topic_score_codex":0.0041974937,"about_ca_topic_score_gemma":0.0021660444,"teacher_disagreement_score":0.0042512165,"about_ca_system_score_codex":0.0011110499,"about_ca_system_score_gemma":0.0017279456,"threshold_uncertainty_score":0.016518712},"labels":[],"label_agreement":null},{"id":"W3155954770","doi":"10.36001/phmconf.2011.v3i1.1990","title":"Condition Based Maintenance Optimization for Multi-component Systems","year":2011,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Component (thermodynamics); Computer science; Physics","score_opus":0.04395768489596985,"score_gpt":0.2322739693166734,"score_spread":0.18831628442070356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155954770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044607278,0.0006582366,0.95120466,0.00023459051,0.00003440352,0.000058186346,0.000078004115,0.00036750326,0.0027571262],"genre_scores_gemma":[0.8983729,0.0003120528,0.098023415,0.00004640649,0.0000370181,0.00013295891,0.00014784904,0.00007314536,0.0028541419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995234,0.00014587537,0.00001942206,0.00009928927,0.00015416079,0.000057818845],"domain_scores_gemma":[0.99917763,0.00053913944,0.00009358739,0.000033528057,0.00012628472,0.000029824734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010311636,0.0008600033,0.0010496071,0.0007495793,0.000360827,0.00074976723,0.0007652183,0.0008800824,0.0016999746],"category_scores_gemma":[0.0023773727,0.00042609574,0.00049817417,0.0006388997,0.00047079296,0.00076363713,0.0005435624,0.00069593516,0.0001784446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035251178,0.000019852081,0.00023177727,0.000035898756,0.000012944386,0.000024368414,0.000016534646,0.9847051,0.001023283,0.0027077424,0.00032587635,0.010861437],"study_design_scores_gemma":[0.0000036993129,0.000009242177,0.000104414394,0.0000013176251,0.0000024184249,0.000004453121,0.0000016979918,0.9987295,0.00013094352,0.0009268402,0.00008379002,0.0000016635148],"about_ca_topic_score_codex":0.006046943,"about_ca_topic_score_gemma":0.00328613,"teacher_disagreement_score":0.006046943,"about_ca_system_score_codex":0.0012609655,"about_ca_system_score_gemma":0.00092575036,"threshold_uncertainty_score":0.012023509},"labels":[],"label_agreement":null},{"id":"W3159071455","doi":"10.1002/nav.21994","title":"Dynamically scheduling and maintaining a flexible server","year":2021,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scheduling (production processes); Computer science; Markov decision process; Markov chain; Mathematical optimization; Semiconductor device fabrication; Schedule; Fair-share scheduling; Job shop scheduling; Dynamic priority scheduling; Operations research; Distributed computing; Real-time computing; Markov process; Mathematics; Engineering; Operating system","score_opus":0.06027633868240124,"score_gpt":0.3365222179903641,"score_spread":0.27624587930796285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159071455","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6045593,0.00013603218,0.39036956,0.0002901383,0.000052140964,0.00007375522,0.000096381256,0.0004929633,0.003929686],"genre_scores_gemma":[0.98274326,0.000026420119,0.016404547,0.000020520592,0.000009328746,0.000018506094,0.000033171422,0.000020988598,0.0007231957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993062,0.0001533413,0.000040389783,0.00018327277,0.0001394634,0.00017730183],"domain_scores_gemma":[0.99787736,0.00085697364,0.00036874673,0.000269246,0.00031471413,0.00031290058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014028517,0.00049891975,0.0006140443,0.000586776,0.0006608296,0.0010750935,0.0014208679,0.0006339729,0.0015020274],"category_scores_gemma":[0.0027828855,0.0003969243,0.00032474223,0.0006651807,0.0006828724,0.00081516354,0.0007496961,0.0006720767,0.00036288745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027315976,0.00014122677,0.002509956,0.00004032378,0.000033364166,0.0001542433,0.00006742653,0.95056385,0.015764877,0.008267483,0.00091567246,0.021268431],"study_design_scores_gemma":[0.000014095541,0.000052729614,0.00037348806,0.0000023883786,0.0000069690204,0.000022983088,0.0000193915,0.99436635,0.0022493722,0.0026988103,0.00018746153,0.000005882932],"about_ca_topic_score_codex":0.004563262,"about_ca_topic_score_gemma":0.0028837356,"teacher_disagreement_score":0.004563262,"about_ca_system_score_codex":0.00089801755,"about_ca_system_score_gemma":0.0011886428,"threshold_uncertainty_score":0.009073436},"labels":[],"label_agreement":null},{"id":"W3163732491","doi":"10.1007/s11432-020-3134-8","title":"Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion","year":2021,"lang":"en","type":"article","venue":"Science China Information Sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fractional Brownian motion; Markov process; Degradation (telecommunications); Computer science; Brownian motion; Stochastic process; Geometric Brownian motion; Diffusion process; Process (computing); Statistical physics; Applied mathematics; Mathematics; Physics; Statistics","score_opus":0.011367877923528032,"score_gpt":0.2342485209836466,"score_spread":0.22288064306011857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163732491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049973935,0.0008575916,0.9476896,0.00032889226,0.00006932459,0.000017663267,0.00005774135,0.00013179328,0.00087355665],"genre_scores_gemma":[0.9698295,0.0010071582,0.026556566,0.000080610655,0.000115621726,0.00004146348,0.00012984843,0.000038877857,0.0022003362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994535,0.00017248694,0.000033785782,0.00015737799,0.00011102394,0.000071725735],"domain_scores_gemma":[0.9980288,0.0013201201,0.0002455587,0.00011679381,0.00023015936,0.000058495756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017934751,0.0009301091,0.0016485873,0.0008166715,0.0003177162,0.0012283507,0.0013837314,0.0014453785,0.00077219703],"category_scores_gemma":[0.005684732,0.00061727274,0.0010469377,0.0008951102,0.0008885763,0.0017770377,0.00080871675,0.0015544269,0.00013231776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027525586,0.000030008187,0.0008807503,0.000046184956,0.000038429767,0.000062824474,0.00003522561,0.9690053,0.0007670266,0.015286373,0.00032203775,0.013498387],"study_design_scores_gemma":[7.0514716e-7,0.000003325526,0.000083665975,0.0000014683891,0.0000026886971,0.0000040050318,0.0000010185444,0.99757725,0.00003963715,0.0022531303,0.000031134976,0.0000019320487],"about_ca_topic_score_codex":0.006432867,"about_ca_topic_score_gemma":0.0023687063,"teacher_disagreement_score":0.006432867,"about_ca_system_score_codex":0.0008370071,"about_ca_system_score_gemma":0.00078690524,"threshold_uncertainty_score":0.012790859},"labels":[],"label_agreement":null},{"id":"W3164058326","doi":"10.1002/net.22055","title":"The node cop‐win reliability of unicyclic and bicyclic graphs","year":2021,"lang":"en","type":"article","venue":"Networks","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Combinatorics; Graph; Random graph; Mathematics; Discrete mathematics; Computer science","score_opus":0.003734307403357608,"score_gpt":0.18515590220383016,"score_spread":0.18142159480047254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164058326","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68221533,0.0015228292,0.3035898,0.0010093247,0.000056647128,0.00008469772,0.00069797965,0.00023344578,0.010589861],"genre_scores_gemma":[0.9939295,0.00030380726,0.004377986,0.00003948664,0.000029688006,0.000028580755,0.000101078374,0.000028790962,0.0011609241],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988656,0.00050213205,0.000044113363,0.00019300735,0.0002193259,0.00017592651],"domain_scores_gemma":[0.9842465,0.010048316,0.002699511,0.0011638066,0.001252242,0.00058950484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002609409,0.0005847605,0.00082464755,0.00185118,0.00046865767,0.0012807619,0.001311378,0.00070405495,0.0017391341],"category_scores_gemma":[0.01446521,0.00047528086,0.00040872794,0.0013045432,0.0017853115,0.0016654751,0.00095814123,0.0008684798,0.00015350735],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028728298,0.000058964826,0.0074226265,0.00019677468,0.00011829309,0.00028863188,0.000522782,0.6405971,0.0028606188,0.32829565,0.004756424,0.014594868],"study_design_scores_gemma":[0.000016383578,0.00005859373,0.0021630554,0.000039918872,0.00003086772,0.00025422726,0.0001274069,0.84782004,0.0006448074,0.14769985,0.0011226354,0.00002211565],"about_ca_topic_score_codex":0.0034899272,"about_ca_topic_score_gemma":0.0024431346,"teacher_disagreement_score":0.0034899272,"about_ca_system_score_codex":0.0014721944,"about_ca_system_score_gemma":0.00046837406,"threshold_uncertainty_score":0.013800025},"labels":[],"label_agreement":null},{"id":"W3168966671","doi":"10.1016/j.compchemeng.2021.107362","title":"Optimum Maintenance Interval Determination for Field Instrument Devices in Oil and Gas Industries Based on Expected Utility Theory","year":2021,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Failure rate; Interval (graph theory); Heuristic; Refinery; Process (computing); Engineering; Oil refinery; Planned maintenance; Duration (music); Operations research; Computer science; Waste management; Mathematics","score_opus":0.00825241083338773,"score_gpt":0.1975507490133539,"score_spread":0.18929833817996616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168966671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08602851,0.00053082005,0.9119553,0.00004671076,0.00001594193,0.000026861466,0.000029989173,0.00021180713,0.0011539903],"genre_scores_gemma":[0.9421853,0.00014197607,0.057123452,0.000011617873,0.000016882912,0.000026236094,0.00004785461,0.00003196834,0.0004147766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948186,0.00017054842,0.000025639061,0.00011281435,0.00015633486,0.000052896205],"domain_scores_gemma":[0.9984931,0.0010665806,0.0001332591,0.000055081677,0.00022248102,0.000029515899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008150025,0.0005457722,0.0008739875,0.00092438556,0.00031544472,0.0006582927,0.00073317206,0.0005079819,0.00063701515],"category_scores_gemma":[0.003020444,0.00035880844,0.00054095977,0.00044289327,0.00027665068,0.0007054651,0.0002458779,0.0005324381,0.00009657213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036251612,0.00011756107,0.0019542677,0.00020187571,0.000057246518,0.00009882354,0.00012082828,0.89484906,0.01729301,0.0063824477,0.000773544,0.07778874],"study_design_scores_gemma":[0.000008641525,0.000049217717,0.00079063646,0.000005101039,0.000014200789,0.00002104553,0.00000900304,0.9950848,0.0024287044,0.0014810029,0.00010127309,0.000006408774],"about_ca_topic_score_codex":0.0018627589,"about_ca_topic_score_gemma":0.0014745966,"teacher_disagreement_score":0.0018627589,"about_ca_system_score_codex":0.00063441106,"about_ca_system_score_gemma":0.00053452316,"threshold_uncertainty_score":0.0046030283},"labels":[],"label_agreement":null},{"id":"W3169825082","doi":"10.1108/jqme-06-2020-0050","title":"Appropriate strategy selection for reliability-centered maintenance of one-shot systems using fuzzy model","year":2021,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Reliability (semiconductor); Fuzzy logic; Computer science; Data mining; Expert system; Artificial intelligence; Machine learning; One shot; Reliability engineering; Engineering","score_opus":0.07032335816318719,"score_gpt":0.2972927037030867,"score_spread":0.2269693455398995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169825082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09957809,0.00026491802,0.89383554,0.00010057907,0.000021998267,0.0000870935,0.00003587687,0.00017357436,0.005902279],"genre_scores_gemma":[0.960223,0.00007980693,0.038094655,0.000024545006,0.0000058046962,0.00005563219,0.000032875436,0.000008450835,0.0014753025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995704,0.00011847413,0.000019773237,0.00010829123,0.000128943,0.00005411348],"domain_scores_gemma":[0.9995152,0.00024001847,0.0000647406,0.000033647637,0.00012515236,0.000021329604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007087124,0.0005133396,0.0006285596,0.00068728765,0.00033852344,0.00089188275,0.00081553066,0.00072012155,0.0017745525],"category_scores_gemma":[0.0019852973,0.0002173508,0.0005285728,0.00021279116,0.00034281742,0.0005944182,0.00040284643,0.00040781117,0.00016873301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013514693,0.000109778994,0.002312809,0.00018362862,0.000073697694,0.00021749656,0.00033480764,0.8958005,0.011032602,0.0139095485,0.0007428248,0.075147256],"study_design_scores_gemma":[0.0000064545457,0.00007052569,0.00047480655,0.000012888406,0.000017070572,0.00003678263,0.000042703112,0.9956442,0.0011388953,0.002194126,0.00035306253,0.000008516076],"about_ca_topic_score_codex":0.0054487525,"about_ca_topic_score_gemma":0.004299749,"teacher_disagreement_score":0.0054487525,"about_ca_system_score_codex":0.0007010765,"about_ca_system_score_gemma":0.00073914387,"threshold_uncertainty_score":0.010834098},"labels":[],"label_agreement":null},{"id":"W3172720700","doi":"10.1002/qre.2867","title":"A multi‐state <i>k</i>‐out‐of‐<i>n</i>:F balanced system with a rebalancing mechanism","year":2021,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reliability (semiconductor); Component (thermodynamics); State (computer science); Markov chain; Markov process; Reliability engineering; Computer science; Process (computing); Product (mathematics); Mathematical optimization; Mechanism (biology); Engineering; Mathematics; Algorithm; Power (physics); Statistics; Physics","score_opus":0.008388500919382256,"score_gpt":0.21846437986288672,"score_spread":0.21007587894350446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172720700","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57824475,0.0005096627,0.40431365,0.0008033765,0.00013406014,0.00021187971,0.0004808677,0.0008343499,0.014467402],"genre_scores_gemma":[0.9936371,0.000040979743,0.0043788045,0.00002061843,0.000010440854,0.000034694414,0.00004119121,0.0000068859354,0.0018293103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995441,0.00008244339,0.000027753975,0.0001392101,0.00007618683,0.00013033091],"domain_scores_gemma":[0.99938893,0.00015651768,0.00017710762,0.000041788535,0.0001655747,0.0000700395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064562587,0.0007100036,0.0009849122,0.00061256334,0.0011492727,0.0011198763,0.0010941217,0.0011049254,0.0029263634],"category_scores_gemma":[0.0006916295,0.00031917257,0.00056479505,0.000615483,0.00069179304,0.0009586858,0.00092790375,0.0004522518,0.00030609392],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039113403,0.0000794925,0.0019482968,0.00011378091,0.00005424424,0.0007992241,0.00013795486,0.9673275,0.010855104,0.006100869,0.0008145491,0.011377728],"study_design_scores_gemma":[0.000023919578,0.0000771396,0.00060983706,0.0000062808044,0.000020438594,0.000039466366,0.000021357942,0.9969241,0.00084959966,0.0011768511,0.00023869594,0.00001225672],"about_ca_topic_score_codex":0.011877323,"about_ca_topic_score_gemma":0.007005991,"teacher_disagreement_score":0.011877323,"about_ca_system_score_codex":0.0010453375,"about_ca_system_score_gemma":0.0006165557,"threshold_uncertainty_score":0.023616374},"labels":[],"label_agreement":null},{"id":"W317510280","doi":"10.1016/j.ress.2015.04.013","title":"Search for all d-MPs for all d levels in multistate two-terminal networks","year":2015,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Path (computing); Binary number; Heuristic; Reliability (semiconductor); Algorithm; Binary search algorithm; Value (mathematics); Mathematics; Integer (computer science); Computer science; Mathematical optimization; Search algorithm; Statistics; Arithmetic; Power (physics); Physics","score_opus":0.023872578345819625,"score_gpt":0.2593243840548468,"score_spread":0.23545180570902718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W317510280","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49629685,0.000866446,0.4859183,0.0024771441,0.00007827623,0.00026791668,0.0013945748,0.0008403791,0.011860073],"genre_scores_gemma":[0.8910581,0.00017327047,0.10359755,0.00024517594,0.000030752755,0.00016448689,0.0006278551,0.00009076226,0.004012071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972576,0.00010607977,0.000018042703,0.00006650456,0.000026046142,0.000057537443],"domain_scores_gemma":[0.9974624,0.0019784817,0.00020258712,0.00008872932,0.00014145032,0.0001262902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083798025,0.0012400186,0.0013269603,0.0016421981,0.00068541366,0.0010736376,0.0012970336,0.0020843947,0.0054374076],"category_scores_gemma":[0.004596673,0.0009554552,0.00082397874,0.0009119731,0.00085878646,0.0019713016,0.0013686012,0.0008872349,0.00040524275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044157924,0.0001216239,0.0019491975,0.00021370876,0.00008568465,0.000103423954,0.00011803537,0.94897306,0.001151439,0.009148413,0.0020259095,0.035667922],"study_design_scores_gemma":[0.000047302135,0.000084647145,0.00032972448,0.000020816364,0.00002241316,0.000020278672,0.000087618275,0.98840886,0.0005766475,0.010166234,0.00022719408,0.000008255783],"about_ca_topic_score_codex":0.0046067154,"about_ca_topic_score_gemma":0.006102474,"teacher_disagreement_score":0.0054374076,"about_ca_system_score_codex":0.00083487114,"about_ca_system_score_gemma":0.0011816774,"threshold_uncertainty_score":0.018189907},"labels":[],"label_agreement":null},{"id":"W3186467491","doi":"10.21203/rs.3.rs-741517/v1","title":"Integrated Production and Maintenance Control Policies for Failure-prone Manufacturing Systems Producing Perishable Products","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preventive maintenance; Corrective maintenance; Operations research; Control (management); Computer science; Robustness (evolution); Reliability engineering; Cost reduction; Minification; Reduction (mathematics); Production (economics); Operations management; Business; Engineering; Economics; Mathematics; Microeconomics","score_opus":0.02658475226615818,"score_gpt":0.28393242690435955,"score_spread":0.25734767463820135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186467491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41874632,0.0006364298,0.578414,0.00023539069,0.000025914987,0.00010215519,0.000083160005,0.0002169376,0.0015396705],"genre_scores_gemma":[0.9940919,0.00006263437,0.0054860185,0.000009953292,0.000005614361,0.00002379208,0.00002023495,0.0000067681117,0.00029314216],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913174,0.00031159457,0.00005329596,0.00015658265,0.00017157056,0.00017537578],"domain_scores_gemma":[0.9969394,0.001547118,0.0009237727,0.00014770273,0.0002922615,0.00014973454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002406904,0.00090966205,0.0009264492,0.00076013437,0.0003019788,0.0010968492,0.0010344303,0.0008951302,0.0008609474],"category_scores_gemma":[0.004397797,0.0004129526,0.00042723355,0.00049384875,0.0007181232,0.0008155672,0.0008741706,0.00067552604,0.00010270118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010185358,0.00005152368,0.0004920188,0.000030405468,0.000017611066,0.00003472304,0.000025438465,0.9906375,0.0017584568,0.0008689706,0.00007188904,0.0059095607],"study_design_scores_gemma":[0.000010238843,0.00009732528,0.000482425,0.000003918279,0.000010745648,0.000009895381,0.0000095178475,0.99791926,0.00081097445,0.0005950349,0.000046860907,0.000003850255],"about_ca_topic_score_codex":0.0031822748,"about_ca_topic_score_gemma":0.0012448397,"teacher_disagreement_score":0.0031822748,"about_ca_system_score_codex":0.0009442749,"about_ca_system_score_gemma":0.00079100527,"threshold_uncertainty_score":0.012729108},"labels":[],"label_agreement":null},{"id":"W3197962962","doi":"10.32920/ryerson.14648742.v1","title":"Inspection and maintenance optimisation of multicomponent systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Downtime; Reliability engineering; Component (thermodynamics); Reliability (semiconductor); Computer science; Preventive maintenance; Type (biology); Monte Carlo method; Engineering; Mathematics; Statistics","score_opus":0.00911424362121756,"score_gpt":0.19389391133619266,"score_spread":0.1847796677149751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197962962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09009625,0.0005567009,0.9046595,0.00023504542,0.00003430407,0.00010018556,0.00012291885,0.00018317159,0.004011935],"genre_scores_gemma":[0.92039144,0.00047673986,0.07425093,0.000050107996,0.000019769326,0.00021868851,0.00011190168,0.000051089708,0.004429307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940205,0.00016383258,0.000025523479,0.00015659395,0.00015888347,0.0000930625],"domain_scores_gemma":[0.9979876,0.0014599835,0.00032056982,0.000058568028,0.00010250033,0.00007058935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001135398,0.0009362396,0.0012935392,0.00082165434,0.00035956712,0.0011758752,0.0014716282,0.0013103287,0.0018125043],"category_scores_gemma":[0.003872583,0.0007149459,0.0009529319,0.00093281333,0.0011856514,0.0009674541,0.0007158485,0.0009042089,0.0001628151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009881573,0.000012423783,0.00015954227,0.000019827607,0.000008965549,0.000016886695,0.00001073996,0.9956442,0.00022320548,0.002086428,0.00004064316,0.0017672372],"study_design_scores_gemma":[0.0000030939104,0.000008637761,0.00006416448,0.0000021616204,0.0000027308718,0.000003966126,0.0000024113124,0.9985654,0.00008244682,0.0012089399,0.0000544681,0.0000016287445],"about_ca_topic_score_codex":0.008687332,"about_ca_topic_score_gemma":0.004429955,"teacher_disagreement_score":0.008687332,"about_ca_system_score_codex":0.0016362874,"about_ca_system_score_gemma":0.0013656457,"threshold_uncertainty_score":0.017273486},"labels":[],"label_agreement":null},{"id":"W3198201971","doi":"10.1016/j.ifacol.2021.08.297","title":"Integration of Planning, Scheduling, and Control for Multi-product Chemical Systems under Preventive Maintenance","year":2021,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Scheduling (production processes); Heuristics; Preventive maintenance; Robustness (evolution); A priori and a posteriori; Computer science; Reliability engineering; Work in process; Operations research; Risk analysis (engineering); Operations management; Engineering; Manufacturing engineering; Business","score_opus":0.019545376788629124,"score_gpt":0.26064803091998706,"score_spread":0.24110265413135795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198201971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036787648,0.00035308415,0.95846254,0.00014902758,0.000036213427,0.00008722439,0.00002062806,0.00022113962,0.0038825017],"genre_scores_gemma":[0.90721434,0.00019297781,0.09150078,0.000031023465,0.000035886853,0.00008637748,0.0000277333,0.000025219446,0.00088561093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992347,0.00019498775,0.000026495314,0.00013019281,0.0002755693,0.00013822444],"domain_scores_gemma":[0.99936014,0.00031852018,0.0001288956,0.00006006092,0.000086323664,0.000045951117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010333197,0.0010478985,0.0007812641,0.00051504694,0.00053897465,0.00093350234,0.00096483895,0.000732548,0.0010227341],"category_scores_gemma":[0.0013826974,0.00045224384,0.00071706594,0.00043178047,0.0008270032,0.0007943041,0.0009651143,0.0009996741,0.000096452844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004947604,0.000070839254,0.00035466292,0.00006781437,0.000027498802,0.00009988251,0.00005967693,0.95794815,0.0028835419,0.009613407,0.00015436282,0.028670728],"study_design_scores_gemma":[0.00000760497,0.00010501072,0.0001870382,0.000005091882,0.000012703516,0.000022052413,0.000010796253,0.9962794,0.00093074184,0.0020649678,0.00036991594,0.000004653576],"about_ca_topic_score_codex":0.007667805,"about_ca_topic_score_gemma":0.0061700507,"teacher_disagreement_score":0.007667805,"about_ca_system_score_codex":0.00083174935,"about_ca_system_score_gemma":0.0022018745,"threshold_uncertainty_score":0.015246332},"labels":[],"label_agreement":null},{"id":"W3200952508","doi":"10.1007/s11356-021-16234-x","title":"RETRACTED ARTICLE: Preventive maintenance for the flexible flowshop scheduling under uncertainty: a waste-to-energy system","year":2021,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":78,"is_retracted":true,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; Université Laval","funders":"","keywords":"Preventive maintenance; Scheduling (production processes); Job shop scheduling; Computer science; Mathematical optimization; Genetic algorithm; Reliability engineering; Operations research; Engineering; Schedule; Machine learning; Mathematics","score_opus":0.019606288864081646,"score_gpt":0.27305148110562144,"score_spread":0.2534451922415398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200952508","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027132572,0.0034063805,0.027636426,0.12455866,0.8332862,0.00006721437,0.0013253333,0.00096352573,0.0060430346],"genre_scores_gemma":[0.11160751,0.010384491,0.024353664,0.072367206,0.6374664,0.00018527589,0.0031610513,0.0017575218,0.13871692],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828404,0.00032096912,0.00018873879,0.00030521568,0.0007390444,0.00016198617],"domain_scores_gemma":[0.9875245,0.005346319,0.00039918564,0.00059557945,0.0054524206,0.00068197097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021817237,0.001231844,0.0016195971,0.0013969064,0.0017284869,0.0021706705,0.0035939373,0.0049374904,0.028028104],"category_scores_gemma":[0.02338249,0.00053583086,0.0010718968,0.0016126067,0.0013651242,0.0018399111,0.0013719696,0.0048289653,0.008874404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019309767,0.000048114613,0.0002881367,0.00035003692,0.000057325815,0.0010841709,0.000053550666,0.0031934753,0.0005492168,0.0035029876,0.96541816,0.025261715],"study_design_scores_gemma":[0.00020517767,0.00026383073,0.003383145,0.0003759898,0.00023815874,0.0025324004,0.00023096788,0.045720942,0.0033136331,0.026562303,0.91701484,0.00015860586],"about_ca_topic_score_codex":0.008589984,"about_ca_topic_score_gemma":0.008168421,"teacher_disagreement_score":0.028028104,"about_ca_system_score_codex":0.0023504717,"about_ca_system_score_gemma":0.0023785264,"threshold_uncertainty_score":0.09376335},"labels":[],"label_agreement":null},{"id":"W3201997504","doi":"10.1007/s11009-021-09896-0","title":"On the Derivative Counting Processes of First- and Second-order Aggregated Semi-Markov Systems","year":2021,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Imperfect; Markov chain; Reliability (semiconductor); Markov process; State space; Applied mathematics; Order (exchange); State (computer science); Joint probability distribution; Statistical physics; Space (punctuation); Algorithm; Statistics; Computer science; Finance","score_opus":0.031179590506700806,"score_gpt":0.24127219393110538,"score_spread":0.21009260342440458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201997504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10136099,0.0015167587,0.8851578,0.0009609069,0.00017211493,0.000045699086,0.0001290395,0.00013417806,0.01052247],"genre_scores_gemma":[0.9409062,0.001793784,0.045966253,0.00026469218,0.0002914598,0.000095160496,0.00023526633,0.00014596155,0.010301238],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987301,0.0005656372,0.00006447529,0.00017267716,0.00029002965,0.00017700657],"domain_scores_gemma":[0.9835433,0.012439916,0.0011355247,0.0006216009,0.0015346004,0.0007250847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051728305,0.00087454985,0.0015777752,0.0024165246,0.0010745479,0.002594653,0.0020129832,0.0014604672,0.003720892],"category_scores_gemma":[0.018779688,0.000648186,0.0014860875,0.0016581087,0.0032548958,0.0045202044,0.0017446829,0.0025899198,0.0002912923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027981145,0.00003252538,0.00084509444,0.000056761204,0.00002830172,0.000090786176,0.00016160004,0.10770573,0.0007651247,0.8856819,0.0006397235,0.003964406],"study_design_scores_gemma":[0.000003631743,0.000009012178,0.00026139838,0.00001757762,0.000011337532,0.000030534335,0.000018215851,0.77561,0.00016897876,0.22350425,0.0003511018,0.000013967855],"about_ca_topic_score_codex":0.006638659,"about_ca_topic_score_gemma":0.0050048274,"teacher_disagreement_score":0.006638659,"about_ca_system_score_codex":0.002733602,"about_ca_system_score_gemma":0.0021124522,"threshold_uncertainty_score":0.027356863},"labels":[],"label_agreement":null},{"id":"W3203889513","doi":"10.1016/j.aej.2021.09.056","title":"Qualitative and quantitative analysis of the reliability of NPC and ANPC power converters for aeronautical applications","year":2021,"lang":"en","type":"article","venue":"Alexandria Engineering Journal","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Converters; Reliability engineering; Reliability (semiconductor); Failure mode and effects analysis; Actuator; Engineering; Power (physics); Hazard; Computer science; Electrical engineering; Voltage","score_opus":0.010857159433138535,"score_gpt":0.2688556749899902,"score_spread":0.25799851555685166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203889513","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77518564,0.00060250633,0.2141841,0.00010894836,0.000027124075,0.00016279801,0.0011871632,0.00047748798,0.008064094],"genre_scores_gemma":[0.991484,0.000054947017,0.007747295,0.00000612264,0.0000053450717,0.000042152486,0.00015009228,0.000015177891,0.0004948857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822825,0.0003830904,0.00008181761,0.0001300999,0.0010912216,0.00008548905],"domain_scores_gemma":[0.9866742,0.009870618,0.0007955274,0.0006395895,0.0019576552,0.000062537874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025744874,0.0002986988,0.00020457634,0.0018366212,0.00017193904,0.0004177084,0.0003971811,0.00036001435,0.001538164],"category_scores_gemma":[0.0080570625,0.00013971784,0.00029391737,0.0006421338,0.0007347384,0.0006191228,0.00014456846,0.00026602045,0.00014761083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007902109,0.0002871115,0.038909625,0.0015723548,0.00017408266,0.00063425873,0.001256454,0.6023933,0.20125118,0.027273148,0.001294711,0.124163546],"study_design_scores_gemma":[0.00002932343,0.0014309258,0.06967763,0.000095073716,0.000100912075,0.00078114314,0.0007303135,0.7628731,0.1487439,0.012709477,0.0027148542,0.00011337975],"about_ca_topic_score_codex":0.0007883012,"about_ca_topic_score_gemma":0.0008136135,"teacher_disagreement_score":0.0025744874,"about_ca_system_score_codex":0.00052777963,"about_ca_system_score_gemma":0.0002459499,"threshold_uncertainty_score":0.01361537},"labels":[],"label_agreement":null},{"id":"W3206390449","doi":"10.1108/jqme-10-2020-0109","title":"Models for maintenance planning and scheduling – a citation-based literature review and content analysis","year":2021,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Citation; Co-citation; Thematic analysis; Computer science; Exploratory analysis; Data science; Scheduling (production processes); Citation analysis; Operations research; Management science; Engineering; Operations management; Sociology; Social science; Qualitative research; Library science","score_opus":0.0379132787457484,"score_gpt":0.2849896022973017,"score_spread":0.2470763235515533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206390449","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015640208,0.93765265,0.018128121,0.009191826,0.0013959988,0.0008628151,0.0022675043,0.00020068084,0.014660221],"genre_scores_gemma":[0.118248075,0.8471977,0.026808145,0.0010846865,0.0011667183,0.0010559836,0.0022032792,0.000069982336,0.0021653485],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9899076,0.004171581,0.0017256071,0.0006969297,0.0032428622,0.0002554209],"domain_scores_gemma":[0.9450102,0.03922554,0.004953324,0.0014715996,0.008939337,0.00040004036],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.013523063,0.001132261,0.0017411768,0.06234851,0.001302869,0.005536824,0.001875831,0.0016837119,0.003278779],"category_scores_gemma":[0.04760858,0.00068295444,0.0024418985,0.048568025,0.001197047,0.007037683,0.0017108407,0.001152544,0.0006419374],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013487162,0.00021148221,0.00785153,0.11132779,0.0013976368,0.0008865419,0.004029953,0.012246021,0.0011814957,0.060384553,0.04509739,0.75525075],"study_design_scores_gemma":[0.00006432377,0.00046256112,0.027430145,0.25994548,0.005206029,0.0021548439,0.009171752,0.0265362,0.0020036567,0.061221156,0.60551095,0.00029300802],"about_ca_topic_score_codex":0.0065551326,"about_ca_topic_score_gemma":0.0076752566,"teacher_disagreement_score":0.9376515,"about_ca_system_score_codex":0.006334532,"about_ca_system_score_gemma":0.015268289,"threshold_uncertainty_score":0.07151759},"labels":[],"label_agreement":null},{"id":"W3208976819","doi":"10.32920/ryerson.14662383.v1","title":"A Methodology for Maintenance Evaluation and Improvement of Repairable Systems in a Mine","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preventive maintenance; Reliability engineering; Predictive maintenance; Planned maintenance; Context (archaeology); Corrective maintenance; Process (computing); Truck; Optimal maintenance; Condition-based maintenance; Function (biology); Computer science; Maintenance engineering; Reliability (semiconductor); Engineering; Risk analysis (engineering); Power (physics); Automotive engineering; Business","score_opus":0.05459362330474142,"score_gpt":0.30129055309687663,"score_spread":0.2466969297921352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208976819","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011220556,0.00008799253,0.99814796,0.00003294868,0.0000076118913,0.00007205409,0.00004703787,0.00015205616,0.000330367],"genre_scores_gemma":[0.047460742,0.00017673447,0.95120335,0.000034982797,0.000022792457,0.00033438666,0.00016493665,0.00006053577,0.00054148433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99583924,0.001602524,0.0003462903,0.00065587467,0.0014031011,0.00015303478],"domain_scores_gemma":[0.9924541,0.003963438,0.00091691356,0.00092626625,0.00163287,0.0001063617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063183424,0.0012075698,0.0011950834,0.0031322783,0.0005344931,0.0015789504,0.0019642287,0.0010205329,0.0024082325],"category_scores_gemma":[0.015952272,0.0006835913,0.0014959673,0.0018549133,0.0009935015,0.0014865884,0.0012434337,0.0015537011,0.0005215448],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009848627,0.00022176611,0.005414408,0.0009654763,0.00031284755,0.0003505018,0.0004961525,0.43920627,0.0145637905,0.1200316,0.0029355357,0.41540325],"study_design_scores_gemma":[0.000037948474,0.00026995823,0.001354622,0.00013882112,0.00009433613,0.00028924394,0.00009703142,0.9207935,0.008383734,0.056814518,0.01167849,0.00004776202],"about_ca_topic_score_codex":0.0021142683,"about_ca_topic_score_gemma":0.0018628706,"teacher_disagreement_score":0.0063183424,"about_ca_system_score_codex":0.0012246013,"about_ca_system_score_gemma":0.0017409298,"threshold_uncertainty_score":0.03341502},"labels":[],"label_agreement":null},{"id":"W3210918092","doi":"10.5281/zenodo.2548012","title":"The Fleet Life Cycle Assessment and Material-Flow Estimation (FLAME) model","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Environmental science; Flow (mathematics); Estimation; Material flow analysis; Life-cycle assessment; Computer science; Engineering; Waste management; Mathematics; Economics; Systems engineering","score_opus":0.011653818441235258,"score_gpt":0.21992266272358768,"score_spread":0.20826884428235243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210918092","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17093156,0.0010974957,0.7435103,0.0011234782,0.00018184436,0.00042167088,0.022379983,0.0017391779,0.058614444],"genre_scores_gemma":[0.8966747,0.00066437153,0.05885301,0.00012267454,0.000051862142,0.00051730574,0.012625347,0.00023995315,0.030250898],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997093,0.00007546632,0.000013793926,0.00006404902,0.00008953629,0.00004788471],"domain_scores_gemma":[0.99959236,0.00016919553,0.00005547409,0.000030555315,0.00012395077,0.000028428503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007761158,0.0010185122,0.0005471892,0.0008519941,0.0004037896,0.00081325497,0.0012980946,0.0010525164,0.0052865543],"category_scores_gemma":[0.0016482822,0.00045376606,0.0012031472,0.00070401636,0.00030015243,0.0010338638,0.00059410295,0.0008572416,0.0011377129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001426352,0.0000056582526,0.00025719637,0.0000088639235,0.0000071787654,0.000010393263,0.000002870144,0.9961747,0.00011718119,0.00075335347,0.00043102508,0.0022173417],"study_design_scores_gemma":[0.0000062155227,0.000015349728,0.00026069352,0.000005140049,0.000008383153,0.00000811521,0.0000021787491,0.9973455,0.00012876019,0.00081380346,0.0014001515,0.000005741318],"about_ca_topic_score_codex":0.059303377,"about_ca_topic_score_gemma":0.023833847,"teacher_disagreement_score":0.059303377,"about_ca_system_score_codex":0.0013796175,"about_ca_system_score_gemma":0.001903541,"threshold_uncertainty_score":0.117916405},"labels":[],"label_agreement":null},{"id":"W3212263828","doi":"10.1016/j.ress.2021.108191","title":"A deep learning predictive model for selective maintenance optimization","year":2021,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Predictive maintenance; Component (thermodynamics); Benchmarking; Modular design; Reliability engineering; Turbofan; Computer science; Set (abstract data type); Maintenance actions; Engineering; Machine learning","score_opus":0.00378129399622761,"score_gpt":0.17741711056902398,"score_spread":0.17363581657279636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212263828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06429047,0.0015433469,0.92425567,0.0008616763,0.00020667356,0.00004257655,0.00093700667,0.001949849,0.0059127645],"genre_scores_gemma":[0.91740507,0.00047584283,0.06969254,0.00036302514,0.00012424827,0.0001156938,0.0012586337,0.00012740222,0.010437597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998286,0.000025624144,0.000008208039,0.000057652232,0.000046616482,0.000033239954],"domain_scores_gemma":[0.9995009,0.00025762335,0.000042695287,0.00004512567,0.0001295457,0.00002419013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049295917,0.0006056473,0.00084295153,0.00044866465,0.0002705444,0.0006434093,0.0015816701,0.0011849761,0.0028992281],"category_scores_gemma":[0.0015712836,0.0005512155,0.0005487545,0.0006190499,0.00036772937,0.0009273809,0.00072733575,0.0017494306,0.00067612925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055050645,0.000058285197,0.00042061045,0.000028578263,0.000030151865,0.000027523867,0.00000918007,0.9418269,0.0008207437,0.0025472513,0.0022486062,0.051927157],"study_design_scores_gemma":[0.0000015000899,0.0000032129772,0.000031352116,0.0000012688092,0.0000023144237,0.0000015877686,3.8609573e-7,0.9992231,0.00008948156,0.0005708769,0.00007406428,8.973674e-7],"about_ca_topic_score_codex":0.01605339,"about_ca_topic_score_gemma":0.01973684,"teacher_disagreement_score":0.01605339,"about_ca_system_score_codex":0.00081230386,"about_ca_system_score_gemma":0.0011235683,"threshold_uncertainty_score":0.031919897},"labels":[],"label_agreement":null},{"id":"W3216336623","doi":"10.1016/j.ress.2021.108172","title":"Reliability modelling for linear and circular k-out-of-n: F systems with shared components","year":2021,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Markov chain; Reliability (semiconductor); Disjoint sets; Algorithm; Markov model; Mathematics; Field (mathematics); Applied mathematics; Computer science; Discrete mathematics; Statistics; Pure mathematics; Physics","score_opus":0.011869295988380225,"score_gpt":0.18877912424661625,"score_spread":0.17690982825823603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216336623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35958922,0.0006153511,0.6218455,0.00041334232,0.00009328116,0.000071837305,0.000268509,0.0004513745,0.016651627],"genre_scores_gemma":[0.985107,0.000110588815,0.009171205,0.00002300708,0.000015719092,0.000024843159,0.000065861954,0.00004648356,0.005435143],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957126,0.00008727714,0.00002028344,0.00010626192,0.00008791587,0.00012696111],"domain_scores_gemma":[0.9988657,0.00052551326,0.00018381394,0.00009424548,0.00027894363,0.000051676507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084185257,0.0005749075,0.0009155247,0.00054993166,0.00067755673,0.0010115823,0.0016447877,0.0013658239,0.0022947437],"category_scores_gemma":[0.0026455023,0.0004531237,0.00100881,0.000527541,0.0011303304,0.0014010032,0.0008460685,0.00058037374,0.000450707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047996687,0.000012355133,0.0003522963,0.0000238996,0.000007855948,0.0000556825,0.00005314594,0.9922127,0.0008159978,0.003942169,0.00021642461,0.0022593995],"study_design_scores_gemma":[0.000001528217,0.0000090900285,0.00012001167,0.0000016585602,0.0000030543958,0.000009561555,0.00000803437,0.99875164,0.0001237352,0.00088725344,0.00008129384,0.000003068293],"about_ca_topic_score_codex":0.018418139,"about_ca_topic_score_gemma":0.01422512,"teacher_disagreement_score":0.018418139,"about_ca_system_score_codex":0.0012672517,"about_ca_system_score_gemma":0.0010915643,"threshold_uncertainty_score":0.03662187},"labels":[],"label_agreement":null},{"id":"W33804416","doi":"10.3390/idr13010023","title":"Systems Failures - Analysing Stakeholder Influence through Case Histories.","year":2005,"lang":"en","type":"article","venue":"IASTED Conference on Software Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Stakeholder; Risk analysis (engineering); Business; Political science","score_opus":0.02588025133627527,"score_gpt":0.21336235142741503,"score_spread":0.18748210009113975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W33804416","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9260888,0.004839789,0.024253488,0.0035928856,0.00015272436,0.002480723,0.020289533,0.00023294082,0.01806911],"genre_scores_gemma":[0.98203766,0.0011787895,0.009962817,0.00018169133,0.000050776354,0.0010870709,0.0044869906,0.000025943797,0.000988224],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.96794015,0.02121156,0.00360261,0.0021132994,0.0040713646,0.0010609634],"domain_scores_gemma":[0.7554363,0.17289689,0.04117505,0.012520782,0.014677818,0.003293217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032997493,0.00043529435,0.00042135816,0.01008108,0.00092708814,0.0018378551,0.001332673,0.00072067283,0.005854904],"category_scores_gemma":[0.16477528,0.00052120414,0.00093872624,0.007350797,0.000740398,0.0027786342,0.0029037707,0.00076482864,0.0006034036],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103323735,0.000115529205,0.9430677,0.00037173263,0.0002197043,0.0003136309,0.0043799663,0.0005583304,0.000048530004,0.0009490647,0.0025946195,0.04727785],"study_design_scores_gemma":[0.000059922477,0.00043666537,0.93088824,0.0014969895,0.00048475034,0.00141352,0.023093686,0.017351285,0.00053901697,0.005878817,0.018278833,0.000078329445],"about_ca_topic_score_codex":0.024494398,"about_ca_topic_score_gemma":0.024517026,"teacher_disagreement_score":0.032997493,"about_ca_system_score_codex":0.0029167235,"about_ca_system_score_gemma":0.0048511634,"threshold_uncertainty_score":0.17450947},"labels":[],"label_agreement":null},{"id":"W36686218","doi":"10.1142/9789812795250_0025","title":"MODELING THE INFLUENCE OF MAINTENANCE ACTIONS","year":2003,"lang":"en","type":"book-chapter","venue":"Series on quality, reliability and engineering statistics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science","score_opus":0.017880302363694037,"score_gpt":0.23695214421824304,"score_spread":0.219071841854549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W36686218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025583003,0.0028293694,0.94854355,0.000927646,0.00030932078,0.00004456583,0.00058403355,0.00064557197,0.020532994],"genre_scores_gemma":[0.78010285,0.0071483343,0.12420233,0.00040152893,0.0008560593,0.00040209203,0.001003362,0.00070933986,0.085174136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996747,0.0000961937,0.000015696662,0.00008109148,0.000088761655,0.000043579308],"domain_scores_gemma":[0.9978993,0.0016584318,0.0001755347,0.00010130223,0.000103329694,0.000061966515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010393922,0.0015072892,0.002062451,0.0009013416,0.0003676628,0.0014042698,0.0022290777,0.0018280891,0.008326493],"category_scores_gemma":[0.003474618,0.0012272408,0.0012715408,0.0010451882,0.0010319453,0.0016663047,0.00072882517,0.0017819885,0.001030485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026971198,0.000041084964,0.00041806232,0.0000488417,0.000040421703,0.000047828682,0.000034568682,0.9320106,0.00068101427,0.05548262,0.0022074142,0.0089605795],"study_design_scores_gemma":[0.000006439054,0.000008222036,0.00009045426,0.000003876614,0.0000145573285,0.000013707975,0.0000023286173,0.97962254,0.00016080496,0.019391943,0.0006812438,0.0000038134713],"about_ca_topic_score_codex":0.009642983,"about_ca_topic_score_gemma":0.009923091,"teacher_disagreement_score":0.009642983,"about_ca_system_score_codex":0.00093131297,"about_ca_system_score_gemma":0.0009255035,"threshold_uncertainty_score":0.02785492},"labels":[],"label_agreement":null},{"id":"W41976007","doi":"","title":"Assessing processes in uncertain, complex physical phenomena and manufacturing","year":2002,"lang":"en","type":"article","venue":"University of North Texas Digital Library (University of North Texas)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Janeway Children's Health and Rehabilitation Centre","funders":"Los Alamos National Laboratory; U.S. Department of Energy","keywords":"Automotive industry; Reliability (semiconductor); Computer science; Quality (philosophy); Reliability engineering; Certification; Systems engineering; Risk analysis (engineering); Cyber-physical system; Manufacturing engineering; Engineering","score_opus":0.014509523949736773,"score_gpt":0.17085982112844622,"score_spread":0.15635029717870944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W41976007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06033478,0.0028204112,0.92627084,0.0010209418,0.000037062928,0.00011852809,0.00020713087,0.00020331104,0.008987035],"genre_scores_gemma":[0.7408718,0.0035035636,0.2535965,0.00014820592,0.00009941756,0.00018209674,0.00019967968,0.00005909143,0.001339699],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99519855,0.0019661444,0.00019307167,0.0005716361,0.0018662737,0.00020443137],"domain_scores_gemma":[0.990126,0.00766461,0.001185936,0.0005224814,0.00040509665,0.00009576981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005015952,0.0011824978,0.001010375,0.0023783478,0.0007721224,0.0034824396,0.00107811,0.0015523059,0.0016111995],"category_scores_gemma":[0.016123943,0.0007037806,0.0007852722,0.0019694695,0.0032813903,0.0058845324,0.0022654752,0.001144226,0.0002031839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006846991,0.00007669086,0.0053602126,0.00037637053,0.00013093241,0.00021111852,0.0005233567,0.72561973,0.0025809503,0.1913945,0.0006941229,0.07296355],"study_design_scores_gemma":[0.000017054927,0.00013844488,0.0050858953,0.00012342124,0.000059598602,0.00018042761,0.00039005544,0.582078,0.0037251776,0.40285194,0.0052754777,0.00007457944],"about_ca_topic_score_codex":0.002027727,"about_ca_topic_score_gemma":0.0017784974,"teacher_disagreement_score":0.005015952,"about_ca_system_score_codex":0.0016828721,"about_ca_system_score_gemma":0.001252205,"threshold_uncertainty_score":0.026527226},"labels":[],"label_agreement":null},{"id":"W4200131186","doi":"10.1016/j.ress.2021.108277","title":"Redundancy strategies assessment and optimization of k-out-of-n systems based on Markov chains and genetic algorithms","year":2021,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Redundancy (engineering); Markov chain; Computation; Reliability (semiconductor); Computer science; Mathematical optimization; Algorithm; Markov chain Monte Carlo; Genetic algorithm; Component (thermodynamics); Markov model; Reliability engineering; Mathematics; Engineering; Machine learning; Bayesian probability; Artificial intelligence","score_opus":0.005622536361153442,"score_gpt":0.20940934179838916,"score_spread":0.2037868054372357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200131186","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21597305,0.0010562375,0.7753249,0.00035873926,0.00006907926,0.000116465526,0.0000803473,0.00023859578,0.0067826053],"genre_scores_gemma":[0.9719254,0.00017751606,0.02631757,0.000038712424,0.000016605749,0.000072719035,0.000047511447,0.00002288411,0.0013811458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994717,0.00016767075,0.000029085862,0.00010014675,0.00011836012,0.00011310783],"domain_scores_gemma":[0.9980586,0.0013537352,0.00022520912,0.00004134303,0.0002570436,0.00006405741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014945811,0.0009421769,0.0018048648,0.001344936,0.0007823522,0.001193501,0.001006869,0.0011771185,0.0012377788],"category_scores_gemma":[0.0031124684,0.0008920532,0.0012723415,0.0008043162,0.0009622789,0.0010369809,0.0008156738,0.00069664646,0.00011766547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001733229,0.000009156351,0.00014477341,0.000012839094,0.000014701728,0.000011800594,0.000009844318,0.9965346,0.00017358847,0.001002425,0.000039874587,0.0020290718],"study_design_scores_gemma":[0.0000027003596,0.000008574567,0.00005464986,0.000001688558,0.0000048309507,0.0000021846186,0.0000022781612,0.99942976,0.000049393977,0.00042747313,0.000014826434,0.0000016910396],"about_ca_topic_score_codex":0.016889067,"about_ca_topic_score_gemma":0.013012398,"teacher_disagreement_score":0.016889067,"about_ca_system_score_codex":0.0016011692,"about_ca_system_score_gemma":0.0021293599,"threshold_uncertainty_score":0.033581495},"labels":[],"label_agreement":null},{"id":"W4200243350","doi":"10.1016/j.isatra.2021.11.041","title":"Robust optimization of uncertainty-based preventive maintenance model for scheduling series–parallel production systems (real case: disposable appliances production)","year":2021,"lang":"en","type":"article","venue":"ISA Transactions","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Preventive maintenance; Scheduling (production processes); Particle swarm optimization; Agile software development; Production (economics); Production planning; Computer science; Genetic algorithm; Industrial engineering; Engineering; Reliability engineering; Operations research; Mathematical optimization; Operations management; Algorithm","score_opus":0.021230309146385344,"score_gpt":0.2228931262012861,"score_spread":0.20166281705490074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200243350","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12374849,0.00097410445,0.8651955,0.0005727983,0.00012824385,0.00010687171,0.00045019452,0.00047797195,0.008345694],"genre_scores_gemma":[0.9872465,0.00016436125,0.0100095915,0.000035853693,0.000022113752,0.00008009708,0.00015048253,0.00003959259,0.0022514486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931216,0.0001873047,0.000030104931,0.0001709552,0.00015826672,0.00014114022],"domain_scores_gemma":[0.9988355,0.0006165279,0.00023965612,0.000048771166,0.00020725442,0.000052200117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018195448,0.0012436173,0.002145938,0.0006621179,0.00044526556,0.0015456752,0.0013485003,0.0014852675,0.0021933934],"category_scores_gemma":[0.0025742133,0.001015463,0.0011730073,0.0006968321,0.00085601443,0.0008074378,0.0008349418,0.0014466654,0.00020970005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015544063,0.00000482532,0.00004120097,0.000009189059,0.000009100046,0.000008614928,0.0000039490114,0.9988249,0.00009480404,0.0004035228,0.000047660964,0.000536604],"study_design_scores_gemma":[0.0000028132642,0.000008143757,0.000046594963,0.0000010054381,0.000003723637,0.0000014097429,0.0000010815174,0.9996873,0.000036237667,0.00018904681,0.000021051765,0.0000014489511],"about_ca_topic_score_codex":0.023435311,"about_ca_topic_score_gemma":0.010009415,"teacher_disagreement_score":0.023435311,"about_ca_system_score_codex":0.0016257006,"about_ca_system_score_gemma":0.0016424231,"threshold_uncertainty_score":0.04659784},"labels":[],"label_agreement":null},{"id":"W4200485852","doi":"10.1080/03610918.2021.2001528","title":"Inference for a gradually deteriorating system with imperfect maintenance","year":2021,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Imperfect; Inference; Monte Carlo method; Gamma process; Computer science; Process (computing); Econometrics; Reliability engineering; Statistical inference; Likelihood function; Preventive maintenance; Mathematical optimization; Algorithm; Engineering; Mathematics; Statistics; Artificial intelligence; Estimation theory","score_opus":0.05410209551862766,"score_gpt":0.354485744382581,"score_spread":0.30038364886395336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200485852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025186533,0.00016029814,0.9740302,0.000090672074,0.000006531036,0.000012667542,0.000027481481,0.00007082077,0.000414797],"genre_scores_gemma":[0.8633447,0.0005431761,0.13438717,0.00008096754,0.000058597707,0.000045632107,0.00017895416,0.00004250461,0.0013182957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998691,0.00057007617,0.00007439206,0.00027798855,0.00030055406,0.00008595659],"domain_scores_gemma":[0.9872088,0.010984667,0.000796885,0.00041828858,0.00046635658,0.00012509729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044506146,0.00062351255,0.0009280651,0.0010828928,0.00037664722,0.00096819585,0.0013749662,0.00097216223,0.0008588548],"category_scores_gemma":[0.023626216,0.0005974521,0.00092274667,0.00061477185,0.0013660319,0.0017524068,0.0011185213,0.0014160817,0.00018257208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008100599,0.000022837894,0.0031854608,0.00007539536,0.000056505793,0.00025789428,0.00014860072,0.92721975,0.0010315133,0.036473274,0.00020037839,0.03124725],"study_design_scores_gemma":[0.0000033885362,0.000012310638,0.000324514,0.0000065631943,0.000009102314,0.0000376536,0.000009334481,0.9853732,0.00037020093,0.013729661,0.00011789221,0.000006165998],"about_ca_topic_score_codex":0.004332668,"about_ca_topic_score_gemma":0.0029868819,"teacher_disagreement_score":0.0044506146,"about_ca_system_score_codex":0.00090113754,"about_ca_system_score_gemma":0.0011608105,"threshold_uncertainty_score":0.023537397},"labels":[],"label_agreement":null},{"id":"W4206023438","doi":"10.1016/j.knosys.2022.108153","title":"Condition-based optimization of non-identical inspection intervals for a k-out-of-n load sharing system with hybrid mixed redundancy strategy","year":2022,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Downtime; Redundancy (engineering); Interval (graph theory); Computer science; Load sharing; Reliability engineering; Mathematical optimization; Mathematics; Engineering","score_opus":0.01377114010794447,"score_gpt":0.2394726419519847,"score_spread":0.22570150184404023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206023438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23156363,0.0006190389,0.75684,0.00035495617,0.000075995275,0.00014785962,0.0001739707,0.00046487091,0.009759658],"genre_scores_gemma":[0.9883437,0.000042662414,0.010534612,0.0000226528,0.000008747353,0.00004288297,0.00003196605,0.00001639719,0.00095631427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995054,0.00012656371,0.000023889088,0.000105722334,0.000120109115,0.0001184043],"domain_scores_gemma":[0.9989649,0.0005564434,0.0001832549,0.000049327144,0.00018955357,0.000056438545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012582439,0.0009693788,0.0018926626,0.000596118,0.0005187619,0.0013882429,0.0012844072,0.0012019142,0.0025973113],"category_scores_gemma":[0.0019022695,0.00065835094,0.00063296495,0.00058718945,0.00075570226,0.0010325689,0.00088770385,0.0005770719,0.00021526712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013291524,0.000036155772,0.00014263901,0.000047405276,0.000022446377,0.000039213683,0.000029644,0.9893216,0.0016357839,0.0014067437,0.00021772369,0.0069678356],"study_design_scores_gemma":[0.00000923395,0.000032104585,0.00009654877,0.0000026379298,0.000007417339,0.0000052064456,0.000005196188,0.9992337,0.00019800584,0.00037411574,0.00003270169,0.000003068035],"about_ca_topic_score_codex":0.009255819,"about_ca_topic_score_gemma":0.00759779,"teacher_disagreement_score":0.009255819,"about_ca_system_score_codex":0.0011583247,"about_ca_system_score_gemma":0.0012537625,"threshold_uncertainty_score":0.018403888},"labels":[],"label_agreement":null},{"id":"W4206982770","doi":"10.33889/ijmems.2022.7.1.001","title":"Condition-based Maintenance Optimization of Degradable Systems","year":2022,"lang":"en","type":"article","venue":"International Journal of Mathematical Engineering and Management Sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton; Université Laval","funders":"China Scholarship Council","keywords":"Preventive maintenance; Component (thermodynamics); Condition-based maintenance; Markov chain; Reliability engineering; Process (computing); Degradation (telecommunications); Computer science; Optimal maintenance; Series (stratigraphy); Mathematical optimization; Markov process; State (computer science); Markov model; Feature (linguistics); Engineering; Mathematics; Algorithm","score_opus":0.007240334992123754,"score_gpt":0.211310596749278,"score_spread":0.20407026175715423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206982770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055834047,0.0007435546,0.9348675,0.00046741223,0.00004897072,0.00006061107,0.0002109112,0.00022661783,0.0075403405],"genre_scores_gemma":[0.9685981,0.00055330043,0.024717622,0.00005730101,0.000028783537,0.00012566715,0.00014936883,0.00006068978,0.0057092267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940526,0.00016894557,0.000024485145,0.0001239647,0.00017921277,0.00009807387],"domain_scores_gemma":[0.99892896,0.00060412637,0.00020948381,0.000039244613,0.00016034853,0.00005781411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001233949,0.0009862013,0.0013549041,0.0008062903,0.0004021316,0.0013826783,0.0011693292,0.0011968673,0.0025165288],"category_scores_gemma":[0.002775321,0.0006431279,0.00076896825,0.0005677322,0.0009943632,0.0011936955,0.00079625024,0.0010579681,0.00026960098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019573294,0.000014589217,0.00015258904,0.000029967923,0.0000125495535,0.00004132494,0.000021086775,0.986621,0.0007276659,0.010103565,0.00023822105,0.002017866],"study_design_scores_gemma":[0.0000032263597,0.000008393781,0.00006636967,0.0000024061528,0.0000032666956,0.000005862012,0.0000026548375,0.9977685,0.000082166276,0.0019522785,0.00010251126,0.000002338428],"about_ca_topic_score_codex":0.0070154294,"about_ca_topic_score_gemma":0.0038391922,"teacher_disagreement_score":0.0070154294,"about_ca_system_score_codex":0.00192481,"about_ca_system_score_gemma":0.0011332295,"threshold_uncertainty_score":0.013965547},"labels":[],"label_agreement":null},{"id":"W4210468896","doi":"10.1115/imece2021-73021","title":"A Framework for Integrating Reliability, Robustness, Resilience, and Vulnerability to Assess System Adaptivity","year":2021,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Robustness (evolution); Computer science; Vulnerability (computing); Risk analysis (engineering); Resilience (materials science); Reliability (semiconductor); Function (biology); Reliability engineering; Management science; Systems engineering; Engineering; Computer security","score_opus":0.024821683697825753,"score_gpt":0.27102177595940635,"score_spread":0.2462000922615806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210468896","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077495184,0.0007107215,0.9845956,0.00043006323,0.000052582815,0.00012210944,0.00010345095,0.00011305356,0.0061229295],"genre_scores_gemma":[0.650208,0.0011244987,0.34564474,0.00017990332,0.00016293247,0.0008083256,0.00020649497,0.000089837675,0.0015752154],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9943758,0.002987158,0.00035728337,0.000618562,0.001287922,0.00037330113],"domain_scores_gemma":[0.9959122,0.0019389133,0.00068548863,0.00029870254,0.0009134075,0.0002513184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008617907,0.0027460011,0.0011795183,0.0072256313,0.00071605155,0.003399679,0.0019874696,0.0016007685,0.0018136607],"category_scores_gemma":[0.008698309,0.00048359958,0.0017779607,0.0024744528,0.0032256395,0.0043645054,0.0034550042,0.0022902156,0.00026663824],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022822003,0.00013140573,0.0022045474,0.00033326264,0.00021355918,0.00017832208,0.00039531328,0.4606197,0.0037593623,0.50599337,0.0014385732,0.02470984],"study_design_scores_gemma":[0.000010943884,0.00013840522,0.0013667253,0.0002651053,0.00008271493,0.000097843455,0.0002750706,0.80361897,0.0010583452,0.18797185,0.005028141,0.000085825224],"about_ca_topic_score_codex":0.00387443,"about_ca_topic_score_gemma":0.0026365146,"teacher_disagreement_score":0.008617907,"about_ca_system_score_codex":0.0027909349,"about_ca_system_score_gemma":0.002382915,"threshold_uncertainty_score":0.045576394},"labels":[],"label_agreement":null},{"id":"W4210736991","doi":"10.1007/s00170-021-08571-5","title":"Opportunistic maintenance integrated model for a two-stage manufacturing process","year":2022,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Manufacturing engineering; Process (computing); Stage (stratigraphy); Industrial and production engineering; Engineering; Computer science; Industrial engineering; Reliability engineering; Mechanical engineering; Operating system; Geology","score_opus":0.011733122704646616,"score_gpt":0.2525933892931301,"score_spread":0.24086026658848347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210736991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18229812,0.0015278169,0.78096205,0.0012247994,0.0002215898,0.00038551164,0.0016309437,0.0009701748,0.03077898],"genre_scores_gemma":[0.9500486,0.0005594627,0.021869976,0.000103091974,0.00006229809,0.0002815542,0.00040688325,0.000083618295,0.026584638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988061,0.00027154732,0.000051611165,0.0003185905,0.00023109243,0.00032105675],"domain_scores_gemma":[0.99818206,0.0009774793,0.00031087565,0.00012999539,0.00027865675,0.00012093354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015299661,0.0014529992,0.0028435304,0.0011991846,0.00093632785,0.0025480422,0.0048224707,0.0041033924,0.010720215],"category_scores_gemma":[0.002713194,0.0012366081,0.001585796,0.0015969679,0.0012538662,0.001880823,0.00167797,0.0015879008,0.0010951316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012598965,0.000057869,0.0003104725,0.0000740208,0.00003799915,0.00016695057,0.000038946895,0.98722804,0.00072666584,0.0072088367,0.00035488332,0.0036692333],"study_design_scores_gemma":[0.000017483744,0.000028640965,0.0001352681,0.0000034360032,0.000019418594,0.000016873017,0.000005585796,0.99819154,0.00007188989,0.0013695185,0.00013367229,0.00000669527],"about_ca_topic_score_codex":0.022112217,"about_ca_topic_score_gemma":0.012277206,"teacher_disagreement_score":0.022112217,"about_ca_system_score_codex":0.0020399224,"about_ca_system_score_gemma":0.0022200656,"threshold_uncertainty_score":0.04396701},"labels":[],"label_agreement":null},{"id":"W4212767643","doi":"10.1177/1748006x221078128","title":"Optimizing a joint reliability-redundancy allocation problem with common cause multi-state failures using immune algorithm","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Toronto Metropolitan University","funders":"Canada Research Chairs; Ryerson University","keywords":"Redundancy (engineering); Reliability (semiconductor); Component (thermodynamics); Reliability engineering; Computer science; Mathematical optimization; State (computer science); Function (biology); Set (abstract data type); Optimal allocation; Algorithm; Engineering; Mathematics","score_opus":0.010533831006841916,"score_gpt":0.2101583616257019,"score_spread":0.19962453061885999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212767643","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12815745,0.0006417851,0.8647187,0.0006292356,0.00006207461,0.00012886027,0.00009098178,0.0002758949,0.0052949777],"genre_scores_gemma":[0.89246595,0.00024742508,0.103159495,0.00017197135,0.00005591287,0.00030485832,0.0001231618,0.000057366302,0.0034137599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914193,0.00034881174,0.00003376607,0.00017821889,0.00011517458,0.00018213423],"domain_scores_gemma":[0.9982973,0.0012040125,0.00019439858,0.00004972772,0.00016941957,0.00008508762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017779082,0.001161587,0.0017832852,0.0009707012,0.00047439194,0.0010428345,0.0010914993,0.001821118,0.0020308585],"category_scores_gemma":[0.0028354297,0.00074926397,0.0010439428,0.0007617024,0.0007685734,0.0010353043,0.0011635127,0.0009953225,0.00016293096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004260908,0.00004882966,0.00028336994,0.000042649168,0.000034904042,0.000043313074,0.000015901214,0.9920563,0.00032697304,0.0019382207,0.00025445706,0.0049125887],"study_design_scores_gemma":[0.000013526429,0.000030843126,0.00006796364,0.0000025146992,0.000008625484,0.000009458355,0.0000060117054,0.998362,0.00009721389,0.0013322642,0.00006711408,0.0000024435021],"about_ca_topic_score_codex":0.0035832433,"about_ca_topic_score_gemma":0.0021174205,"teacher_disagreement_score":0.0035832433,"about_ca_system_score_codex":0.0009546535,"about_ca_system_score_gemma":0.0014773435,"threshold_uncertainty_score":0.009402573},"labels":[],"label_agreement":null},{"id":"W4213149237","doi":"10.1016/j.ress.2022.108394","title":"A multi-objective model for optimizing the redundancy allocation, component supplier selection, and reliable activities for multi-state systems","year":2022,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sorting; Mathematical optimization; Redundancy (engineering); Evolutionary algorithm; Multi-objective optimization; Genetic algorithm; Heuristics; Computer science; Benchmark (surveying); Pareto principle; Algorithm; Mathematics","score_opus":0.012598309500584503,"score_gpt":0.213945886008081,"score_spread":0.20134757650749652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213149237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0388109,0.00082016713,0.9491862,0.000531277,0.00011208008,0.00017751184,0.00044300724,0.00040642396,0.009512399],"genre_scores_gemma":[0.8708072,0.00065889116,0.11594465,0.00017102661,0.000078174555,0.00062176737,0.0005124393,0.00014662175,0.011059245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891555,0.00045847677,0.000043397715,0.00015648593,0.0002532454,0.00017288439],"domain_scores_gemma":[0.99851555,0.0009720594,0.00016308439,0.000045102774,0.00022780645,0.000076400545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024803542,0.00197543,0.0027129103,0.0014541529,0.0008190719,0.0021827216,0.002498549,0.0027566957,0.004959416],"category_scores_gemma":[0.0031329186,0.0014512718,0.0014479299,0.0015518928,0.0011152799,0.0017622815,0.0014152735,0.0018523372,0.0005264931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011193551,0.000009855059,0.00003833694,0.000015458949,0.000012733816,0.000011309231,0.000005487814,0.9978313,0.0000660847,0.0009618981,0.00009187455,0.00094446307],"study_design_scores_gemma":[0.000004816304,0.000008393882,0.000024995823,0.0000018494528,0.0000040112222,0.0000014745236,0.0000018619471,0.9994566,0.000020133843,0.0004207764,0.000053385276,0.000001763041],"about_ca_topic_score_codex":0.019596877,"about_ca_topic_score_gemma":0.016352825,"teacher_disagreement_score":0.019596877,"about_ca_system_score_codex":0.0022015925,"about_ca_system_score_gemma":0.002314603,"threshold_uncertainty_score":0.038965642},"labels":[],"label_agreement":null},{"id":"W4213364704","doi":"10.1016/j.ress.2022.108379","title":"A recursive method for the health assessment of systems using the proportional hazards model","year":2022,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; Ministry of Science and Technology of the People's Republic of China","keywords":"Covariate; Equidistant; Process (computing); Computer science; Mathematical optimization; Upper and lower bounds; Algorithm; Stochastic matrix; Proportional hazards model; Mathematics; Applied mathematics; Statistics; Markov chain","score_opus":0.013704097759265416,"score_gpt":0.27921259624964334,"score_spread":0.26550849849037794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213364704","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008108235,0.000040212675,0.9988174,0.000019194451,0.0000067162337,0.000009929596,0.000010478356,0.00009911841,0.00018608132],"genre_scores_gemma":[0.20518468,0.0003797018,0.7873907,0.00011436924,0.00009506706,0.00033042877,0.00020649878,0.00024350303,0.006055054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994455,0.00027669803,0.000018362558,0.000080189515,0.00013510493,0.000044130724],"domain_scores_gemma":[0.9987717,0.0009406978,0.000041480584,0.00007852594,0.00014129508,0.000026272886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012069409,0.00056099304,0.00083343795,0.00051693164,0.0004036941,0.0005614442,0.0014870621,0.00075497275,0.0035418188],"category_scores_gemma":[0.0038367112,0.00046988777,0.00096864335,0.0003997415,0.0004810888,0.0008770478,0.00094486214,0.0014151657,0.000600278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008989711,0.00008273113,0.00066951534,0.0001613439,0.00012034232,0.00008843681,0.00012498356,0.73988485,0.0066445405,0.057938233,0.0017413811,0.1924537],"study_design_scores_gemma":[0.0000064426936,0.000017900878,0.00009411628,0.0000044936005,0.000012810101,0.000017730601,0.000003447529,0.9931452,0.00038349963,0.005739015,0.00056818576,0.000007095802],"about_ca_topic_score_codex":0.008514321,"about_ca_topic_score_gemma":0.0076392842,"teacher_disagreement_score":0.008514321,"about_ca_system_score_codex":0.00048780133,"about_ca_system_score_gemma":0.0013504416,"threshold_uncertainty_score":0.016929507},"labels":[],"label_agreement":null},{"id":"W4214484890","doi":"10.1016/j.ress.2022.108405","title":"Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study","year":2022,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prognostics; Context (archaeology); Cluster analysis; Computer science; Condition-based maintenance; Downtime; Reliability (semiconductor); Predictive maintenance; Machine learning; Fault (geology); Reliability engineering; Artificial intelligence; Data mining; Engineering","score_opus":0.010327396159176508,"score_gpt":0.23366881403846726,"score_spread":0.22334141787929074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214484890","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5421257,0.0006678769,0.45115077,0.0007639587,0.00010299792,0.0003013626,0.0005197672,0.0011616796,0.0032058337],"genre_scores_gemma":[0.8777651,0.00014070346,0.11927905,0.000044990662,0.000020504365,0.00007530538,0.00024077162,0.00006347569,0.002370013],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992906,0.00024353317,0.00005099502,0.00013197454,0.00021572798,0.00006710377],"domain_scores_gemma":[0.99802303,0.00095273013,0.00010560037,0.00022246708,0.00059857394,0.00009755249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014983175,0.0005739756,0.00075006334,0.00093607494,0.0008866104,0.0006602373,0.001453988,0.0017022508,0.0014468692],"category_scores_gemma":[0.00278362,0.00024150619,0.0006318402,0.0008790285,0.00043794143,0.0005304137,0.00047922184,0.0004757747,0.0004269508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018556602,0.0013842294,0.026822831,0.0006466192,0.00030836987,0.006117836,0.0010749871,0.39256635,0.031694446,0.0031874473,0.009605478,0.5247358],"study_design_scores_gemma":[0.000045115812,0.00022535285,0.004822196,0.000017161075,0.000055816523,0.0017284956,0.0002010174,0.9803509,0.009676484,0.0013675963,0.0014647064,0.000045123383],"about_ca_topic_score_codex":0.0083445925,"about_ca_topic_score_gemma":0.013192004,"teacher_disagreement_score":0.0083445925,"about_ca_system_score_codex":0.00069073035,"about_ca_system_score_gemma":0.00087678846,"threshold_uncertainty_score":0.016592026},"labels":[],"label_agreement":null},{"id":"W4220947047","doi":"10.1007/978-3-030-96794-9_14","title":"Methodology for Optimizing Preventive Maintenance Programs for Equipment on an Electrical Distribution Network","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in mechanical engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Université du Québec à Montréal","funders":"","keywords":"Preventive maintenance; Reliability engineering; Reliability (semiconductor); Overhead (engineering); Engineering; Distribution (mathematics); Planned maintenance; Predictive maintenance; Phase (matter); Operations research; Computer science; Electrical engineering; Mathematics","score_opus":0.0238898837409379,"score_gpt":0.25447149534568914,"score_spread":0.23058161160475124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220947047","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003373217,0.00013718073,0.9947643,0.000039435847,0.00001761485,0.00005379324,0.000035488087,0.00017473212,0.0014043066],"genre_scores_gemma":[0.2313638,0.000601363,0.7621095,0.0001083589,0.000084559666,0.0005657464,0.0002364768,0.00027420596,0.004655994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995908,0.00012322301,0.000020146224,0.00007727273,0.0001323813,0.000056192934],"domain_scores_gemma":[0.9991335,0.00056452514,0.00006527366,0.000043745247,0.00017000095,0.0000230405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014501695,0.0014252653,0.0014620898,0.0010476505,0.0004498029,0.0010450707,0.0016479094,0.0011075363,0.0039245007],"category_scores_gemma":[0.0026980964,0.0008072839,0.0011744334,0.0010625871,0.00054568064,0.00079966325,0.0007036525,0.0010740428,0.00037800567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027116186,0.000052562787,0.00019077648,0.00012241898,0.000040681225,0.000030638414,0.000024354802,0.9311947,0.0021236388,0.0072483723,0.00079457235,0.05815015],"study_design_scores_gemma":[0.0000071116856,0.000021874408,0.00006351647,0.000008114058,0.000012588723,0.00001101159,0.0000058136625,0.99580884,0.00037870905,0.0033721828,0.00030706517,0.000003187934],"about_ca_topic_score_codex":0.00567614,"about_ca_topic_score_gemma":0.00491692,"teacher_disagreement_score":0.00567614,"about_ca_system_score_codex":0.0013163465,"about_ca_system_score_gemma":0.0018600178,"threshold_uncertainty_score":0.013128698},"labels":[],"label_agreement":null},{"id":"W4220959268","doi":"10.1002/asmb.2679","title":"An overview of some classical models and discussion of the signature‐based models of preventive maintenance","year":2022,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Institute for Research in Fundamental Sciences","keywords":"Signature (topology); Reliability (semiconductor); Component (thermodynamics); Computer science; Preventive maintenance; Reliability engineering; Stochastic ordering; Function (biology); Complex system; Operations research; Mathematics; Artificial intelligence; Engineering; Statistics","score_opus":0.027413016773107483,"score_gpt":0.2389014293862069,"score_spread":0.21148841261309942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220959268","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0098677855,0.08658494,0.8502622,0.002744087,0.00067313085,0.00010070109,0.0006219514,0.00020959304,0.048935633],"genre_scores_gemma":[0.61526465,0.1571267,0.18675077,0.0015718172,0.0040604654,0.00074153376,0.0010668462,0.0002115104,0.033205695],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993531,0.00024633232,0.000051025403,0.00010852219,0.00018593165,0.0000550543],"domain_scores_gemma":[0.9991217,0.00050752016,0.00012268678,0.00007689207,0.00014328363,0.000027879687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011650082,0.0014136214,0.0010030154,0.0020442759,0.0005377157,0.0015987045,0.0027796533,0.0023075796,0.0034891248],"category_scores_gemma":[0.00223324,0.0005777227,0.0015908782,0.0026258966,0.0011763453,0.0021174417,0.0009872138,0.002386988,0.0009453182],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031670483,0.0000925229,0.0007742516,0.000620696,0.00006289946,0.00024345174,0.0001908975,0.15884256,0.0010931252,0.7963312,0.0058222115,0.03589438],"study_design_scores_gemma":[0.000012618123,0.000096384734,0.0008454572,0.00030667864,0.00007067276,0.0004503379,0.00005528563,0.5307386,0.00059568486,0.41757706,0.04919143,0.000059851605],"about_ca_topic_score_codex":0.0031369212,"about_ca_topic_score_gemma":0.0014511261,"teacher_disagreement_score":0.0034891248,"about_ca_system_score_codex":0.0016619911,"about_ca_system_score_gemma":0.0008737997,"threshold_uncertainty_score":0.012058616},"labels":[],"label_agreement":null},{"id":"W4225982922","doi":"10.1007/s00170-021-08273-y","title":"Integrated production and maintenance control policies for failure-prone manufacturing systems producing perishable products","year":2022,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preventive maintenance; Corrective maintenance; Robustness (evolution); Operations research; Reliability engineering; Control (management); Cost reduction; Control system; Production (economics); Reduction (mathematics); Computer science; Engineering; Operations management; Business; Economics; Mathematics","score_opus":0.005467687286461031,"score_gpt":0.2018562741089933,"score_spread":0.19638858682253227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225982922","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64171475,0.00046680792,0.35222277,0.0003151837,0.00006327921,0.00025365135,0.00024351085,0.000790067,0.0039300397],"genre_scores_gemma":[0.99378234,0.0000362462,0.0057278685,0.000017062419,0.000009108197,0.00002698191,0.000046832363,0.000013313292,0.00034018184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989281,0.00019530456,0.00007600929,0.00022506145,0.0002686475,0.0003068159],"domain_scores_gemma":[0.99591184,0.0016591549,0.0011834016,0.000287095,0.0007007331,0.0002577777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022829552,0.00090218696,0.00075206533,0.0008727363,0.0004889887,0.0015446366,0.0012031741,0.00061807217,0.0015700317],"category_scores_gemma":[0.004314,0.00044274362,0.00037203517,0.00048213778,0.00052250363,0.001043473,0.0007796487,0.00070347515,0.00022845593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010109175,0.00040764362,0.0035870655,0.000098771714,0.00007032207,0.00010484116,0.00016056345,0.9303822,0.013784502,0.0035896592,0.00070353365,0.046099942],"study_design_scores_gemma":[0.000053001877,0.00033457496,0.0033290114,0.000013432962,0.000049687013,0.000033417185,0.00004266284,0.98980534,0.003904463,0.0022463114,0.00017510641,0.000013030032],"about_ca_topic_score_codex":0.0031936078,"about_ca_topic_score_gemma":0.0034800011,"teacher_disagreement_score":0.0031936078,"about_ca_system_score_codex":0.001161713,"about_ca_system_score_gemma":0.0018722222,"threshold_uncertainty_score":0.012073576},"labels":[],"label_agreement":null},{"id":"W4230850650","doi":"10.1017/s0021900200005015","title":"Conditional Ordering of k-out-of-n Systems with Independent But Nonidentical Components","year":2008,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China; China Scholarship Council; McMaster University","keywords":"Residual; Monotone polygon; Stochastic ordering; Mathematics; Applied mathematics; Computer science; Statistics; Algorithm","score_opus":0.01842217880571664,"score_gpt":0.19973099351546578,"score_spread":0.18130881470974913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230850650","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5365444,0.00020202965,0.45293927,0.0004788675,0.00005340255,0.00009759886,0.00051583536,0.0002528601,0.008915769],"genre_scores_gemma":[0.97886604,0.000099155935,0.01791946,0.000037879152,0.000047420934,0.000041164552,0.0002678211,0.00004031396,0.0026807373],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99856806,0.00039434773,0.000104846,0.00023121324,0.00041888218,0.00028252968],"domain_scores_gemma":[0.9896332,0.005650465,0.0014546699,0.00078167534,0.0015373506,0.0009426858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024412898,0.0005011991,0.00071008515,0.0009553258,0.0005285275,0.0010579152,0.0012232559,0.00058577186,0.0043392167],"category_scores_gemma":[0.0104506565,0.000325344,0.0005220962,0.00058960315,0.001272872,0.001621569,0.0009151789,0.0007781109,0.0002489549],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001185883,0.00019107347,0.012107176,0.00030165745,0.000089361485,0.0013784035,0.0005619656,0.4246748,0.021760581,0.51037705,0.0017250967,0.025646925],"study_design_scores_gemma":[0.00003702982,0.00017124994,0.004560959,0.000014438601,0.000033939727,0.00018041806,0.0000756416,0.9118658,0.0035289663,0.07848939,0.0009959703,0.00004630736],"about_ca_topic_score_codex":0.0024765215,"about_ca_topic_score_gemma":0.0033090317,"teacher_disagreement_score":0.0043392167,"about_ca_system_score_codex":0.0010862708,"about_ca_system_score_gemma":0.0009038545,"threshold_uncertainty_score":0.014516115},"labels":[],"label_agreement":null},{"id":"W4231085816","doi":"10.1177/0954410020919582","title":"Design of hazard identification system for aircraft power supply system based on SIMPLORER and MATLAB co-simulation","year":2020,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"MATLAB; Identification (biology); Fault (geology); Interface (matter); Power (physics); Hazard; Electric power system; Computer science; Engineering; Control engineering; Process (computing); Automotive engineering; Simulation; Operating system","score_opus":0.01263365354207126,"score_gpt":0.2058312384182825,"score_spread":0.19319758487621122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231085816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020527763,0.00008122349,0.9618505,0.00006349238,0.000049343907,0.00019967061,0.00013773626,0.010868552,0.0062217177],"genre_scores_gemma":[0.7423764,0.0002459945,0.2452377,0.000102301274,0.00003382694,0.00079498894,0.0006358495,0.000465102,0.010107926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996307,0.00007429813,0.000037217982,0.00008250338,0.00014358074,0.000031665153],"domain_scores_gemma":[0.9996846,0.00008330464,0.000043921496,0.000046580353,0.000120926285,0.000020600202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041592412,0.00071306725,0.0004877174,0.0005764793,0.0003360693,0.00055686646,0.0009115548,0.00036028217,0.0064951214],"category_scores_gemma":[0.000900083,0.00035280403,0.00043802473,0.00016975566,0.00023948035,0.00057869125,0.00069039874,0.0003936217,0.0009251702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089440646,0.00019867047,0.008086113,0.00074176845,0.00014089014,0.00060164026,0.0006753698,0.67330265,0.06171156,0.016159294,0.0072095175,0.23027807],"study_design_scores_gemma":[0.000047640293,0.00008114017,0.00059390237,0.00001467173,0.000028224837,0.00014325103,0.00002331161,0.9793575,0.01280867,0.0010968443,0.0057873107,0.000017601495],"about_ca_topic_score_codex":0.00231544,"about_ca_topic_score_gemma":0.0014205012,"teacher_disagreement_score":0.0064951214,"about_ca_system_score_codex":0.00030587922,"about_ca_system_score_gemma":0.0006167005,"threshold_uncertainty_score":0.021728337},"labels":[],"label_agreement":null},{"id":"W4232133303","doi":"10.1016/b978-0-12-801913-9.00005-1","title":"Bathtub Distributions","year":2018,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Bathtub; Monotone polygon; Mathematics; Applied mathematics; Hazard; Residual; Convolution (computer science); Discretization; Function (biology); Closure (psychology); Statistics; Computer science; Mathematical analysis; Algorithm; Geometry; Materials science","score_opus":0.007971064222712316,"score_gpt":0.19728329465701533,"score_spread":0.189312230434303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232133303","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046312413,0.0064410926,0.2621366,0.0014479961,0.0008891915,0.000041837582,0.0006897802,0.0011208267,0.72260135],"genre_scores_gemma":[0.089235954,0.008268981,0.037075017,0.0004086086,0.00043808718,0.0001210657,0.0011456418,0.0014999924,0.86180675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997942,0.000034276465,0.000008241586,0.00005304688,0.00009004013,0.000020260082],"domain_scores_gemma":[0.9997681,0.00006846075,0.000014290241,0.000058051817,0.000066867586,0.00002425852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028880133,0.00078277645,0.000798567,0.001403473,0.0007391195,0.0018245226,0.00097671,0.0010110384,0.092332944],"category_scores_gemma":[0.0012674079,0.00056323124,0.0004591752,0.0018794868,0.0009983934,0.0025698033,0.0016205411,0.0020297687,0.026600206],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037682887,0.000035425866,0.00017478148,0.00017786263,0.00001594964,0.000076934906,0.00009201662,0.014248593,0.0016341988,0.67813104,0.07862082,0.22675459],"study_design_scores_gemma":[0.000015790498,0.000025800658,0.00046258364,0.00016236826,0.000013593839,0.00035073602,0.00006971182,0.040865064,0.002077124,0.57112914,0.38480175,0.000026330363],"about_ca_topic_score_codex":0.0012928348,"about_ca_topic_score_gemma":0.0014549792,"teacher_disagreement_score":0.092332944,"about_ca_system_score_codex":0.0009924907,"about_ca_system_score_gemma":0.0006826955,"threshold_uncertainty_score":0.30888444},"labels":[],"label_agreement":null},{"id":"W4232398195","doi":"10.32920/ryerson.14662998.v1","title":"Inspection optimization of load-sharing systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Load sharing; Interval (graph theory); Computer science; Reliability engineering; Order (exchange); Mathematical optimization; Mathematics; Engineering; Distributed computing; Economics","score_opus":0.010509856321696842,"score_gpt":0.20240622146529177,"score_spread":0.19189636514359493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232398195","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6082446,0.00034455978,0.3873411,0.00016430872,0.000023786437,0.000049353577,0.00007444838,0.0002939555,0.0034638387],"genre_scores_gemma":[0.99461514,0.00004435265,0.0045020524,0.000006967673,0.0000029908888,0.0000106111165,0.000020419688,0.000013807853,0.0007835565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995751,0.000105394465,0.000018362312,0.00011065171,0.00009130129,0.000099242345],"domain_scores_gemma":[0.99888915,0.00051825773,0.0003035823,0.00008969178,0.00013111815,0.00006827648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008120411,0.0006329034,0.00086449867,0.0003850888,0.00031314383,0.00060449156,0.00080883934,0.00055883854,0.0013350244],"category_scores_gemma":[0.0025359737,0.00036378094,0.0005058132,0.00042783248,0.0005579447,0.0006778445,0.0005509468,0.00038016078,0.00010504863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008733576,0.00003099558,0.00067263504,0.00003662468,0.000016251577,0.000073802,0.000044216657,0.9878034,0.003206203,0.0017368343,0.000118025164,0.006173788],"study_design_scores_gemma":[0.0000034251914,0.000035517496,0.0003887615,0.000001710238,0.0000053164304,0.000015426545,0.000009788527,0.99819946,0.0005056373,0.0007683597,0.000063894135,0.0000026673],"about_ca_topic_score_codex":0.0056100176,"about_ca_topic_score_gemma":0.002505661,"teacher_disagreement_score":0.0056100176,"about_ca_system_score_codex":0.000943372,"about_ca_system_score_gemma":0.00060442934,"threshold_uncertainty_score":0.011154711},"labels":[],"label_agreement":null},{"id":"W4232955327","doi":"10.32920/ryerson.14648742","title":"Inspection and maintenance optimisation of multicomponent systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Downtime; Reliability engineering; Component (thermodynamics); Reliability (semiconductor); Computer science; Preventive maintenance; Type (biology); Monte Carlo method; Corrective maintenance; Engineering; Mathematics; Statistics","score_opus":0.00911424362121756,"score_gpt":0.19389391133619266,"score_spread":0.1847796677149751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232955327","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09009625,0.0005567009,0.9046595,0.00023504542,0.00003430407,0.00010018556,0.00012291885,0.00018317159,0.004011935],"genre_scores_gemma":[0.92039144,0.00047673986,0.07425093,0.000050107996,0.000019769326,0.00021868851,0.00011190168,0.000051089708,0.004429307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940205,0.00016383258,0.000025523479,0.00015659395,0.00015888347,0.0000930625],"domain_scores_gemma":[0.9979876,0.0014599835,0.00032056982,0.000058568028,0.00010250033,0.00007058935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001135398,0.0009362396,0.0012935392,0.00082165434,0.00035956712,0.0011758752,0.0014716282,0.0013103287,0.0018125043],"category_scores_gemma":[0.003872583,0.0007149459,0.0009529319,0.00093281333,0.0011856514,0.0009674541,0.0007158485,0.0009042089,0.0001628151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009881573,0.000012423783,0.00015954227,0.000019827607,0.000008965549,0.000016886695,0.00001073996,0.9956442,0.00022320548,0.002086428,0.00004064316,0.0017672372],"study_design_scores_gemma":[0.0000030939104,0.000008637761,0.00006416448,0.0000021616204,0.0000027308718,0.000003966126,0.0000024113124,0.9985654,0.00008244682,0.0012089399,0.0000544681,0.0000016287445],"about_ca_topic_score_codex":0.008687332,"about_ca_topic_score_gemma":0.004429955,"teacher_disagreement_score":0.008687332,"about_ca_system_score_codex":0.0016362874,"about_ca_system_score_gemma":0.0013656457,"threshold_uncertainty_score":0.017273486},"labels":[],"label_agreement":null},{"id":"W4235439810","doi":"10.1017/s0001867800010703","title":"Optimal repair/replacement policy for a general repair model","year":2001,"lang":"en","type":"article","venue":"Advances in Applied Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Preventive maintenance; Limit (mathematics); Optimal maintenance; Reliability engineering; Function (biology); Mathematical optimization; Average cost; Computer science; Mathematics; Engineering","score_opus":0.010738226307536203,"score_gpt":0.2555185162644116,"score_spread":0.24478028995687537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235439810","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15651017,0.001871945,0.8301089,0.0011988288,0.00012932197,0.0001034999,0.00033281228,0.00044052594,0.009304015],"genre_scores_gemma":[0.94596225,0.0008116012,0.044190444,0.00013899132,0.00011431229,0.000112877664,0.00021776132,0.0000785197,0.008373198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990609,0.00027291154,0.00003710373,0.00024618892,0.00016575382,0.00021701517],"domain_scores_gemma":[0.99849236,0.0008290273,0.00029037485,0.00012630707,0.00015718586,0.00010457404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017792993,0.0009647852,0.0020398535,0.00083528605,0.0004831271,0.0011888988,0.0018858677,0.0023882168,0.0034215737],"category_scores_gemma":[0.004878884,0.0006791399,0.0009654817,0.00075759605,0.0012556955,0.0018756185,0.00079211744,0.0015104365,0.0004219693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044503962,0.000038729115,0.0001986166,0.000051365856,0.00001717013,0.00009377458,0.000027281838,0.9699855,0.0006678919,0.024694538,0.00068006665,0.0035005948],"study_design_scores_gemma":[0.000015234509,0.000021651402,0.00011615147,0.0000046924306,0.0000140169905,0.00003504513,0.000011004464,0.9874887,0.0001209076,0.011854824,0.0003113501,0.000006317958],"about_ca_topic_score_codex":0.0066551766,"about_ca_topic_score_gemma":0.0045295455,"teacher_disagreement_score":0.0066551766,"about_ca_system_score_codex":0.0017737977,"about_ca_system_score_gemma":0.0015524529,"threshold_uncertainty_score":0.013232887},"labels":[],"label_agreement":null},{"id":"W4235673564","doi":"10.1016/j.jlp.2006.03.005","title":"","year":2006,"lang":"en","type":"article","venue":"Journal of Loss Prevention in the Process Industries","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Common cause failure; Reliability engineering; Reliability (semiconductor); Common cause and special cause; Component (thermodynamics); Computer science; Engineering; Operations management","score_opus":0.010572327970341954,"score_gpt":0.24795129826996545,"score_spread":0.2373789702996235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235673564","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017514497,0.005848331,0.040753085,0.009511954,0.009327371,0.00012434021,0.0014221302,0.0011636646,0.91433454],"genre_scores_gemma":[0.14423226,0.004924371,0.009464635,0.0010370914,0.0017880106,0.000056474317,0.0023072215,0.00023429123,0.8359556],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99977845,0.000017887536,0.000012050586,0.00003939165,0.00012410019,0.000028217417],"domain_scores_gemma":[0.99936837,0.00004595548,0.000026158936,0.000108575914,0.00036663844,0.000084196494],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00039210738,0.00035708584,0.00034146328,0.0009971319,0.0005960163,0.0021322952,0.00057720393,0.0007744831,0.22917242],"category_scores_gemma":[0.0010391928,0.00013455428,0.00017256464,0.0006784335,0.00042258663,0.0011217614,0.00069469505,0.00061749277,0.11146345],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002544712,0.00024812765,0.0023615258,0.00019425873,0.00003559943,0.00016208943,0.00008847268,0.0023725713,0.009742489,0.1209832,0.40815577,0.45540148],"study_design_scores_gemma":[0.00003084966,0.0000780066,0.0029358033,0.00007056535,0.000019731828,0.00040362118,0.00012703097,0.005246498,0.005778382,0.022682386,0.9626139,0.000013244304],"about_ca_topic_score_codex":0.0015518286,"about_ca_topic_score_gemma":0.002174924,"teacher_disagreement_score":0.7708276,"about_ca_system_score_codex":0.0005381831,"about_ca_system_score_gemma":0.0009765355,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4237106922","doi":"10.1109/ias.2001.955756","title":"Zone-branch reliability methodology applied to Gold Book standard network","year":2002,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reliability (semiconductor); Computer science; Gold standard (test); Reliability engineering; Engineering; Mathematics; Statistics","score_opus":0.022325615230072662,"score_gpt":0.22759002049224977,"score_spread":0.2052644052621771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237106922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038393028,0.00013310771,0.99278474,0.000040353614,0.000020144245,0.000030973315,0.00003329534,0.00021178553,0.0029063376],"genre_scores_gemma":[0.32622716,0.000865631,0.6646288,0.00010992079,0.00008147675,0.00028012283,0.00026857705,0.00032601377,0.0072122575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99826103,0.00078659295,0.00005793867,0.0001884136,0.0006347066,0.000071222705],"domain_scores_gemma":[0.9979069,0.0010196107,0.00015609582,0.00022248829,0.0006632538,0.000031702173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026156912,0.000715777,0.00057043013,0.002354507,0.00044434247,0.0010752006,0.0013253228,0.00078364223,0.0032817367],"category_scores_gemma":[0.00598603,0.00033500727,0.000605434,0.0018107995,0.0007337953,0.0017348058,0.00057928474,0.0013320709,0.0010229313],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096155054,0.00004315344,0.0013687423,0.00022995983,0.000067352,0.0002020482,0.0003505001,0.5800948,0.01142542,0.25102198,0.004916413,0.15018342],"study_design_scores_gemma":[0.000013222533,0.00009386715,0.0004728752,0.00003623909,0.000020571402,0.00013194814,0.00006400426,0.9208744,0.005829634,0.06499057,0.007452071,0.000020513904],"about_ca_topic_score_codex":0.0028853114,"about_ca_topic_score_gemma":0.0025867834,"teacher_disagreement_score":0.0032817367,"about_ca_system_score_codex":0.0009355988,"about_ca_system_score_gemma":0.0008040265,"threshold_uncertainty_score":0.013833284},"labels":[],"label_agreement":null},{"id":"W4237332381","doi":"10.1017/s0001867800012520","title":"On the collapsibility of lifetime regression models","year":2003,"lang":"en","type":"article","venue":"Advances in Applied Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Université Laval","funders":"","keywords":"Mathematics; Simple (philosophy); Infinitesimal; Function (biology); Applied mathematics; Covariate; Econometrics; Statistics; Mathematical analysis","score_opus":0.009192959822511398,"score_gpt":0.2176656447050595,"score_spread":0.2084726848825481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237332381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04089783,0.0013293939,0.9537318,0.0009871696,0.00004403779,0.0000625979,0.00028686223,0.00022764545,0.0024327103],"genre_scores_gemma":[0.8716637,0.0051340433,0.10822053,0.00061155495,0.0006677084,0.00060939643,0.0015949241,0.0003711747,0.011127036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99251163,0.003515148,0.00045114197,0.0018398045,0.0010827159,0.0005995225],"domain_scores_gemma":[0.8981682,0.082105696,0.01086927,0.004473672,0.0033768236,0.0010063342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020011142,0.0028636164,0.003070155,0.0027768235,0.0010148546,0.0025313413,0.0026694261,0.0024937333,0.003758455],"category_scores_gemma":[0.080267586,0.0017037279,0.0034085421,0.0018807926,0.004762782,0.0063002226,0.005482183,0.0048682583,0.00082948443],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009073716,0.000056814468,0.004211065,0.00020410761,0.0002708291,0.00064680836,0.0005772117,0.5233556,0.0009448052,0.4539072,0.001234724,0.0145000685],"study_design_scores_gemma":[0.000025390003,0.00010502068,0.00069371256,0.000059511858,0.000047780693,0.00017338613,0.000058909125,0.6981917,0.00036077132,0.2986308,0.0016001988,0.00005282655],"about_ca_topic_score_codex":0.0055725113,"about_ca_topic_score_gemma":0.002386862,"teacher_disagreement_score":0.020011142,"about_ca_system_score_codex":0.0017302107,"about_ca_system_score_gemma":0.001632642,"threshold_uncertainty_score":0.10583031},"labels":[],"label_agreement":null},{"id":"W4237564985","doi":"10.1109/tr.2019.2954020","title":"IEEE Transactions on Reliability publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Singapore Management University; Wuhan University; Universidade de São Paulo; Xidian University; Sichuan University; Universidad de Murcia; TU Graz, Internationale Beziehungen und Mobilitätsprogramme; University of Hong Kong; Northwestern Polytechnical University; Centre National de la Recherche Scientifique; Chinese Academy of Sciences; National University of Singapore; University of Alberta; Northwestern University; Arizona State University; Southern Methodist University","keywords":"Reliability theory; Reliability (semiconductor); Reliability engineering; Computer science; Engineering; Failure rate","score_opus":0.005725311708535436,"score_gpt":0.19682998077939048,"score_spread":0.19110466907085505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237564985","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035631026,0.024386872,0.10456581,0.013860011,0.02205692,0.0006630234,0.034506604,0.0045633838,0.7918343],"genre_scores_gemma":[0.04601225,0.032086957,0.035218954,0.002379416,0.0072361208,0.0005608694,0.057924286,0.0012891038,0.81729203],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988135,0.00014254634,0.00012702271,0.00012829456,0.0007086015,0.00008010535],"domain_scores_gemma":[0.996447,0.00043821713,0.00021887603,0.00069528545,0.0020064602,0.00019411564],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0015678147,0.001329969,0.0012555645,0.003017873,0.0007825042,0.002572261,0.0012508406,0.0017640708,0.32144228],"category_scores_gemma":[0.0060508824,0.0005011098,0.00055220275,0.0024828552,0.0004141183,0.002222951,0.001289394,0.00173204,0.18698487],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007274083,0.00006378008,0.00039707916,0.000408753,0.000023316268,0.00007690888,0.000033537566,0.001835586,0.0011402915,0.024698533,0.7977493,0.17350021],"study_design_scores_gemma":[0.000022412354,0.000049459413,0.0006500035,0.00030349646,0.00002346616,0.00019462862,0.000024971047,0.0038681854,0.00071531784,0.01348346,0.9806378,0.000026893798],"about_ca_topic_score_codex":0.0019152745,"about_ca_topic_score_gemma":0.0028726798,"teacher_disagreement_score":0.67855775,"about_ca_system_score_codex":0.0008231857,"about_ca_system_score_gemma":0.0019173056,"threshold_uncertainty_score":0.96788025},"labels":[],"label_agreement":null},{"id":"W4237979468","doi":"10.1007/978-94-007-0753-5_102338","title":"Loss Productivity","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University; University of Northern British Columbia","funders":"","keywords":"Productivity; Economics; Macroeconomics","score_opus":0.006504169250933199,"score_gpt":0.17084082373715379,"score_spread":0.1643366544862206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237979468","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038999673,0.003145716,0.08286929,0.0016573984,0.0011517891,0.00009325427,0.00044348612,0.0014114997,0.9053276],"genre_scores_gemma":[0.07705387,0.005105134,0.012403414,0.0005359176,0.00061867485,0.000112458336,0.0007096648,0.00090398116,0.90255696],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987091,0.00008813272,0.000036795915,0.00021451052,0.00082277664,0.00012872282],"domain_scores_gemma":[0.9990005,0.0001675658,0.00008420597,0.0002254681,0.0004448429,0.00007747732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097448565,0.0014367958,0.0006329414,0.0019975947,0.0008481208,0.0031461047,0.0020451478,0.00090378296,0.10306058],"category_scores_gemma":[0.0024685182,0.00033037155,0.0005967831,0.0011933879,0.0008348719,0.0032873736,0.0017811677,0.0017780564,0.041112717],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009135525,0.00012543595,0.00044935464,0.00033935762,0.000020678594,0.00010695004,0.00017431838,0.0065054656,0.005038034,0.20454219,0.11786329,0.66474354],"study_design_scores_gemma":[0.00001635245,0.00014538449,0.0016317212,0.00026530534,0.00003768411,0.0008211526,0.00020738142,0.008378632,0.011178703,0.14317732,0.8341027,0.00003769252],"about_ca_topic_score_codex":0.0009791902,"about_ca_topic_score_gemma":0.0009559309,"teacher_disagreement_score":0.10306058,"about_ca_system_score_codex":0.0020412197,"about_ca_system_score_gemma":0.0011581905,"threshold_uncertainty_score":0.34477198},"labels":[],"label_agreement":null},{"id":"W4238545762","doi":"10.32920/ryerson.14662326","title":"Non-periodic inspection of optimization of repairable systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability engineering; Component (thermodynamics); Time horizon; Process (computing); Computer science; Interval (graph theory); Mathematical optimization; Mathematics; Engineering","score_opus":0.006781369052610318,"score_gpt":0.1926203435621223,"score_spread":0.18583897450951198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238545762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16799968,0.0021303464,0.8150734,0.0010310593,0.0001439135,0.00016241295,0.00042603214,0.00034881898,0.012684297],"genre_scores_gemma":[0.9590079,0.0006733099,0.03303386,0.00007464117,0.00004219849,0.00020799172,0.00019579659,0.00006410302,0.0067001046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999008,0.00046550712,0.000027539001,0.00020229493,0.00013547779,0.00016124359],"domain_scores_gemma":[0.99765044,0.0015624446,0.0004150305,0.00007053785,0.00015729571,0.00014419269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018168368,0.001418822,0.0015957541,0.0008056106,0.00040240987,0.0012106276,0.0017470082,0.0014827331,0.0030456812],"category_scores_gemma":[0.005395186,0.0011622144,0.0010158157,0.0006733756,0.0013623318,0.0013670817,0.00070306065,0.0013826961,0.00027700965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002242609,0.0000146322045,0.00017974172,0.000022689379,0.00000966546,0.000025232297,0.000010255746,0.99476236,0.00009972889,0.0037824723,0.000113569,0.0009572316],"study_design_scores_gemma":[0.000007006755,0.000017587166,0.00010076491,0.000004392646,0.000003985953,0.0000045719967,0.000005341333,0.9973736,0.00004577627,0.0023277602,0.00010628077,0.0000029284115],"about_ca_topic_score_codex":0.01226146,"about_ca_topic_score_gemma":0.0065270714,"teacher_disagreement_score":0.01226146,"about_ca_system_score_codex":0.002145202,"about_ca_system_score_gemma":0.0014687806,"threshold_uncertainty_score":0.024380147},"labels":[],"label_agreement":null},{"id":"W4239507838","doi":"10.1109/tr.2019.2934379","title":"IEEE Transactions on Reliability publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Singapore Management University; Wuhan University; Universidade de São Paulo; Xidian University; Sichuan University; Universidad de Murcia; TU Graz, Internationale Beziehungen und Mobilitätsprogramme; University of Hong Kong; Northwestern Polytechnical University; Centre National de la Recherche Scientifique; Chinese Academy of Sciences; National University of Singapore; University of Alberta; Northwestern University; Arizona State University; Southern Methodist University","keywords":"Reliability (semiconductor); Reliability engineering; Reliability theory; Computer science; Engineering; Failure rate","score_opus":0.005725311708535436,"score_gpt":0.19682998077939048,"score_spread":0.19110466907085505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239507838","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035631026,0.024386872,0.10456581,0.013860011,0.02205692,0.0006630234,0.034506604,0.0045633838,0.7918343],"genre_scores_gemma":[0.04601225,0.032086957,0.035218954,0.002379416,0.0072361208,0.0005608694,0.057924286,0.0012891038,0.81729203],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988135,0.00014254634,0.00012702271,0.00012829456,0.0007086015,0.00008010535],"domain_scores_gemma":[0.996447,0.00043821713,0.00021887603,0.00069528545,0.0020064602,0.00019411564],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0015678147,0.001329969,0.0012555645,0.003017873,0.0007825042,0.002572261,0.0012508406,0.0017640708,0.32144228],"category_scores_gemma":[0.0060508824,0.0005011098,0.00055220275,0.0024828552,0.0004141183,0.002222951,0.001289394,0.00173204,0.18698487],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007274083,0.00006378008,0.00039707916,0.000408753,0.000023316268,0.00007690888,0.000033537566,0.001835586,0.0011402915,0.024698533,0.7977493,0.17350021],"study_design_scores_gemma":[0.000022412354,0.000049459413,0.0006500035,0.00030349646,0.00002346616,0.00019462862,0.000024971047,0.0038681854,0.00071531784,0.01348346,0.9806378,0.000026893798],"about_ca_topic_score_codex":0.0019152745,"about_ca_topic_score_gemma":0.0028726798,"teacher_disagreement_score":0.67855775,"about_ca_system_score_codex":0.0008231857,"about_ca_system_score_gemma":0.0019173056,"threshold_uncertainty_score":0.96788025},"labels":[],"label_agreement":null},{"id":"W4242156936","doi":"10.1002/asmb.790","title":"Optimal corrective maintenance contract planning for aging multi‐state system","year":2009,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Failure rate; Reliability engineering; Corrective maintenance; Computer science; Order (exchange); Optimal maintenance; Piecewise; Total cost; Function (biology); Mathematical optimization; Series (stratigraphy); Maintenance actions; State (computer science); Genetic algorithm; Operations research; Preventive maintenance; Engineering; Economics; Mathematics; Algorithm","score_opus":0.016226921536985503,"score_gpt":0.2268139392469692,"score_spread":0.21058701770998367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242156936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4555736,0.00086272275,0.5375852,0.00047795984,0.000052438176,0.00013088339,0.00017617241,0.0003514245,0.0047896984],"genre_scores_gemma":[0.98500603,0.00007052972,0.013970842,0.00001756868,0.0000062713234,0.00003377379,0.000048308186,0.00001547125,0.00083124737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992731,0.0002868796,0.000026017124,0.00010156366,0.00016888425,0.00014358207],"domain_scores_gemma":[0.9982461,0.0009747403,0.00028110095,0.00008565592,0.00022927894,0.00018316515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019445411,0.0006771894,0.001073497,0.00083468616,0.00041875755,0.00080420123,0.0009853969,0.0008711203,0.001908978],"category_scores_gemma":[0.0033658526,0.0005367098,0.00042931546,0.00056447455,0.0007193431,0.00075276615,0.0005145435,0.0008158723,0.0001302624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006095871,0.000023982262,0.00026052978,0.000019354642,0.000009427592,0.00004925451,0.000020900392,0.99034894,0.00064785033,0.0023376343,0.00016303785,0.006058091],"study_design_scores_gemma":[0.000007996045,0.000030019492,0.00012639677,0.000002048363,0.0000037408936,0.000009097887,0.0000059941094,0.99836797,0.00018617525,0.001172665,0.00008467286,0.0000032215567],"about_ca_topic_score_codex":0.0094268005,"about_ca_topic_score_gemma":0.0041580405,"teacher_disagreement_score":0.0094268005,"about_ca_system_score_codex":0.0015537136,"about_ca_system_score_gemma":0.0013700873,"threshold_uncertainty_score":0.018743873},"labels":[],"label_agreement":null},{"id":"W4242347261","doi":"10.32920/ryerson.14662998","title":"Inspection optimization of load-sharing systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Load sharing; Interval (graph theory); Computer science; Reliability engineering; Order (exchange); Mathematical optimization; Mathematics; Engineering; Distributed computing","score_opus":0.010509856321696842,"score_gpt":0.20240622146529177,"score_spread":0.19189636514359493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242347261","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6082446,0.00034455978,0.3873411,0.00016430872,0.000023786437,0.000049353577,0.00007444838,0.0002939555,0.0034638387],"genre_scores_gemma":[0.99461514,0.00004435265,0.0045020524,0.000006967673,0.0000029908888,0.0000106111165,0.000020419688,0.000013807853,0.0007835565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995751,0.000105394465,0.000018362312,0.00011065171,0.00009130129,0.000099242345],"domain_scores_gemma":[0.99888915,0.00051825773,0.0003035823,0.00008969178,0.00013111815,0.00006827648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008120411,0.0006329034,0.00086449867,0.0003850888,0.00031314383,0.00060449156,0.00080883934,0.00055883854,0.0013350244],"category_scores_gemma":[0.0025359737,0.00036378094,0.0005058132,0.00042783248,0.0005579447,0.0006778445,0.0005509468,0.00038016078,0.00010504863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008733576,0.00003099558,0.00067263504,0.00003662468,0.000016251577,0.000073802,0.000044216657,0.9878034,0.003206203,0.0017368343,0.000118025164,0.006173788],"study_design_scores_gemma":[0.0000034251914,0.000035517496,0.0003887615,0.000001710238,0.0000053164304,0.000015426545,0.000009788527,0.99819946,0.0005056373,0.0007683597,0.000063894135,0.0000026673],"about_ca_topic_score_codex":0.0056100176,"about_ca_topic_score_gemma":0.002505661,"teacher_disagreement_score":0.0056100176,"about_ca_system_score_codex":0.000943372,"about_ca_system_score_gemma":0.00060442934,"threshold_uncertainty_score":0.011154711},"labels":[],"label_agreement":null},{"id":"W4242619820","doi":"10.1504/ijpqm.2018.094760","title":"Maintenance policy selection using fuzzy failure modes and effective analysis and key performance indicators","year":2018,"lang":"en","type":"article","venue":"International Journal of Productivity and Quality Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Reliability engineering; Analytic hierarchy process; Fuzzy logic; Key (lock); Failure mode, effects, and criticality analysis; Criticality; Selection (genetic algorithm); Computer science; Failure mode and effects analysis; Process (computing); Condition-based maintenance; Operations research; Risk analysis (engineering); Engineering; Machine learning; Artificial intelligence","score_opus":0.008530499353165055,"score_gpt":0.26642558400306304,"score_spread":0.25789508464989797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242619820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1053546,0.000250373,0.8914684,0.00010077959,0.000018921102,0.00011549048,0.000083973944,0.00017017806,0.002437363],"genre_scores_gemma":[0.9122845,0.00016887469,0.0865274,0.00001584794,0.00001776829,0.000111605514,0.00009887427,0.000012521756,0.00076271576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884593,0.00024782453,0.0000854376,0.00019071224,0.00049472845,0.00013523393],"domain_scores_gemma":[0.9985858,0.0007399054,0.00021460518,0.000048279235,0.00035098864,0.00006043411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019045542,0.0010182395,0.0007768684,0.00343218,0.0005064075,0.0012586778,0.00077606854,0.00061514875,0.0009173803],"category_scores_gemma":[0.004279795,0.0003064262,0.0011072472,0.0011996626,0.00036030466,0.0011339205,0.00060240494,0.000447088,0.00010186618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033681444,0.00021070977,0.01217002,0.00030152404,0.00019601091,0.0003045967,0.00044434858,0.7299472,0.0132539235,0.01476409,0.0010597792,0.22701097],"study_design_scores_gemma":[0.000009477696,0.00007334192,0.0021217554,0.000017525379,0.000046783756,0.000047707712,0.000063779364,0.99137336,0.0015634621,0.004373493,0.00028998303,0.000019352947],"about_ca_topic_score_codex":0.0046779,"about_ca_topic_score_gemma":0.003115469,"teacher_disagreement_score":0.0046779,"about_ca_system_score_codex":0.001247331,"about_ca_system_score_gemma":0.0013037372,"threshold_uncertainty_score":0.0100723505},"labels":[],"label_agreement":null},{"id":"W4242713036","doi":"10.1017/s0021900200019343","title":"On exact and large deviation approximation for the distribution of the longest run in a sequence of two-state Markov dependent trials","year":2003,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Markov chain; Sequence (biology); Applied mathematics; Variable-order Markov model; Markov process; Markov chain mixing time; Markov model; Distribution (mathematics); Statistics; Mathematical analysis","score_opus":0.022231204504160832,"score_gpt":0.25457408826277816,"score_spread":0.23234288375861734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242713036","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024739197,0.0008072782,0.97138447,0.00030637946,0.000044610755,0.000051160634,0.00009387974,0.00019585651,0.0023772004],"genre_scores_gemma":[0.73129046,0.0027520417,0.25836867,0.00034202187,0.0002154761,0.0006791805,0.00068877294,0.00043120608,0.005232191],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974022,0.001247067,0.00010806395,0.00036654106,0.0006449333,0.00023124942],"domain_scores_gemma":[0.95059747,0.04153136,0.0024483586,0.002303678,0.002361198,0.0007578652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012270559,0.0012643358,0.0019020911,0.0020071226,0.000646641,0.002036928,0.0029905704,0.0016978614,0.0032535212],"category_scores_gemma":[0.05901421,0.0007849722,0.0011156439,0.002144452,0.0041198833,0.006030752,0.0024103732,0.0033173254,0.0007386031],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012167762,0.000056279445,0.0014335781,0.00017215763,0.00005028722,0.00019250516,0.00021214518,0.70022684,0.00096565456,0.28199372,0.0009374842,0.013637632],"study_design_scores_gemma":[0.000009917199,0.000014786721,0.00016835758,0.000029923658,0.000005996128,0.00003614497,0.00001907562,0.93574387,0.00029521226,0.06339148,0.0002676665,0.00001760487],"about_ca_topic_score_codex":0.0027949084,"about_ca_topic_score_gemma":0.002061821,"teacher_disagreement_score":0.012270559,"about_ca_system_score_codex":0.0025921331,"about_ca_system_score_gemma":0.0019657575,"threshold_uncertainty_score":0.06489366},"labels":[],"label_agreement":null},{"id":"W4245549591","doi":"10.18178/wcse.2016.06.011","title":"Tolerating One and Two Node Failures in a Bipartite Network K(n,n)","year":2016,"lang":"en","type":"article","venue":"Proceedings of 2016 the 6th International Workshop on Computer Science and Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Bipartite graph; Computer science; Node (physics); Computer network; Theoretical computer science; Engineering; Graph","score_opus":0.007834587471028779,"score_gpt":0.20326321357423804,"score_spread":0.19542862610320927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245549591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11977674,0.00018859463,0.870043,0.0003483083,0.000037221933,0.000047878126,0.00018685768,0.00035546135,0.009015947],"genre_scores_gemma":[0.81324065,0.0004524352,0.17723714,0.00022395457,0.00003571237,0.00016931584,0.0003956033,0.00015456778,0.00809062],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994068,0.00023476795,0.000020226793,0.00012339753,0.000076071025,0.00013861526],"domain_scores_gemma":[0.9991128,0.00038223635,0.00014005585,0.00011357981,0.000106366846,0.00014501443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007010551,0.0005545782,0.00046576973,0.00062482356,0.00068709377,0.00059624726,0.000844043,0.0010261579,0.0029343923],"category_scores_gemma":[0.0023457387,0.00034600397,0.00034179178,0.0007611513,0.0006568447,0.0012826144,0.0010054479,0.00063322287,0.0006011738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043354311,0.0001766178,0.001999401,0.0003555465,0.000090151814,0.00036932967,0.00022929159,0.73728985,0.02702741,0.16585837,0.00537546,0.060795087],"study_design_scores_gemma":[0.0000490743,0.00016359145,0.0009614529,0.000036014877,0.000037980095,0.00040239102,0.00013585521,0.8438091,0.003637722,0.14563592,0.0051026405,0.00002823367],"about_ca_topic_score_codex":0.0017088556,"about_ca_topic_score_gemma":0.0018853822,"teacher_disagreement_score":0.0029343923,"about_ca_system_score_codex":0.00054324203,"about_ca_system_score_gemma":0.0006847669,"threshold_uncertainty_score":0.009816527},"labels":[],"label_agreement":null},{"id":"W4246352112","doi":"10.1002/net.20148","title":"Uniformly optimal digraphs for strongly connected reliability","year":2006,"lang":"en","type":"article","venue":"Networks","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Digraph; Counterexample; Conjecture; Combinatorics; Vertex (graph theory); Strongly connected component; Reliability (semiconductor); Mathematics; Enhanced Data Rates for GSM Evolution; Graph; Simple (philosophy); Connectivity; Terminal (telecommunication); Vertex connectivity; Directed graph; Discrete mathematics; Computer science","score_opus":0.0031287280909298274,"score_gpt":0.17559408778692287,"score_spread":0.17246535969599305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246352112","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54046917,0.0008455503,0.43256974,0.0015814113,0.00009752892,0.00022573341,0.0014107963,0.0005737721,0.02222626],"genre_scores_gemma":[0.92984813,0.00036606003,0.06508986,0.00023940933,0.000029874109,0.00013952056,0.00055618753,0.00004092355,0.0036900123],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99951947,0.00013388203,0.000035435674,0.00014174798,0.00007757377,0.00009193052],"domain_scores_gemma":[0.99711645,0.0015116663,0.00035988822,0.00035257108,0.0003007449,0.00035860992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087782746,0.0004901885,0.00057626807,0.0011854401,0.00064377213,0.00091420807,0.0005465869,0.0006459884,0.003870652],"category_scores_gemma":[0.0062424894,0.0004944552,0.00040314242,0.0006065091,0.00081357243,0.0012654635,0.0008609249,0.0007422365,0.00031545103],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053504563,0.00023297667,0.0058554746,0.00048208123,0.00011725941,0.00031676047,0.00047802864,0.24892096,0.011169323,0.63105965,0.012042582,0.0887899],"study_design_scores_gemma":[0.000110907735,0.00018187541,0.002986837,0.00007234228,0.00007817851,0.00024693736,0.00017052291,0.338505,0.005383515,0.6439137,0.008311586,0.00003872746],"about_ca_topic_score_codex":0.0013833572,"about_ca_topic_score_gemma":0.0022901315,"teacher_disagreement_score":0.003870652,"about_ca_system_score_codex":0.0016751058,"about_ca_system_score_gemma":0.0008485569,"threshold_uncertainty_score":0.012948692},"labels":[],"label_agreement":null},{"id":"W4249414392","doi":"10.1002/0471667196.ess7151","title":"Optimal Sample Size Allocation for Accelerated Degradation Test Based on Wiener Process","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Statistical Sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Reliability (semiconductor); Degradation (telecommunications); Wiener process; Reliability engineering; Accelerated life testing; Computer science; Process (computing); Product (mathematics); Variance (accounting); Statistics; Mathematics; Weibull distribution; Engineering; Power (physics)","score_opus":0.012421432497651003,"score_gpt":0.26824912194416034,"score_spread":0.25582768944650935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249414392","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058888886,0.0001949762,0.939483,0.00014748641,0.000019352705,0.00022061651,0.000040843206,0.00013472789,0.000870094],"genre_scores_gemma":[0.6321712,0.0002128787,0.36505806,0.00010962666,0.00005160319,0.0008076859,0.00020261832,0.000058739333,0.001327497],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937324,0.004617914,0.00019423998,0.0005130824,0.00066189613,0.00028051288],"domain_scores_gemma":[0.96721655,0.029153176,0.00094898685,0.00069771835,0.0016667299,0.00031683964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011609606,0.0007248299,0.0019703347,0.0012177605,0.00040007068,0.0007739231,0.001032776,0.0010165157,0.0019521166],"category_scores_gemma":[0.036747668,0.00052889716,0.0006326905,0.0005955475,0.0012676639,0.0011986078,0.0012162206,0.0011511155,0.00026356554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017364903,0.0003648436,0.003673289,0.00025730077,0.00013657505,0.00017795937,0.00016236115,0.8158523,0.008316222,0.04540624,0.0012499513,0.12266645],"study_design_scores_gemma":[0.000087079665,0.00021776865,0.0008900917,0.000016555472,0.000023449293,0.000024321791,0.000020004021,0.98747265,0.0020743317,0.008932895,0.00022619318,0.00001465281],"about_ca_topic_score_codex":0.0013814488,"about_ca_topic_score_gemma":0.00088549516,"teacher_disagreement_score":0.011609606,"about_ca_system_score_codex":0.000923918,"about_ca_system_score_gemma":0.001731749,"threshold_uncertainty_score":0.06139815},"labels":[],"label_agreement":null},{"id":"W4250530401","doi":"10.1002/9781118445112.stat04159","title":"Group Maintenance Policies","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Generalization; Feature (linguistics); Computer science; State (computer science); Group (periodic table); Mathematics; Algorithm","score_opus":0.013824247786860663,"score_gpt":0.24514150839796037,"score_spread":0.2313172606110997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250530401","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09266927,0.0007357297,0.88325095,0.00080798357,0.00032481362,0.0002412391,0.00028225235,0.0015206051,0.020167137],"genre_scores_gemma":[0.87250656,0.00029555577,0.11613772,0.00018979337,0.00017931701,0.00021573869,0.00028306595,0.00019009443,0.010002248],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.997905,0.00067802024,0.00011482944,0.00042925164,0.0006061547,0.000266792],"domain_scores_gemma":[0.99577063,0.0015048652,0.00057477166,0.0013452418,0.00054625445,0.00025822653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002550717,0.00061973056,0.0007741739,0.0007728806,0.00063212065,0.0011067985,0.0023042427,0.0009029258,0.007499755],"category_scores_gemma":[0.0063111163,0.00024619952,0.0004700392,0.0007258927,0.0007738361,0.0020158188,0.0012985137,0.0009940194,0.0012671154],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007315915,0.00048720813,0.0056920843,0.00035554057,0.00015724714,0.00045125245,0.00041162735,0.42557696,0.010543762,0.19833478,0.031311586,0.32594627],"study_design_scores_gemma":[0.00012828308,0.00037028367,0.0014234543,0.00004912969,0.00006316745,0.00044664738,0.00008510556,0.831227,0.006308206,0.1345679,0.02529435,0.00003650138],"about_ca_topic_score_codex":0.0007988841,"about_ca_topic_score_gemma":0.00071257574,"teacher_disagreement_score":0.007499755,"about_ca_system_score_codex":0.0009942146,"about_ca_system_score_gemma":0.0008291127,"threshold_uncertainty_score":0.025089204},"labels":[],"label_agreement":null},{"id":"W4250867502","doi":"10.24200/sci.2021.56113.4591","title":"Reliability Optimization of a k-out-of-n Series-Parallel System with Warm Standby Components","year":2021,"lang":"en","type":"article","venue":"Scientia Iranica","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Redundancy (engineering); Series and parallel circuits; Computer science; Mathematical optimization; Reliability engineering; Imperfect; Genetic algorithm; Algorithm; Mathematics; Engineering; Voltage","score_opus":0.009388558374429347,"score_gpt":0.19468989658609417,"score_spread":0.18530133821166483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250867502","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4209369,0.0009299496,0.56467813,0.0004993763,0.00009619779,0.00013764671,0.00030995658,0.0003838968,0.012027914],"genre_scores_gemma":[0.97754914,0.00016530888,0.018363811,0.00003086567,0.000023732702,0.00008448951,0.0000823017,0.000027499924,0.003672839],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959475,0.00014345265,0.000014262727,0.00010951879,0.00006603874,0.00007196439],"domain_scores_gemma":[0.99966574,0.00014033545,0.00008363167,0.000024751718,0.00005189605,0.000033714863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006831276,0.0010986117,0.0011801567,0.00057174277,0.0005752635,0.0008534977,0.0010623853,0.0009906145,0.002070205],"category_scores_gemma":[0.00073305256,0.00053830235,0.0010537313,0.0005945278,0.00059348013,0.0007839724,0.00064310123,0.00063861755,0.00017135052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030364854,0.000015170234,0.00018218116,0.000024569417,0.000018457993,0.000059330632,0.000010048163,0.9963037,0.0005881218,0.000641045,0.00011336616,0.0020135676],"study_design_scores_gemma":[0.0000075337025,0.00004332945,0.00015793322,0.0000023629918,0.000009803336,0.000022749227,0.00001147734,0.99867874,0.00021783044,0.000736054,0.00010905402,0.0000030830981],"about_ca_topic_score_codex":0.008395882,"about_ca_topic_score_gemma":0.007370412,"teacher_disagreement_score":0.008395882,"about_ca_system_score_codex":0.0010213465,"about_ca_system_score_gemma":0.0010029682,"threshold_uncertainty_score":0.01669401},"labels":[],"label_agreement":null},{"id":"W4252906998","doi":"10.32920/ryerson.14662326.v1","title":"Non-periodic inspection of optimization of repairable systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability engineering; Component (thermodynamics); Time horizon; Process (computing); Computer science; Interval (graph theory); Type (biology); Total cost; Poisson process; Mathematical optimization; Poisson distribution; Mathematics; Engineering; Statistics","score_opus":0.006781369052610318,"score_gpt":0.1926203435621223,"score_spread":0.18583897450951198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252906998","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16799968,0.0021303464,0.8150734,0.0010310593,0.0001439135,0.00016241295,0.00042603214,0.00034881898,0.012684297],"genre_scores_gemma":[0.9590079,0.0006733099,0.03303386,0.00007464117,0.00004219849,0.00020799172,0.00019579659,0.00006410302,0.0067001046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999008,0.00046550712,0.000027539001,0.00020229493,0.00013547779,0.00016124359],"domain_scores_gemma":[0.99765044,0.0015624446,0.0004150305,0.00007053785,0.00015729571,0.00014419269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018168368,0.001418822,0.0015957541,0.0008056106,0.00040240987,0.0012106276,0.0017470082,0.0014827331,0.0030456812],"category_scores_gemma":[0.005395186,0.0011622144,0.0010158157,0.0006733756,0.0013623318,0.0013670817,0.00070306065,0.0013826961,0.00027700965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002242609,0.0000146322045,0.00017974172,0.000022689379,0.00000966546,0.000025232297,0.000010255746,0.99476236,0.00009972889,0.0037824723,0.000113569,0.0009572316],"study_design_scores_gemma":[0.000007006755,0.000017587166,0.00010076491,0.000004392646,0.000003985953,0.0000045719967,0.000005341333,0.9973736,0.00004577627,0.0023277602,0.00010628077,0.0000029284115],"about_ca_topic_score_codex":0.01226146,"about_ca_topic_score_gemma":0.0065270714,"teacher_disagreement_score":0.01226146,"about_ca_system_score_codex":0.002145202,"about_ca_system_score_gemma":0.0014687806,"threshold_uncertainty_score":0.024380147},"labels":[],"label_agreement":null},{"id":"W4253592151","doi":"10.1017/s0021900200022658","title":"On the behaviour of some new ageing properties based upon the residual life of k-out-of-n systems","year":2002,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Lanzhou University","keywords":"Mathematics; Residual; Monotonic function; Independent and identically distributed random variables; Complement (music); Pure mathematics; Combinatorics; Discrete mathematics; Statistics; Random variable; Mathematical analysis; Algorithm; Chemistry","score_opus":0.033301876597087283,"score_gpt":0.19668396143782277,"score_spread":0.1633820848407355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253592151","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8782153,0.001235918,0.116946064,0.00026184376,0.000028514027,0.000039844796,0.00011782452,0.00015134453,0.0030033982],"genre_scores_gemma":[0.9950258,0.00032223677,0.004078988,0.00002690868,0.000042709813,0.000015886602,0.00007826717,0.000037107107,0.00037212626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99953854,0.00014118353,0.000030745887,0.00008838148,0.00012122533,0.00008005005],"domain_scores_gemma":[0.98563564,0.008798841,0.003306283,0.0006969716,0.0011123181,0.00044989272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026259131,0.0005434803,0.00061861024,0.0012673053,0.0004188474,0.00074628356,0.00071538007,0.00064521894,0.00089633366],"category_scores_gemma":[0.014765084,0.00019926271,0.00058682775,0.00046311252,0.0015707539,0.0019737105,0.0005677441,0.00071702455,0.00011230915],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072060945,0.00023937841,0.041233297,0.0007281862,0.00019529542,0.0014958312,0.0018002219,0.5977953,0.07698702,0.23338462,0.0013392341,0.044081006],"study_design_scores_gemma":[0.000017621025,0.00021671347,0.011587338,0.000045626282,0.000032994805,0.0006343509,0.00014662267,0.94695354,0.005644237,0.034010194,0.0006590006,0.000051842762],"about_ca_topic_score_codex":0.0006967487,"about_ca_topic_score_gemma":0.00043001754,"teacher_disagreement_score":0.0026259131,"about_ca_system_score_codex":0.00048564927,"about_ca_system_score_gemma":0.00025557526,"threshold_uncertainty_score":0.013887346},"labels":[],"label_agreement":null},{"id":"W4255712498","doi":"10.1007/978-1-4471-2757-4","title":"Maintenance Management in Network Utilities","year":2012,"lang":"en","type":"book","venue":"Springer series in reliability engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science","score_opus":0.004873413267438001,"score_gpt":0.17467660131363677,"score_spread":0.16980318804619876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255712498","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022016887,0.22865939,0.28858215,0.0053715864,0.0041898554,0.00013527644,0.0004881053,0.0017933048,0.44876346],"genre_scores_gemma":[0.18617643,0.098862395,0.04431458,0.0006421166,0.0028081106,0.00009133238,0.0007805803,0.0003387943,0.66598564],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998846,0.000019359331,0.0000060929156,0.000020006883,0.0000592003,0.000010669705],"domain_scores_gemma":[0.9999131,0.000028646507,0.0000100352345,0.000016482938,0.00002602629,0.0000058196806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000177757,0.0005887569,0.00036031753,0.0005880842,0.00018922421,0.0009236825,0.00053149014,0.0004327897,0.00929364],"category_scores_gemma":[0.00038914732,0.00021653416,0.00016748146,0.0012143105,0.00021992445,0.0011517726,0.00030605803,0.0005466361,0.0025688242],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023180946,0.00006330816,0.00027510905,0.00030946566,0.000013950632,0.00009199044,0.0001615459,0.015156482,0.0020334553,0.039688952,0.10918669,0.83299595],"study_design_scores_gemma":[0.000010119513,0.0000915655,0.0030049854,0.0004083554,0.000031313393,0.00045207673,0.0002010726,0.04651549,0.0020149234,0.09307972,0.85416764,0.000022736443],"about_ca_topic_score_codex":0.001396036,"about_ca_topic_score_gemma":0.0027565355,"teacher_disagreement_score":0.00929364,"about_ca_system_score_codex":0.00062278943,"about_ca_system_score_gemma":0.00038684942,"threshold_uncertainty_score":0.03109032},"labels":[],"label_agreement":null},{"id":"W4280573633","doi":"10.1177/1748006x211070641","title":"Guest Editorial: Reliability analysis for infrastructure systems","year":2022,"lang":"en","type":"editorial","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Alberta","keywords":"Reliability (semiconductor); Computer science; Reliability engineering; Engineering; Physics","score_opus":0.0035580909791516997,"score_gpt":0.20220504226633576,"score_spread":0.19864695128718407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280573633","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005563899,0.0045187636,0.0003370412,0.019148178,0.9742698,0.000012587974,0.000038833234,0.000059046135,0.0015601424],"genre_scores_gemma":[0.0005500471,0.004095546,0.00012969376,0.0048637292,0.9842242,0.000010158225,0.000024456902,0.00004834959,0.006053852],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99621886,0.0005052716,0.00048391763,0.0004821179,0.0020703415,0.00023948372],"domain_scores_gemma":[0.98226446,0.0061750202,0.0007762614,0.0005004486,0.008385342,0.0018986302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004846028,0.004626889,0.003149699,0.0035827837,0.0022989078,0.00628997,0.0032039678,0.012737188,0.014937527],"category_scores_gemma":[0.017036123,0.0011042751,0.0027996968,0.0013340337,0.0019411321,0.0040820385,0.0010559004,0.014802218,0.012795908],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004104041,0.000021521599,0.000028459519,0.00019710297,0.00002112674,0.00016069626,0.000007577846,0.000107253196,0.00010208993,0.00045724306,0.9931664,0.005689622],"study_design_scores_gemma":[0.00007077169,0.00006192368,0.00042138653,0.0004304439,0.000072811075,0.0005734533,0.000032396016,0.0009074128,0.00032257827,0.002708333,0.99436814,0.000030258041],"about_ca_topic_score_codex":0.00078651647,"about_ca_topic_score_gemma":0.0016375585,"teacher_disagreement_score":0.014937527,"about_ca_system_score_codex":0.0020692428,"about_ca_system_score_gemma":0.0017397942,"threshold_uncertainty_score":0.049970984},"labels":[],"label_agreement":null},{"id":"W4281400630","doi":"10.1109/cscwd54268.2022.9776083","title":"Transfer Learning-enabled Modelling Framework for Digital Twin","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Regional Municipality of Niagara; Brock University; National Research Council Canada","funders":"","keywords":"Computer science; Key (lock); Data modeling; Transfer of learning; Data science; Machine learning; Artificial intelligence; Software engineering","score_opus":0.04403060809417175,"score_gpt":0.25139104820857955,"score_spread":0.2073604401144078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281400630","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004389944,0.0001722188,0.99209917,0.00012296741,0.000018884155,0.000022241744,0.00006241428,0.00021791247,0.0028941873],"genre_scores_gemma":[0.7451728,0.0010158262,0.23851474,0.00012775279,0.00008657015,0.00036719034,0.00040746454,0.0001885516,0.014119003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961627,0.00009898158,0.000022812032,0.00009475148,0.00012960467,0.00003758146],"domain_scores_gemma":[0.99961025,0.00014160035,0.000057777343,0.000045787267,0.00011545901,0.00002908321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009247207,0.00068381446,0.0007203014,0.00092022505,0.0004697033,0.0012648177,0.0017026277,0.0010984103,0.0029087826],"category_scores_gemma":[0.0017622882,0.0002964578,0.001017947,0.00074885174,0.00080204004,0.001736969,0.0014546571,0.0013488849,0.00057440036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001404137,0.000018149201,0.00028793566,0.000033620017,0.000016049597,0.0000730731,0.00008190388,0.9119943,0.0008660218,0.07234291,0.00044457786,0.013827373],"study_design_scores_gemma":[0.0000010900714,0.000007746815,0.000033079454,0.0000031594525,0.0000031272273,0.00001212353,0.0000070676283,0.9865433,0.00012762555,0.012332471,0.00092589203,0.0000034334837],"about_ca_topic_score_codex":0.009652482,"about_ca_topic_score_gemma":0.0046153828,"teacher_disagreement_score":0.009652482,"about_ca_system_score_codex":0.0012836343,"about_ca_system_score_gemma":0.00117222,"threshold_uncertainty_score":0.019192636},"labels":[],"label_agreement":null},{"id":"W4281674093","doi":"10.1007/s00170-022-09078-3","title":"Production, maintenance and quality inspection planning of a hybrid manufacturing/remanufacturing system under production rate-dependent deterioration","year":2022,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remanufacturing; Production (economics); Preventive maintenance; Production line; Robustness (evolution); Hamilton–Jacobi–Bellman equation; Reliability engineering; Quality (philosophy); Production planning; Computer science; Mathematical optimization; Sensitivity (control systems); Engineering; Manufacturing engineering; Optimal control; Mathematics; Economics","score_opus":0.010802128652378314,"score_gpt":0.23590704261300569,"score_spread":0.22510491396062737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281674093","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7641828,0.0002899047,0.23156406,0.0002460839,0.000030114634,0.00009967106,0.0001295903,0.00037605478,0.0030816908],"genre_scores_gemma":[0.9935173,0.000022939998,0.0056295195,0.0000068777417,0.0000027707295,0.000013872395,0.000025042069,0.0000059480635,0.00077590515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969614,0.00006328509,0.00001322174,0.00007313227,0.00006907871,0.0000850925],"domain_scores_gemma":[0.99931586,0.00028579653,0.00017370032,0.000037388803,0.00012723963,0.000060087703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007711866,0.0005744852,0.00077035744,0.000537737,0.0006233253,0.0007907901,0.00073566014,0.0007542508,0.0010096653],"category_scores_gemma":[0.00088436704,0.00045693683,0.00055774715,0.0003813268,0.0005752446,0.00043564264,0.00053546735,0.0003912777,0.00009416133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000167893,0.00005070971,0.0015751345,0.000042678857,0.000021688533,0.00031049276,0.000054893684,0.98163027,0.007612753,0.0006290211,0.0001474861,0.0077570765],"study_design_scores_gemma":[0.0000063917405,0.00007269602,0.00097372755,0.0000014032103,0.000012187217,0.000021468177,0.000012876723,0.99788386,0.0007805068,0.00019146898,0.000039069713,0.0000042870965],"about_ca_topic_score_codex":0.013252347,"about_ca_topic_score_gemma":0.00867417,"teacher_disagreement_score":0.013252347,"about_ca_system_score_codex":0.00087069056,"about_ca_system_score_gemma":0.0009827941,"threshold_uncertainty_score":0.026350439},"labels":[],"label_agreement":null},{"id":"W4281761125","doi":"10.1080/00207543.2022.2077670","title":"A hybrid column-generation and genetic algorithm approach for solving large-scale multimission selective maintenance problems in serial <i>K</i>-out-of-<i>n</i>:<i>G</i> systems","year":2022,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Metaheuristic; Genetic algorithm; Mathematical optimization; Reliability (semiconductor); Algorithm; Computer science; Component (thermodynamics); Column generation; Nonlinear system; Scale (ratio); Integer (computer science); Sequence (biology); Mathematics","score_opus":0.03231597847503569,"score_gpt":0.2914385858612608,"score_spread":0.2591226073862251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281761125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02010636,0.00044853298,0.9760656,0.00011344087,0.000041844745,0.00007959517,0.000043446762,0.00046219162,0.0026390902],"genre_scores_gemma":[0.32287696,0.0003304469,0.67346436,0.00016225482,0.000041507545,0.00027336914,0.00016085309,0.00009043277,0.0025997153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999803,0.000055689026,0.000008699365,0.000036337417,0.00006485436,0.00003141609],"domain_scores_gemma":[0.9997384,0.00014959248,0.000030903942,0.000016616188,0.000046570065,0.00001790293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044418365,0.0009221622,0.0006947818,0.0007012613,0.00034034823,0.00048386288,0.00096152705,0.0009148503,0.0013084318],"category_scores_gemma":[0.0006839109,0.00040422397,0.0005992228,0.0008335063,0.00037613232,0.0004734094,0.00057186786,0.0007052665,0.00020678395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038501672,0.00004671555,0.00032156162,0.00006526142,0.000043471777,0.000045247274,0.000027964812,0.9352817,0.0033004726,0.0028854616,0.0006946926,0.05724893],"study_design_scores_gemma":[0.000010309141,0.00002865948,0.000068245754,0.0000035481871,0.000008189151,0.000014335584,0.0000047905396,0.9979656,0.00060756283,0.00071446836,0.0005709024,0.000003438306],"about_ca_topic_score_codex":0.0068306997,"about_ca_topic_score_gemma":0.0073655294,"teacher_disagreement_score":0.0068306997,"about_ca_system_score_codex":0.0005272741,"about_ca_system_score_gemma":0.0010984208,"threshold_uncertainty_score":0.013581872},"labels":[],"label_agreement":null},{"id":"W4283208352","doi":"10.2514/6.2022-3881","title":"A Scoring Approach to Assess Maintenance Risk for Aircraft Systems in Conceptual Design","year":2022,"lang":"en","type":"article","venue":"AIAA AVIATION 2022 Forum","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada); Concordia University","funders":"","keywords":"Maintainability; Conceptual design; Aircraft maintenance; Avionics; Component (thermodynamics); Reliability engineering; Maintenance actions; Systems engineering; Maintenance engineering; Engineering; Computer science; Risk analysis (engineering); Aeronautics; Mechanical engineering; Aerospace engineering","score_opus":0.02702326239318503,"score_gpt":0.2266006954804229,"score_spread":0.19957743308723785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283208352","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028389586,0.00010119635,0.96626186,0.00006921589,0.000021272155,0.00023089397,0.00021201179,0.0005424177,0.004171484],"genre_scores_gemma":[0.34723243,0.00012653974,0.6490914,0.000034447803,0.00003293559,0.00042082078,0.00050119584,0.00014748353,0.0024126917],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9959757,0.001277146,0.00033360824,0.0003324043,0.001928045,0.00015308493],"domain_scores_gemma":[0.99385166,0.0026220446,0.00092287385,0.0005902749,0.0018349136,0.00017811375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045423345,0.0020865293,0.0008484235,0.0059961504,0.00063595147,0.0022672887,0.0012410855,0.0007805831,0.0048809475],"category_scores_gemma":[0.012654191,0.00051030534,0.0010275934,0.0023156372,0.0007417306,0.0015693479,0.0017541919,0.0011232112,0.0008183971],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022329096,0.00034488403,0.022424862,0.00036256632,0.00024913804,0.00019377723,0.00053662225,0.3247058,0.015050044,0.03119484,0.004158483,0.60055566],"study_design_scores_gemma":[0.000022745366,0.0003648297,0.0069498476,0.000057666723,0.00006213207,0.00017573868,0.00019721387,0.97244847,0.003754047,0.013051891,0.002844047,0.000071414615],"about_ca_topic_score_codex":0.002151244,"about_ca_topic_score_gemma":0.0026839948,"teacher_disagreement_score":0.0059961504,"about_ca_system_score_codex":0.0011022923,"about_ca_system_score_gemma":0.0010455601,"threshold_uncertainty_score":0.02402246},"labels":[],"label_agreement":null},{"id":"W4283765801","doi":"10.24867/jpe-2022-01-048","title":"THE APPLICATION OF NEW INDUSTRIAL MAINTENANCE CONCEPTS - AN EASY WAY TO SAVING MONEY","year":2022,"lang":"en","type":"article","venue":"Journal of Production Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cytodiagnostics (Canada)","funders":"","keywords":"Plan (archaeology); Investment (military); Production (economics); Business; Risk analysis (engineering); Computer science; Engineering management; Management science; Industrial organization; Economics; Engineering; Political science; Macroeconomics","score_opus":0.013011324147477585,"score_gpt":0.22831014551950093,"score_spread":0.21529882137202336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283765801","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07875162,0.14796378,0.49249607,0.062232617,0.004022435,0.00024468446,0.00020140751,0.0005327333,0.21355465],"genre_scores_gemma":[0.8257941,0.04447012,0.09830513,0.0040052547,0.003865918,0.00026020184,0.00008822091,0.00013340425,0.02307767],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979583,0.0005921004,0.00009486923,0.00035978592,0.00090479845,0.000090131005],"domain_scores_gemma":[0.9969735,0.0016559873,0.000573316,0.00037747578,0.00033859466,0.000081059385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002607387,0.00080659194,0.00046092653,0.0018226556,0.00070214097,0.003075924,0.0015461072,0.0018899154,0.0042843493],"category_scores_gemma":[0.0046590376,0.00025407667,0.0005553534,0.0008894158,0.0063291616,0.007167459,0.002259669,0.0018484481,0.00073819916],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058918737,0.0000765951,0.0010122763,0.00076362176,0.00003936563,0.00014582416,0.0005282585,0.0034206433,0.0019463155,0.86481893,0.004717492,0.122471765],"study_design_scores_gemma":[0.000044600234,0.00044975663,0.0033987882,0.00083011086,0.000064318425,0.0009768162,0.0006038517,0.009233469,0.0028189374,0.7927957,0.18870766,0.000075956974],"about_ca_topic_score_codex":0.00032715485,"about_ca_topic_score_gemma":0.00034389255,"teacher_disagreement_score":0.0042843493,"about_ca_system_score_codex":0.0013073562,"about_ca_system_score_gemma":0.00065194385,"threshold_uncertainty_score":0.0143325925},"labels":[],"label_agreement":null},{"id":"W4285042929","doi":"10.1155/2022/5275843","title":"Reliability Assessment Model and Simulation of Journal Bearing of Railway Freight Cars Based on Bayesian Method under Small Sample Sizes","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Sichuan Province Science and Technology Support Program; National Natural Science Foundation of China; National Science Foundation","keywords":"Reliability (semiconductor); Weibull distribution; Bearing (navigation); Reliability engineering; Sample size determination; Bayes' theorem; Sample (material); Monte Carlo method; Bayesian probability; Engineering; Test data; Computer science; Statistics; Mathematics","score_opus":0.011925586693493195,"score_gpt":0.26349086993434645,"score_spread":0.25156528324085325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285042929","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.108896755,0.00029007715,0.8878991,0.00021187412,0.000026064254,0.0001114208,0.00015672803,0.00025263103,0.002155251],"genre_scores_gemma":[0.9255206,0.00042195836,0.069946386,0.000055741522,0.000028296286,0.0003482095,0.00039501544,0.00004594553,0.0032378798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985689,0.00054415927,0.000078425655,0.00029074406,0.00039381775,0.00012393823],"domain_scores_gemma":[0.99638855,0.0022840085,0.00035818448,0.00015847557,0.0007223564,0.00008832766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034183785,0.00063350995,0.00091025943,0.0008983556,0.00041849498,0.0007697942,0.0014305109,0.0010430987,0.0019146121],"category_scores_gemma":[0.008809243,0.00063147297,0.0011116645,0.0005166058,0.0007248572,0.00143891,0.000711748,0.0009439596,0.00022128237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006239484,0.000026666723,0.0021799593,0.00004713818,0.000021826334,0.00006259635,0.000094477124,0.98021036,0.0008821059,0.008256296,0.00019990603,0.007956227],"study_design_scores_gemma":[0.000007209593,0.000015845539,0.0003825787,0.0000031438103,0.000008102957,0.000011063215,0.0000065859304,0.99786747,0.00016291639,0.0014441502,0.00008515259,0.0000057107245],"about_ca_topic_score_codex":0.016864698,"about_ca_topic_score_gemma":0.007966727,"teacher_disagreement_score":0.016864698,"about_ca_system_score_codex":0.0008843029,"about_ca_system_score_gemma":0.0011698311,"threshold_uncertainty_score":0.033533037},"labels":[],"label_agreement":null},{"id":"W4285291624","doi":"10.5267/j.jpm.2022.4.001","title":"Aircraft total turnaround time estimation using fuzzy critical path method","year":2022,"lang":"en","type":"article","venue":"Journal of Project Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Turnaround time; Fuzzy logic; Randomness; Fuzzy set; Computer science; Critical path method; Mathematical optimization; Operations research; Mathematics; Statistics; Engineering; Artificial intelligence","score_opus":0.012617472228050394,"score_gpt":0.28349910351432445,"score_spread":0.27088163128627407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285291624","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2004927,0.00025908428,0.7971563,0.00004384465,0.000020492313,0.000081313214,0.000148941,0.00037329615,0.0014240555],"genre_scores_gemma":[0.8973101,0.000105601874,0.102020346,0.000008719096,0.000009936696,0.000057210706,0.00013762931,0.000023697658,0.00032674024],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994467,0.000116649615,0.000034755583,0.0001589271,0.0001844902,0.000058471138],"domain_scores_gemma":[0.9969303,0.0018110235,0.0004696042,0.00010583784,0.0006155116,0.00006784431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011519772,0.0007328909,0.00042888965,0.0035084763,0.00044188494,0.0006738173,0.00064201024,0.0005797131,0.0011172397],"category_scores_gemma":[0.004496023,0.000288817,0.00071955356,0.0013207054,0.00030488407,0.0008205049,0.00031388536,0.00047283032,0.00014092152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018918466,0.000058149653,0.012830801,0.00010665493,0.00006753923,0.0001438079,0.00017896823,0.8866393,0.0067637335,0.0038947866,0.00047155604,0.088655524],"study_design_scores_gemma":[0.000004854219,0.000040919662,0.0022733188,0.000008257745,0.00001135297,0.000037590657,0.00002474772,0.9941673,0.0017208157,0.0014706949,0.00022554869,0.000014501378],"about_ca_topic_score_codex":0.010859348,"about_ca_topic_score_gemma":0.004201382,"teacher_disagreement_score":0.010859348,"about_ca_system_score_codex":0.0009188639,"about_ca_system_score_gemma":0.0009207811,"threshold_uncertainty_score":0.02159226},"labels":[],"label_agreement":null},{"id":"W4285395612","doi":"10.22215/jphm.v2i1.3321","title":"Jet Engine Optimal Preventive Maintenance Scheduling Using Golden Section Search and Genetic Algorithm","year":2022,"lang":"en","type":"article","venue":"Journal of Prognostics and Health Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Chongqing Municipal Education Commission; Research Manitoba","keywords":"Preventive maintenance; Corrective maintenance; Jet engine; Predictive maintenance; Reliability engineering; Schedule; Aircraft maintenance; Optimal maintenance; Scheduling (production processes); Planned maintenance; Reliability (semiconductor); Engineering; Job shop scheduling; Computer science; Automotive engineering; Operations management; Mechanical engineering; Aeronautics; Power (physics)","score_opus":0.019293107480640403,"score_gpt":0.2665848716997915,"score_spread":0.2472917642191511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285395612","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09608523,0.0005682874,0.8984818,0.00012802191,0.000038129172,0.00007620007,0.00005739959,0.0003999422,0.0041649146],"genre_scores_gemma":[0.8251894,0.00032105087,0.17199148,0.00005838296,0.000023625975,0.00012982184,0.00014270097,0.00006262675,0.0020809416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970347,0.00008289862,0.000011984631,0.00005831434,0.00008407121,0.000059190592],"domain_scores_gemma":[0.9994979,0.00028705387,0.00008122543,0.000025244064,0.00007870904,0.000029739502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068689836,0.00072109944,0.0010550527,0.0011775745,0.00029942102,0.00060679304,0.0009772237,0.0008176795,0.0010488734],"category_scores_gemma":[0.001627824,0.00048376352,0.0008754377,0.0007952729,0.00043963254,0.00049614423,0.00042461173,0.0004945582,0.00011661859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019887872,0.000016104521,0.00025014745,0.000015121888,0.000012003321,0.000017943188,0.000012120226,0.98854476,0.0005406536,0.001558695,0.00012917334,0.00888332],"study_design_scores_gemma":[0.000005216028,0.000012533669,0.00006519971,0.0000017466491,0.0000049366845,0.000003379229,0.0000020449554,0.9992157,0.00014664758,0.0004806629,0.000060119284,0.0000017014596],"about_ca_topic_score_codex":0.01267333,"about_ca_topic_score_gemma":0.006287068,"teacher_disagreement_score":0.01267333,"about_ca_system_score_codex":0.0012056603,"about_ca_system_score_gemma":0.0017414322,"threshold_uncertainty_score":0.025199115},"labels":[],"label_agreement":null},{"id":"W4287845199","doi":"10.1016/j.ress.2022.108734","title":"Reliability optimization of dynamic k-out-of-n systems with competing failure modes","year":2022,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Redundancy (engineering); Reliability engineering; Reliability (semiconductor); Computer science; Process (computing); Mathematical optimization; Failure mode and effects analysis; Task (project management); Engineering; Mathematics","score_opus":0.0031647745411509907,"score_gpt":0.16933011769842862,"score_spread":0.16616534315727763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287845199","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6341345,0.0012515781,0.33630395,0.0015091595,0.00019011399,0.000112806076,0.0003367384,0.00032029173,0.02584091],"genre_scores_gemma":[0.9937734,0.000084163505,0.0035097874,0.000031636715,0.000019186953,0.000019478139,0.000040393068,0.000025499192,0.002496408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999419,0.00017195819,0.000021350283,0.00011593196,0.00009219598,0.00017967008],"domain_scores_gemma":[0.9982204,0.0010885402,0.00025117304,0.00006504059,0.0002532478,0.00012157327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012160683,0.0011618768,0.0014689637,0.00061308226,0.00078912155,0.0011119356,0.0012717624,0.0011833931,0.0023253805],"category_scores_gemma":[0.0038602233,0.00086499593,0.0007812447,0.00051877723,0.0011682755,0.0010878112,0.0013276828,0.000817784,0.00026521183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010238185,0.000016596809,0.00025281816,0.000023413624,0.000015914804,0.000055694152,0.000018452994,0.9956208,0.000649293,0.001569052,0.00017798232,0.0014975084],"study_design_scores_gemma":[0.00000737508,0.000036131725,0.00017279206,0.0000025921427,0.0000066210173,0.000012584906,0.000012753742,0.9985896,0.00011924192,0.0009918492,0.000044002027,0.0000044172452],"about_ca_topic_score_codex":0.00979842,"about_ca_topic_score_gemma":0.008291696,"teacher_disagreement_score":0.00979842,"about_ca_system_score_codex":0.0013742829,"about_ca_system_score_gemma":0.0011536969,"threshold_uncertainty_score":0.019482732},"labels":[],"label_agreement":null},{"id":"W4293084081","doi":"10.1080/00207721.2022.2083258","title":"An optimal control problem for the maintenance of a machine","year":2022,"lang":"en","type":"article","venue":"International Journal of Systems Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Dynamic programming; Random variable; Bellman equation; Variable (mathematics); Computer science; Process (computing); Function (biology); Control (management); Optimal control; Control variable; Mathematics; Artificial intelligence; Machine learning; Statistics","score_opus":0.00600066833445333,"score_gpt":0.23334451630525635,"score_spread":0.227343847970803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293084081","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030612286,0.0010232559,0.95856005,0.001115374,0.00015766977,0.00011865161,0.00021402819,0.00014629598,0.008052501],"genre_scores_gemma":[0.9209811,0.00084436423,0.066912346,0.0001473091,0.00013556678,0.00032604017,0.00024560496,0.00004772221,0.010359949],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988304,0.00043258505,0.000045743735,0.00032918845,0.00020586929,0.00015630972],"domain_scores_gemma":[0.998486,0.001045155,0.00017185387,0.00004731479,0.00018845065,0.000061289946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018427823,0.0013368763,0.0015308296,0.0007400577,0.00056399807,0.0019193552,0.0012402713,0.002370032,0.0037155787],"category_scores_gemma":[0.0034104234,0.00056283246,0.00089794147,0.00068782363,0.0017823669,0.0012726384,0.00087104854,0.0017244403,0.00026641614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006677102,0.000048038586,0.00024481473,0.00014755933,0.000034135028,0.000085803425,0.00006120549,0.9448366,0.0010889441,0.045688394,0.00069396297,0.0070037963],"study_design_scores_gemma":[0.000022956347,0.00004828509,0.0001062464,0.0000067139476,0.00001009783,0.000015366166,0.000009599353,0.9910256,0.00015196353,0.008097715,0.0004981025,0.0000074033887],"about_ca_topic_score_codex":0.009588618,"about_ca_topic_score_gemma":0.0048654894,"teacher_disagreement_score":0.009588618,"about_ca_system_score_codex":0.0019453474,"about_ca_system_score_gemma":0.0022865073,"threshold_uncertainty_score":0.019065619},"labels":[],"label_agreement":null},{"id":"W4296426248","doi":"10.1109/rams51457.2022.9894014","title":"General Transitions Probabilities and Maintenance Inspection Interval Optimization of a Weighted k-out-of-n System","year":2022,"lang":"en","type":"article","venue":"2022 Annual Reliability and Maintainability Symposium (RAMS)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Reliability engineering; Production (economics); Interval (graph theory); Production line; Maintenance engineering; Preventive maintenance; Planned maintenance; Computer science; Line (geometry); Operational maintenance; Operations management; Risk analysis (engineering); Engineering; Business; Mathematics; Computerized maintenance management system; Mechanical engineering","score_opus":0.003880012021495324,"score_gpt":0.1904228210579949,"score_spread":0.18654280903649959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296426248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07760618,0.00066203,0.9128843,0.00045496837,0.00008028731,0.00013043008,0.00045294402,0.00044636146,0.00728259],"genre_scores_gemma":[0.92272335,0.0005932786,0.06316726,0.000105715095,0.000101244324,0.00024403221,0.00058079354,0.00017156242,0.012312813],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989807,0.0002504773,0.000037660862,0.00031146605,0.00018472664,0.00023493994],"domain_scores_gemma":[0.9978381,0.0013137237,0.00032862063,0.000076630145,0.0003311387,0.000111770445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015458417,0.0016143849,0.0018342293,0.0011711083,0.00068296585,0.0013060725,0.0024467923,0.0014807908,0.0073634936],"category_scores_gemma":[0.005387702,0.00085937744,0.0010982293,0.0013993118,0.0011333859,0.0015904367,0.0009615435,0.0012311871,0.00054271857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008312189,0.000028388808,0.0005039639,0.000069806265,0.00003874322,0.000077067874,0.000038343518,0.9851119,0.00075515325,0.007477376,0.0005408198,0.0052753203],"study_design_scores_gemma":[0.0000073099923,0.000019033852,0.00022712609,0.000004142955,0.000013551089,0.0000172923,0.0000049748846,0.9958819,0.00010713013,0.0035937254,0.000118820986,0.0000049919367],"about_ca_topic_score_codex":0.011762291,"about_ca_topic_score_gemma":0.007696882,"teacher_disagreement_score":0.011762291,"about_ca_system_score_codex":0.0017777712,"about_ca_system_score_gemma":0.0015574879,"threshold_uncertainty_score":0.024633348},"labels":[],"label_agreement":null},{"id":"W4296474017","doi":"10.1109/rams51457.2022.9894004","title":"Dynamic k-out-of-n System Reliability under Uncertain Conditions","year":2022,"lang":"en","type":"article","venue":"2022 Annual Reliability and Maintainability Symposium (RAMS)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Computer science; Engineering; Physics; Thermodynamics","score_opus":0.004461345621007182,"score_gpt":0.22153586931506808,"score_spread":0.21707452369406088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296474017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3752543,0.0012601056,0.6100501,0.0019135475,0.00023451087,0.00017543025,0.0007695892,0.000554617,0.009787798],"genre_scores_gemma":[0.99395084,0.0001616688,0.0041806377,0.000047713926,0.00002614535,0.000047171896,0.00008420567,0.000023544317,0.0014780128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986456,0.00029537996,0.00007924957,0.00040278718,0.00026223852,0.00031474233],"domain_scores_gemma":[0.99389064,0.004021529,0.0008940482,0.00022622685,0.0008023564,0.00016513618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025707616,0.0009784069,0.0020284902,0.00072483544,0.0010406551,0.0013189543,0.0016877138,0.0017006601,0.0020977387],"category_scores_gemma":[0.010221208,0.00078905385,0.0007691313,0.00062507053,0.001323642,0.0021124682,0.0014488568,0.0014618302,0.00025856172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013009245,0.000016589998,0.0012067618,0.00009369029,0.000042030963,0.00027640403,0.00009345684,0.99026734,0.0009724941,0.0033864453,0.00038276977,0.0031318946],"study_design_scores_gemma":[0.00000630356,0.000028655715,0.000705556,0.00000772962,0.000015793285,0.00007396156,0.000027936674,0.99597484,0.00019699677,0.0028343631,0.000116412695,0.000011372288],"about_ca_topic_score_codex":0.011348134,"about_ca_topic_score_gemma":0.00884496,"teacher_disagreement_score":0.011348134,"about_ca_system_score_codex":0.0014557282,"about_ca_system_score_gemma":0.0008917672,"threshold_uncertainty_score":0.022564173},"labels":[],"label_agreement":null},{"id":"W4297998800","doi":"10.1287/mnsc.2022.4547","title":"Analytical Solution to a Partially Observable Machine Maintenance Problem with Obvious Failures","year":2022,"lang":"en","type":"article","venue":"Management Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Unobservable; Observable; Mathematical optimization; Partially observable Markov decision process; Computer science; Markov decision process; Preventive maintenance; Markov chain; Markov process; Mathematics; Markov model; Reliability engineering; Econometrics; Engineering; Machine learning; Statistics","score_opus":0.0072911417287016824,"score_gpt":0.19741568181712696,"score_spread":0.19012454008842528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297998800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10272721,0.0016926655,0.85716796,0.0028899482,0.00014610229,0.00013685919,0.00088935083,0.0002929915,0.03405689],"genre_scores_gemma":[0.9151886,0.0011371773,0.06941959,0.00014130566,0.00008769518,0.00025126373,0.00033323342,0.00006757166,0.013373431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99963856,0.0001337502,0.000012620745,0.00006393452,0.00007170098,0.00007936121],"domain_scores_gemma":[0.9975689,0.0017741447,0.0002991184,0.000053541993,0.00020400729,0.000100292775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010645984,0.0008107391,0.0009919108,0.0006933077,0.0004537299,0.001064947,0.0011001198,0.0016063352,0.00533621],"category_scores_gemma":[0.0050321487,0.0005842346,0.0006582701,0.0006208476,0.00097074895,0.0008854522,0.0008617058,0.0009403456,0.00030112523],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036242764,0.000027007001,0.00031734002,0.000081412574,0.000016228936,0.00008960305,0.000043229396,0.9412735,0.0003958006,0.052822106,0.0013379543,0.003559484],"study_design_scores_gemma":[0.000012992311,0.00001607919,0.00014234909,0.0000132790965,0.0000073091855,0.000013545757,0.000018132743,0.98044246,0.00007214231,0.018727545,0.0005291698,0.000004914414],"about_ca_topic_score_codex":0.006925889,"about_ca_topic_score_gemma":0.0067173424,"teacher_disagreement_score":0.006925889,"about_ca_system_score_codex":0.0016335291,"about_ca_system_score_gemma":0.0021876518,"threshold_uncertainty_score":0.017851353},"labels":[],"label_agreement":null},{"id":"W4306246669","doi":"","title":"Industrial system example modeling for the assessment of maintenance strategies","year":2022,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Computer science; Systems engineering; Reliability engineering; Engineering","score_opus":0.03456471551125331,"score_gpt":0.24561930002661939,"score_spread":0.21105458451536607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306246669","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30202702,0.00076194166,0.6501915,0.00063842325,0.00008017858,0.00017270609,0.0018057565,0.0014449971,0.04287751],"genre_scores_gemma":[0.93686265,0.00026543057,0.055085406,0.000031806212,0.000018268845,0.0001217999,0.0004288003,0.00012474517,0.00706104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999782,0.00011007611,0.000010222905,0.000026827229,0.0000474624,0.000023398366],"domain_scores_gemma":[0.9992461,0.0005447943,0.000036353493,0.000049660717,0.00010867379,0.000014475763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041645757,0.000545391,0.0006126845,0.0004172792,0.00033151623,0.000649941,0.0008024719,0.0011155604,0.007069038],"category_scores_gemma":[0.0016236255,0.00028262276,0.0006683446,0.0004731829,0.0001818001,0.0004943607,0.0003449704,0.0006897979,0.0004427607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025328907,0.000017627435,0.00022515336,0.00003706843,0.000007818414,0.00004089678,0.000020140958,0.9932846,0.0005062932,0.0018652417,0.0003235638,0.0036462953],"study_design_scores_gemma":[0.0000036460121,0.000010592004,0.000100331636,0.0000033274212,0.0000053251715,0.000007272124,0.0000050075573,0.99868375,0.00017474116,0.0006340126,0.00037046295,0.0000014044102],"about_ca_topic_score_codex":0.013081086,"about_ca_topic_score_gemma":0.011863022,"teacher_disagreement_score":0.013081086,"about_ca_system_score_codex":0.0005729696,"about_ca_system_score_gemma":0.0005409348,"threshold_uncertainty_score":0.026009858},"labels":[],"label_agreement":null},{"id":"W4306409156","doi":"10.1049/gtd2.12625","title":"An exact MILP model for joint switch placement and preventive maintenance scheduling considering incentive regulation","year":2022,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; Université Laval","funders":"","keywords":"Preventive maintenance; Incentive; Scheduling (production processes); Computer science; Joint (building); Mathematical optimization; Operations research; Business; Reliability engineering; Engineering; Microeconomics; Economics; Mathematics; Civil engineering","score_opus":0.019790090383515955,"score_gpt":0.2329326396629384,"score_spread":0.21314254927942244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306409156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046042465,0.00044757753,0.91682863,0.00071482215,0.00011643816,0.00016240242,0.0006381685,0.00033340612,0.034716085],"genre_scores_gemma":[0.93097615,0.0003700826,0.05252899,0.00012167509,0.00004961333,0.0003671063,0.00027631063,0.000063313746,0.015246841],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962616,0.00014248921,0.000010829288,0.000047577985,0.00008338562,0.000089472676],"domain_scores_gemma":[0.9994804,0.00030655228,0.000065907436,0.000020463585,0.000091399284,0.000035323123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008063337,0.00080619653,0.0009231177,0.0005820922,0.00053373404,0.0015880859,0.0010434894,0.0015727137,0.006229319],"category_scores_gemma":[0.0012867992,0.0007219531,0.0007203024,0.0008122308,0.000668051,0.0007157895,0.0007527708,0.0013497736,0.0004970893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010315644,0.000008598511,0.000057371784,0.000012449085,0.00000417478,0.000027560221,0.0000063653415,0.9957129,0.000106483225,0.002992303,0.00018504339,0.00087642984],"study_design_scores_gemma":[0.000003040237,0.000005630133,0.000018782293,0.0000022354516,0.0000021576363,0.0000020707935,0.000004315344,0.9991393,0.000032688713,0.0006490681,0.00013946804,0.0000011718073],"about_ca_topic_score_codex":0.0148842465,"about_ca_topic_score_gemma":0.011228313,"teacher_disagreement_score":0.0148842465,"about_ca_system_score_codex":0.0013266192,"about_ca_system_score_gemma":0.0017812593,"threshold_uncertainty_score":0.029595196},"labels":[],"label_agreement":null},{"id":"W4308588613","doi":"10.1177/1748006x221130540","title":"Efficient reliability computation of consecutive-k-out-of-n: F systems with shared components","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Markov chain; Reliability (semiconductor); Disjoint sets; Computation; Component (thermodynamics); Computer science; Markov model; Reliability theory; Markov process; Algorithm; Function (biology); State space; Reliability engineering; Theoretical computer science; Mathematics; Mathematical optimization; Applied mathematics; Discrete mathematics; Failure rate; Statistics; Engineering","score_opus":0.0085190713278502,"score_gpt":0.19835309440730436,"score_spread":0.18983402307945416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308588613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058218375,0.00018059021,0.9393834,0.00006625845,0.000013525253,0.000030696603,0.000048705937,0.00018464444,0.0018738507],"genre_scores_gemma":[0.87896514,0.00020999064,0.11850204,0.000022744465,0.000020475769,0.0000691743,0.0001276631,0.00009233506,0.0019904354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976724,0.000064332264,0.000016049065,0.000045100693,0.000062415376,0.000044798177],"domain_scores_gemma":[0.998429,0.0010608038,0.00016689453,0.00008202096,0.00021855869,0.000042701515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083014707,0.0006260077,0.0008468799,0.00064621435,0.00037889482,0.0006473304,0.0008608252,0.0005892911,0.0022732855],"category_scores_gemma":[0.0037766062,0.0003528686,0.0005854225,0.00049518014,0.0005642844,0.0009866698,0.00057402987,0.0005615205,0.0002537414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003121519,0.000013951964,0.0007051744,0.000048034286,0.000017088862,0.00009991671,0.00006165531,0.9703926,0.0017622192,0.012379733,0.00022007214,0.014268431],"study_design_scores_gemma":[0.000001096715,0.000005355741,0.000060784856,0.0000016965693,0.0000022218107,0.000013136699,0.0000043767513,0.9973724,0.00030624404,0.0021764366,0.00005440458,0.0000018570245],"about_ca_topic_score_codex":0.0063341176,"about_ca_topic_score_gemma":0.006941997,"teacher_disagreement_score":0.0063341176,"about_ca_system_score_codex":0.000754374,"about_ca_system_score_gemma":0.0011912476,"threshold_uncertainty_score":0.012594521},"labels":[],"label_agreement":null},{"id":"W4311238859","doi":"10.18280/mmep.090522","title":"Validating the Reliability Simulation Using Bohlamp Circuit with Accelerated Life Test Method","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Computer science; Reliability engineering; Series (stratigraphy); Fidelity; Similarity (geometry); Test (biology); Artificial intelligence; Engineering","score_opus":0.06009212696997052,"score_gpt":0.24691374496889726,"score_spread":0.18682161799892674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311238859","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16650338,0.0003035655,0.8093557,0.00028971283,0.000115432194,0.00020979693,0.0004682931,0.0019984154,0.020755745],"genre_scores_gemma":[0.88317406,0.00021608388,0.11132123,0.000031184383,0.000017064738,0.00028716968,0.00030187238,0.000116567164,0.0045348057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996443,0.00009708454,0.000021552862,0.000041849704,0.00016695146,0.000028301727],"domain_scores_gemma":[0.999231,0.00034915382,0.00006755964,0.00009668137,0.0002382417,0.000017420256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005777158,0.00038390065,0.00026715736,0.00052598224,0.00033818925,0.0003627522,0.000766023,0.0005985764,0.0051724645],"category_scores_gemma":[0.0016308003,0.0002054229,0.0004808861,0.00031665692,0.00024600312,0.00051653833,0.00034797986,0.0004776605,0.0004466167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121707184,0.00010741451,0.0059838714,0.0003868669,0.00003971113,0.00022763628,0.000333275,0.89573765,0.027692003,0.014511224,0.002312132,0.052546576],"study_design_scores_gemma":[0.000013174044,0.00008313335,0.00080985605,0.000015626538,0.000009794387,0.000049475064,0.000020660322,0.9888454,0.0063265758,0.0012734842,0.0025426971,0.000010146431],"about_ca_topic_score_codex":0.0034472405,"about_ca_topic_score_gemma":0.0017709067,"teacher_disagreement_score":0.0051724645,"about_ca_system_score_codex":0.00029021036,"about_ca_system_score_gemma":0.00059120386,"threshold_uncertainty_score":0.017303586},"labels":[],"label_agreement":null},{"id":"W4312221932","doi":"10.1016/j.cie.2022.108930","title":"Optimal replacement in a proportional hazards model with cumulative and dependent risks","year":2022,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"Hubei Polytechnic University; Hubei Provincial Department of Education","keywords":"Curse of dimensionality; Mathematical optimization; Markov decision process; Limit (mathematics); Markov process; Function (biology); Minor (academic); Computer science; Hazard; Mathematics; Statistics","score_opus":0.026609316968709017,"score_gpt":0.22431699842169503,"score_spread":0.197707681452986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312221932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08806916,0.0011681981,0.9029027,0.0023782225,0.00020398611,0.00012719422,0.00038834094,0.0003931313,0.0043691043],"genre_scores_gemma":[0.9264012,0.000996032,0.0500833,0.00031782157,0.00021628638,0.00027547244,0.00038608292,0.00015237369,0.021171503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966558,0.001848291,0.00012495283,0.00049811474,0.0003581487,0.0005147111],"domain_scores_gemma":[0.9886052,0.009538299,0.000619382,0.00044006333,0.00045988458,0.00033709695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066827554,0.0013174791,0.0032278358,0.0013375138,0.0006543423,0.0023001835,0.0039424174,0.0031227772,0.004289617],"category_scores_gemma":[0.018228205,0.0018872819,0.0017355516,0.0012347472,0.0020819036,0.0025560267,0.0017099027,0.0024490047,0.0006059264],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023025538,0.00006300802,0.00068417756,0.00007299389,0.00005802844,0.000102745464,0.000050572387,0.9668813,0.00026072626,0.024675662,0.000660349,0.006260107],"study_design_scores_gemma":[0.000046561003,0.00006269455,0.0002159803,0.000010472016,0.000044570774,0.00004349874,0.000017514143,0.97909445,0.00010668567,0.02004218,0.0003015216,0.000013824352],"about_ca_topic_score_codex":0.009002803,"about_ca_topic_score_gemma":0.003912911,"teacher_disagreement_score":0.009002803,"about_ca_system_score_codex":0.0016894926,"about_ca_system_score_gemma":0.0026078187,"threshold_uncertainty_score":0.035342216},"labels":[],"label_agreement":null},{"id":"W4312357394","doi":"10.1016/j.ifacol.2022.09.189","title":"Reliability Assessment of an Electrical Network with Digital Twins","year":2022,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Computer science; Task (project management); Electric power system; Power (physics); Engineering; Systems engineering","score_opus":0.0045252812969137305,"score_gpt":0.2148100437553863,"score_spread":0.21028476245847255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312357394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35022596,0.0064144437,0.62224966,0.00023461157,0.0001125098,0.00020792232,0.0003610391,0.0002359491,0.019957973],"genre_scores_gemma":[0.92698646,0.0039548227,0.066668585,0.000029780978,0.000039290993,0.000072458075,0.00020208197,0.000015404044,0.0020310462],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991008,0.00040585254,0.00007546154,0.000095789605,0.00029128953,0.000030748542],"domain_scores_gemma":[0.9987822,0.0005433218,0.0002353507,0.00006127551,0.00035573548,0.000022029813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012353085,0.0004554446,0.00038792312,0.0028473998,0.00024427497,0.000869483,0.00044101427,0.00030420613,0.00086474995],"category_scores_gemma":[0.00469181,0.0001712757,0.0003579983,0.0017803786,0.00029742828,0.0015723282,0.00058031647,0.00022488544,0.00016625917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005194296,0.0000923393,0.042723257,0.0026305898,0.00040030532,0.0016045033,0.0007445718,0.23778634,0.037416097,0.051168364,0.002294554,0.6226197],"study_design_scores_gemma":[0.000030641124,0.0011997916,0.02952733,0.00064723066,0.0008780308,0.0019027115,0.0012529234,0.8884546,0.026754305,0.028356388,0.02090547,0.000090620924],"about_ca_topic_score_codex":0.0011131464,"about_ca_topic_score_gemma":0.0013622333,"teacher_disagreement_score":0.0028473998,"about_ca_system_score_codex":0.0004098553,"about_ca_system_score_gemma":0.00030950172,"threshold_uncertainty_score":0.0065330267},"labels":[],"label_agreement":null},{"id":"W4312686432","doi":"10.1016/j.ifacol.2022.09.613","title":"A Remaining Useful Life Model for Optimizing Maintenance cost and Spare-parts replacement of Production Systems in the Context of Sustainability","year":2022,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Spare part; Reliability engineering; Predictive maintenance; Residual; Computer science; Preventive maintenance; Context (archaeology); Scheduling (production processes); Reliability (semiconductor); Production (economics); Maintenance engineering; Operations research; Mathematical optimization; Engineering; Power (physics); Operations management; Algorithm; Mathematics","score_opus":0.020726036187370723,"score_gpt":0.23947975212925518,"score_spread":0.21875371594188445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312686432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13275851,0.0011772729,0.85666704,0.00048359163,0.000047887122,0.00007395248,0.00042099517,0.00044195002,0.007928833],"genre_scores_gemma":[0.96957165,0.00035247966,0.025693446,0.000043564523,0.00002083486,0.00009924317,0.00023398103,0.000040907442,0.0039439234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971694,0.000077719444,0.000011825842,0.000060392693,0.00008166885,0.00005149046],"domain_scores_gemma":[0.9995809,0.00023146917,0.0000631727,0.00002130976,0.00007802417,0.000025058987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006683844,0.0007982232,0.00077943294,0.00077381247,0.0002915479,0.00084968156,0.001359151,0.0010862341,0.0017627683],"category_scores_gemma":[0.0014989539,0.0003017874,0.00074543094,0.0006889642,0.00037820535,0.00091853313,0.0004302461,0.0008422518,0.00025723517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013526883,0.000016750817,0.0002690967,0.000018166114,0.0000069039884,0.000029055875,0.000012616248,0.99242824,0.0004832752,0.0022015597,0.00015778908,0.0043629725],"study_design_scores_gemma":[0.0000012289348,0.000009960701,0.00008621046,0.000001930707,0.0000039940946,0.000006737955,0.0000029554792,0.9990496,0.0000971705,0.0006114124,0.00012695379,0.0000018363085],"about_ca_topic_score_codex":0.0073179617,"about_ca_topic_score_gemma":0.005488423,"teacher_disagreement_score":0.0073179617,"about_ca_system_score_codex":0.001290686,"about_ca_system_score_gemma":0.001243037,"threshold_uncertainty_score":0.0145507455},"labels":[],"label_agreement":null},{"id":"W4312686935","doi":"10.1016/j.ifacol.2022.09.614","title":"A new integrated strategy for optimizing the maintenance cost of Production systems using reliability importance measures","year":2022,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Spare part; Reliability engineering; Component (thermodynamics); Reliability (semiconductor); Ranking (information retrieval); Computer science; Selection (genetic algorithm); Production (economics); Preventive maintenance; Engineering; Operations management; Artificial intelligence","score_opus":0.03169700221743066,"score_gpt":0.24737786244835283,"score_spread":0.21568086023092217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312686935","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02232058,0.00019629535,0.9747473,0.000060732382,0.000017909946,0.00006818902,0.00002209144,0.00012358358,0.0024433488],"genre_scores_gemma":[0.7301018,0.00022194623,0.26715302,0.000050148392,0.000048333415,0.0001796289,0.000080398546,0.00005788063,0.0021068288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945885,0.00009886269,0.00002526285,0.00010550731,0.0002541733,0.000057409794],"domain_scores_gemma":[0.9996086,0.00014906115,0.00007086088,0.000046357363,0.00009873899,0.00002637381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000785136,0.001017899,0.00078662107,0.0011663096,0.00028987302,0.0008293233,0.001179443,0.000555915,0.0011454015],"category_scores_gemma":[0.0013606987,0.00031386412,0.00052574393,0.0006233871,0.00034693212,0.00097484456,0.00067546905,0.00045686384,0.00013268719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006968985,0.0002457164,0.0015943778,0.00013859152,0.00011045865,0.00008369388,0.0000906581,0.77933365,0.013530231,0.023360582,0.0011280028,0.1803143],"study_design_scores_gemma":[0.000010316995,0.0001418961,0.0005476301,0.00000706796,0.00003422835,0.000038964867,0.000009651768,0.99360716,0.0016958027,0.0029316272,0.0009667448,0.00000892052],"about_ca_topic_score_codex":0.0020810815,"about_ca_topic_score_gemma":0.0021769146,"teacher_disagreement_score":0.0020810815,"about_ca_system_score_codex":0.0008231249,"about_ca_system_score_gemma":0.00097140396,"threshold_uncertainty_score":0.0059722066},"labels":[],"label_agreement":null},{"id":"W4312942651","doi":"10.1016/j.ifacol.2022.09.555","title":"Selective maintenance optimization: a condensed critical review and future research directions","year":2022,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Component (thermodynamics); Computer science; Heuristic; Set (abstract data type); Field (mathematics); Operations research; Limited resources; Reliability engineering; Management science; Systems engineering; Risk analysis (engineering); Industrial engineering; Engineering; Artificial intelligence; Mathematics","score_opus":0.02122800093463845,"score_gpt":0.2927288206122783,"score_spread":0.27150081967763984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312942651","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002392021,0.99492776,0.001628808,0.0012315388,0.0005343888,0.0000065042022,0.000023856908,0.000015613725,0.0013923877],"genre_scores_gemma":[0.0024037743,0.99419564,0.0015945006,0.0004262529,0.00092096627,0.000011924947,0.00004577957,0.000007773236,0.000393349],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951255,0.00011662259,0.000062501254,0.00012805371,0.000130406,0.000049909242],"domain_scores_gemma":[0.9966628,0.002249816,0.00019332918,0.00010246877,0.00069301156,0.000098611505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018267601,0.0011502994,0.0014992551,0.0026903206,0.00050198106,0.0021834665,0.0015091124,0.0015797324,0.0052023227],"category_scores_gemma":[0.004154691,0.0005058846,0.00095460296,0.004323397,0.00083908404,0.0035214317,0.00086493,0.002036616,0.0015714467],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012480376,0.00018835858,0.0006643894,0.028634716,0.00017859488,0.000170913,0.00018921179,0.0064490484,0.0008421772,0.043968216,0.059012752,0.8595769],"study_design_scores_gemma":[0.000029651692,0.0003097383,0.0016900456,0.01694037,0.00035079307,0.0007842117,0.0004795146,0.0045905425,0.0007169998,0.038042787,0.9359646,0.00010064787],"about_ca_topic_score_codex":0.0020685447,"about_ca_topic_score_gemma":0.0017395811,"teacher_disagreement_score":0.0052023227,"about_ca_system_score_codex":0.0012175263,"about_ca_system_score_gemma":0.002441227,"threshold_uncertainty_score":0.017403483},"labels":[],"label_agreement":null},{"id":"W4312999026","doi":"10.1016/j.ifacol.2022.09.554","title":"A Novel Predictive Selective Maintenance Strategy Using Deep Learning and Mathematical Programming","year":2022,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prognostics; Predictive maintenance; Component (thermodynamics); Reliability (semiconductor); Computer science; Process (computing); Modular design; Reliability engineering; Maintenance actions; Machine learning; Engineering; Data mining","score_opus":0.009644971950502216,"score_gpt":0.2270474517347121,"score_spread":0.2174024797842099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312999026","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04304976,0.0007702818,0.9511209,0.00046514673,0.000064524924,0.000047955196,0.00014481948,0.0010190245,0.0033175508],"genre_scores_gemma":[0.8488924,0.00030159543,0.14591026,0.00041534047,0.00006382161,0.00015604675,0.00038802676,0.00011104867,0.0037614873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997439,0.000040165967,0.000012804482,0.00007050852,0.000076338,0.00005632463],"domain_scores_gemma":[0.9993536,0.00033366593,0.00008864418,0.0000384746,0.00013810872,0.00004758543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005901052,0.000944242,0.0009777653,0.0005518589,0.0003022873,0.00073672127,0.0016961391,0.00095857074,0.0015671119],"category_scores_gemma":[0.0014845782,0.00043599802,0.00059673167,0.00046484006,0.000498538,0.00094714016,0.0009256084,0.00118861,0.00021683105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005474689,0.000064412015,0.0008934398,0.000055175224,0.000030244228,0.00007260446,0.000030407518,0.92664194,0.0012474145,0.0031175727,0.0019651863,0.0658268],"study_design_scores_gemma":[0.0000027742171,0.000008781628,0.000033045166,0.000001789397,0.0000028445002,0.0000047090184,0.0000014100849,0.99908805,0.00014324531,0.0006149126,0.00009722392,0.0000011364601],"about_ca_topic_score_codex":0.008931038,"about_ca_topic_score_gemma":0.008060192,"teacher_disagreement_score":0.008931038,"about_ca_system_score_codex":0.00089668465,"about_ca_system_score_gemma":0.0013530428,"threshold_uncertainty_score":0.017758131},"labels":[],"label_agreement":null},{"id":"W4317564516","doi":"10.1109/ispce54918.2022.10017206","title":"Component Interchangeability for Compliance - Considerations, Challenges and Improvements","year":2022,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Intertek (Canada)","funders":"","keywords":"Interchangeability; Documentation; Component (thermodynamics); Risk analysis (engineering); Context (archaeology); Leverage (statistics); Computer science; Systems engineering; Engineering; Rework; Safety standards; Reliability engineering; Process management; Business","score_opus":0.06537358559369455,"score_gpt":0.2523769830022023,"score_spread":0.18700339740850774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317564516","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017364968,0.022257345,0.78857726,0.059772637,0.0017802555,0.0008816056,0.0005505473,0.0032770068,0.10553832],"genre_scores_gemma":[0.17930433,0.021595264,0.76331216,0.007278481,0.0010546823,0.0011121164,0.0019671784,0.0016712103,0.022704614],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.91517574,0.031556662,0.007559302,0.0049234303,0.038943954,0.00184093],"domain_scores_gemma":[0.89517444,0.030629173,0.006568344,0.02940435,0.036695514,0.0015281772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07033228,0.0014631059,0.0011254578,0.004622582,0.0019496592,0.013059982,0.0053675305,0.0038782104,0.009898474],"category_scores_gemma":[0.0964104,0.0009846799,0.001535899,0.0060317293,0.00441003,0.020736717,0.0055393814,0.00569528,0.0041443557],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006712685,0.0002397114,0.0021990675,0.0015616537,0.000054065975,0.00017543165,0.0018457792,0.0050451197,0.0028181034,0.5044043,0.025821296,0.4557682],"study_design_scores_gemma":[0.00004671632,0.00038332545,0.0038768435,0.0064391843,0.00008135925,0.001107468,0.0030083498,0.022054633,0.00824109,0.30873904,0.64589214,0.0001298526],"about_ca_topic_score_codex":0.004491232,"about_ca_topic_score_gemma":0.0025456632,"teacher_disagreement_score":0.07033228,"about_ca_system_score_codex":0.004919674,"about_ca_system_score_gemma":0.013937042,"threshold_uncertainty_score":0.37195712},"labels":[],"label_agreement":null},{"id":"W4317932088","doi":"10.3390/app13031541","title":"Warranty Cost Analysis for Multi-State Products Protected by Lemon Laws","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Warranty; Product (mathematics); Quality (philosophy); Reliability engineering; Actuarial science; Duration (music); Business; Operations research; Risk analysis (engineering); Computer science; Engineering; Law; Mathematics","score_opus":0.03769049162840287,"score_gpt":0.2616556071385514,"score_spread":0.22396511551014853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317932088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4079813,0.0043397164,0.5656866,0.0010840753,0.00011851176,0.00017685149,0.0005255711,0.00039336662,0.019693937],"genre_scores_gemma":[0.9855202,0.00074472657,0.009082872,0.000037086505,0.000034727025,0.000061984065,0.00017688527,0.00003876116,0.004302705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989942,0.0002990856,0.000052733925,0.00012898263,0.00028386127,0.00024110322],"domain_scores_gemma":[0.99529135,0.003124025,0.0006959941,0.00020000024,0.000540605,0.00014810375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031943077,0.0012003611,0.0012010741,0.0016678117,0.00053909386,0.0014634423,0.001684075,0.0016116227,0.0033064599],"category_scores_gemma":[0.008622849,0.0007040476,0.0014865877,0.0008965612,0.0010715286,0.0022205971,0.0007124233,0.00123832,0.00022866359],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011365451,0.000044341712,0.00086669275,0.000081986815,0.000021439102,0.00021495187,0.000055687604,0.9599237,0.00075033086,0.03253683,0.0006155565,0.0047748685],"study_design_scores_gemma":[0.0000030766607,0.000024694955,0.00034481508,0.000006877143,0.000010687293,0.000026224945,0.000012257356,0.9969729,0.00010870612,0.0023287565,0.00015478481,0.0000062120985],"about_ca_topic_score_codex":0.01131904,"about_ca_topic_score_gemma":0.0043723946,"teacher_disagreement_score":0.01131904,"about_ca_system_score_codex":0.0033478707,"about_ca_system_score_gemma":0.0013115222,"threshold_uncertainty_score":0.024290621},"labels":[],"label_agreement":null},{"id":"W4318069113","doi":"","title":"Optimisation des périodicités de maintenance préventive pour l'appareillage électrique","year":2022,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Computer science","score_opus":0.010822619430829444,"score_gpt":0.214094173104553,"score_spread":0.20327155367372357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318069113","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7178737,0.0017642417,0.26409033,0.00046781515,0.000098235716,0.00008084612,0.00019022127,0.0005507231,0.014883942],"genre_scores_gemma":[0.9814456,0.00020739269,0.014638243,0.00002133292,0.000016812824,0.000046644647,0.00005388463,0.000046842306,0.0035230909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998331,0.000050065668,0.0000059787726,0.000029923585,0.00004603336,0.000034939036],"domain_scores_gemma":[0.9993724,0.00043419795,0.00005864941,0.00002866014,0.00007321785,0.000032821245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004990651,0.00084358954,0.0006885677,0.0006542375,0.0002602961,0.00064509903,0.00041767993,0.0010923009,0.0044342703],"category_scores_gemma":[0.0014771185,0.00033123788,0.00045194392,0.00037430058,0.00023025674,0.0003264277,0.00027768128,0.0004942227,0.00029886432],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002808767,0.00009464111,0.0005349414,0.000113656875,0.00003721901,0.000043163564,0.000026518504,0.94704604,0.013361934,0.0006529695,0.00032321128,0.037484918],"study_design_scores_gemma":[0.00006097128,0.00047554064,0.0027485907,0.000019874906,0.000041965333,0.000040408966,0.000028193439,0.98863447,0.0066461335,0.0007043508,0.0005872194,0.000012307442],"about_ca_topic_score_codex":0.0023950646,"about_ca_topic_score_gemma":0.002234003,"teacher_disagreement_score":0.0044342703,"about_ca_system_score_codex":0.000500898,"about_ca_system_score_gemma":0.00048128827,"threshold_uncertainty_score":0.014834046},"labels":[],"label_agreement":null},{"id":"W4318426825","doi":"10.2139/ssrn.4341529","title":"Strategies for Improving Ship Overhaul Project Planning","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Solver; Minification; Job shop scheduling; Computer science; Benchmark (surveying); Plan (archaeology); Operations research; Mathematical optimization; Resource (disambiguation); Work (physics); Graph; Scheduling (production processes); Linear programming; Industrial engineering; Engineering; Routing (electronic design automation); Algorithm; Mathematics; Theoretical computer science","score_opus":0.014092101362850482,"score_gpt":0.2537981650374333,"score_spread":0.2397060636745828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318426825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05505844,0.0016573389,0.9000864,0.0029772783,0.00017816015,0.00037454447,0.0002639319,0.00073474494,0.03866915],"genre_scores_gemma":[0.7207607,0.0011999195,0.2686083,0.00028271388,0.000073811185,0.00024985365,0.00024036515,0.00010135121,0.008482998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99930227,0.00033475316,0.000032014203,0.00009420506,0.00015608736,0.00008064659],"domain_scores_gemma":[0.99878365,0.0005031316,0.00016885047,0.00009860708,0.00036570895,0.00008001118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015862527,0.0010231526,0.00071667717,0.0013955694,0.0005156976,0.0022021553,0.0012407068,0.0009110312,0.0071511734],"category_scores_gemma":[0.0046783173,0.00043232826,0.0003641345,0.0014530843,0.00032633092,0.001967607,0.0009972678,0.000880944,0.0005979876],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010137756,0.00016329483,0.0016966064,0.00024035542,0.000068622576,0.00010147729,0.0002308792,0.7275609,0.003925659,0.041414116,0.005736692,0.21876009],"study_design_scores_gemma":[0.000047394005,0.00018670707,0.0011426924,0.00008084081,0.000058365047,0.000036125122,0.00049033196,0.9477461,0.001766866,0.04007102,0.008352614,0.000020891572],"about_ca_topic_score_codex":0.008752207,"about_ca_topic_score_gemma":0.019284006,"teacher_disagreement_score":0.008752207,"about_ca_system_score_codex":0.0011434028,"about_ca_system_score_gemma":0.0024940348,"threshold_uncertainty_score":0.023923099},"labels":[],"label_agreement":null},{"id":"W4319793250","doi":"10.1016/j.ress.2023.109131","title":"Reliability of three-dimensional consecutive k-type systems","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National University's Basic Research Foundation of China; National Natural Science Foundation of China","keywords":"Markov chain; Reliability (semiconductor); Type (biology); Algorithm; Mathematics; Linear system; State space; Task (project management); Computer science; Discrete mathematics; Applied mathematics; Statistics; Engineering; Mathematical analysis","score_opus":0.007508915798313607,"score_gpt":0.19137319753708892,"score_spread":0.1838642817387753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319793250","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93063664,0.00023780913,0.062376954,0.00014908875,0.000042554282,0.000018867704,0.00017693013,0.00014778992,0.006213196],"genre_scores_gemma":[0.9976641,0.00003450913,0.0017917784,0.000005945356,0.0000044981894,0.0000044154585,0.00003395873,0.000005931537,0.00045486027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975795,0.00005727618,0.000017934944,0.000054797147,0.00006137704,0.000050728882],"domain_scores_gemma":[0.9984237,0.0007363279,0.00028404975,0.00016956631,0.00030382077,0.00008241933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047398894,0.0003078355,0.00042822174,0.00038651653,0.00041069367,0.0009468438,0.00067239325,0.0006819125,0.0014861731],"category_scores_gemma":[0.0027552159,0.00026434896,0.0003764842,0.00046703545,0.0011410785,0.00073144276,0.00043574124,0.00036458418,0.00021402097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005168618,0.00006240167,0.0063552507,0.000070858056,0.000040750052,0.0002335654,0.00010262446,0.9581476,0.009140959,0.016741734,0.0005075502,0.008079806],"study_design_scores_gemma":[0.000011779512,0.000057843874,0.0029986193,0.000004063625,0.000010424629,0.00008152044,0.000028496648,0.9900732,0.0012194348,0.0053584795,0.0001385689,0.00001758892],"about_ca_topic_score_codex":0.0026391714,"about_ca_topic_score_gemma":0.0014907399,"teacher_disagreement_score":0.0026391714,"about_ca_system_score_codex":0.00058490963,"about_ca_system_score_gemma":0.00041477493,"threshold_uncertainty_score":0.0052476525},"labels":[],"label_agreement":null},{"id":"W4319991343","doi":"10.23977/acss.2023.070103","title":"Overall Design of Storage Reliability Evaluation System for Large Weaponry","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Computer data storage; Computer science; Engineering; Computer hardware","score_opus":0.02248327463006881,"score_gpt":0.2652983441768251,"score_spread":0.24281506954675627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319991343","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021205977,0.00022975782,0.96727663,0.00013004613,0.00007095275,0.0007344086,0.00029559137,0.0054225437,0.004634153],"genre_scores_gemma":[0.5307074,0.0004817998,0.45286098,0.00018664692,0.00022076817,0.0023146754,0.0015722067,0.00051549857,0.011139948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987167,0.00023071875,0.000105019935,0.00034365623,0.0004924352,0.0001114929],"domain_scores_gemma":[0.99875116,0.00011938912,0.0001011167,0.0001006854,0.00086617266,0.000061557315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014534919,0.0011423988,0.0010145009,0.001491824,0.0010805401,0.0012980934,0.0014748514,0.00066688756,0.006068674],"category_scores_gemma":[0.001389904,0.0005170918,0.0006462861,0.000700812,0.00033558984,0.0010528609,0.00079781335,0.0005112691,0.0023503364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012075717,0.0002514268,0.018000482,0.0012960852,0.00025187622,0.00070506695,0.0008022328,0.20533894,0.22907235,0.015320309,0.019902121,0.5078514],"study_design_scores_gemma":[0.00020415316,0.0011714896,0.014988438,0.00013465778,0.00036738877,0.0007348648,0.00019214278,0.82687527,0.10694193,0.0042594806,0.043884154,0.00024611838],"about_ca_topic_score_codex":0.0027616695,"about_ca_topic_score_gemma":0.001824323,"teacher_disagreement_score":0.006068674,"about_ca_system_score_codex":0.00083931716,"about_ca_system_score_gemma":0.0016855724,"threshold_uncertainty_score":0.02030176},"labels":[],"label_agreement":null},{"id":"W4320180606","doi":"10.1016/j.cor.2023.106191","title":"Branch-and-price algorithms for large-scale mission-oriented maintenance planning problems","year":2023,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Column generation; Mathematical optimization; Computer science; Branch and bound; Algorithm; Convergence (economics); Integer (computer science); Embedding; Piecewise linear function; Computation; Mathematics","score_opus":0.05046480676281146,"score_gpt":0.34094365545552496,"score_spread":0.2904788486927135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320180606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027454607,0.0019006298,0.9618858,0.00087507255,0.00014274938,0.00013722459,0.00019219678,0.000601276,0.00681056],"genre_scores_gemma":[0.5220611,0.0018938477,0.46595016,0.00026907824,0.00032357732,0.0005459422,0.0007179695,0.00037780774,0.007860545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919456,0.00038913343,0.000036507674,0.000097643904,0.00014965034,0.00013263927],"domain_scores_gemma":[0.9938519,0.0053482377,0.00020934641,0.00014857999,0.00023811724,0.00020384815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003304168,0.001491761,0.002590872,0.0017090337,0.0010966095,0.0016769216,0.0021570134,0.0025076903,0.007027098],"category_scores_gemma":[0.007686935,0.0012416518,0.00087986153,0.0030743186,0.0011241812,0.0026586093,0.0014968914,0.0027293882,0.00074001076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013922525,0.00014501427,0.00031522743,0.000100442136,0.000042826505,0.00002765986,0.000032921318,0.92109567,0.00021018177,0.014599744,0.0036763866,0.05961485],"study_design_scores_gemma":[0.00003823508,0.000020229529,0.000041093823,0.0000060977095,0.0000061983314,0.0000041888006,0.000004705079,0.98850864,0.000060727114,0.0110978,0.00020942056,0.0000025810243],"about_ca_topic_score_codex":0.009029218,"about_ca_topic_score_gemma":0.009665683,"teacher_disagreement_score":0.009029218,"about_ca_system_score_codex":0.001621915,"about_ca_system_score_gemma":0.002502668,"threshold_uncertainty_score":0.023507953},"labels":[],"label_agreement":null},{"id":"W4320924181","doi":"10.1007/978-3-031-25448-2_6","title":"Methods for Comparing Asset Portfolio Reliability","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in mechanical engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Reliability (semiconductor); Portfolio; Asset (computer security); Order (exchange); Asset management; Investment (military); Ranking (information retrieval); Process (computing); Asset allocation; Business; Risk analysis (engineering); Field (mathematics); Computer science; Actuarial science; Power (physics); Finance; Artificial intelligence; Mathematics; Computer security","score_opus":0.020441087926010564,"score_gpt":0.2751582960352164,"score_spread":0.25471720810920584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320924181","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028137115,0.00083717407,0.98986864,0.000045222812,0.00011633063,0.00005627737,0.00035736102,0.0007019385,0.0052034385],"genre_scores_gemma":[0.06961432,0.0010890694,0.91565585,0.0000715832,0.00021205802,0.0006661273,0.0014796259,0.00084623956,0.010365215],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99583447,0.001804206,0.0002548944,0.0004914933,0.0015078732,0.0001070074],"domain_scores_gemma":[0.9777198,0.01691977,0.0007840663,0.0025857328,0.0018454005,0.00014528769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070857797,0.0012773564,0.0016170279,0.004415166,0.00039347578,0.0018763097,0.002476709,0.0012707162,0.012743112],"category_scores_gemma":[0.033991765,0.0007014521,0.0012347221,0.0035404155,0.00078854995,0.0027866382,0.0016011891,0.0018406213,0.0031143303],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018948063,0.00012155081,0.002856692,0.0004913284,0.00034311195,0.00006189816,0.00014440146,0.0782833,0.0029893755,0.089202575,0.012812205,0.81250405],"study_design_scores_gemma":[0.00006962564,0.00018742822,0.005471801,0.00021122758,0.00016652794,0.0003924778,0.00014023621,0.71105146,0.0051212274,0.2565579,0.020534089,0.00009600616],"about_ca_topic_score_codex":0.0010879242,"about_ca_topic_score_gemma":0.0012276202,"teacher_disagreement_score":0.012743112,"about_ca_system_score_codex":0.0006009205,"about_ca_system_score_gemma":0.00060718117,"threshold_uncertainty_score":0.042629957},"labels":[],"label_agreement":null},{"id":"W4321189550","doi":"10.1016/j.ress.2023.109167","title":"Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Prognostics; Condition-based maintenance; Component (thermodynamics); Reliability engineering; Maintenance actions; Turbine; Degradation (telecommunications); Computer science; Optimal maintenance; Engineering","score_opus":0.011840247797534531,"score_gpt":0.20365726484338317,"score_spread":0.19181701704584864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321189550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29452243,0.0010714888,0.69848436,0.0004877376,0.00009288008,0.00014372412,0.0002563675,0.0005283542,0.0044127316],"genre_scores_gemma":[0.98610175,0.00010552763,0.012343586,0.000019301398,0.000020364847,0.00005106191,0.00010012894,0.000035673685,0.0012225314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951696,0.00013860794,0.00002328879,0.00011531829,0.000113904476,0.000092012735],"domain_scores_gemma":[0.99863297,0.0008873985,0.00015172914,0.00005520362,0.0002105982,0.00006198417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012017711,0.001188695,0.0020932925,0.0011123186,0.00048033067,0.0011956572,0.001288626,0.0017176928,0.0018760819],"category_scores_gemma":[0.003295516,0.00094585464,0.0008816145,0.0007551577,0.0007538879,0.0014272219,0.0008917617,0.0007445836,0.00019763525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007333037,0.000027982913,0.00021553016,0.00002721592,0.000021967686,0.000028615683,0.000011701715,0.9953046,0.0006232526,0.00033588015,0.00011660037,0.0032133292],"study_design_scores_gemma":[0.000005384469,0.000015300313,0.00016411812,0.0000012144538,0.0000053751573,0.0000035758017,0.000002583211,0.99950206,0.00007860503,0.00020610717,0.0000138031055,0.0000019267443],"about_ca_topic_score_codex":0.009990482,"about_ca_topic_score_gemma":0.005106384,"teacher_disagreement_score":0.009990482,"about_ca_system_score_codex":0.0010486136,"about_ca_system_score_gemma":0.001060756,"threshold_uncertainty_score":0.019864678},"labels":[],"label_agreement":null},{"id":"W4321350275","doi":"10.1016/j.ress.2023.109179","title":"A deep reinforcement learning approach for repair-based maintenance of multi-unit systems using proportional hazards model","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Reinforcement learning; Reliability (semiconductor); Electric power system; Computer science; Condition-based maintenance; Markov decision process; Dependency (UML); Reliability engineering; Mathematical optimization; Artificial intelligence; Engineering; Markov process; Power (physics); Mathematics; Statistics","score_opus":0.018072162240533048,"score_gpt":0.2261103614807574,"score_spread":0.20803819924022435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321350275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054828368,0.00056219037,0.9414079,0.00027102782,0.0000660557,0.0000325669,0.00009105912,0.0007360074,0.0020049184],"genre_scores_gemma":[0.94518304,0.00014299313,0.05140363,0.00011050665,0.000045239696,0.000062016996,0.00013828871,0.000051736162,0.0028626039],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998247,0.000039293966,0.000008015086,0.00004765674,0.00004164682,0.00003877404],"domain_scores_gemma":[0.99946624,0.0003235251,0.00005027011,0.000029611292,0.00009305641,0.000037253332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005570833,0.00052105513,0.001007774,0.0003019028,0.00024631058,0.00040710598,0.0014577251,0.00079088804,0.002631211],"category_scores_gemma":[0.001250282,0.00039814416,0.0005063984,0.00028810033,0.00035167337,0.000609868,0.00073207176,0.0012124677,0.00022988996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057654972,0.000041741492,0.00031056182,0.000030817868,0.000021850954,0.00003170254,0.000016895756,0.9614889,0.0007358823,0.0016800251,0.00056451815,0.03501953],"study_design_scores_gemma":[0.0000015873148,0.0000058734886,0.000019599705,7.054962e-7,0.0000016400868,0.0000015004365,5.8209343e-7,0.999537,0.000047775382,0.00035789737,0.000025210671,5.692229e-7],"about_ca_topic_score_codex":0.010958668,"about_ca_topic_score_gemma":0.009383029,"teacher_disagreement_score":0.010958668,"about_ca_system_score_codex":0.0006831042,"about_ca_system_score_gemma":0.00094744755,"threshold_uncertainty_score":0.02178979},"labels":[],"label_agreement":null},{"id":"W4321351689","doi":"10.1007/s11009-023-09984-3","title":"Joint Reliability of Two Consecutive-(1, l) or (2, k)-out-of-(2, n): F Type Systems and Its Application in Smart Street Light Deployment","year":2023,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Reliability (semiconductor); Joint (building); Markov chain; Type (biology); Mathematics; Software deployment; Algorithm; Process (computing); Computational complexity theory; Markov process; Theoretical computer science; Applied mathematics; Discrete mathematics; Mathematical optimization; Computer science; Statistics; Structural engineering; Engineering; Programming language","score_opus":0.07793048609960566,"score_gpt":0.30633832862008836,"score_spread":0.22840784252048268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321351689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42493722,0.0002984687,0.5703486,0.0002520504,0.00005270597,0.000042563985,0.00025529906,0.00043578076,0.0033773363],"genre_scores_gemma":[0.98926467,0.000038288013,0.009186871,0.000011894319,0.000019198003,0.000015166117,0.0000762227,0.000027383752,0.001360347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992574,0.00021835715,0.00004107887,0.000201761,0.00012496542,0.00015638012],"domain_scores_gemma":[0.99514234,0.0027499155,0.00080060086,0.0003849758,0.0007223403,0.00019984694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017645656,0.00059094554,0.00089177914,0.0007553164,0.00042239655,0.0009681803,0.0011141102,0.0009119752,0.0018682219],"category_scores_gemma":[0.0065134736,0.00041385053,0.00065003545,0.00074227655,0.00095031474,0.0012516058,0.0006356758,0.00053314224,0.0002470884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007942472,0.00007817572,0.0081835585,0.000111220346,0.00008820382,0.00050371344,0.00018217388,0.93590873,0.008362451,0.023883626,0.00130312,0.020600831],"study_design_scores_gemma":[0.000004258401,0.000038622336,0.0012017657,0.0000026012372,0.00001206483,0.00006623328,0.0000134830325,0.9948107,0.0005522626,0.003197821,0.00009034955,0.000009878763],"about_ca_topic_score_codex":0.0030747193,"about_ca_topic_score_gemma":0.003026527,"teacher_disagreement_score":0.0030747193,"about_ca_system_score_codex":0.0008468715,"about_ca_system_score_gemma":0.0004948131,"threshold_uncertainty_score":0.009332001},"labels":[],"label_agreement":null},{"id":"W4321381493","doi":"10.1016/j.engappai.2023.105980","title":"Hybridization in nature inspired algorithms as an approach for problems with multiple goals: An application on reliability–redundancy allocation problems","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Redundancy (engineering); Reliability (semiconductor); Algorithm; Firefly algorithm; Scheme (mathematics); Mathematics; Particle swarm optimization","score_opus":0.014731764645668824,"score_gpt":0.2538218993466544,"score_spread":0.23909013470098556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321381493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043811034,0.0007922703,0.9380881,0.000946757,0.00019576469,0.00012933089,0.000019926241,0.00023298748,0.015783835],"genre_scores_gemma":[0.44080684,0.00058624474,0.5482249,0.0006845773,0.00016308743,0.00058552873,0.000048090176,0.00011782268,0.008782784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889755,0.00061864714,0.000031947326,0.000109743196,0.00028159545,0.000060509385],"domain_scores_gemma":[0.9976394,0.0019239255,0.00009062073,0.00009859411,0.00017453298,0.00007285894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026164027,0.001022832,0.0012789706,0.0010110023,0.0009036663,0.0012214634,0.0014864102,0.0031295947,0.0024644695],"category_scores_gemma":[0.0049284375,0.00065750914,0.0013527748,0.0011776693,0.0016373277,0.0013852562,0.0019903935,0.0025018682,0.00027957588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102852384,0.0001998138,0.0009271929,0.00013850613,0.00014151404,0.00016948923,0.00024521703,0.8436994,0.0038875767,0.08405471,0.0012793243,0.06515449],"study_design_scores_gemma":[0.00002789299,0.00010443799,0.000104658735,0.000011972624,0.000021702934,0.00004853766,0.000027550102,0.9773791,0.00048854895,0.020538941,0.0012372984,0.000009305741],"about_ca_topic_score_codex":0.0010966277,"about_ca_topic_score_gemma":0.0012313507,"teacher_disagreement_score":0.0031295947,"about_ca_system_score_codex":0.0010274539,"about_ca_system_score_gemma":0.00072987704,"threshold_uncertainty_score":0.0138370395},"labels":[],"label_agreement":null},{"id":"W4327795815","doi":"10.2139/ssrn.4387680","title":"Using Skewed Exponential Power Mixture for VaR and CVaR Forecasts to Comply with Market Risk Regulation","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"CVAR; Econometrics; Expected shortfall; Pareto principle; Generalized Pareto distribution; Economics; Computer science; Mathematics; Statistics; Risk management; Finance; Extreme value theory","score_opus":0.006818519640069952,"score_gpt":0.21142401299468402,"score_spread":0.20460549335461406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327795815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051525783,0.000098143886,0.9453019,0.00021163063,0.00010446865,0.000035644618,0.00011378486,0.000772146,0.0018365637],"genre_scores_gemma":[0.87717986,0.00014673977,0.11963423,0.00021604614,0.00013436824,0.00005153743,0.00066670193,0.00026089608,0.0017095931],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976593,0.0009761375,0.00014917653,0.00040287973,0.0006221785,0.00019035366],"domain_scores_gemma":[0.9899749,0.006449496,0.0006979475,0.001257605,0.0014642568,0.00015586731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057463767,0.0008048606,0.00087917136,0.000679239,0.00039713475,0.001770905,0.0011285299,0.0010945762,0.0018639358],"category_scores_gemma":[0.037974134,0.0005926264,0.00059416785,0.0007686738,0.0005290721,0.003481286,0.0014617633,0.0021168767,0.0008715674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008200708,0.00012349979,0.0062289694,0.00007858813,0.00016500271,0.00018761915,0.00014544658,0.829602,0.008830393,0.026933499,0.0027446346,0.12414029],"study_design_scores_gemma":[0.000012257421,0.000023247105,0.0006830421,0.000005461148,0.000009559445,0.000018415842,0.0000066563553,0.993948,0.0012011433,0.003771025,0.00031260433,0.000008473966],"about_ca_topic_score_codex":0.0025324118,"about_ca_topic_score_gemma":0.00265554,"teacher_disagreement_score":0.0057463767,"about_ca_system_score_codex":0.0005170674,"about_ca_system_score_gemma":0.0009893662,"threshold_uncertainty_score":0.030390143},"labels":[],"label_agreement":null},{"id":"W4327923865","doi":"10.1016/j.ress.2023.109261","title":"Opportunistic condition-based maintenance optimization for electrical distribution systems","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; Mitacs","keywords":"Condition-based maintenance; Reliability engineering; Preventive maintenance; Dependency (UML); Matching (statistics); Computer science; Optimal maintenance; Monte Carlo method; Operations research; Predictive maintenance; Risk analysis (engineering); Engineering; Statistics; Mathematics; Business; Artificial intelligence","score_opus":0.006477533025136558,"score_gpt":0.19861367583640258,"score_spread":0.192136142811266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327923865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41409758,0.00092131045,0.57457614,0.0008612893,0.00013268912,0.00022801914,0.00049866905,0.00091403176,0.0077702375],"genre_scores_gemma":[0.9911259,0.00004419781,0.0077373055,0.000029450346,0.000027110653,0.000027345313,0.00006902636,0.000021450507,0.0009182763],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929523,0.0002631193,0.000025850211,0.00010580903,0.00013232727,0.0001775862],"domain_scores_gemma":[0.9967332,0.0024469967,0.00029413638,0.00016161641,0.00021472818,0.0001494173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012532221,0.0007857558,0.0017329173,0.00057439186,0.00039022925,0.0008173493,0.0013441631,0.0009103184,0.0030706308],"category_scores_gemma":[0.004651908,0.0006139905,0.00035216656,0.00069185667,0.0008119267,0.0011575625,0.0008265392,0.0006797464,0.00021410875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039819,0.00011853082,0.00057782215,0.000036056656,0.00002932375,0.000044401906,0.000017871482,0.9794041,0.0006862358,0.0017822328,0.0009589243,0.015946316],"study_design_scores_gemma":[0.000021376949,0.000029741375,0.0001871235,0.0000011845344,0.00000437306,0.0000060455486,0.0000032622404,0.9984681,0.00008180934,0.0011521244,0.00004296184,0.0000019225827],"about_ca_topic_score_codex":0.0057425885,"about_ca_topic_score_gemma":0.0061056563,"teacher_disagreement_score":0.0057425885,"about_ca_system_score_codex":0.0009367736,"about_ca_system_score_gemma":0.0010456538,"threshold_uncertainty_score":0.011418283},"labels":[],"label_agreement":null},{"id":"W4360604684","doi":"10.1016/j.ress.2023.109257","title":"An optimizing maintenance policy for airborne redundant systems operating with faults by using Markov process and NSGA-II","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Reliability engineering; Redundancy (engineering); Markov chain; Reliability (semiconductor); Markov process; Process (computing); Markov model; Mathematical optimization; Computer science; Markov decision process; Constraint (computer-aided design); Engineering; Mathematics","score_opus":0.004862179205169025,"score_gpt":0.21977934239616465,"score_spread":0.21491716319099563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360604684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.201227,0.00041237753,0.792052,0.00043275708,0.00011492063,0.00018461052,0.00014993151,0.000473011,0.0049534575],"genre_scores_gemma":[0.9749162,0.00008842265,0.023564583,0.000056334986,0.000018278899,0.000108568136,0.00007328426,0.000019216412,0.0011551402],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995703,0.00013624887,0.00002085564,0.000067712,0.00009569836,0.000109211665],"domain_scores_gemma":[0.9990019,0.0005736497,0.00013414094,0.00002772525,0.00019690102,0.00006571462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010338436,0.0007130964,0.0011357756,0.00057984627,0.0005518479,0.0007238104,0.00081511197,0.0007337452,0.001664914],"category_scores_gemma":[0.0021712894,0.0005654904,0.00063382153,0.00043143486,0.00046975832,0.0005341511,0.0006376581,0.00079887075,0.00012593892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038681177,0.000014386315,0.00012998484,0.000011556996,0.000009180035,0.00000872858,0.0000070698125,0.9964385,0.00020000765,0.00073788705,0.00013444177,0.0022696054],"study_design_scores_gemma":[0.0000055633504,0.000011801505,0.000060185004,0.0000016493514,0.000004472738,0.0000015977068,0.0000025573734,0.999548,0.000044255077,0.00029886127,0.00001972697,0.000001233546],"about_ca_topic_score_codex":0.023911841,"about_ca_topic_score_gemma":0.01613303,"teacher_disagreement_score":0.023911841,"about_ca_system_score_codex":0.001392097,"about_ca_system_score_gemma":0.0030361316,"threshold_uncertainty_score":0.047545314},"labels":[],"label_agreement":null},{"id":"W4362647313","doi":"10.1109/rams51473.2023.10088200","title":"Dynamic Maintenance for a Large Scale Identical Parallel Manufacturing Systems Using Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Reinforcement learning; Markov decision process; Optimal maintenance; Computer science; State space; Markov process; Component (thermodynamics); Mathematical optimization; Process (computing); State (computer science); Minification; Q-learning; Stochastic process; Artificial intelligence; Mathematics; Algorithm","score_opus":0.010632590745165022,"score_gpt":0.23877437567902457,"score_spread":0.22814178493385956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362647313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048685953,0.00035093384,0.94822264,0.00031343565,0.00002945608,0.000059868573,0.000035800884,0.00020077104,0.002101092],"genre_scores_gemma":[0.9648336,0.00014083822,0.03330583,0.000055190452,0.000024489096,0.00008806776,0.000036656445,0.000016886657,0.0014984884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996057,0.0001086132,0.00001748432,0.000107592874,0.00009822647,0.00006236858],"domain_scores_gemma":[0.9993087,0.00037428693,0.0001417143,0.000031300548,0.00010359343,0.000040338688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009787602,0.0006849921,0.0010367256,0.00038753188,0.00041459643,0.0006739002,0.00116034,0.0008954045,0.0013127213],"category_scores_gemma":[0.0016167853,0.00037537774,0.00062566384,0.0003323282,0.0007472553,0.000681149,0.00071989855,0.0009244789,0.00010884916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011516896,0.00001605129,0.00029254856,0.000015119642,0.00001128997,0.000030279092,0.000010517199,0.99363756,0.00024596794,0.0011804603,0.00007446827,0.0044742753],"study_design_scores_gemma":[0.0000023691098,0.000008067732,0.000043795924,9.20824e-7,0.000001962887,0.0000032618316,0.0000014031292,0.9994242,0.0000354298,0.00044372183,0.000033900367,9.766392e-7],"about_ca_topic_score_codex":0.011235918,"about_ca_topic_score_gemma":0.0071040597,"teacher_disagreement_score":0.011235918,"about_ca_system_score_codex":0.0011931685,"about_ca_system_score_gemma":0.001166729,"threshold_uncertainty_score":0.022341073},"labels":[],"label_agreement":null},{"id":"W4362647360","doi":"10.1109/rams51473.2023.10088255","title":"A Decision-making Framework for Repair vs Replacement of a Multi-Component System Subject to Environmental Shocks","year":2023,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Downtime; Profit (economics); Reliability engineering; Computer science; Imperfect; Operations research; Risk analysis (engineering); Engineering; Economics; Business; Microeconomics","score_opus":0.010646280013738626,"score_gpt":0.24704394734473228,"score_spread":0.23639766733099365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362647360","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08986554,0.0013155325,0.8870168,0.0021323806,0.00019142409,0.0003070628,0.000372651,0.00022226897,0.018576391],"genre_scores_gemma":[0.94838184,0.0004885611,0.0443592,0.00012099657,0.000073160125,0.00025081975,0.0001379866,0.000038435493,0.006148978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972652,0.0014837519,0.00007954955,0.00035392278,0.00038186225,0.00043569045],"domain_scores_gemma":[0.9971156,0.0018924588,0.00028290114,0.000067143665,0.00036708507,0.0002748098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005613951,0.0016210497,0.0018242456,0.0013148361,0.0014510723,0.0035516655,0.0021974056,0.0030168276,0.007601911],"category_scores_gemma":[0.004539147,0.0007480632,0.0016326125,0.0011157894,0.0019203165,0.002025371,0.0018189377,0.0025043006,0.0003848222],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032655993,0.000023685348,0.00019752205,0.000022655378,0.000017520566,0.00009784506,0.000023645378,0.9804067,0.00021397063,0.017094705,0.0002041025,0.0016650467],"study_design_scores_gemma":[0.0000123348955,0.000030808624,0.00013352252,0.000007939551,0.0000095556325,0.00001181258,0.000029362296,0.98954225,0.00008663447,0.009839008,0.00028608725,0.00001074464],"about_ca_topic_score_codex":0.01497445,"about_ca_topic_score_gemma":0.0091674905,"teacher_disagreement_score":0.01497445,"about_ca_system_score_codex":0.0049802912,"about_ca_system_score_gemma":0.0032814466,"threshold_uncertainty_score":0.03613472},"labels":[],"label_agreement":null},{"id":"W4362647392","doi":"10.1109/rams51473.2023.10088213","title":"Dynamic Multilevel Redundancy Allocation Optimization Under Uncertainty","year":2023,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Redundancy (engineering); Computer science; Reliability engineering; Process (computing); Variety (cybernetics); Key (lock); Risk analysis (engineering); Systems engineering; Industrial engineering; Operations research; Engineering; Artificial intelligence","score_opus":0.009790343237056494,"score_gpt":0.22822903861441368,"score_spread":0.2184386953773572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362647392","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08470695,0.0017520777,0.8933731,0.0006565671,0.00008796289,0.00012602349,0.0002782165,0.00034490766,0.018674262],"genre_scores_gemma":[0.9555773,0.0005428872,0.03873021,0.00010400823,0.000029231971,0.00017293633,0.00016926115,0.000055561006,0.004618552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995111,0.00015903937,0.000018186378,0.00009482096,0.0001099397,0.00010679822],"domain_scores_gemma":[0.99905604,0.00056367414,0.00014276584,0.000040162642,0.00014836695,0.00004897763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010064135,0.0010918899,0.0013794532,0.00077703304,0.00045297583,0.0011996226,0.00086204446,0.001019192,0.0032459048],"category_scores_gemma":[0.0022898428,0.00052425044,0.00079496484,0.0006910665,0.0007199692,0.0007916617,0.0012029513,0.00093334715,0.0003562358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005264634,0.000012529778,0.0001957439,0.000071678754,0.000027091564,0.000075419455,0.00002231956,0.9867737,0.0007967872,0.0049078367,0.00042341193,0.006640746],"study_design_scores_gemma":[0.0000107619535,0.000028700402,0.00012717176,0.000008170025,0.00001096048,0.000014300397,0.000008692327,0.9963862,0.00018047243,0.0029065618,0.00031324476,0.0000048818115],"about_ca_topic_score_codex":0.004361821,"about_ca_topic_score_gemma":0.0021859945,"teacher_disagreement_score":0.004361821,"about_ca_system_score_codex":0.0010259369,"about_ca_system_score_gemma":0.00093192223,"threshold_uncertainty_score":0.010858595},"labels":[],"label_agreement":null},{"id":"W4362669277","doi":"10.3390/su15076270","title":"Machine Learning Applications for Reliability Engineering: A Review","year":2023,"lang":"en","type":"review","venue":"Sustainability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec; Université du Québec à Trois-Rivières","keywords":"Maintainability; Reliability (semiconductor); Computer science; Prognostics; Big data; Artificial intelligence; Cloud computing; Machine learning; Realization (probability); Reliability engineering; Systems engineering; Software engineering; Engineering; Data mining","score_opus":0.01881194137560345,"score_gpt":0.30272239726073735,"score_spread":0.2839104558851339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362669277","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020684465,0.9962942,0.0011289254,0.00029496846,0.00022440165,0.00001597685,0.000037573198,0.000020799183,0.0017762097],"genre_scores_gemma":[0.0013693506,0.9964644,0.0010819886,0.00014809995,0.00024428128,0.000015218039,0.00006246582,0.000005683887,0.0006084665],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996075,0.00007520274,0.00006561777,0.00006845499,0.00015505996,0.000028192635],"domain_scores_gemma":[0.99844533,0.00095776445,0.00013192404,0.000040866285,0.0003798729,0.00004423008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010563391,0.0010709068,0.0013502545,0.0032432615,0.00037427148,0.001285527,0.0011867293,0.0012654503,0.0058466448],"category_scores_gemma":[0.0020582627,0.00049421017,0.0010329644,0.0051836357,0.00040331137,0.0019099705,0.0007586722,0.0015100461,0.0027914273],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039191626,0.00009844997,0.0003350654,0.029854232,0.00012473593,0.0001459023,0.00009314871,0.0016143069,0.0009081247,0.006469962,0.02532474,0.93499213],"study_design_scores_gemma":[0.000011244729,0.00013037752,0.0011850591,0.012373297,0.00024607085,0.00080340914,0.00009751489,0.0010633358,0.00068210275,0.005027609,0.97833484,0.000045092514],"about_ca_topic_score_codex":0.0016640931,"about_ca_topic_score_gemma":0.0022276477,"teacher_disagreement_score":0.0058466448,"about_ca_system_score_codex":0.0005501945,"about_ca_system_score_gemma":0.0015312735,"threshold_uncertainty_score":0.019558966},"labels":[],"label_agreement":null},{"id":"W4366278346","doi":"10.37965/jdmd.2023.158","title":"Wind Turbine Optimal Preventive Maintenance Scheduling Using Fibonacci Search and Genetic Algorithm","year":2023,"lang":"en","type":"article","venue":"Journal of Dynamics Monitoring and Diagnostics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preventive maintenance; Optimal maintenance; Reliability engineering; Turbine; Wind power; Corrective maintenance; Predictive maintenance; Schedule; Scheduling (production processes); Reliability (semiconductor); Genetic algorithm; Maintenance engineering; Computer science; Engineering; Operations management","score_opus":0.009920592283896548,"score_gpt":0.24576247331486273,"score_spread":0.23584188103096618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366278346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0937236,0.0006373083,0.89864475,0.00021566574,0.000060939725,0.000113816306,0.000070248236,0.00037351722,0.006160179],"genre_scores_gemma":[0.7734506,0.00030168224,0.22326438,0.00010047371,0.000026259195,0.00023607734,0.00013723833,0.00005890097,0.0024243603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997831,0.00006949932,0.000008307891,0.000040469993,0.000055100896,0.00004349165],"domain_scores_gemma":[0.99961156,0.00024103606,0.000049230966,0.000012722067,0.00006610204,0.000019358198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005150671,0.00066294917,0.00088415213,0.00087139517,0.0003878335,0.0006234477,0.0008421399,0.0010216789,0.0012869849],"category_scores_gemma":[0.0013556579,0.00053045014,0.00058979366,0.0007802165,0.00039297552,0.00041752227,0.0003154411,0.0004594438,0.00015212366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016721315,0.000018523198,0.00023277043,0.000016159536,0.00001234037,0.000024323754,0.000013576712,0.98742896,0.00052111683,0.0018208654,0.00029826298,0.009596316],"study_design_scores_gemma":[0.0000065994773,0.000010122735,0.00006368412,0.0000020514067,0.0000032016235,0.0000038806284,0.0000022873705,0.9992318,0.00007265275,0.00052717427,0.000074557815,0.0000019800207],"about_ca_topic_score_codex":0.015832962,"about_ca_topic_score_gemma":0.011603945,"teacher_disagreement_score":0.015832962,"about_ca_system_score_codex":0.0010846303,"about_ca_system_score_gemma":0.0017495312,"threshold_uncertainty_score":0.031481624},"labels":[],"label_agreement":null},{"id":"W4366505762","doi":"10.1007/978-1-4471-7503-2_52","title":"Accelerated Life Testing Data Analyses for One-Shot Devices","year":2023,"lang":"en","type":"book-chapter","venue":"Springer handbooks","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Accelerated life testing; Reliability (semiconductor); One shot; Reliability engineering; Shot (pellet); Computer science; Explosive material; Inference; Statistical inference; Statistical hypothesis testing; Engineering; Statistics; Artificial intelligence; Weibull distribution; Mathematics; Mechanical engineering","score_opus":0.5255605658270567,"score_gpt":0.36481935667351273,"score_spread":0.16074120915354395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366505762","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028497873,0.01219308,0.87670463,0.0005373694,0.000803224,0.00028497912,0.008034328,0.014685917,0.058258638],"genre_scores_gemma":[0.20632386,0.010660392,0.57241714,0.0004954374,0.00050829083,0.000503118,0.023257352,0.0073707895,0.17846358],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999161,0.00010518887,0.00004149232,0.000104069724,0.0005558652,0.00003238632],"domain_scores_gemma":[0.9971623,0.0014210555,0.00012717556,0.00045353523,0.00080599886,0.000029991761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007906714,0.0011958521,0.000986924,0.002347698,0.00040809353,0.0012485796,0.002080238,0.00068091403,0.027738316],"category_scores_gemma":[0.0029096843,0.0005243996,0.0006984723,0.0018125742,0.0002830399,0.0014574927,0.00059883227,0.0010449324,0.0088676205],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017959115,0.00014519968,0.0020915086,0.0010258537,0.00014008052,0.00029764697,0.00022628243,0.013204497,0.07851783,0.012491965,0.06477933,0.8269002],"study_design_scores_gemma":[0.000046482346,0.0011867142,0.030506313,0.0008287011,0.00034174926,0.0044420054,0.0006053593,0.26109096,0.24464475,0.086610675,0.36938575,0.0003105401],"about_ca_topic_score_codex":0.00078131916,"about_ca_topic_score_gemma":0.00191818,"teacher_disagreement_score":0.027738316,"about_ca_system_score_codex":0.00037278587,"about_ca_system_score_gemma":0.0004193502,"threshold_uncertainty_score":0.09279388},"labels":[],"label_agreement":null},{"id":"W4367020722","doi":"10.2139/ssrn.4425015","title":"Loss Ratio as a Risk Constraint: Analysis and Optimization","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Mathematical optimization; CVAR; Probabilistic logic; Computer science; Expected shortfall; Sensitivity (control systems); Metric (unit); Linear programming; Constraint (computer-aided design); Risk management; Mathematics; Engineering; Economics","score_opus":0.0029268239326857336,"score_gpt":0.19913592752051293,"score_spread":0.1962091035878272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367020722","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029936578,0.0067977784,0.92079264,0.0031633805,0.00024527108,0.0001421781,0.00040250638,0.00014170678,0.038377937],"genre_scores_gemma":[0.76165175,0.008205831,0.17677644,0.00080850197,0.0009028523,0.00055706705,0.0005618258,0.0006315425,0.04990422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998988,0.00044547734,0.000030465197,0.00014771744,0.00022907875,0.0001592549],"domain_scores_gemma":[0.9942167,0.0047571305,0.00033973984,0.00013439362,0.00038914382,0.00016284705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004220912,0.0019267742,0.002691741,0.0018511959,0.0006855436,0.0037577853,0.0022792262,0.0029632542,0.0070624147],"category_scores_gemma":[0.013143401,0.0017365945,0.0015919026,0.0020075066,0.001972654,0.0034567118,0.0018853543,0.0035901435,0.0007078133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005955555,0.00009275511,0.000424694,0.00018000194,0.00006414666,0.00014447598,0.00005469087,0.92909795,0.0005324231,0.056568872,0.0042876955,0.008492727],"study_design_scores_gemma":[0.000009549209,0.000016891185,0.00020337336,0.000029886776,0.000026779708,0.00004430395,0.000026425543,0.9803668,0.00011417626,0.018333413,0.00081623276,0.000012224917],"about_ca_topic_score_codex":0.01039725,"about_ca_topic_score_gemma":0.007796525,"teacher_disagreement_score":0.01039725,"about_ca_system_score_codex":0.0025886956,"about_ca_system_score_gemma":0.0027936937,"threshold_uncertainty_score":0.02362609},"labels":[],"label_agreement":null},{"id":"W4372352878","doi":"10.1002/net.22150","title":"Existence of optimally‐greatest digraphs for strongly connected node reliability","year":2023,"lang":"en","type":"article","venue":"Networks","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Mount Saint Vincent University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Digraph; Strongly connected component; Node (physics); Reliability (semiconductor); Mathematics; Combinatorics; Directed graph; Graph; Discrete mathematics; Computer science","score_opus":0.011205896267939299,"score_gpt":0.21777872761502384,"score_spread":0.20657283134708454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4372352878","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5139297,0.00044128086,0.46653333,0.0010411275,0.000056102002,0.00006933119,0.00047288043,0.00019649803,0.017259618],"genre_scores_gemma":[0.9530052,0.000248553,0.04386124,0.00014647937,0.000032108892,0.000052147756,0.00017189118,0.000039204493,0.0024432284],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994293,0.00016690297,0.000021165875,0.00014391901,0.00011395283,0.00012477303],"domain_scores_gemma":[0.9954026,0.002653489,0.00065800827,0.00043984666,0.0003734495,0.00047266393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008787216,0.00045939698,0.000599669,0.0008915497,0.0006321188,0.0013743718,0.000760911,0.0007492604,0.0026648499],"category_scores_gemma":[0.005130291,0.0006432833,0.00044737768,0.0006924424,0.001266749,0.001960242,0.0009108629,0.0011232524,0.00020419672],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015550335,0.00015686503,0.0034029528,0.00019775474,0.00006157798,0.00021315667,0.00023645697,0.30213168,0.013592813,0.6560885,0.0043923776,0.01937046],"study_design_scores_gemma":[0.000036856967,0.000103001854,0.001240357,0.000040560102,0.00002878826,0.00023300297,0.00017411252,0.39171922,0.003967782,0.5987871,0.0036294137,0.00003984675],"about_ca_topic_score_codex":0.0013203614,"about_ca_topic_score_gemma":0.002571537,"teacher_disagreement_score":0.0026648499,"about_ca_system_score_codex":0.001447539,"about_ca_system_score_gemma":0.00104562,"threshold_uncertainty_score":0.010502696},"labels":[],"label_agreement":null},{"id":"W4376851344","doi":"10.1109/tase.2023.3269059","title":"Addressing a Collaborative Maintenance Planning Using Multiple Operators by a Multi-Objective Metaheuristic Algorithm","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Multi-objective optimization; Metaheuristic; Scheduling (production processes); Computer science; Job shop scheduling; Pareto principle; Preventive maintenance; Maintenance engineering; Quality (philosophy); Algorithm; Engineering; Reliability engineering; Operations management; Machine learning; Routing (electronic design automation)","score_opus":0.027900512763950492,"score_gpt":0.2744670834040074,"score_spread":0.24656657064005688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376851344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04066553,0.00030583408,0.9534558,0.00025771698,0.00004911003,0.00010971643,0.00005426257,0.0002488482,0.004853175],"genre_scores_gemma":[0.6073131,0.00030426666,0.38814196,0.00014344223,0.000047735914,0.00038555206,0.00014217252,0.000071297036,0.0034505515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996309,0.00011780843,0.000018185367,0.00007725247,0.000083780185,0.00007200413],"domain_scores_gemma":[0.99960405,0.00020884776,0.000067348985,0.00002496387,0.00006087794,0.000033990418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008961094,0.0010721623,0.0010006765,0.0009900054,0.00062041136,0.0011295123,0.001384281,0.0017629301,0.0018488307],"category_scores_gemma":[0.0011329817,0.0005435696,0.0012204134,0.0009413639,0.0004979529,0.00092785247,0.0010027193,0.00096332794,0.00018459983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025676114,0.000039329305,0.0002922072,0.000027662387,0.000038791706,0.000048763286,0.000025395377,0.9821116,0.00068097643,0.0025039136,0.00035379283,0.013851919],"study_design_scores_gemma":[0.000007772213,0.000013773431,0.000027811055,0.0000027959777,0.0000068992485,0.00000625746,0.000006316885,0.99904305,0.00011805689,0.0006112282,0.00015428358,0.0000018073911],"about_ca_topic_score_codex":0.009128065,"about_ca_topic_score_gemma":0.006078185,"teacher_disagreement_score":0.009128065,"about_ca_system_score_codex":0.000983492,"about_ca_system_score_gemma":0.0020667019,"threshold_uncertainty_score":0.018149853},"labels":[],"label_agreement":null},{"id":"W4378218810","doi":"10.1177/1748006x231174960","title":"Optimal condition based maintenance using attribute Bayesian control chart","year":2023,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Control chart; Control limits; Condition-based maintenance; Partially observable Markov decision process; Bayesian probability; Computer science; Statistical process control; Markov chain; Reliability engineering; Data mining; Chart; Statistics; Machine learning; Process (computing); Engineering; Artificial intelligence; Markov model; Mathematics","score_opus":0.007363713392144142,"score_gpt":0.21016631024795443,"score_spread":0.20280259685581029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378218810","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030020103,0.00033561606,0.96592474,0.00014282955,0.00003343977,0.00012779736,0.0001316164,0.00055027637,0.0027336013],"genre_scores_gemma":[0.9387833,0.00021813958,0.059598442,0.000038562317,0.000023588706,0.00014511398,0.00016824625,0.00003674701,0.0009878282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853015,0.0003408226,0.00007173085,0.00034830158,0.0005213877,0.00018753567],"domain_scores_gemma":[0.99708456,0.0014491597,0.00052991405,0.00014665628,0.0006632073,0.00012649725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024544331,0.0010826415,0.0012565228,0.0009833495,0.00050798943,0.0019320098,0.0011288761,0.0008535181,0.0017393361],"category_scores_gemma":[0.006044241,0.00040453736,0.0005633869,0.00070078793,0.00086468284,0.0012676512,0.0007686965,0.0010593296,0.00017114819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001083421,0.00004340825,0.0006143201,0.000045759924,0.00001682656,0.000033921155,0.000040032064,0.96384454,0.0014725344,0.0069905836,0.00043755674,0.026352277],"study_design_scores_gemma":[0.000009846106,0.000030165109,0.00016306309,0.0000043566797,0.0000055036317,0.00000546151,0.0000028320217,0.99762374,0.00035399146,0.0016549495,0.00014058755,0.0000054847583],"about_ca_topic_score_codex":0.010768332,"about_ca_topic_score_gemma":0.0040914007,"teacher_disagreement_score":0.010768332,"about_ca_system_score_codex":0.0015927902,"about_ca_system_score_gemma":0.00194934,"threshold_uncertainty_score":0.0214113},"labels":[],"label_agreement":null},{"id":"W4378364512","doi":"10.1016/j.ijpe.2023.108925","title":"Integration of operational lockout/tagout in a joint production and maintenance policy of a smart production system","year":2023,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Preventive maintenance; Production (economics); Corrective maintenance; Computer science; Reliability engineering; Reliability (semiconductor); Maintenance actions; Work (physics); Operations research; Control (management); Condition-based maintenance; Risk analysis (engineering); Engineering; Business; Economics","score_opus":0.01241304617602027,"score_gpt":0.22396206266688148,"score_spread":0.21154901649086122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378364512","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71626186,0.00027083067,0.27057827,0.0013029773,0.0001800074,0.00037361553,0.00021789642,0.0009925403,0.009822055],"genre_scores_gemma":[0.9964972,0.0000115637085,0.0028759665,0.00004208029,0.000009287606,0.000018026452,0.000014837449,0.000013039895,0.00051810563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974847,0.00079117186,0.00018918209,0.0004797471,0.000350065,0.00070509413],"domain_scores_gemma":[0.9940784,0.0028886897,0.0011195764,0.0005331264,0.00076794985,0.0006122519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056349654,0.0006292042,0.0013154804,0.0006983049,0.0006338665,0.002535536,0.0015616281,0.0017855205,0.003469218],"category_scores_gemma":[0.009249839,0.0005948288,0.00049735565,0.00038936734,0.0008895633,0.0021434797,0.0010084161,0.0015984655,0.0003209971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022093768,0.0007758642,0.01005055,0.00017373363,0.00017781815,0.0004898368,0.00026736112,0.8708004,0.01848376,0.036435325,0.001966426,0.058169607],"study_design_scores_gemma":[0.00009801767,0.000695112,0.007211263,0.000027135095,0.000099782315,0.00007926721,0.00014440404,0.97272813,0.0035660707,0.014345952,0.0009519566,0.000052883475],"about_ca_topic_score_codex":0.0038303195,"about_ca_topic_score_gemma":0.0033512579,"teacher_disagreement_score":0.0056349654,"about_ca_system_score_codex":0.0012819617,"about_ca_system_score_gemma":0.0023701198,"threshold_uncertainty_score":0.029800892},"labels":[],"label_agreement":null},{"id":"W4379108563","doi":"10.1007/978-3-031-28859-3_8","title":"Multi-state Signatures for Multi-state Systems with Binary/Multi-state Components","year":2023,"lang":"en","type":"book-chapter","venue":"Springer series in reliability engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Signature (topology); Computer science; State (computer science); Binary number; Notation; Reliability (semiconductor); Theoretical computer science; Transformation (genetics); Algorithm; Mathematics","score_opus":0.025339078993146164,"score_gpt":0.224346262860635,"score_spread":0.19900718386748883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379108563","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044810497,0.0015857253,0.97072643,0.00034225624,0.0009913464,0.00007471377,0.0002678365,0.002256891,0.019273642],"genre_scores_gemma":[0.27873603,0.0029488364,0.66092616,0.00058142166,0.00097168045,0.00035416905,0.0013408728,0.0013976034,0.05274318],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986864,0.00020753636,0.00014520437,0.00025720848,0.00059612695,0.000107516884],"domain_scores_gemma":[0.99814653,0.00068517606,0.00014629144,0.00068292936,0.00028737416,0.000051833744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009060019,0.0009303861,0.0009675237,0.00109921,0.00064382,0.0027090374,0.0013469504,0.0013178305,0.007560192],"category_scores_gemma":[0.0025985392,0.0006188104,0.0008452563,0.001319095,0.0013368827,0.00507829,0.002057391,0.0032655646,0.003890561],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017618986,0.00006730928,0.00020581206,0.00044747838,0.00003490894,0.00030719122,0.00031254938,0.016420936,0.01706919,0.6420334,0.016270412,0.30665463],"study_design_scores_gemma":[0.000026104119,0.00011955127,0.00023074062,0.00017492422,0.0000335982,0.0009899732,0.00008204243,0.14345022,0.023849564,0.7198004,0.11116116,0.00008178323],"about_ca_topic_score_codex":0.0001912857,"about_ca_topic_score_gemma":0.00027119496,"teacher_disagreement_score":0.007560192,"about_ca_system_score_codex":0.0007519625,"about_ca_system_score_gemma":0.00068940094,"threshold_uncertainty_score":0.025291383},"labels":[],"label_agreement":null},{"id":"W4379194119","doi":"10.1016/j.ress.2023.109412","title":"Inspection interval optimization of a weighted-K-out-of-N system with identical multi-state load-sharing components","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Canada Research Chairs","keywords":"Interval (graph theory); State (computer science); Reliability engineering; Computer science; Algorithm; Component (thermodynamics); Interval arithmetic; Load sharing; Mathematical optimization; Mathematics; Engineering; Distributed computing; Combinatorics; Physics; Mathematical analysis","score_opus":0.010556867712190167,"score_gpt":0.205853488006075,"score_spread":0.19529662029388484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379194119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38761383,0.00046088314,0.6013338,0.0004613974,0.000081668986,0.0000897197,0.00019650652,0.00042046327,0.0093418155],"genre_scores_gemma":[0.9883712,0.000044683955,0.009300813,0.000023236822,0.000010919334,0.000020771507,0.000046927136,0.000022815737,0.0021586525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993818,0.00017472528,0.000026100253,0.00014956295,0.000107709515,0.00016009214],"domain_scores_gemma":[0.9983144,0.0008476903,0.00028025874,0.000092939415,0.00032410593,0.00014071935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015217904,0.00085608586,0.0016879098,0.0005173824,0.00055753905,0.0011405961,0.0015482715,0.0012914612,0.002357383],"category_scores_gemma":[0.0033079376,0.00061221374,0.0006235868,0.0005680082,0.0008000485,0.00095152063,0.0009877608,0.0007756001,0.00018749022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002607096,0.000038742193,0.0004964722,0.000058251007,0.000036148598,0.00010745558,0.000041286738,0.9894847,0.0019372879,0.0018275377,0.0002981759,0.005413274],"study_design_scores_gemma":[0.000007870623,0.00003171774,0.00018902599,0.0000018766232,0.000008732349,0.000008317095,0.0000055738847,0.9990502,0.00016739381,0.0004941793,0.000031605745,0.0000035188061],"about_ca_topic_score_codex":0.011555938,"about_ca_topic_score_gemma":0.008276277,"teacher_disagreement_score":0.011555938,"about_ca_system_score_codex":0.0011504161,"about_ca_system_score_gemma":0.0010285807,"threshold_uncertainty_score":0.022977352},"labels":[],"label_agreement":null},{"id":"W4380742523","doi":"10.1016/j.ress.2023.109420","title":"Optimal replacement policy for a two-unit system subject to shocks and cumulative damage","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Science Council","keywords":"Shock (circulatory); Unit (ring theory); Mathematics","score_opus":0.008831886242822945,"score_gpt":0.2408581181009523,"score_spread":0.23202623185812937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380742523","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6425829,0.0016858709,0.33653718,0.003673529,0.00026851392,0.00035250478,0.00076288765,0.00093918253,0.0131974155],"genre_scores_gemma":[0.98726463,0.00019700044,0.0076935655,0.000081792314,0.00003877386,0.00005834022,0.00009591708,0.000044039316,0.004525916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936277,0.00026589577,0.00003133267,0.00010298249,0.000066149776,0.00017092406],"domain_scores_gemma":[0.9972295,0.0017759869,0.00029694525,0.000103384,0.00034407477,0.0002500854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001651739,0.0011940787,0.0018883887,0.0011131783,0.0006599394,0.0016540035,0.0016719557,0.003333821,0.0044856817],"category_scores_gemma":[0.0039010472,0.0010029085,0.0005666542,0.00089358224,0.0012396541,0.0011812239,0.0010422713,0.0013263065,0.0005462299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029148694,0.00004618825,0.00037220432,0.00006649128,0.000028133996,0.00013359595,0.000040410774,0.9909045,0.0016968776,0.0025593648,0.0006453401,0.0032154159],"study_design_scores_gemma":[0.00003640542,0.000066081266,0.00037388044,0.0000068998133,0.000017839313,0.000021850721,0.000023324923,0.99748355,0.0002303113,0.0016235973,0.000107602056,0.000008701959],"about_ca_topic_score_codex":0.011558836,"about_ca_topic_score_gemma":0.0049987067,"teacher_disagreement_score":0.011558836,"about_ca_system_score_codex":0.0019403648,"about_ca_system_score_gemma":0.0016600325,"threshold_uncertainty_score":0.022983074},"labels":[],"label_agreement":null},{"id":"W4380791774","doi":"10.1007/s10287-023-00464-0","title":"Robust selective maintenance optimization of series–parallel mission-critical systems subject to maintenance quality uncertainty","year":2023,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Preventive maintenance; Computer science; Mathematical optimization; Quality (philosophy); Benchmark (surveying); Robust optimization; Reliability (semiconductor); Operations research; Decision maker; Reliability engineering; Optimal maintenance; Mathematics; Engineering","score_opus":0.025675144523067066,"score_gpt":0.27676620739907565,"score_spread":0.2510910628760086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380791774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40346518,0.0008975166,0.5841746,0.0010540461,0.00010248533,0.00009192221,0.0002758433,0.0002580232,0.009680467],"genre_scores_gemma":[0.9929362,0.00012314312,0.005312214,0.000028361732,0.000027106375,0.000030756433,0.0000637166,0.000027279322,0.0014513578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970156,0.000091766364,0.00001213988,0.00006623294,0.00006276613,0.00006550345],"domain_scores_gemma":[0.9984515,0.0009769155,0.0002817818,0.000071121096,0.00015102411,0.000067758876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011428626,0.0009357558,0.0012641973,0.0005877914,0.0003436037,0.0008428013,0.00097101025,0.0009207955,0.0012481402],"category_scores_gemma":[0.0037105621,0.0005808627,0.00053716655,0.00057127135,0.0008976262,0.0007370892,0.0010086794,0.00064777845,0.00010977017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004692676,0.000010492958,0.00013836373,0.0000194154,0.000021731195,0.000026073454,0.000006345809,0.99582726,0.0004396574,0.0012609934,0.0001293451,0.0020735152],"study_design_scores_gemma":[0.000005139394,0.000013080944,0.00008943781,0.0000010891666,0.000004422764,0.0000047926083,0.000002722695,0.99891555,0.00010968511,0.0008253956,0.000027513774,0.0000012096978],"about_ca_topic_score_codex":0.0058393213,"about_ca_topic_score_gemma":0.0028745118,"teacher_disagreement_score":0.0058393213,"about_ca_system_score_codex":0.0009664609,"about_ca_system_score_gemma":0.0009775553,"threshold_uncertainty_score":0.011610687},"labels":[],"label_agreement":null},{"id":"W4381135222","doi":"10.21203/rs.3.rs-3066336/v1","title":"Optimization of Time Dependent Fuzzy Multi-Objective Reliability Redundancy Allocation Problem for n-Stage Series Parallel System","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Heritage College","funders":"","keywords":"Mathematical optimization; Maximization; Particle swarm optimization; Sorting; Fuzzy logic; Computer science; Redundancy (engineering); Mathematics; Algorithm; Artificial intelligence","score_opus":0.04232631070548657,"score_gpt":0.3218317230223515,"score_spread":0.27950541231686493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381135222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24532102,0.0005177184,0.7469564,0.00030742487,0.000044368146,0.0001285191,0.00013526314,0.00014904424,0.006440209],"genre_scores_gemma":[0.95196307,0.00013132904,0.04550867,0.00002377777,0.000016089456,0.0001053554,0.000055441993,0.000017397282,0.0021788625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956566,0.00016910762,0.000021193528,0.00009324815,0.00009096297,0.00005986681],"domain_scores_gemma":[0.99946684,0.00026930787,0.00011041438,0.000026851683,0.00009376289,0.00003281796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010407988,0.00073933403,0.000797238,0.00058962987,0.00032834502,0.00064051105,0.0006672159,0.0008044461,0.0015153049],"category_scores_gemma":[0.0012836881,0.00032436626,0.0006507375,0.00054905383,0.0003924793,0.00041612345,0.00041059972,0.00043606723,0.00010093167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003683217,0.000018212166,0.00019916506,0.00004313922,0.000018722701,0.000044457956,0.000014385948,0.9917365,0.0012536093,0.0009049953,0.00010737019,0.005622686],"study_design_scores_gemma":[0.000007973364,0.00005963149,0.00020975897,0.0000038399826,0.000007250877,0.000013855054,0.00000882544,0.9983217,0.00046578376,0.0008002873,0.00009823492,0.0000028508261],"about_ca_topic_score_codex":0.0041435705,"about_ca_topic_score_gemma":0.002482521,"teacher_disagreement_score":0.0041435705,"about_ca_system_score_codex":0.0008441343,"about_ca_system_score_gemma":0.00073446054,"threshold_uncertainty_score":0.008238912},"labels":[],"label_agreement":null},{"id":"W4381430117","doi":"10.1007/s40995-023-01486-8","title":"An Integrated Model of Maintenance Policies and Economic Design of X-bar Control Chart Under Burr XII Shock Model","year":2023,"lang":"en","type":"article","venue":"Iranian Journal of Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Reliability engineering; Control chart; Sensitivity (control systems); Chart; Bar chart; Shock (circulatory); Reliability (semiconductor); Preventive maintenance; Hazard; Bar (unit); Engineering; Operations research; Statistics; Econometrics; Computer science; Mathematics; Power (physics)","score_opus":0.021451167645127892,"score_gpt":0.23455885406327423,"score_spread":0.21310768641814634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381430117","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2620344,0.0008109149,0.6917354,0.0010956407,0.00017710413,0.00019592034,0.0006180009,0.0006964247,0.04263622],"genre_scores_gemma":[0.9876587,0.00012538007,0.0065259985,0.000028430135,0.000016212633,0.00006118642,0.00009559476,0.000019643048,0.005468817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995715,0.00012345494,0.000013602811,0.00010683982,0.00007051949,0.000114091716],"domain_scores_gemma":[0.99946564,0.0002320445,0.000078306926,0.000019882476,0.00016443846,0.000039733437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008405529,0.00069839705,0.0013504416,0.0006035546,0.0005946845,0.0020183348,0.0014054365,0.0021455286,0.005623451],"category_scores_gemma":[0.0015271766,0.00069697655,0.000770802,0.0005212086,0.00068638596,0.0010798136,0.0007004858,0.0010604777,0.00033911943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025144234,0.000009426751,0.00013831373,0.000012469338,0.0000072541675,0.00003131385,0.000009384942,0.9958188,0.00020471627,0.002667452,0.00016477676,0.00091099745],"study_design_scores_gemma":[0.0000053000704,0.000009575375,0.000076526434,0.0000014202355,0.0000047859594,0.000002214142,0.00000497346,0.99936086,0.00004224382,0.00042300383,0.0000663805,0.0000026295393],"about_ca_topic_score_codex":0.045528665,"about_ca_topic_score_gemma":0.015098899,"teacher_disagreement_score":0.045528665,"about_ca_system_score_codex":0.0017378755,"about_ca_system_score_gemma":0.0019646324,"threshold_uncertainty_score":0.090527296},"labels":[],"label_agreement":null},{"id":"W4381434719","doi":"10.1002/net.22166","title":"On the split reliability of graphs","year":2023,"lang":"en","type":"article","venue":"Networks","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Vertex (graph theory); Combinatorics; Reliability (semiconductor); Graph; Random graph; Discrete mathematics; Computer science","score_opus":0.006908805412771038,"score_gpt":0.1889312714313677,"score_spread":0.18202246601859667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381434719","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30880284,0.0016609384,0.66462827,0.0016104692,0.00008977131,0.00007078047,0.00042604993,0.00020296329,0.022507867],"genre_scores_gemma":[0.9822708,0.0006793295,0.015016819,0.00012112869,0.00010009728,0.000061394756,0.00013831879,0.000060414557,0.0015517712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982659,0.00094679126,0.000038959406,0.00022584377,0.0003160044,0.0002066351],"domain_scores_gemma":[0.98976433,0.007383795,0.00092146336,0.00070809596,0.0008415009,0.00038075764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022274496,0.0008262777,0.00079406874,0.0018637177,0.0005591279,0.0011929452,0.00091949845,0.00089335674,0.002468368],"category_scores_gemma":[0.011352768,0.0004544595,0.0005622191,0.0008580315,0.00259402,0.0024706693,0.001580422,0.0010759097,0.00024227165],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001803785,0.000031036616,0.0013126326,0.0001425509,0.00005771877,0.00016826879,0.00020772389,0.4958863,0.0023343558,0.48409706,0.0029899965,0.012591974],"study_design_scores_gemma":[0.00001644473,0.00006139274,0.00048031926,0.000042280895,0.000018125624,0.000104694926,0.00008154178,0.5389625,0.0005345606,0.45833415,0.0013514501,0.000012606652],"about_ca_topic_score_codex":0.0014825333,"about_ca_topic_score_gemma":0.00085027114,"teacher_disagreement_score":0.002468368,"about_ca_system_score_codex":0.0012501725,"about_ca_system_score_gemma":0.0004580624,"threshold_uncertainty_score":0.011780024},"labels":[],"label_agreement":null},{"id":"W4381744499","doi":"10.1109/iraset57153.2023.10153017","title":"Maintenance Optimization of Multi-Component Systems Subjected to $s$-Dependent Competing Risks and Imperfect Maintenance","year":2023,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Preventive maintenance; Reliability (semiconductor); Reliability engineering; Mathematical optimization; Computer science; Imperfect; Component (thermodynamics); Time horizon; Optimization problem; Mathematics; Engineering","score_opus":0.020072511468956653,"score_gpt":0.23881582602526152,"score_spread":0.21874331455630486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381744499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.096990556,0.0011378966,0.89789504,0.00038033802,0.000051813353,0.000075226104,0.00010516593,0.0001996441,0.0031642425],"genre_scores_gemma":[0.95295095,0.00031976384,0.04363919,0.000052637777,0.000034228156,0.00008923343,0.00010399633,0.000057334495,0.002752734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993747,0.00022719284,0.000025963998,0.00013220056,0.0001413204,0.00009867522],"domain_scores_gemma":[0.9988772,0.0006918379,0.00021844212,0.000049559698,0.00010818125,0.000054713484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001430833,0.0013465964,0.0013049945,0.00060679734,0.00038232343,0.00087213196,0.0011533217,0.0011676591,0.0015342078],"category_scores_gemma":[0.0023346462,0.00071388175,0.00094642123,0.0005610941,0.0007673893,0.0007419526,0.00091276073,0.0010185897,0.00014147608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028469614,0.000012986805,0.0002221198,0.000038689108,0.000018791496,0.000042373464,0.000015694417,0.99414426,0.00066312123,0.0016339825,0.00009793803,0.0030815774],"study_design_scores_gemma":[0.000005621145,0.000026151189,0.00013916125,0.0000025667484,0.0000067112574,0.000011241437,0.000004443389,0.9987771,0.00017252167,0.00077420817,0.000077544915,0.0000026349774],"about_ca_topic_score_codex":0.0054633175,"about_ca_topic_score_gemma":0.0036296092,"teacher_disagreement_score":0.0054633175,"about_ca_system_score_codex":0.0009863813,"about_ca_system_score_gemma":0.0011004715,"threshold_uncertainty_score":0.010863066},"labels":[],"label_agreement":null},{"id":"W4381744613","doi":"10.4050/f-0079-2023-18175","title":"Reliability Prediction with No Observed Field Failure but with Known Design Lives","year":2023,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Mean time between failures; Reliability (semiconductor); Reliability engineering; Component (thermodynamics); Failure rate; Hazard; Field (mathematics); Exponential distribution; Reliability theory; Computer science; Function (biology); Engineering; Statistics; Mathematics","score_opus":0.013422597283747088,"score_gpt":0.17850241112927973,"score_spread":0.16507981384553264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381744613","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7814263,0.00032275033,0.20980912,0.00022970633,0.00003706219,0.00008630894,0.001319122,0.00091767946,0.0058520143],"genre_scores_gemma":[0.9855823,0.0000672049,0.012163348,0.000019600891,0.000008580243,0.000034400768,0.0007466399,0.000028014341,0.0013500081],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99965477,0.0000604537,0.000016504158,0.00011866924,0.00010491787,0.00004482404],"domain_scores_gemma":[0.99838746,0.0007026877,0.00030843835,0.00028332145,0.00025391346,0.00006424567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007847882,0.00080955646,0.00047849037,0.0004343174,0.00014506197,0.00036570107,0.000624008,0.00056801393,0.0009668837],"category_scores_gemma":[0.0026924287,0.00027764105,0.0003236191,0.00020457381,0.00028923972,0.00075010414,0.00022524962,0.00064236554,0.00043017985],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014502728,0.00009990383,0.013595824,0.00009014255,0.000025886193,0.00012387855,0.0000527891,0.9562192,0.006041202,0.00081065076,0.0012047897,0.021590667],"study_design_scores_gemma":[0.0000121772655,0.00021614258,0.010922998,0.000014827371,0.00002030703,0.000061711995,0.00002256456,0.9797283,0.0066446923,0.001531849,0.0008052228,0.000019190204],"about_ca_topic_score_codex":0.0033274463,"about_ca_topic_score_gemma":0.0040274193,"teacher_disagreement_score":0.0033274463,"about_ca_system_score_codex":0.000378258,"about_ca_system_score_gemma":0.0005256177,"threshold_uncertainty_score":0.006616175},"labels":[],"label_agreement":null},{"id":"W4382457010","doi":"10.3390/su15097493","title":"Asset Management, Reliability and Prognostics Modeling Techniques","year":2023,"lang":"en","type":"article","venue":"Sustainability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec; Université du Québec à Trois-Rivières","keywords":"Prognostics; Maintainability; Reliability (semiconductor); Asset management; Asset (computer security); Risk analysis (engineering); Systems engineering; Physics of failure; Engineering; Computer science; Reliability engineering; Process management; Business; Computer security","score_opus":0.006011840982856996,"score_gpt":0.2296390675938341,"score_spread":0.2236272266109771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382457010","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005839258,0.012700367,0.95903444,0.0020361827,0.00023353023,0.000080445774,0.00058499794,0.0006962476,0.01879456],"genre_scores_gemma":[0.52906984,0.059550047,0.38036555,0.0007908537,0.0014711323,0.00070690113,0.0017624302,0.00039633637,0.02588693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993463,0.00020371849,0.000048655027,0.00012179341,0.00023575731,0.00004374522],"domain_scores_gemma":[0.9991923,0.00041901454,0.00013248473,0.00007333876,0.00016069302,0.000022261207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012466593,0.001269035,0.00085768895,0.0018428391,0.0005494939,0.0017186947,0.0018070533,0.0013674553,0.0031859705],"category_scores_gemma":[0.002935005,0.00046841198,0.0011677486,0.0021506965,0.00078276236,0.0020982728,0.0012203221,0.0015685129,0.0013962614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003013007,0.00005471963,0.0023197553,0.00069687265,0.00010115537,0.00023573828,0.00023816318,0.6389179,0.0023319935,0.20789234,0.007085891,0.14009525],"study_design_scores_gemma":[0.000008642823,0.000057024405,0.00090406823,0.00022529835,0.000060033024,0.0003338898,0.00009757955,0.79367346,0.0013567626,0.1539518,0.049287785,0.000043716253],"about_ca_topic_score_codex":0.0053625917,"about_ca_topic_score_gemma":0.002808549,"teacher_disagreement_score":0.0053625917,"about_ca_system_score_codex":0.0009938282,"about_ca_system_score_gemma":0.0013725198,"threshold_uncertainty_score":0.010662794},"labels":[],"label_agreement":null},{"id":"W4383987331","doi":"10.48550/arxiv.2307.03860","title":"Reinforcement and Deep Reinforcement Learning-based Solutions for Machine Maintenance Planning, Scheduling Policies, and Optimization","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Computer science; Categorization; Risk analysis (engineering); Scheduling (production processes); Asset (computer security); Artificial intelligence; Machine learning; Operations research; Engineering; Operations management; Computer security","score_opus":0.047984768794560664,"score_gpt":0.19555490325833788,"score_spread":0.1475701344637772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383987331","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010249087,0.00447107,0.97766566,0.0013693202,0.00018226965,0.000041771247,0.00011615325,0.00041741162,0.00548722],"genre_scores_gemma":[0.7718721,0.005990879,0.21274589,0.0005876812,0.0004209046,0.00031232386,0.00043080922,0.00018852763,0.0074509326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995499,0.00015197841,0.00003147569,0.00010466094,0.00009365834,0.00006829747],"domain_scores_gemma":[0.99812084,0.0013621722,0.00016096496,0.00007226022,0.00021697106,0.00006670104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010486037,0.0011768395,0.0011726052,0.0005805616,0.00030929167,0.0011281756,0.0011539321,0.0013751561,0.0027136987],"category_scores_gemma":[0.004081005,0.00045229582,0.00065217353,0.00071845175,0.0007533801,0.0009811765,0.0011131904,0.002367583,0.00037461077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028339527,0.000047217978,0.0005067223,0.00015122352,0.000050236915,0.00003678192,0.000038799317,0.9368253,0.00031994484,0.013186838,0.001869882,0.046938688],"study_design_scores_gemma":[0.000005849901,0.0000134495185,0.00007051077,0.000017556056,0.0000077661,0.00000701873,0.0000066146285,0.9884871,0.000105219966,0.010466543,0.0008088546,0.0000034780073],"about_ca_topic_score_codex":0.008101839,"about_ca_topic_score_gemma":0.006504384,"teacher_disagreement_score":0.008101839,"about_ca_system_score_codex":0.0013047588,"about_ca_system_score_gemma":0.0019762225,"threshold_uncertainty_score":0.016109407},"labels":[],"label_agreement":null},{"id":"W4384562776","doi":"10.1016/j.cor.2023.106344","title":"MPILS: An Automatic Tuner for MILP Solvers","year":2023,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Fonds de recherche du Québec – Nature et technologies; Institut de Valorisation des Données","keywords":"Tuner; Solver; Computer science; Iterated local search; Set (abstract data type); Mathematical optimization; Heuristic; Local search (optimization); Parameter space; Cluster analysis; Iterated function; Metaheuristic; Algorithm; Artificial intelligence; Mathematics","score_opus":0.09402563793458564,"score_gpt":0.3790488157703011,"score_spread":0.28502317783571546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384562776","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008792873,0.0003098354,0.60025465,0.00023919654,0.00029730189,0.00016081793,0.0024884462,0.37602398,0.011432866],"genre_scores_gemma":[0.17823869,0.0003339039,0.711105,0.0008734515,0.00023033294,0.0010120819,0.008830937,0.080272056,0.019103598],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894553,0.00027265836,0.00007974311,0.00020107442,0.0003914324,0.00010959211],"domain_scores_gemma":[0.9983132,0.0009311736,0.0000963223,0.00029386798,0.00026700879,0.00009849965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013044067,0.0028532492,0.0011682254,0.0018079738,0.0008181027,0.0016847926,0.0031273777,0.0014186051,0.048579473],"category_scores_gemma":[0.0066475957,0.0015442638,0.0011252172,0.0011836033,0.00060611183,0.0018309368,0.002432132,0.002825027,0.015988247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021190986,0.0005403663,0.0022388315,0.0012289003,0.00048251328,0.0006631696,0.00031817218,0.1573779,0.028956512,0.0132587375,0.26887468,0.52394116],"study_design_scores_gemma":[0.00061013363,0.00010806238,0.00058539124,0.00007083492,0.000048836788,0.00012120962,0.000055000077,0.92383516,0.020447211,0.010825227,0.043205455,0.00008752768],"about_ca_topic_score_codex":0.002927553,"about_ca_topic_score_gemma":0.004839745,"teacher_disagreement_score":0.048579473,"about_ca_system_score_codex":0.00064732024,"about_ca_system_score_gemma":0.0012392139,"threshold_uncertainty_score":0.16251457},"labels":[],"label_agreement":null},{"id":"W4385271570","doi":"10.1007/s10479-023-05525-w","title":"Extended replacement policy for a system under shocks effect","year":2023,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Shock (circulatory); Generalization; Unit (ring theory); Theory of computation; Catastrophic failure; Minor (academic); Computer science; Mathematics; Physics; Mathematical analysis; Algorithm; Thermodynamics","score_opus":0.1204904495290139,"score_gpt":0.4389525915870206,"score_spread":0.3184621420580067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385271570","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59538436,0.0008900168,0.38682118,0.0028762002,0.000400909,0.00019016444,0.00063934067,0.00091345457,0.011884419],"genre_scores_gemma":[0.987703,0.00011837974,0.007373169,0.00007016029,0.00005438879,0.000029109695,0.000074405136,0.000026190846,0.004551185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994874,0.00017342539,0.000026785583,0.00008668008,0.00007475825,0.00015100632],"domain_scores_gemma":[0.9983406,0.0008521852,0.0001898373,0.0001740836,0.00028555686,0.00015781772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014350783,0.0007784758,0.0013637455,0.00066907174,0.00050199113,0.0011734193,0.0014127041,0.0018935979,0.005037485],"category_scores_gemma":[0.00361695,0.00039840332,0.00054720225,0.0005421852,0.0007460955,0.0012403623,0.0007996629,0.0012001662,0.00038724978],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006874208,0.00009104036,0.0008310759,0.00014432364,0.0000638412,0.00047332366,0.000084175284,0.9509175,0.0065252893,0.020546421,0.002288461,0.017347055],"study_design_scores_gemma":[0.000046465066,0.00014497289,0.00078316533,0.000011609632,0.000036300436,0.00007742985,0.000031216652,0.9859291,0.00057539664,0.011870602,0.0004798826,0.000013878806],"about_ca_topic_score_codex":0.0034517418,"about_ca_topic_score_gemma":0.001631506,"teacher_disagreement_score":0.005037485,"about_ca_system_score_codex":0.00088821066,"about_ca_system_score_gemma":0.001090446,"threshold_uncertainty_score":0.01685208},"labels":[],"label_agreement":null},{"id":"W4385484908","doi":"10.1109/icphm57936.2023.10194057","title":"A Reinforcement Learning Algorithm for Optimal Dynamic Policies of Joint Condition-based Maintenance and Condition-based Production","year":2023,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Markov decision process; Reinforcement learning; Production (economics); Computer science; Mathematical optimization; Dynamic programming; Q-learning; Time horizon; Production planning; Markov process; Scheduling (production processes); Algorithm; Mathematics; Artificial intelligence; Economics","score_opus":0.0082320604232123,"score_gpt":0.23215250280417754,"score_spread":0.22392044238096523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385484908","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008370586,0.00012843516,0.98883677,0.00013461943,0.000030188055,0.00007947161,0.000021809015,0.00037902978,0.0020191087],"genre_scores_gemma":[0.55360395,0.00021190548,0.44191042,0.00018285356,0.000053453692,0.00055808865,0.00013354792,0.00011337503,0.0032325105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995034,0.00014766952,0.000026863192,0.000120663164,0.00012694618,0.000074623116],"domain_scores_gemma":[0.9985876,0.0009391423,0.00012877434,0.00004109113,0.00023220842,0.00007111159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016275065,0.00088865083,0.0013324007,0.0005480946,0.00051932444,0.00072339934,0.0012855632,0.001211089,0.0028311778],"category_scores_gemma":[0.0047905706,0.00045260484,0.00043295557,0.0004055595,0.00078043516,0.0008052756,0.00087387336,0.0017999241,0.0004207188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006785817,0.00008191833,0.00042040236,0.00004525276,0.000024869694,0.000047257843,0.000047739315,0.9273487,0.00090443826,0.0093125645,0.0010478913,0.060651068],"study_design_scores_gemma":[0.000015024902,0.000020951082,0.00003199067,0.000004730798,0.000002637469,0.000006926945,0.0000022878457,0.99830294,0.0001274537,0.0012785023,0.00020359801,0.0000030193546],"about_ca_topic_score_codex":0.009250997,"about_ca_topic_score_gemma":0.005257255,"teacher_disagreement_score":0.009250997,"about_ca_system_score_codex":0.001319408,"about_ca_system_score_gemma":0.0025722906,"threshold_uncertainty_score":0.018394291},"labels":[],"label_agreement":null},{"id":"W4385484918","doi":"10.1109/icphm57936.2023.10193968","title":"A Reinforcement Learning Algorithm for Optimal Dynamic Policies of Joint Condition-based Maintenance and Condition-based Production","year":2023,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Markov decision process; Reinforcement learning; Production (economics); Q-learning; Dynamic programming; Mathematical optimization; Computer science; Time horizon; Markov process; Production planning; Scheduling (production processes); Algorithm; Mathematics; Artificial intelligence; Economics","score_opus":0.0082320604232123,"score_gpt":0.23215250280417754,"score_spread":0.22392044238096523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385484918","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008370586,0.00012843516,0.98883677,0.00013461943,0.000030188055,0.00007947161,0.000021809015,0.00037902978,0.0020191087],"genre_scores_gemma":[0.55360395,0.00021190548,0.44191042,0.00018285356,0.000053453692,0.00055808865,0.00013354792,0.00011337503,0.0032325105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995034,0.00014766952,0.000026863192,0.000120663164,0.00012694618,0.000074623116],"domain_scores_gemma":[0.9985876,0.0009391423,0.00012877434,0.00004109113,0.00023220842,0.00007111159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016275065,0.00088865083,0.0013324007,0.0005480946,0.00051932444,0.00072339934,0.0012855632,0.001211089,0.0028311778],"category_scores_gemma":[0.0047905706,0.00045260484,0.00043295557,0.0004055595,0.00078043516,0.0008052756,0.00087387336,0.0017999241,0.0004207188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006785817,0.00008191833,0.00042040236,0.00004525276,0.000024869694,0.000047257843,0.000047739315,0.9273487,0.00090443826,0.0093125645,0.0010478913,0.060651068],"study_design_scores_gemma":[0.000015024902,0.000020951082,0.00003199067,0.000004730798,0.000002637469,0.000006926945,0.0000022878457,0.99830294,0.0001274537,0.0012785023,0.00020359801,0.0000030193546],"about_ca_topic_score_codex":0.009250997,"about_ca_topic_score_gemma":0.005257255,"teacher_disagreement_score":0.009250997,"about_ca_system_score_codex":0.001319408,"about_ca_system_score_gemma":0.0025722906,"threshold_uncertainty_score":0.018394291},"labels":[],"label_agreement":null},{"id":"W4385490279","doi":"10.1111/jfpe.14429","title":"A deep reinforcement learning‐based maintenance optimization for vacuum packaging machines considering product quality degradation","year":2023,"lang":"en","type":"article","venue":"Journal of Food Process Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Quality (philosophy); Benchmarking; Reinforcement learning; Reliability engineering; Reliability (semiconductor); Product (mathematics); Preventive maintenance; Computer science; Production (economics); Cost reduction; Condition-based maintenance; Reduction (mathematics); Process (computing); Corrective maintenance; Predictive maintenance; Productivity; Risk analysis (engineering); Engineering; Business; Power (physics); Artificial intelligence; Marketing","score_opus":0.01740170446794785,"score_gpt":0.25326266233819494,"score_spread":0.23586095787024708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385490279","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3125888,0.0011717798,0.6775967,0.0009293016,0.000121996454,0.000096799624,0.00017866956,0.0009562868,0.0063596205],"genre_scores_gemma":[0.98461676,0.00007638991,0.01336451,0.00007472531,0.00001203123,0.00005193307,0.0000649374,0.00001837713,0.0017204252],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997634,0.00005371441,0.000010345911,0.00006904294,0.000045762892,0.00005773012],"domain_scores_gemma":[0.9993606,0.0003631789,0.00008423785,0.000021819867,0.00012499465,0.00004514064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067968515,0.000805788,0.0009982816,0.00033246225,0.00025211246,0.0006436359,0.0008793117,0.0012491316,0.0016853453],"category_scores_gemma":[0.0015516144,0.00043498116,0.0005334505,0.00020413696,0.0005254859,0.00046103002,0.00060221954,0.0009866942,0.0001297707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034667348,0.00002658177,0.0003938835,0.000018626411,0.0000102640415,0.000027057209,0.000006728858,0.99357224,0.00047727925,0.00030269442,0.00016232315,0.0049676634],"study_design_scores_gemma":[0.0000034695913,0.000010352411,0.000051887837,0.0000013501939,0.0000022552506,0.000001596238,7.679297e-7,0.99975723,0.000058882644,0.00008692636,0.00002444693,8.3395594e-7],"about_ca_topic_score_codex":0.013964658,"about_ca_topic_score_gemma":0.007107959,"teacher_disagreement_score":0.013964658,"about_ca_system_score_codex":0.0010282603,"about_ca_system_score_gemma":0.001329355,"threshold_uncertainty_score":0.027766764},"labels":[],"label_agreement":null},{"id":"W4385603717","doi":"10.1016/j.jmsy.2023.07.014","title":"Reinforcement and deep reinforcement learning-based solutions for machine maintenance planning, scheduling policies, and optimization","year":2023,"lang":"en","type":"article","venue":"Journal of Manufacturing Systems","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Reinforcement learning; Computer science; Categorization; Risk analysis (engineering); Asset (computer security); Scheduling (production processes); Predictive maintenance; Artificial intelligence; Machine learning; Engineering; Reliability engineering; Operations management; Computer security","score_opus":0.015703431803264615,"score_gpt":0.2321526244594865,"score_spread":0.2164491926562219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385603717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058406886,0.0010697308,0.93449426,0.0011882256,0.00015507266,0.000058581514,0.00015506023,0.00054691505,0.0039253314],"genre_scores_gemma":[0.90871793,0.0003024533,0.08698742,0.00023293699,0.00010633646,0.00012630598,0.00018177138,0.000082165054,0.0032626586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999617,0.0001484392,0.00001995812,0.000073812276,0.00006378369,0.00007712779],"domain_scores_gemma":[0.9968162,0.0024114887,0.00020820256,0.00010406906,0.00033411148,0.00012596346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001484943,0.0009338765,0.0012470155,0.00052275497,0.00036803223,0.0007258837,0.001362134,0.0015350283,0.002465084],"category_scores_gemma":[0.006443882,0.00057894306,0.0005026037,0.00047793903,0.00087360444,0.0010520979,0.0010438657,0.0021096014,0.00027115384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044687004,0.000040016126,0.00024154344,0.0000254069,0.000013639169,0.0000115751145,0.000015392967,0.9820129,0.00014880378,0.0033021923,0.0006207855,0.013522974],"study_design_scores_gemma":[0.000003955088,0.0000059025297,0.000019827636,0.0000018923225,0.0000012809127,0.0000011064927,0.000001213968,0.9982413,0.000031747473,0.0016570925,0.00003389016,8.5156137e-7],"about_ca_topic_score_codex":0.012227757,"about_ca_topic_score_gemma":0.01021436,"teacher_disagreement_score":0.012227757,"about_ca_system_score_codex":0.0013746821,"about_ca_system_score_gemma":0.002010437,"threshold_uncertainty_score":0.024313152},"labels":[],"label_agreement":null},{"id":"W4385810797","doi":"10.1017/jpr.2023.51","title":"Reliability analyses of linear two-dimensional consecutive <i>k</i>-type systems","year":2023,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Reliability (semiconductor); Type (biology); Markov chain; Linear system; Discrete mathematics; Applied mathematics; Combinatorics; Algorithm; Mathematical analysis; Statistics; Thermodynamics","score_opus":0.0306406236927119,"score_gpt":0.2790673576824543,"score_spread":0.24842673398974238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385810797","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74026984,0.0005926177,0.25096756,0.00023046986,0.00004295202,0.000040336527,0.00009307918,0.00011851563,0.0076446724],"genre_scores_gemma":[0.9965571,0.0000500123,0.002721677,0.000009557886,0.0000067803353,0.000007759605,0.0000188173,0.000004384185,0.00062393036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999828,0.000042466305,0.000008237653,0.00004631703,0.000038019058,0.000036944235],"domain_scores_gemma":[0.9992982,0.00026735538,0.00018805741,0.000047546004,0.0001442933,0.000054525746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003973786,0.00033601656,0.00047145222,0.00048294547,0.00038178748,0.00053881854,0.00059840066,0.00046432033,0.0013944063],"category_scores_gemma":[0.0012513835,0.0002007299,0.0004340126,0.0004204305,0.00076696376,0.0005561445,0.0005238216,0.00032193246,0.00010237685],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009608747,0.000028493394,0.0027204358,0.00008090265,0.000042006504,0.00026519346,0.00009631491,0.9698724,0.0064212666,0.013688796,0.00032227277,0.006365839],"study_design_scores_gemma":[0.0000026395903,0.000027299524,0.00068777206,0.000003084258,0.0000069702965,0.00003623611,0.000023960385,0.99631137,0.00062118145,0.002168034,0.00010457764,0.00000693739],"about_ca_topic_score_codex":0.0032321787,"about_ca_topic_score_gemma":0.0017361069,"teacher_disagreement_score":0.0032321787,"about_ca_system_score_codex":0.0005606985,"about_ca_system_score_gemma":0.00032622204,"threshold_uncertainty_score":0.0064267516},"labels":[],"label_agreement":null},{"id":"W4386401956","doi":"10.1016/j.ress.2023.109624","title":"Stochastic programming for selective maintenance optimization with uncertainty in the next mission conditions","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stochastic programming; Component (thermodynamics); Computer science; Mathematical optimization; Operations research; Sample (material); Reliability engineering; Engineering; Mathematics","score_opus":0.009394295653930738,"score_gpt":0.21577198717167334,"score_spread":0.2063776915177426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386401956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028747963,0.0010233959,0.9619395,0.0012893567,0.00012886443,0.00009353876,0.00036169402,0.00018944066,0.0062262495],"genre_scores_gemma":[0.87338156,0.001357709,0.1073059,0.0005758471,0.00029767494,0.0006948596,0.0008677789,0.00029934186,0.01521938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985071,0.00073381234,0.000047685982,0.0002114077,0.00024960516,0.00025034085],"domain_scores_gemma":[0.9926594,0.00614862,0.00041967447,0.00012125174,0.0004399288,0.00021106061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041422704,0.0018111186,0.0030337465,0.0011498388,0.0005476126,0.0018571792,0.001942791,0.0021337771,0.0040567606],"category_scores_gemma":[0.01144613,0.0018282868,0.0015185681,0.001167316,0.0016092476,0.0016980165,0.0018876179,0.0033614086,0.00032399353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037880374,0.00002031066,0.00013920474,0.00003693085,0.000036819474,0.000026072094,0.0000151761915,0.98377717,0.00015202157,0.013324187,0.0005326471,0.0019015777],"study_design_scores_gemma":[0.0000054754805,0.000008436835,0.000037414833,0.000004474031,0.0000051940965,0.0000025066279,0.0000035346961,0.9952127,0.00003127456,0.0046049026,0.000081044025,0.000003041716],"about_ca_topic_score_codex":0.01152646,"about_ca_topic_score_gemma":0.0067287777,"teacher_disagreement_score":0.01152646,"about_ca_system_score_codex":0.0024923994,"about_ca_system_score_gemma":0.0027440602,"threshold_uncertainty_score":0.02291876},"labels":[],"label_agreement":null},{"id":"W4386514735","doi":"10.1016/j.ress.2023.109632","title":"Redundancy allocation problem with a mix of components for a multi-state system and continuous performance level components","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Canada Research Chairs; Government of Canada","keywords":"Enumeration; Redundancy (engineering); Mathematical optimization; Computer science; Binary number; Component (thermodynamics); Genetic algorithm; Algorithm; Mathematics; Arithmetic","score_opus":0.019249053938026155,"score_gpt":0.2031138302382909,"score_spread":0.18386477630026477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386514735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26419896,0.00086261175,0.71683705,0.0014546312,0.00014785877,0.00039087923,0.0005616698,0.00048627763,0.015059941],"genre_scores_gemma":[0.92744946,0.00020248507,0.062312666,0.0001279586,0.00010296195,0.0002736944,0.00014484751,0.00011425448,0.009271628],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991412,0.00037104965,0.000035501267,0.00016318736,0.0001177287,0.00017137318],"domain_scores_gemma":[0.9978532,0.0015591217,0.00018013865,0.000087215914,0.00016540542,0.00015483302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021544502,0.0015285901,0.0023938413,0.0012290464,0.0005412563,0.0017847175,0.0019001313,0.0024431532,0.007218944],"category_scores_gemma":[0.003541149,0.0012939243,0.0011900181,0.0010677467,0.0010001513,0.0022347854,0.0016486157,0.0013277802,0.00049016374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032890378,0.00009876631,0.00028952336,0.00019725805,0.00010335845,0.00019565206,0.00005678519,0.9751472,0.002581231,0.0107595725,0.00091000943,0.009331698],"study_design_scores_gemma":[0.00006812296,0.00014278019,0.0002976121,0.000016004116,0.00005499294,0.00004832728,0.000025808738,0.9931022,0.00043395945,0.0055482853,0.0002507085,0.00001121049],"about_ca_topic_score_codex":0.0022715179,"about_ca_topic_score_gemma":0.0014274768,"teacher_disagreement_score":0.007218944,"about_ca_system_score_codex":0.0011567412,"about_ca_system_score_gemma":0.00090785546,"threshold_uncertainty_score":0.024149835},"labels":[],"label_agreement":null},{"id":"W4386897708","doi":"10.1016/j.ress.2023.109668","title":"A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Preventive maintenance; Reliability (semiconductor); Condition-based maintenance; Reliability engineering; Maintenance actions; Reinforcement learning; Predictive maintenance; Process (computing); Nonparametric statistics; Optimal maintenance; Computer science; Function (biology); Engineering; Machine learning; Mathematics; Statistics; Power (physics)","score_opus":0.02584545277468162,"score_gpt":0.28210051414613285,"score_spread":0.2562550613714512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386897708","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010746941,0.000019043522,0.99861956,0.000017077815,0.000008587428,0.000016884132,0.000013428611,0.000055875065,0.00017476543],"genre_scores_gemma":[0.25715464,0.00015117569,0.7385613,0.000113838396,0.000121781384,0.00062403583,0.000308269,0.00020390889,0.002761015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983822,0.0007145486,0.00006901227,0.00025845235,0.0004882268,0.00008750799],"domain_scores_gemma":[0.99381036,0.004346188,0.00033107866,0.00047048047,0.0009574626,0.000084393134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003964083,0.00081091374,0.0012553111,0.0011292057,0.00039872705,0.0009289047,0.0020600823,0.0011974542,0.003004846],"category_scores_gemma":[0.0129857855,0.0008235764,0.001284847,0.0008293027,0.00085197826,0.0013392282,0.0014698145,0.0020388414,0.0005200508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110369045,0.0001791193,0.0007037862,0.00014640807,0.000100788726,0.00007336698,0.00007521671,0.81942236,0.0052102115,0.05844581,0.0012191209,0.11431343],"study_design_scores_gemma":[0.000004538198,0.000021037755,0.0000740351,0.0000040800974,0.0000065075164,0.000011031624,0.0000017762536,0.9952904,0.0003402338,0.003980525,0.00025970297,0.000006174533],"about_ca_topic_score_codex":0.0022495931,"about_ca_topic_score_gemma":0.0017869362,"teacher_disagreement_score":0.003964083,"about_ca_system_score_codex":0.0006671078,"about_ca_system_score_gemma":0.0016678126,"threshold_uncertainty_score":0.020964324},"labels":[],"label_agreement":null},{"id":"W4387007840","doi":"10.1016/j.ress.2023.109677","title":"Joint reliability of linear consecutive k-type systems with shared components in a zigzag structure","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Zigzag; Reliability (semiconductor); Linear system; Markov chain; Type (biology); Joint (building); Mathematics; Algorithm; Markov process; Discrete mathematics; Applied mathematics; Combinatorics; Mathematical analysis; Statistics; Physics; Structural engineering; Geometry; Engineering","score_opus":0.010769827538316727,"score_gpt":0.18834508209410722,"score_spread":0.17757525455579048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387007840","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90682507,0.00014360313,0.088111304,0.00009783333,0.000019739507,0.000020135363,0.000090608315,0.00014009634,0.0045515453],"genre_scores_gemma":[0.9948932,0.000028623666,0.0040786755,0.00000520267,0.0000033671563,0.000007916754,0.000026317175,0.000007660656,0.00094903976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997764,0.000069723115,0.000014291069,0.00004478411,0.000047439687,0.000047290352],"domain_scores_gemma":[0.99893695,0.00046823084,0.00024652111,0.00009391232,0.00019372338,0.00006070506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004858873,0.00036692398,0.0005157084,0.00042523813,0.00042054075,0.00090577354,0.00074714853,0.00061700214,0.0012301556],"category_scores_gemma":[0.0015314318,0.00034168555,0.00035089493,0.00052277214,0.0010166682,0.00071083667,0.00047257054,0.00033471547,0.00024672682],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051625207,0.000053679494,0.0031498577,0.000062497274,0.000052221523,0.00026283722,0.00009009003,0.96175945,0.009041874,0.019013856,0.00030204694,0.0056953426],"study_design_scores_gemma":[0.000019446554,0.000081035345,0.0012095321,0.000004823363,0.000017334383,0.00004417019,0.000032826218,0.990274,0.0015369508,0.0066775884,0.00008876799,0.000013626638],"about_ca_topic_score_codex":0.0038095668,"about_ca_topic_score_gemma":0.00347549,"teacher_disagreement_score":0.0038095668,"about_ca_system_score_codex":0.0006435838,"about_ca_system_score_gemma":0.0005156152,"threshold_uncertainty_score":0.0075747967},"labels":[],"label_agreement":null},{"id":"W4387502350","doi":"10.1016/j.ress.2023.109722","title":"Flexible modelling of a bivariate degradation process with a shared frailty and an application to fatigue crack data","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Bivariate analysis; Reliability (semiconductor); Monte Carlo method; Gamma process; Degradation (telecommunications); Computer science; Process (computing); Reliability engineering; Joint probability distribution; Function (biology); Engineering; Mathematics; Statistics; Machine learning","score_opus":0.03778506669141962,"score_gpt":0.2613551994014207,"score_spread":0.2235701327100011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387502350","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19974332,0.00065663824,0.7962692,0.0005849634,0.00009597202,0.00006675436,0.00045749592,0.0003515271,0.001774087],"genre_scores_gemma":[0.9712683,0.00041447903,0.023975618,0.00007536679,0.00005397823,0.000075743934,0.00028000132,0.00010096374,0.0037556305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943584,0.00020396459,0.000038353537,0.00012937482,0.00007519872,0.00011723363],"domain_scores_gemma":[0.99542856,0.0030303746,0.00046713534,0.00037295738,0.0004923714,0.00020856051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038336234,0.00088271487,0.0018060809,0.0009728659,0.0005610219,0.0017794686,0.002274588,0.0029540928,0.0018420989],"category_scores_gemma":[0.011168042,0.0009559504,0.0018007288,0.0014184782,0.0015218178,0.0023613968,0.0013981914,0.0024471856,0.00026418295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003391108,0.000022068338,0.00093023124,0.00002449045,0.000024025192,0.0000970396,0.000050613162,0.98848766,0.00052490865,0.0077123283,0.0001583594,0.0019343256],"study_design_scores_gemma":[0.0000018990199,0.0000036136385,0.00015250065,0.0000020449777,0.000003221844,0.000008912083,0.0000035380208,0.9986456,0.000035079764,0.0011005402,0.000038739727,0.0000042369893],"about_ca_topic_score_codex":0.022853965,"about_ca_topic_score_gemma":0.011570091,"teacher_disagreement_score":0.022853965,"about_ca_system_score_codex":0.0009923999,"about_ca_system_score_gemma":0.0010520542,"threshold_uncertainty_score":0.045441866},"labels":[],"label_agreement":null},{"id":"W4387703901","doi":"10.4043/32798-ms","title":"Offshore Battery Energy Storage System Operational Impacts and Remote Fleet Intelligence","year":2023,"lang":"en","type":"article","venue":"Offshore Technology Conference Brasil","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Scope (computer science); Context (archaeology); Submarine pipeline; Scalability; Range (aeronautics); Computer science; Reliability engineering; Risk analysis (engineering); Function (biology); Mode (computer interface); Engineering; Systems engineering; Marine engineering; Operations research; Business; Database","score_opus":0.012830363840405817,"score_gpt":0.2221252699558052,"score_spread":0.2092949061153994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387703901","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9834585,0.00013542906,0.0027828482,0.00012697,0.000015417141,0.000026642498,0.00067999295,0.00008228292,0.012691856],"genre_scores_gemma":[0.9987111,0.000059157894,0.00024707042,0.000008343797,0.0000032743376,0.0000039943097,0.00027055896,0.0000089229925,0.0006875869],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994985,0.00010225257,0.000033362285,0.000070092894,0.00023782325,0.000057981342],"domain_scores_gemma":[0.9988991,0.00045982245,0.00024433836,0.00011855737,0.00022784178,0.00005040486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006170242,0.00026341784,0.0002476658,0.0006484205,0.00028482167,0.0011267246,0.00031613596,0.00025570445,0.0034882345],"category_scores_gemma":[0.0016601591,0.00009476033,0.00019997267,0.000660926,0.00040337117,0.0009558649,0.00076059706,0.00034847533,0.0005094796],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009554018,0.00018398305,0.49559718,0.00045369272,0.00028681496,0.0033341076,0.0012772004,0.34072298,0.02588301,0.00465518,0.0044710445,0.12217944],"study_design_scores_gemma":[0.00002733361,0.0014289339,0.79179835,0.00018302375,0.00013242898,0.0012463526,0.0054545496,0.16685311,0.013690516,0.004390677,0.014685598,0.000109094995],"about_ca_topic_score_codex":0.0031109478,"about_ca_topic_score_gemma":0.0036831726,"teacher_disagreement_score":0.0034882345,"about_ca_system_score_codex":0.0005064023,"about_ca_system_score_gemma":0.00021814095,"threshold_uncertainty_score":0.011669338},"labels":[],"label_agreement":null},{"id":"W4387908650","doi":"10.1080/00207543.2023.2270689","title":"A critical review of selective maintenance for mission-oriented systems: challenges and a roadmap for novel contributions","year":2023,"lang":"en","type":"review","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Component (thermodynamics); Computer science; Key (lock); Management science; Systems engineering; Risk analysis (engineering); Quality (philosophy); Operations research; Engineering; Computer security","score_opus":0.20485660806059322,"score_gpt":0.4759798635173481,"score_spread":0.27112325545675486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387908650","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012632197,0.9975706,0.0005731626,0.0005078108,0.00029709557,0.000008168931,0.000025811505,0.000009633913,0.00088143314],"genre_scores_gemma":[0.00081553415,0.9978398,0.0005286375,0.00026591576,0.00024630252,0.000009572908,0.000034775236,0.0000034304712,0.00025610847],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992779,0.00014564162,0.00014144261,0.00013961409,0.00024515495,0.000050362694],"domain_scores_gemma":[0.9967789,0.002014119,0.00028916614,0.0000800977,0.00075581693,0.000081849335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015301694,0.0011751446,0.001595698,0.0039357943,0.00042338623,0.0016791672,0.00131,0.001676378,0.003727647],"category_scores_gemma":[0.0044261524,0.0006269263,0.00094830774,0.005247534,0.00059119595,0.0033104303,0.00069548725,0.0016757327,0.0016183048],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008776792,0.00010533155,0.0002954784,0.087679185,0.00020433217,0.00022872727,0.00019935358,0.0017829317,0.0016559185,0.019232253,0.05703386,0.8314949],"study_design_scores_gemma":[0.000012813128,0.0001680095,0.00078138715,0.021265235,0.00034080783,0.0006269647,0.00018661197,0.00047828097,0.00059122057,0.0057198624,0.9697808,0.00004801083],"about_ca_topic_score_codex":0.0019287457,"about_ca_topic_score_gemma":0.0024325612,"teacher_disagreement_score":0.0039357943,"about_ca_system_score_codex":0.0009740099,"about_ca_system_score_gemma":0.0032635133,"threshold_uncertainty_score":0.012470186},"labels":[],"label_agreement":null},{"id":"W4387914158","doi":"10.1109/codit58514.2023.10284108","title":"The Multi-Commodity Flow Problem with Disjoint Signaling Paths: A Branch-and-Benders-Cut Algorithm","year":2023,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Path (computing); Disjoint sets; Jitter; Integer programming; Reliability (semiconductor); Routing (electronic design automation); Algorithm; Limit (mathematics); Flow network; Mathematical optimization; Linear programming; Mathematics; Computer network; Telecommunications","score_opus":0.011474671016477525,"score_gpt":0.19978961018750696,"score_spread":0.18831493917102943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387914158","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028640572,0.00043833329,0.9651219,0.0006392933,0.00005374981,0.00025280728,0.00021012596,0.00030859752,0.0043346398],"genre_scores_gemma":[0.18009654,0.0004080124,0.8138201,0.00018397611,0.00007372443,0.000489684,0.00051312346,0.00013556327,0.0042792927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99927837,0.0003343878,0.00002572985,0.00013723862,0.0001271747,0.00009705845],"domain_scores_gemma":[0.9984559,0.0011780243,0.00012213436,0.000052213232,0.00011043794,0.00008125256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002245234,0.0015515035,0.0015198122,0.0010035603,0.00072786206,0.0014249817,0.0017610035,0.002379363,0.004551773],"category_scores_gemma":[0.003434619,0.0009236641,0.00084763597,0.001390121,0.00080520345,0.0019291826,0.0011973424,0.0019760167,0.00046919784],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099363846,0.00013055054,0.00035912893,0.00007590092,0.00003128713,0.000059411515,0.000047829177,0.94507545,0.00046976577,0.0144053465,0.0023651195,0.036880713],"study_design_scores_gemma":[0.000033153156,0.000036596688,0.000047427293,0.000010022186,0.000006443235,0.000015694533,0.000013916524,0.9894404,0.00016234687,0.009675722,0.0005546444,0.0000036657307],"about_ca_topic_score_codex":0.0035951976,"about_ca_topic_score_gemma":0.0030456886,"teacher_disagreement_score":0.004551773,"about_ca_system_score_codex":0.0011979059,"about_ca_system_score_gemma":0.0019369427,"threshold_uncertainty_score":0.015227199},"labels":[],"label_agreement":null},{"id":"W4387939880","doi":"10.1017/apr.2023.44","title":"An inaccuracy measure between non-explosive point processes with applications to Markov chains","year":2023,"lang":"en","type":"article","venue":"Advances in Applied Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Point process; Mathematics; Residual; Markov process; Markov chain; Measure (data warehouse); Markov property; Martingale (probability theory); Gamma process; Cox process; Component (thermodynamics); Markov renewal process; Time reversibility; Statistical physics; Entropy (arrow of time); Applied mathematics; Poisson distribution; Statistics; Markov model; Computer science; Algorithm; Poisson process; Data mining","score_opus":0.010779376514791418,"score_gpt":0.25294527916725357,"score_spread":0.24216590265246216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387939880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.108101636,0.00078204,0.88752633,0.0005100375,0.00007216823,0.00004733816,0.00009388531,0.00011908815,0.002747529],"genre_scores_gemma":[0.9457979,0.00049503095,0.051789757,0.00008163276,0.00014745712,0.000059523023,0.00008268494,0.000044172924,0.0015018092],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969886,0.00093038747,0.00021383059,0.0005004412,0.0011656715,0.00020106135],"domain_scores_gemma":[0.96418643,0.02530522,0.0055002263,0.0015701931,0.0020679215,0.0013700419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062694745,0.00074798486,0.0011074935,0.0035235942,0.00066426524,0.0019481102,0.0014369327,0.0015220004,0.0013940033],"category_scores_gemma":[0.030324027,0.000548889,0.0009810732,0.0018438569,0.00404429,0.004009802,0.003640872,0.002182381,0.000110294575],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116044255,0.00006023605,0.0053482405,0.00013365022,0.00008784167,0.00045040494,0.0003669821,0.38964865,0.003013519,0.5857285,0.000438068,0.014607773],"study_design_scores_gemma":[0.000007967398,0.000063909465,0.0011320168,0.000041509655,0.000019197796,0.00013620399,0.000039511826,0.77559876,0.0008495411,0.22158962,0.00048432217,0.000037407834],"about_ca_topic_score_codex":0.0011685414,"about_ca_topic_score_gemma":0.00051383424,"teacher_disagreement_score":0.0062694745,"about_ca_system_score_codex":0.001744883,"about_ca_system_score_gemma":0.00064672914,"threshold_uncertainty_score":0.033156514},"labels":[],"label_agreement":null},{"id":"W4388090033","doi":"10.1016/j.datak.2023.102240","title":"Hierarchical framework for interpretable and specialized deep reinforcement learning-based predictive maintenance","year":2023,"lang":"en","type":"article","venue":"Data & Knowledge Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"H2020 Marie Skłodowska-Curie Actions; Canadian Institute of Steel Construction; Horizon 2020 Framework Programme; Österreichische Forschungsförderungsgesellschaft; Science Foundation Ireland; Horizon 2020","keywords":"Interpretability; Reinforcement learning; Machine learning; Computer science; Artificial intelligence; Probabilistic logic; Black box; Context (archaeology); Markov decision process; Risk analysis (engineering); Markov process","score_opus":0.015179626367081898,"score_gpt":0.25194097941082105,"score_spread":0.23676135304373916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388090033","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010300418,0.0003218107,0.98549485,0.00016712604,0.000042300875,0.000030973137,0.00022451016,0.001593722,0.0018242949],"genre_scores_gemma":[0.7552218,0.00029599495,0.23792507,0.0002112266,0.000086434615,0.0001721658,0.0007470979,0.00024075214,0.005099514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996811,0.000053819815,0.000017216586,0.00010405977,0.00008196046,0.00006183515],"domain_scores_gemma":[0.99944085,0.00022293784,0.00005810237,0.00008880058,0.00014730515,0.000041939944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066042354,0.0007725386,0.0009458678,0.0005279849,0.0003220009,0.0008779212,0.0025200478,0.0011693347,0.0048991274],"category_scores_gemma":[0.0020494666,0.00045966372,0.0006757112,0.00044515755,0.0005899517,0.0011040244,0.0011521006,0.0016766326,0.000789146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011221818,0.000097507254,0.0006341479,0.0000809222,0.00004457537,0.00008462937,0.00005986927,0.8639094,0.003155795,0.02021226,0.0033433074,0.10826527],"study_design_scores_gemma":[0.000003140622,0.000008387973,0.00004199818,0.0000029598614,0.0000035450146,0.0000043131736,0.0000017220862,0.9956185,0.0002360683,0.0038871877,0.00019008823,0.0000019767933],"about_ca_topic_score_codex":0.010754869,"about_ca_topic_score_gemma":0.017191842,"teacher_disagreement_score":0.010754869,"about_ca_system_score_codex":0.001137878,"about_ca_system_score_gemma":0.0016728514,"threshold_uncertainty_score":0.021384537},"labels":[],"label_agreement":null},{"id":"W4388130690","doi":"10.1080/00207543.2023.2275635","title":"A novel approach for predicting Lockout/Tagout safety procedures for smart maintenance strategies","year":2023,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Task (project management); Sensitivity (control systems); Artificial neural network; Random forest; Computer science; Predictive maintenance; Engineering; Machine learning; Artificial intelligence; Data mining; Reliability engineering; Systems engineering","score_opus":0.06247765858059513,"score_gpt":0.3536041135988805,"score_spread":0.2911264550182854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388130690","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.175254,0.0010350115,0.8165623,0.00034605767,0.00014390289,0.00014450897,0.0008568478,0.0018767697,0.0037806598],"genre_scores_gemma":[0.87002635,0.00028807917,0.12514992,0.00010359669,0.0000852546,0.00008809141,0.0009701522,0.00005830171,0.0032301957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997707,0.000024325582,0.000016329102,0.00008084614,0.000066978944,0.000040766754],"domain_scores_gemma":[0.99960524,0.00011956212,0.00007149048,0.000026187156,0.00014337292,0.00003419092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004017896,0.00096515473,0.0006492736,0.0011658547,0.00027952658,0.0005738001,0.00090917293,0.00084711605,0.0013633657],"category_scores_gemma":[0.0010694391,0.00027858393,0.0005777474,0.00047404834,0.00019754087,0.0005695739,0.0003978164,0.000562972,0.00041527738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030213525,0.00043374344,0.018635584,0.00016072331,0.00008994344,0.00021868036,0.00010234373,0.6296611,0.012037386,0.0011971975,0.004239104,0.3329221],"study_design_scores_gemma":[0.0000056791896,0.000058788304,0.0018868605,0.00000913115,0.00001519688,0.00002745388,0.000014811048,0.99565035,0.0013350026,0.0005134022,0.00047643742,0.00000692898],"about_ca_topic_score_codex":0.007856302,"about_ca_topic_score_gemma":0.010573028,"teacher_disagreement_score":0.007856302,"about_ca_system_score_codex":0.000528627,"about_ca_system_score_gemma":0.0010795693,"threshold_uncertainty_score":0.015621185},"labels":[],"label_agreement":null},{"id":"W4388312160","doi":"10.1016/j.eswa.2023.122303","title":"Distributionally-robust chance-constrained optimization of selective maintenance under uncertain repair duration","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Mathematical optimization; Computer science; Probabilistic logic; Benchmark (surveying); Piecewise linear function; Preventive maintenance; Linear programming; Ambiguity; Mathematics; Reliability engineering; Artificial intelligence","score_opus":0.012011024546715664,"score_gpt":0.2229362957933242,"score_spread":0.21092527124660854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388312160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07820184,0.0012027179,0.91283774,0.000988347,0.00011065916,0.000081011945,0.00045209276,0.00025212162,0.0058735115],"genre_scores_gemma":[0.9598232,0.00051344326,0.033299223,0.0001380954,0.00008774212,0.00014395328,0.00032935868,0.00016151302,0.0055034827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872845,0.00051141245,0.000058918507,0.00024957958,0.00020772687,0.00024389155],"domain_scores_gemma":[0.9922891,0.005943069,0.0007749157,0.00021740013,0.0005352188,0.00024024492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004450418,0.0016018508,0.0032678468,0.0011403554,0.00041082466,0.0019880515,0.002279029,0.0023845886,0.0023980446],"category_scores_gemma":[0.013645613,0.0015488488,0.00124635,0.001262743,0.0019334722,0.0020872392,0.0018008153,0.0016021233,0.00033255198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053530715,0.000010809186,0.0001094534,0.000040327683,0.000028896855,0.000023860843,0.000011358078,0.9932961,0.00025233303,0.0044482956,0.00019796367,0.0015270183],"study_design_scores_gemma":[0.0000071701097,0.000015013472,0.000080360296,0.0000043936884,0.000006229404,0.0000053920458,0.0000040859004,0.99729246,0.00009396566,0.0024380733,0.00004857305,0.0000042372526],"about_ca_topic_score_codex":0.00996354,"about_ca_topic_score_gemma":0.0050406507,"teacher_disagreement_score":0.00996354,"about_ca_system_score_codex":0.002305419,"about_ca_system_score_gemma":0.0020226366,"threshold_uncertainty_score":0.023536384},"labels":[],"label_agreement":null},{"id":"W4388543763","doi":"10.1109/tr.2023.3325665","title":"An Availability-Constrained Integrated Maintenance–Monitoring Model for a System With Failures Following an NHPP","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Canada Research Chairs","keywords":"Reliability engineering; Control chart; Corrective maintenance; Preventive maintenance; Control limits; Optimal maintenance; Production (economics); Maintenance actions; Interval (graph theory); Chart; Sensitivity (control systems); Maintenance engineering; Statistical process control; Computer science; Process (computing); Function (biology); Poisson distribution; Planned maintenance; Engineering; Statistics; Mathematics","score_opus":0.013609162318019506,"score_gpt":0.23625005895799486,"score_spread":0.22264089663997536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388543763","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10747297,0.0009225965,0.8761613,0.0009145163,0.00013377723,0.0002743423,0.0013005523,0.0007757003,0.012044292],"genre_scores_gemma":[0.9614157,0.00048204273,0.02221654,0.00012092133,0.00006786625,0.0004647497,0.00050993456,0.000078609555,0.014643523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986034,0.00035190914,0.00006551885,0.00042829648,0.00028693405,0.0002640357],"domain_scores_gemma":[0.99766123,0.0012571935,0.00044842652,0.000069525,0.0004459487,0.00011764808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002512619,0.0017982082,0.0019747976,0.0011914247,0.00067969004,0.0018496396,0.0038714095,0.0030531713,0.0048146783],"category_scores_gemma":[0.0046971347,0.0013128933,0.0013427294,0.0012172022,0.0012330296,0.0016300209,0.001606576,0.0020825078,0.0005675209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041132345,0.00002027415,0.00033886032,0.00003288765,0.000017890732,0.00008350698,0.000029461633,0.99555534,0.0002525888,0.0021856017,0.00013891583,0.0013035338],"study_design_scores_gemma":[0.000007313644,0.00001770247,0.00012396567,0.000003034144,0.000010608733,0.000010528443,0.0000064113797,0.9992036,0.00003738525,0.00050577807,0.00007006292,0.000003606432],"about_ca_topic_score_codex":0.031328235,"about_ca_topic_score_gemma":0.012662327,"teacher_disagreement_score":0.031328235,"about_ca_system_score_codex":0.002195227,"about_ca_system_score_gemma":0.0018589434,"threshold_uncertainty_score":0.06229174},"labels":[],"label_agreement":null},{"id":"W4388562518","doi":"10.1016/j.ress.2023.109796","title":"Joint optimization of selective maintenance and repairpersons assignment problem for mission-oriented systems operating under <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si5.svg\" display=\"inline\" id=\"d1e7882\"><mml:mi>s</mml:mi></mml:math>-dependent competing risks","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Mathematical optimization; Computer science; Robustness (evolution); Reliability engineering; Operations research; Engineering; Mathematics","score_opus":0.01333719931605585,"score_gpt":0.22216649039718697,"score_spread":0.20882929108113113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388562518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2913862,0.0016351664,0.6757051,0.0033321066,0.00018982776,0.0004881095,0.0015431658,0.0006050892,0.025115304],"genre_scores_gemma":[0.9364746,0.0004909691,0.048551098,0.00017648225,0.000078713885,0.00032980283,0.0007363115,0.00017081187,0.012991224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902296,0.0004270264,0.00003326015,0.0001584787,0.00011672009,0.00024157786],"domain_scores_gemma":[0.9963637,0.0025145595,0.00031969094,0.00012775468,0.0003011071,0.0003731579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026872274,0.0014215505,0.0018130402,0.0011143602,0.0005230381,0.0015897169,0.0016048973,0.0019472704,0.009707766],"category_scores_gemma":[0.0043597007,0.000862498,0.0010033959,0.0008052384,0.0008867017,0.0014491493,0.0014781858,0.0014204145,0.0006548991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042291795,0.00011777801,0.00083192624,0.00018778579,0.00007352591,0.0001080596,0.000051963194,0.9771967,0.0005993543,0.0056403093,0.0029276724,0.01184196],"study_design_scores_gemma":[0.000040434763,0.00010570218,0.0004366644,0.000013640156,0.000025061041,0.000020062565,0.000037219852,0.99509996,0.000253847,0.003641981,0.00031767998,0.000007684473],"about_ca_topic_score_codex":0.0101220375,"about_ca_topic_score_gemma":0.0075575844,"teacher_disagreement_score":0.0101220375,"about_ca_system_score_codex":0.0014037417,"about_ca_system_score_gemma":0.0023684697,"threshold_uncertainty_score":0.03247577},"labels":[],"label_agreement":null},{"id":"W4388716749","doi":"10.1080/00207543.2023.2280882","title":"Condition-based maintenance optimisation for multi-component systems using mean residual life","year":2023,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Component (thermodynamics); Residual; Reliability engineering; Condition-based maintenance; Computer science; Engineering; Algorithm","score_opus":0.16887623450024145,"score_gpt":0.4049592125975391,"score_spread":0.23608297809729764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388716749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029289128,0.00043253077,0.968088,0.00012107054,0.000025309411,0.00004329011,0.00006578562,0.00027710284,0.0016578354],"genre_scores_gemma":[0.9405542,0.00028133285,0.057074115,0.000045061184,0.000029806188,0.0001369694,0.00010994764,0.000043807537,0.0017248046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941397,0.00016707017,0.0000249626,0.00012801337,0.00019105212,0.000074816446],"domain_scores_gemma":[0.9990478,0.0006391173,0.00012596909,0.00003566939,0.00012061208,0.000030778396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009738759,0.0009448153,0.0014497594,0.00061736244,0.00025440173,0.0010994038,0.0012622367,0.0012989368,0.0015733787],"category_scores_gemma":[0.0020372914,0.00046390845,0.00083440024,0.00049324235,0.0005615941,0.0008710382,0.00060198363,0.0009854485,0.00020968266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002134941,0.000015981052,0.00016500165,0.00002520032,0.0000112652515,0.000016456,0.000012290396,0.9934202,0.00048283368,0.0010065498,0.0000850077,0.0047379946],"study_design_scores_gemma":[0.0000025502898,0.000013420287,0.000052366166,0.0000016889663,0.0000028511897,0.000003817941,0.0000012122086,0.9994999,0.00007697372,0.00029311614,0.00005055619,0.000001526851],"about_ca_topic_score_codex":0.005624928,"about_ca_topic_score_gemma":0.003426556,"teacher_disagreement_score":0.005624928,"about_ca_system_score_codex":0.0009616054,"about_ca_system_score_gemma":0.0010758748,"threshold_uncertainty_score":0.011184335},"labels":[],"label_agreement":null},{"id":"W4390817300","doi":"10.1016/j.ress.2024.109942","title":"Integrated degradation-based burn-in and maintenance model for heterogeneous and highly reliable items","year":2024,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Burn-in; Reliability engineering; Reliability (semiconductor); Optimal maintenance; Constraint (computer-aided design); Preventive maintenance; Computer science; Function (biology); Degradation (telecommunications); Population; Risk analysis (engineering); Engineering; Operations research; Power (physics)","score_opus":0.005800617488607081,"score_gpt":0.18579187339650458,"score_spread":0.1799912559078975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390817300","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25073677,0.002197783,0.720454,0.0006871974,0.00018881427,0.00020560238,0.0014096953,0.0009107394,0.023209505],"genre_scores_gemma":[0.9781979,0.0004032027,0.009507166,0.000059942642,0.0000300675,0.00010509211,0.00034107632,0.000076287164,0.011279347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960726,0.00006343875,0.000017684846,0.00011074544,0.00008992727,0.0001110297],"domain_scores_gemma":[0.99942434,0.00020281824,0.00010091608,0.00003942376,0.00018104777,0.00005141789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007938312,0.0009640612,0.0016765504,0.0008940029,0.00051926624,0.0011066594,0.0030312221,0.0017807405,0.0034945917],"category_scores_gemma":[0.001424405,0.00071895856,0.0011066883,0.00096155045,0.00081019005,0.001469079,0.00080592884,0.0010340337,0.00042378475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022438524,0.000012829141,0.00019821078,0.000019459505,0.000009897768,0.000038260583,0.000010941505,0.9971239,0.0003456841,0.0009890606,0.00012026506,0.0011089914],"study_design_scores_gemma":[0.000002100948,0.000005764347,0.00009947354,0.0000015918854,0.0000063773264,0.000006524583,0.0000027265019,0.9993654,0.000058475976,0.00040270537,0.00004707674,0.0000018853444],"about_ca_topic_score_codex":0.02322359,"about_ca_topic_score_gemma":0.013632649,"teacher_disagreement_score":0.02322359,"about_ca_system_score_codex":0.0016737793,"about_ca_system_score_gemma":0.0012449678,"threshold_uncertainty_score":0.04617679},"labels":[],"label_agreement":null},{"id":"W4391422737","doi":"10.1109/ieem58616.2023.10406556","title":"A Data-driven Approach to Predict Maintenance Delays for Time-based Maintenance","year":2023,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Reliability (semiconductor); Computer science; Predictive maintenance; Random forest; Reliability engineering; Machine learning; Predictive modelling; Artificial neural network; Nuclear power; Work (physics); Artificial intelligence; Data mining; Power (physics); Engineering","score_opus":0.022788368731795176,"score_gpt":0.23124265868519242,"score_spread":0.20845428995339724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391422737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14403631,0.00050504965,0.8489496,0.0007162161,0.000114927505,0.00015083628,0.0023589714,0.0013412158,0.0018268056],"genre_scores_gemma":[0.8535629,0.00018739568,0.14103013,0.000090293404,0.000071348244,0.00020061203,0.0026217694,0.00008207309,0.0021535105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970263,0.000053153275,0.000022214537,0.00011915168,0.00006500115,0.00003795304],"domain_scores_gemma":[0.9984836,0.0008753617,0.0001694218,0.00008829721,0.00030966089,0.00007364405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009985399,0.0007431025,0.0006859079,0.001041196,0.0002909512,0.00076284364,0.0015360189,0.00095887086,0.0016406275],"category_scores_gemma":[0.0033403693,0.00048624218,0.00068230456,0.0009333258,0.00023986152,0.00079496746,0.00041651633,0.0014878672,0.00031129352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007939615,0.00013200067,0.0033735582,0.000048164973,0.00004187248,0.000047790196,0.00002952231,0.95767415,0.0010966961,0.0020626846,0.0010634436,0.034350768],"study_design_scores_gemma":[0.000001939153,0.000011010331,0.0002485118,0.0000018165861,0.0000030455446,0.0000049506675,0.0000030222589,0.9986487,0.00019825014,0.0007355101,0.00014106052,0.0000022460829],"about_ca_topic_score_codex":0.013024743,"about_ca_topic_score_gemma":0.01895687,"teacher_disagreement_score":0.013024743,"about_ca_system_score_codex":0.0011353721,"about_ca_system_score_gemma":0.0013452665,"threshold_uncertainty_score":0.02589786},"labels":[],"label_agreement":null},{"id":"W4391575423","doi":"10.1007/s13243-024-00135-6","title":"A mathematical model for aerospace product MRO scheduling with remanufacturing","year":2024,"lang":"en","type":"article","venue":"Journal of remanufacturing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remanufacturing; Aerospace; Scheduling (production processes); Reliability engineering; Preventive maintenance; Component (thermodynamics); Operations research; Risk analysis (engineering); Operations management; Computer science; Manufacturing engineering; Engineering; Business","score_opus":0.010501610000030578,"score_gpt":0.22274581599400317,"score_spread":0.2122442059939726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391575423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029278692,0.0014856196,0.9334427,0.0014499803,0.00026536558,0.00019167432,0.0007835642,0.00027100672,0.03283137],"genre_scores_gemma":[0.84504664,0.0024031217,0.098982014,0.00036762565,0.00026263227,0.00064620934,0.0007239645,0.00018568139,0.05138213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924386,0.00022902447,0.000028573138,0.00017483984,0.00016878045,0.00015496142],"domain_scores_gemma":[0.9988342,0.00068572856,0.00019635775,0.00004470309,0.00016117803,0.000077844335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014693512,0.0013308161,0.0021336845,0.0011670159,0.0009193645,0.0024350455,0.003143217,0.0026736723,0.0081121065],"category_scores_gemma":[0.0030506572,0.0010902289,0.0014153091,0.0015662346,0.00102988,0.0019284567,0.0013434445,0.001865885,0.0012570855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020332525,0.000025868017,0.00010530218,0.00005920819,0.000012917337,0.00007507507,0.000027909005,0.96912855,0.00040730333,0.026497997,0.0007280041,0.0029115125],"study_design_scores_gemma":[0.0000076155798,0.000014358455,0.000044838926,0.0000056648128,0.0000059088206,0.000011846918,0.0000077717705,0.99490154,0.00005867053,0.004319203,0.00061676966,0.000005748453],"about_ca_topic_score_codex":0.018790087,"about_ca_topic_score_gemma":0.011734311,"teacher_disagreement_score":0.018790087,"about_ca_system_score_codex":0.0028513032,"about_ca_system_score_gemma":0.0024764168,"threshold_uncertainty_score":0.037361443},"labels":[],"label_agreement":null},{"id":"W4392905041","doi":"10.1109/rams51492.2024.10457802","title":"A Cumulative Shock Model with Random Failure Threshold and a Change Point","year":2024,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Point (geometry); Computer science; Shock (circulatory); Mathematics; Medicine","score_opus":0.013935964164521752,"score_gpt":0.21053603853846836,"score_spread":0.1966000743739466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392905041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21512793,0.0017720399,0.74845195,0.0046562734,0.00049547764,0.00019553759,0.0010504212,0.00098243,0.02726792],"genre_scores_gemma":[0.967389,0.0005217061,0.01120361,0.0002105858,0.000117372634,0.00008936327,0.00022365045,0.00009544802,0.020149225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99879164,0.0003616143,0.00006460008,0.00025313598,0.00025951557,0.00026942434],"domain_scores_gemma":[0.99425536,0.0028298462,0.000795799,0.00050367735,0.0010714486,0.000543873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027640972,0.00092834025,0.0016036875,0.0017833606,0.00069329754,0.0021814986,0.0033316652,0.0023936399,0.011365031],"category_scores_gemma":[0.0074006254,0.00055110594,0.0018047652,0.0013471877,0.001967472,0.0023240976,0.0015747478,0.0028365324,0.0011533815],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000176151,0.00007987861,0.0031352818,0.00010966365,0.0000669196,0.0005049851,0.00018689691,0.8512099,0.0008971796,0.13147584,0.0030914035,0.009065917],"study_design_scores_gemma":[0.000013003867,0.000046608668,0.0004697015,0.000014607136,0.000025261503,0.0000850728,0.000044572513,0.97335386,0.00015478472,0.025046574,0.0007234854,0.000022448085],"about_ca_topic_score_codex":0.018740576,"about_ca_topic_score_gemma":0.007756553,"teacher_disagreement_score":0.018740576,"about_ca_system_score_codex":0.002199705,"about_ca_system_score_gemma":0.0011667213,"threshold_uncertainty_score":0.038019776},"labels":[],"label_agreement":null},{"id":"W4392905317","doi":"10.1109/rams51492.2024.10457834","title":"A New Maintenance Plan for Wind Turbine Farms Using Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Reinforcement learning; Plan (archaeology); Turbine; Reinforcement; Computer science; Wind power; Engineering; Marine engineering; Artificial intelligence; Electrical engineering; Structural engineering; Mechanical engineering; Geology","score_opus":0.01435257853677909,"score_gpt":0.22688538752000845,"score_spread":0.21253280898322935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392905317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09242538,0.000257257,0.9012401,0.00033544286,0.000045122957,0.00023026044,0.00016472394,0.00088842673,0.0044132867],"genre_scores_gemma":[0.883306,0.00011281418,0.114394315,0.000052429652,0.00001595404,0.00015155434,0.00018199968,0.000041556,0.0017432931],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999795,0.000034198,0.000011958213,0.00006105559,0.000055169596,0.000042662752],"domain_scores_gemma":[0.99959,0.00014023145,0.00008725872,0.000025577503,0.00009885115,0.000058089485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048313013,0.0006130309,0.0006181228,0.0005551576,0.00038371078,0.00063471985,0.0008947968,0.0006893382,0.0020555444],"category_scores_gemma":[0.0013336036,0.00032054144,0.00036698452,0.00030133658,0.00030552305,0.0008185141,0.00050608424,0.0006533973,0.00022995574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005925511,0.00006218986,0.0010867211,0.00003504681,0.000016141525,0.00010988552,0.000036904476,0.9544373,0.0013531105,0.0021132687,0.00073745695,0.03995273],"study_design_scores_gemma":[0.0000094133775,0.000023596676,0.00011724108,0.0000034644877,0.0000039912916,0.000010198955,0.0000052365663,0.9987866,0.00018303395,0.00066971336,0.00018465889,0.0000029044631],"about_ca_topic_score_codex":0.007248613,"about_ca_topic_score_gemma":0.008142562,"teacher_disagreement_score":0.007248613,"about_ca_system_score_codex":0.0007605927,"about_ca_system_score_gemma":0.0013290447,"threshold_uncertainty_score":0.01441282},"labels":[],"label_agreement":null},{"id":"W4393135520","doi":"10.36001/phmap.2017.v1i1.1825","title":"A Bayesian approach to reliability prediction for one-shot devices","year":2017,"lang":"en","type":"article","venue":"PHM Society Asia-Pacific Conference","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Korea Institute of Energy Technology Evaluation and Planning; Ministry of Trade, Industry and Energy; McMaster University","keywords":"Reliability (semiconductor); Bayesian probability; Shot (pellet); Reliability engineering; Computer science; One shot; Artificial intelligence; Data mining; Machine learning; Engineering; Materials science; Physics","score_opus":0.04893112573165968,"score_gpt":0.2595196536544122,"score_spread":0.2105885279227525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393135520","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015721481,0.00059652154,0.98087573,0.00032988918,0.000038306356,0.000048095342,0.00016673293,0.00020245729,0.0020207718],"genre_scores_gemma":[0.7852943,0.002057151,0.19799873,0.0003031385,0.00027064487,0.0004144704,0.0010958662,0.00013459941,0.01243106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987192,0.00041289418,0.000057155587,0.00032312857,0.00036615419,0.00012154195],"domain_scores_gemma":[0.9962788,0.0025374074,0.00026309397,0.00013920464,0.0006890603,0.0000923559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025191095,0.0008719321,0.0014190017,0.0013216812,0.00049262354,0.0010871317,0.0023507099,0.0013786323,0.0034182603],"category_scores_gemma":[0.007879942,0.000947903,0.00097529276,0.0009878969,0.0008155843,0.0018663913,0.00078950514,0.0017213811,0.00056106626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099573175,0.00006797616,0.0030885926,0.00013756203,0.00007337551,0.00014358886,0.0001291545,0.9152639,0.0012963411,0.028960017,0.0019569574,0.048782907],"study_design_scores_gemma":[0.0000054292937,0.000025520654,0.000545989,0.000013944705,0.000017353406,0.000030185323,0.00000976319,0.98757774,0.00019643041,0.011109008,0.0004560265,0.000012720287],"about_ca_topic_score_codex":0.011964062,"about_ca_topic_score_gemma":0.010899592,"teacher_disagreement_score":0.011964062,"about_ca_system_score_codex":0.0011735237,"about_ca_system_score_gemma":0.0012326961,"threshold_uncertainty_score":0.02378881},"labels":[],"label_agreement":null},{"id":"W4394917640","doi":"10.1007/s10479-024-05930-9","title":"A mathematical maintenance model for a production system subject to deterioration according to a stochastic geometric process","year":2024,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subject (documents); Process (computing); Production (economics); Computer science; Economics; Programming language","score_opus":0.14406912029629854,"score_gpt":0.41939483199546657,"score_spread":0.27532571169916803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394917640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07531514,0.0018968572,0.9072862,0.002900585,0.0002654807,0.00013873595,0.0007655858,0.00038876262,0.0110427495],"genre_scores_gemma":[0.93135995,0.0021510324,0.041102614,0.0003556865,0.00034869878,0.0003196404,0.00064236316,0.00017132483,0.023548717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987953,0.00037239635,0.00007285908,0.00034010742,0.00024153548,0.00017774977],"domain_scores_gemma":[0.99632144,0.0022231357,0.00067578803,0.00010923222,0.0005327081,0.00013767234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027496729,0.001829238,0.0024197246,0.0023134856,0.0008051954,0.0028331992,0.0042005284,0.004479896,0.0046217088],"category_scores_gemma":[0.0062396503,0.0014349102,0.0018512884,0.0020219146,0.0025115365,0.0026043742,0.0016778989,0.0021021783,0.0007452335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002591005,0.000027998403,0.00022756757,0.00007215971,0.000025873234,0.00009716459,0.000051201998,0.9649742,0.0006379308,0.031593543,0.00045041923,0.0018158406],"study_design_scores_gemma":[0.000010160625,0.000020909818,0.00011878947,0.000005997365,0.000016506989,0.000030710566,0.000007436842,0.994995,0.00005589,0.004534801,0.00019289361,0.000010985972],"about_ca_topic_score_codex":0.01145082,"about_ca_topic_score_gemma":0.0057318746,"teacher_disagreement_score":0.01145082,"about_ca_system_score_codex":0.0030757412,"about_ca_system_score_gemma":0.0021758303,"threshold_uncertainty_score":0.022768378},"labels":[],"label_agreement":null},{"id":"W4395450092","doi":"10.1016/j.ress.2024.110147","title":"On the residual lifetimes of dependent components upon system failure","year":2024,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Politechnika Warszawska; McMaster University","keywords":"Residual; Reliability engineering; Component (thermodynamics); Computer science; Engineering; Algorithm; Physics; Thermodynamics","score_opus":0.004290552595567029,"score_gpt":0.16985227492423327,"score_spread":0.16556172232866623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395450092","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55714077,0.011000525,0.39308804,0.001986105,0.00038999302,0.000095851836,0.0006769934,0.00030803418,0.035313677],"genre_scores_gemma":[0.9745871,0.0037067884,0.010831331,0.0001746196,0.00021051217,0.000047709396,0.00046873974,0.00018670026,0.009786547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993356,0.0002711547,0.000026797277,0.00011641994,0.00014536016,0.00010463321],"domain_scores_gemma":[0.98859465,0.009141957,0.0007369402,0.00049403426,0.00085217494,0.00018025297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003501668,0.00097032124,0.0006181694,0.0015920352,0.00030483116,0.00075171527,0.0011923062,0.0006682503,0.0044532563],"category_scores_gemma":[0.014198814,0.000360865,0.00043350793,0.00068311585,0.0016219304,0.0027228934,0.0010150388,0.0012327454,0.00044499905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005412718,0.000102230275,0.0035693701,0.00039085522,0.00009808099,0.00026484672,0.00034331912,0.80221665,0.009039124,0.14155562,0.002737767,0.03914084],"study_design_scores_gemma":[0.000014030614,0.00008576174,0.004055493,0.00008109836,0.000055151228,0.000092923714,0.00009039846,0.95970416,0.0021822168,0.03196413,0.0016472983,0.000027387843],"about_ca_topic_score_codex":0.003063985,"about_ca_topic_score_gemma":0.0031740447,"teacher_disagreement_score":0.0044532563,"about_ca_system_score_codex":0.0010584656,"about_ca_system_score_gemma":0.00062562927,"threshold_uncertainty_score":0.018518806},"labels":[],"label_agreement":null},{"id":"W4395690405","doi":"10.1002/net.22222","title":"Network design with vulnerability constraints and probabilistic edge reliability","year":2024,"lang":"en","type":"article","venue":"Networks","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Backup; Enhanced Data Rates for GSM Evolution; Probabilistic logic; Reliability (semiconductor); Computer science; Path (computing); Mathematical optimization; Network planning and design; Mathematics; Computer network; Artificial intelligence","score_opus":0.010452731007824328,"score_gpt":0.20145504541877496,"score_spread":0.19100231441095064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395690405","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15794007,0.0007408111,0.83264285,0.0009676107,0.00007657748,0.00015594714,0.0009667021,0.0002853337,0.0062240167],"genre_scores_gemma":[0.8561195,0.00039066188,0.14002982,0.00011247382,0.000047402205,0.00018693498,0.00067925063,0.00007904124,0.0023549995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986675,0.0006560006,0.00004916501,0.0002197658,0.0002451814,0.00016242481],"domain_scores_gemma":[0.99614906,0.002745332,0.0004363688,0.0002440731,0.00029785634,0.00012726705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017298161,0.0010107163,0.0008943336,0.0008616204,0.00038642166,0.0009481501,0.001151075,0.0010694569,0.0023297966],"category_scores_gemma":[0.006023816,0.0006438359,0.0007226742,0.0009828059,0.0007354961,0.0015527088,0.00081720593,0.001346463,0.0001492539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025204476,0.000014619416,0.00025749687,0.000033633878,0.000011039599,0.000032982807,0.000007347873,0.98950654,0.00025986592,0.004804527,0.0004696203,0.004577096],"study_design_scores_gemma":[0.000009997712,0.000023568695,0.0001520841,0.000007902825,0.000006566409,0.000035796118,0.0000097984785,0.9883052,0.00032615077,0.010562268,0.000556885,0.0000038000778],"about_ca_topic_score_codex":0.0042064264,"about_ca_topic_score_gemma":0.0038170153,"teacher_disagreement_score":0.0042064264,"about_ca_system_score_codex":0.0013016,"about_ca_system_score_gemma":0.0011411933,"threshold_uncertainty_score":0.0094438195},"labels":[],"label_agreement":null},{"id":"W4396734039","doi":"10.1080/00207543.2024.2349257","title":"Optimal predictive selective maintenance for fleets of mission-oriented systems","year":2024,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Predictive maintenance; Computer science; Business; Systems engineering; Engineering; Operations research; Reliability engineering","score_opus":0.03140687701698398,"score_gpt":0.3534756340140226,"score_spread":0.32206875699703863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396734039","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22907661,0.0008687782,0.7640381,0.00046836087,0.000046122248,0.000080553626,0.00018554254,0.0004012243,0.0048347665],"genre_scores_gemma":[0.9769867,0.00012266151,0.021674702,0.000033615153,0.000010577871,0.000041227217,0.00009977715,0.000029542882,0.0010012225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997776,0.00006441285,0.00000871262,0.000046407717,0.000053381904,0.00004944275],"domain_scores_gemma":[0.99907374,0.00064227486,0.000114699345,0.00003905256,0.00007754702,0.000052692987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008716675,0.0008096926,0.0008497309,0.00054719026,0.00041963972,0.0006685797,0.00088395923,0.00080816064,0.0012536268],"category_scores_gemma":[0.0023214368,0.00054899836,0.0005246326,0.00045629762,0.00077839714,0.0008111016,0.0008329148,0.00088364934,0.00011603281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019733805,0.000005755635,0.00014901563,0.000008586042,0.0000039850743,0.0000130686,0.000007596525,0.9964684,0.00016935452,0.00045329984,0.000074783486,0.0026263345],"study_design_scores_gemma":[0.0000027439391,0.000008697538,0.000085579486,0.0000013814113,0.0000015722546,0.0000023425341,0.0000039789975,0.99913836,0.00006583614,0.0006501451,0.00003840547,9.713799e-7],"about_ca_topic_score_codex":0.01179665,"about_ca_topic_score_gemma":0.00827378,"teacher_disagreement_score":0.01179665,"about_ca_system_score_codex":0.0010223711,"about_ca_system_score_gemma":0.0007677111,"threshold_uncertainty_score":0.023455977},"labels":[],"label_agreement":null},{"id":"W4396938226","doi":"10.1680/jinam.23.00045","title":"Decision making for road infrastructures in a network based on a policy gradient method","year":2024,"lang":"en","type":"article","venue":"Infrastructure Asset Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; McGill University","funders":"","keywords":"Computer science","score_opus":0.004157412555366968,"score_gpt":0.273447385723387,"score_spread":0.26928997316802006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396938226","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038601607,0.00037108414,0.95634764,0.00043628117,0.00006206299,0.00012873726,0.000050901206,0.00014532119,0.003856371],"genre_scores_gemma":[0.89628965,0.00035639,0.09857728,0.00022645447,0.00006943302,0.00037759406,0.00008489459,0.00004485413,0.0039734202],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992167,0.00045567873,0.000025173287,0.00009854866,0.00010743486,0.00009646098],"domain_scores_gemma":[0.99792683,0.0015719164,0.00013411281,0.000035137655,0.00024094418,0.00009110677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022299306,0.00086161995,0.0019937644,0.00075871847,0.0004347146,0.001045604,0.0010437781,0.0014397585,0.0027883123],"category_scores_gemma":[0.0031222382,0.0006101243,0.00075148646,0.0005815468,0.0012333937,0.0009816465,0.0010516645,0.0012886997,0.0002425707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023344868,0.000019027017,0.00015974558,0.000017950499,0.000014377665,0.000026348185,0.000010469325,0.9924159,0.00015132906,0.0030190446,0.000120564706,0.004021782],"study_design_scores_gemma":[0.0000040863206,0.0000072614384,0.000017611195,0.0000011419963,0.0000013616204,0.0000012881154,0.0000014718415,0.9993641,0.000021100866,0.0005443156,0.000035125464,0.0000010861563],"about_ca_topic_score_codex":0.009353558,"about_ca_topic_score_gemma":0.0043930975,"teacher_disagreement_score":0.009353558,"about_ca_system_score_codex":0.001406426,"about_ca_system_score_gemma":0.0019379033,"threshold_uncertainty_score":0.018598199},"labels":[],"label_agreement":null},{"id":"W4397012017","doi":"10.23940/ijpe.12.4.p409.mag","title":"Production Rate Maximization of a Multi-State System under Inspection and Repair Policy","year":2012,"lang":"en","type":"article","venue":"International Journal of Performability Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Maximization; State (computer science); Production (economics); Computer science; Production system (computer science); Reliability engineering; Business; Mathematical optimization; Engineering; Mathematics; Economics; Algorithm; Microeconomics","score_opus":0.008439984556181961,"score_gpt":0.22171149819798738,"score_spread":0.2132715136418054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4397012017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14736667,0.0009889675,0.8440044,0.0006261973,0.00004469905,0.000097806624,0.0002667536,0.0002832853,0.0063211913],"genre_scores_gemma":[0.96842784,0.00041482662,0.026819695,0.00003525794,0.000027298282,0.00010357461,0.000109524146,0.000040712235,0.0040214714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990841,0.00030176743,0.000036696034,0.00023527752,0.00015800817,0.00018408976],"domain_scores_gemma":[0.99691355,0.0018811559,0.000580178,0.00012195523,0.00032973007,0.00017329764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019964657,0.0012706775,0.0015601284,0.000794596,0.000531324,0.0017049464,0.0012582451,0.0014154036,0.0023967708],"category_scores_gemma":[0.0036648607,0.00065725675,0.00089995476,0.00090608606,0.0011874704,0.0014094817,0.001071837,0.00076993945,0.00037503458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017393698,0.000049057668,0.00086448435,0.00021281226,0.00004926555,0.0002877464,0.000094332245,0.96955305,0.007862131,0.014411709,0.00045592134,0.0059855254],"study_design_scores_gemma":[0.000012975539,0.00005863817,0.00034083077,0.000007635407,0.000012488359,0.00003193683,0.000013657173,0.9950406,0.00075489,0.0035892755,0.00012834593,0.000008697808],"about_ca_topic_score_codex":0.0025705453,"about_ca_topic_score_gemma":0.0012796066,"teacher_disagreement_score":0.0025705453,"about_ca_system_score_codex":0.0013146945,"about_ca_system_score_gemma":0.0010568248,"threshold_uncertainty_score":0.010558486},"labels":[],"label_agreement":null},{"id":"W4398182762","doi":"10.1287/mnsc.2022.01108","title":"Self-Adapting Network Relaxations for Weakly Coupled Markov Decision Processes","year":2024,"lang":"en","type":"article","venue":"Management Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Markov decision process; Computer science; Markov chain; Statistical physics; Markov process; Mathematical optimization; Econometrics; Mathematics; Machine learning; Physics; Statistics","score_opus":0.00595439663458187,"score_gpt":0.22412371452424498,"score_spread":0.21816931788966312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398182762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036359627,0.00022050619,0.95585203,0.0004882426,0.00004531996,0.00010427951,0.0002959624,0.00022564946,0.0064084334],"genre_scores_gemma":[0.6990309,0.0004758255,0.29278943,0.00030210297,0.00007151234,0.00058466475,0.00067146413,0.00022471293,0.005849423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910223,0.000411358,0.00003733742,0.00016917741,0.00014833293,0.00013164975],"domain_scores_gemma":[0.99303985,0.0055038994,0.0005720339,0.00028577633,0.00030070308,0.00029774904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027058122,0.0011929736,0.000879137,0.00063048286,0.0004973815,0.001188353,0.0012550905,0.0010662813,0.006669601],"category_scores_gemma":[0.0116023915,0.00068181084,0.0011767431,0.00050315855,0.001125957,0.0018229262,0.0017426129,0.0031370255,0.00040124857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059039427,0.00004111974,0.00042180825,0.00005438505,0.00002066753,0.00004821641,0.00005347479,0.9529414,0.00052638014,0.038212888,0.00084412756,0.0067764935],"study_design_scores_gemma":[0.0000065490076,0.000010274217,0.00003987304,0.000005117249,0.000002984694,0.000004748152,0.0000068253257,0.98825383,0.000103607505,0.011341206,0.00022218049,0.0000027387082],"about_ca_topic_score_codex":0.004100784,"about_ca_topic_score_gemma":0.0042963517,"teacher_disagreement_score":0.006669601,"about_ca_system_score_codex":0.0017389935,"about_ca_system_score_gemma":0.0017697731,"threshold_uncertainty_score":0.022312045},"labels":[],"label_agreement":null},{"id":"W4399052648","doi":"10.1002/qre.3580","title":"Advances and novel applications in systems reliability and safety engineering (selected papers of the International Conference of SRSE 2022)","year":2024,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Christian ministry; China; Beijing; Reliability (semiconductor); Engineering; Engineering management; Political science; Law","score_opus":0.00881505897396057,"score_gpt":0.2330666372378419,"score_spread":0.22425157826388134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399052648","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022222975,0.31534445,0.012237259,0.030641718,0.529596,0.00024854927,0.0007216788,0.00033588812,0.1086522],"genre_scores_gemma":[0.02480864,0.25237352,0.008752358,0.0082993405,0.33765122,0.00033113427,0.0021261089,0.0010093426,0.36464834],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99544877,0.0005320694,0.00037224364,0.0006257136,0.0025714661,0.000449698],"domain_scores_gemma":[0.99143654,0.0021101253,0.00043949997,0.0004083317,0.0044721942,0.0011332405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053014625,0.0019069305,0.0017408296,0.004561532,0.0013593232,0.0058211405,0.0015433921,0.0029039227,0.04869839],"category_scores_gemma":[0.007004893,0.00053834415,0.0016950897,0.003250816,0.0010415368,0.0036937057,0.0019336859,0.004316811,0.018857146],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101966776,0.000059058322,0.0002676011,0.0012607756,0.00005645422,0.00020121972,0.00012466332,0.0005653617,0.0015096777,0.013724521,0.8563739,0.12575482],"study_design_scores_gemma":[0.000010834676,0.00006253254,0.0006064074,0.00036303356,0.000028611252,0.00024346435,0.000052041225,0.00051870797,0.000530553,0.003815454,0.99374974,0.000018587343],"about_ca_topic_score_codex":0.0009928193,"about_ca_topic_score_gemma":0.001646056,"teacher_disagreement_score":0.04869839,"about_ca_system_score_codex":0.0020982297,"about_ca_system_score_gemma":0.002335468,"threshold_uncertainty_score":0.16291237},"labels":[],"label_agreement":null},{"id":"W4399246352","doi":"10.1002/qre.3595","title":"Reliability and maintainability estimation of a multi‐failure‐cause system under imperfect maintenance","year":2024,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Maintainability; Reliability engineering; Reliability (semiconductor); Covariate; Computer science; Estimation; Imperfect; Engineering","score_opus":0.010028671866666696,"score_gpt":0.2558451672653867,"score_spread":0.24581649539872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399246352","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64595735,0.00028030606,0.35187542,0.00023612907,0.000016680551,0.00004368498,0.00023401169,0.0001622628,0.0011941306],"genre_scores_gemma":[0.9945707,0.000037252456,0.004828842,0.000006432698,0.0000053842577,0.000014104745,0.00007356773,0.0000043152636,0.00045937186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936706,0.00025722184,0.000031189735,0.00016166037,0.000113309754,0.00006961671],"domain_scores_gemma":[0.9951696,0.0033839114,0.00081273296,0.00023119719,0.0003264616,0.00007600799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029382745,0.0005138465,0.0008916975,0.00087942526,0.00030572995,0.0006844782,0.0008857829,0.00097067317,0.0007011627],"category_scores_gemma":[0.008696721,0.00037148266,0.00066719466,0.0005385071,0.00061838416,0.000477549,0.0005825426,0.0005640416,0.00009313795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057067613,0.000021365016,0.008886877,0.000036670066,0.000050723258,0.00014921551,0.00007019563,0.9799289,0.0013457898,0.003234815,0.00012818667,0.006090124],"study_design_scores_gemma":[0.000003228107,0.000031328153,0.0052236523,0.0000041267253,0.000020191012,0.000032422304,0.00001419793,0.9929404,0.00036277683,0.0012791201,0.000079673824,0.0000089114155],"about_ca_topic_score_codex":0.010952829,"about_ca_topic_score_gemma":0.0057687485,"teacher_disagreement_score":0.010952829,"about_ca_system_score_codex":0.00083102286,"about_ca_system_score_gemma":0.00049667776,"threshold_uncertainty_score":0.021778166},"labels":[],"label_agreement":null},{"id":"W4399665261","doi":"10.1117/12.3019334","title":"NFIRAOS integration phase: planning for capacity, integration, and other logistics","year":2024,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ontario Ministry of Research and Innovation; British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; National Astronomical Observatory of Japan; Association of Canadian Universities for Research in Astronomy; National Institutes of Natural Sciences; California Institute of Technology; Gordon and Betty Moore Foundation; National Science Foundation","keywords":"Phase (matter); Capacity planning; Computer science; Process management; Business; Operating system","score_opus":0.040742113026394675,"score_gpt":0.29272536194107424,"score_spread":0.25198324891467955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399665261","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1569038,0.002623791,0.22227427,0.019010918,0.0012750311,0.011031886,0.009874986,0.0076420167,0.5693634],"genre_scores_gemma":[0.44768062,0.0021410352,0.27964738,0.002547464,0.00024288073,0.005241928,0.014407003,0.0026423621,0.24544936],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958727,0.00046804134,0.00009772031,0.00023168577,0.0022068336,0.0011230331],"domain_scores_gemma":[0.9916447,0.00039494713,0.00040652536,0.00037504628,0.004988359,0.002190379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069410177,0.0008708203,0.00041675544,0.0020406866,0.0030861043,0.005821058,0.0023982455,0.0015086562,0.029563274],"category_scores_gemma":[0.007147623,0.00076297484,0.00063561276,0.0012457762,0.0008079931,0.0037672655,0.0022607578,0.0024483767,0.01020561],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009757605,0.0012677389,0.020570384,0.00092479575,0.000089220775,0.0015063131,0.0023279514,0.0840298,0.038926456,0.063475706,0.2878424,0.49806353],"study_design_scores_gemma":[0.00013634523,0.0014192413,0.022001911,0.0004223476,0.00003910598,0.0005175587,0.0036520374,0.030908354,0.016323952,0.010141245,0.91428643,0.00015139698],"about_ca_topic_score_codex":0.098198615,"about_ca_topic_score_gemma":0.10983457,"teacher_disagreement_score":0.098198615,"about_ca_system_score_codex":0.0110500455,"about_ca_system_score_gemma":0.039696056,"threshold_uncertainty_score":0.19525409},"labels":[],"label_agreement":null},{"id":"W4400066539","doi":"10.32372/chjs.15-01-05","title":"Unveiling patterns and trends in research on cumulative damage models for statistical and reliability analyses: Bibliometric and thematic explorations with data analytics","year":2024,"lang":"en","type":"article","venue":"Chilean Journal of Statistics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"CHIST-ERA; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Agencia Nacional de Investigación y Desarrollo; Universidade do Minho; Agenția Națională pentru Cercetare și Dezvoltare","keywords":"Reliability (semiconductor); Thematic map; Data science; Analytics; Computer science; Data analysis; Statistics; Data mining; Geography; Cartography; Mathematics; Physics","score_opus":0.3249776292588378,"score_gpt":0.44338529320727854,"score_spread":0.11840766394844077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400066539","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6444979,0.14925277,0.10232095,0.016792635,0.00076788047,0.0009260133,0.022540126,0.0010472022,0.061854437],"genre_scores_gemma":[0.8704312,0.05329703,0.06239414,0.0006695785,0.0007668999,0.0010095169,0.008629266,0.00021656053,0.0025857547],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9888409,0.0039238934,0.0014781484,0.0011384477,0.004185618,0.00043297224],"domain_scores_gemma":[0.8993628,0.079599656,0.009292505,0.00333225,0.007690984,0.0007217507],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.015318303,0.0007393027,0.0011434277,0.10776637,0.0014794075,0.008583412,0.00096000894,0.0009058612,0.002156878],"category_scores_gemma":[0.061113134,0.0004114751,0.0013688821,0.13736638,0.0018117152,0.008835705,0.003544455,0.0009024265,0.0005031444],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021062375,0.00016284622,0.20366323,0.027041763,0.0011473316,0.0017639565,0.047930866,0.007819471,0.0068314895,0.083010696,0.018862087,0.6015557],"study_design_scores_gemma":[0.0000760497,0.00024965635,0.3229414,0.020187166,0.001731471,0.0028931384,0.118598595,0.04466733,0.006921467,0.16305056,0.31827593,0.00040721655],"about_ca_topic_score_codex":0.0033149044,"about_ca_topic_score_gemma":0.005377585,"teacher_disagreement_score":0.8922336,"about_ca_system_score_codex":0.0027666118,"about_ca_system_score_gemma":0.0047516855,"threshold_uncertainty_score":0.08101189},"labels":[],"label_agreement":null},{"id":"W4400967131","doi":"10.1016/j.ress.2024.110366","title":"Optimal replacement policy based on number of failures for a system with multiple attempt minimal repairs","year":2024,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science and Technology, Taiwan; National Science and Technology Council","keywords":"Computer science; Reliability engineering; Operations research; Engineering","score_opus":0.003675263865330713,"score_gpt":0.20464117797152728,"score_spread":0.20096591410619657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400967131","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.623366,0.0014713927,0.36950862,0.0008522913,0.00014272335,0.00013452908,0.0002030815,0.0009224184,0.0033989844],"genre_scores_gemma":[0.9887088,0.00008602969,0.0105525935,0.000031604883,0.00002281561,0.00001907654,0.000027757631,0.000018445095,0.00053284515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995228,0.00012952743,0.000029532253,0.00010964477,0.00007765427,0.0001308074],"domain_scores_gemma":[0.99760276,0.0013616307,0.00031590293,0.00014379896,0.00035224852,0.00022374035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008555904,0.0007359746,0.0012949605,0.0008686971,0.00053022464,0.0008510432,0.0010210333,0.00089914823,0.0014789581],"category_scores_gemma":[0.0038376881,0.00038988475,0.00037305904,0.00041048427,0.00057182426,0.0006871671,0.00039031895,0.0004980576,0.00014413941],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087871583,0.00012570359,0.001578472,0.00016314872,0.000056379846,0.00012830703,0.000073167466,0.95732474,0.010523272,0.00345942,0.0011233877,0.024565287],"study_design_scores_gemma":[0.000032121527,0.0001301258,0.0009004105,0.000009518115,0.000039941173,0.000052940257,0.000026144518,0.99569243,0.0012307042,0.0017390439,0.00013676715,0.000009779011],"about_ca_topic_score_codex":0.004270014,"about_ca_topic_score_gemma":0.0030554729,"teacher_disagreement_score":0.004270014,"about_ca_system_score_codex":0.00091113104,"about_ca_system_score_gemma":0.0013844807,"threshold_uncertainty_score":0.008490324},"labels":[],"label_agreement":null},{"id":"W4401026935","doi":"10.1016/j.ress.2024.110397","title":"A general inspection and replacement policy for protection systems subject to shocks with state dependent effect","year":2024,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Subject (documents); State (computer science); Reliability engineering; Computer science; Engineering; Forensic engineering; Algorithm","score_opus":0.0031717983863211695,"score_gpt":0.19291203537638224,"score_spread":0.18974023699006107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401026935","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22681433,0.00058812066,0.7516905,0.0027841872,0.00020590513,0.0006063847,0.00083321263,0.001573867,0.014903486],"genre_scores_gemma":[0.9690944,0.00025725807,0.021460852,0.0003316025,0.00008153022,0.00012617475,0.00021092666,0.000056410496,0.008380775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987986,0.00034008684,0.00005635816,0.00027583336,0.00022889754,0.00030027243],"domain_scores_gemma":[0.9981468,0.00088707934,0.0002786023,0.00017826515,0.00038279255,0.00012658795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027826803,0.0014371367,0.0017421668,0.00082721753,0.00060419086,0.001466427,0.0019561788,0.0030166353,0.00491472],"category_scores_gemma":[0.005158282,0.0007260138,0.0010926062,0.00069401803,0.0011452177,0.0015369429,0.0015367116,0.0014392636,0.00045643857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007873724,0.0001603554,0.0010186771,0.00021132767,0.000079873775,0.00027252932,0.00007138171,0.9394537,0.011197671,0.02722197,0.0029775943,0.016547397],"study_design_scores_gemma":[0.00012130572,0.00033812533,0.0028508164,0.000030011983,0.00008020808,0.000094822666,0.000047131398,0.97434074,0.0017730063,0.019472962,0.0008181193,0.000032835876],"about_ca_topic_score_codex":0.006916541,"about_ca_topic_score_gemma":0.004507864,"teacher_disagreement_score":0.006916541,"about_ca_system_score_codex":0.0018711444,"about_ca_system_score_gemma":0.002334766,"threshold_uncertainty_score":0.016441405},"labels":[],"label_agreement":null},{"id":"W4401445036","doi":"10.1016/j.tbench.2024.100172","title":"Analyzing the impact of opportunistic maintenance optimization on manufacturing industries in Bangladesh: An empirical study","year":2024,"lang":"en","type":"article","venue":"BenchCouncil Transactions on Benchmarks Standards and Evaluations","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Preventive maintenance; Corrective maintenance; Unit (ring theory); Total productive maintenance; Proactive maintenance; Risk analysis (engineering); Computer science; Operations management; Business; Reliability engineering; Engineering; Production (economics); Economics","score_opus":0.037100751369721366,"score_gpt":0.3341958532264288,"score_spread":0.29709510185670746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401445036","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9969771,0.00010066676,0.00026528782,0.000077994184,0.0000015007113,0.00003030273,0.00025505575,0.0000058391074,0.0022862917],"genre_scores_gemma":[0.99902236,0.00015480847,0.00021335838,0.000010862888,0.0000014601194,0.000012320211,0.00018918916,0.0000016891288,0.00039395958],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992518,0.0002251816,0.00006808678,0.000088260036,0.00019561914,0.00017107207],"domain_scores_gemma":[0.9913316,0.005233429,0.001596841,0.0003631261,0.0010955534,0.0003794284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012127415,0.000327065,0.00028925436,0.00090385665,0.00040824234,0.0008230966,0.0005812617,0.0005102708,0.0022627402],"category_scores_gemma":[0.0055986997,0.00017751021,0.00023699044,0.0016543446,0.0003967758,0.00079999096,0.0004265997,0.0005441196,0.00033691924],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061359577,0.0021149984,0.8417346,0.00051244185,0.00020149858,0.0014918676,0.0018931704,0.07139155,0.0033039113,0.002724746,0.0025126252,0.07150517],"study_design_scores_gemma":[0.000054353735,0.0018337757,0.9090377,0.0001260363,0.000129775,0.00053822773,0.012981777,0.06606271,0.0023553246,0.00072730874,0.0060825045,0.00007055992],"about_ca_topic_score_codex":0.027617028,"about_ca_topic_score_gemma":0.03621552,"teacher_disagreement_score":0.027617028,"about_ca_system_score_codex":0.0017137253,"about_ca_system_score_gemma":0.0008988416,"threshold_uncertainty_score":0.054912567},"labels":[],"label_agreement":null},{"id":"W4401551138","doi":"10.1002/asmb.2886","title":"An EM‐based likelihood inference for degradation data analysis using gamma process","year":2024,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Gamma process; Inference; Computer science; Process (computing); Degradation (telecommunications); Econometrics; Statistics; Artificial intelligence; Mathematics","score_opus":0.045155421335977594,"score_gpt":0.29537361744147644,"score_spread":0.25021819610549884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401551138","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004183385,0.0001432817,0.99525875,0.000046908783,0.000007269543,0.000015522222,0.00003016451,0.00010524795,0.00020953103],"genre_scores_gemma":[0.35081726,0.00088274793,0.6443718,0.00023109157,0.000113273265,0.00031665797,0.00082317385,0.00016421311,0.0022798036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99817836,0.0010952816,0.00009574964,0.00027270318,0.0002911744,0.00006665891],"domain_scores_gemma":[0.992155,0.0062339054,0.00041752003,0.00038901708,0.00072312844,0.00008144635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005334345,0.00082342146,0.0012815497,0.0017108994,0.00044145866,0.0010527059,0.0016842843,0.0011027193,0.0017093038],"category_scores_gemma":[0.018214097,0.00075414294,0.0012768625,0.0014982997,0.0010995682,0.0017614337,0.001494974,0.0015089328,0.00040295246],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016172825,0.00008032898,0.0026902682,0.00021170166,0.00014552816,0.0001480102,0.00011918296,0.85204995,0.0025041215,0.036376197,0.0011709738,0.10434203],"study_design_scores_gemma":[0.000008503239,0.000015131636,0.0002489166,0.0000119710685,0.000008820962,0.000037899306,0.000008074663,0.9918174,0.0005274708,0.006999064,0.00030665274,0.000010164317],"about_ca_topic_score_codex":0.0023696576,"about_ca_topic_score_gemma":0.0015566362,"teacher_disagreement_score":0.005334345,"about_ca_system_score_codex":0.00075383217,"about_ca_system_score_gemma":0.001315837,"threshold_uncertainty_score":0.028211057},"labels":[],"label_agreement":null},{"id":"W4401884971","doi":"10.4203/ccc.8.4.1","title":"Stochastic Projection Based Gradient Free PINN for Reliability Analysis of System using PDEM","year":2024,"lang":"en","type":"article","venue":"Civil-comp conferences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Computer science; Projection (relational algebra); Control theory (sociology); Algorithm; Physics; Artificial intelligence","score_opus":0.022436263318625447,"score_gpt":0.24530044354143846,"score_spread":0.222864180222813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401884971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035994402,0.00021267544,0.99435174,0.000092571834,0.000021167656,0.000023185328,0.000020287274,0.00007479357,0.0016040484],"genre_scores_gemma":[0.5476695,0.001335513,0.4390659,0.00025720216,0.000099146375,0.0004939598,0.000295144,0.00026917952,0.010514475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997199,0.00010815126,0.000011385989,0.000044965855,0.00009336897,0.000022242735],"domain_scores_gemma":[0.9994537,0.00031176367,0.000044698238,0.000026933785,0.00014109616,0.000021826647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008935831,0.0007742004,0.00077404996,0.00046979933,0.00037054694,0.0005331486,0.0008976326,0.0007300475,0.001926896],"category_scores_gemma":[0.0021064037,0.00045009307,0.00069715624,0.00040337906,0.00070341746,0.0009177598,0.00092414423,0.0011685526,0.00025350467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018020399,0.000016357544,0.0004402977,0.000109007524,0.0000230828,0.000059575148,0.000051330102,0.95943105,0.0010521356,0.022272926,0.00066182425,0.015864344],"study_design_scores_gemma":[0.0000010754951,0.000003833873,0.000027660912,0.0000025993338,0.0000014400516,0.0000064602737,0.0000022252455,0.99754435,0.00008535678,0.0021101532,0.00021330458,0.0000015891848],"about_ca_topic_score_codex":0.0053072013,"about_ca_topic_score_gemma":0.003512987,"teacher_disagreement_score":0.0053072013,"about_ca_system_score_codex":0.00065141066,"about_ca_system_score_gemma":0.00136338,"threshold_uncertainty_score":0.010552585},"labels":[],"label_agreement":null},{"id":"W4402795113","doi":"10.1080/00207543.2024.2403114","title":"Optimising resource-constrained fleet selective maintenance with asynchronous maintenance breaks","year":2024,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Asynchronous communication; Resource (disambiguation); Computer science; Optimal maintenance; Operations research; Engineering; Reliability engineering; Computer network","score_opus":0.019504169405180443,"score_gpt":0.3028560488073723,"score_spread":0.28335187940219186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402795113","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2183251,0.00047358414,0.775678,0.00021101502,0.00003902299,0.00011261413,0.00021193975,0.00023024104,0.0047186064],"genre_scores_gemma":[0.947567,0.000120826,0.050468665,0.000027471795,0.000011478528,0.00008330329,0.00012840719,0.000037225134,0.0015555493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997367,0.00009007171,0.000011100697,0.000054091106,0.000048483904,0.00005946505],"domain_scores_gemma":[0.9994012,0.00035977594,0.00010338154,0.0000389464,0.000044966095,0.00005176188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007766463,0.0006455816,0.00061557506,0.00030669713,0.00021829345,0.0005624179,0.0008732,0.00069828157,0.0018581641],"category_scores_gemma":[0.0014617048,0.0003854717,0.00043059365,0.0003615866,0.00037262612,0.0007972057,0.0006864023,0.00065041287,0.00015390718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055523025,0.000019236706,0.00024913801,0.000036116842,0.000014698701,0.000045955854,0.000015122943,0.9888409,0.0012146767,0.0015909668,0.00020354832,0.007714132],"study_design_scores_gemma":[0.000018558247,0.00009229788,0.00026262467,0.0000045524157,0.0000077973655,0.000020554276,0.000018863948,0.99668473,0.00039174614,0.0021829153,0.00031255372,0.0000028295153],"about_ca_topic_score_codex":0.0025596253,"about_ca_topic_score_gemma":0.002438625,"teacher_disagreement_score":0.0025596253,"about_ca_system_score_codex":0.00040781076,"about_ca_system_score_gemma":0.00060633227,"threshold_uncertainty_score":0.006216228},"labels":[],"label_agreement":null},{"id":"W4403035467","doi":"10.1007/s13369-024-09496-3","title":"Production/Preventive Maintenance Comprehensive Approach for Manufacturing Systems Susceptible to Quality Degradation","year":2024,"lang":"en","type":"article","venue":"Arabian Journal for Science and Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Preventive maintenance; Degradation (telecommunications); Quality (philosophy); Production (economics); Reliability engineering; Risk analysis (engineering); Computer science; Manufacturing engineering; Engineering; Business; Physics","score_opus":0.023054729990615086,"score_gpt":0.26479537451840596,"score_spread":0.24174064452779087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403035467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.098564684,0.0012638884,0.89318645,0.00026466558,0.00004606726,0.00011580336,0.00012265182,0.00047124134,0.0059645586],"genre_scores_gemma":[0.9557993,0.0003823324,0.041417122,0.000041894098,0.000046764468,0.00006942368,0.00014544094,0.00004299267,0.0020548431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970454,0.00005436926,0.000013401444,0.000052323565,0.00012148818,0.00005396478],"domain_scores_gemma":[0.999587,0.00013955556,0.00006910301,0.000046389006,0.00013296028,0.00002493401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007046215,0.0009660104,0.0010482555,0.0010109239,0.000563861,0.0009469015,0.0011928771,0.0007651475,0.001574155],"category_scores_gemma":[0.0012470816,0.00039180362,0.00078967406,0.0005468701,0.000311871,0.0008208077,0.0007332461,0.00048542483,0.00011771058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006760037,0.000091236965,0.0013079448,0.00018264029,0.0000770728,0.00021226869,0.00007994517,0.94501585,0.0069462834,0.0032906681,0.0008038764,0.041924622],"study_design_scores_gemma":[0.000003661921,0.000052124207,0.0008150278,0.000007535281,0.00004527386,0.000039262315,0.000020808791,0.9964978,0.0006748832,0.0016180725,0.00022195387,0.0000036451372],"about_ca_topic_score_codex":0.0041632066,"about_ca_topic_score_gemma":0.0050389264,"teacher_disagreement_score":0.0041632066,"about_ca_system_score_codex":0.0005250611,"about_ca_system_score_gemma":0.0011993919,"threshold_uncertainty_score":0.008277953},"labels":[],"label_agreement":null},{"id":"W4403150608","doi":"10.1016/j.ress.2024.110551","title":"A multi-stage stochastic programming model for multi-mission selective maintenance optimization","year":2024,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stochastic programming; Stage (stratigraphy); Computer science; Mathematical optimization; Operations research; Engineering; Mathematics; Geology","score_opus":0.019720727114085845,"score_gpt":0.2519016100204082,"score_spread":0.23218088290632233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403150608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018032055,0.00050390547,0.9744992,0.00051969587,0.00008535521,0.0000892407,0.00035377216,0.00018625076,0.005730552],"genre_scores_gemma":[0.8729255,0.0008704551,0.1011778,0.00026820204,0.00013784453,0.0006381372,0.0006338542,0.0001540807,0.02319412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889576,0.0004668846,0.000042656015,0.00019555057,0.00019395442,0.00020511885],"domain_scores_gemma":[0.99805605,0.0013330071,0.00020029585,0.000056205892,0.00022532068,0.00012912083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026310845,0.001365175,0.0025825212,0.00085356156,0.0005790413,0.0017878219,0.0031308357,0.0030993538,0.005176139],"category_scores_gemma":[0.004095327,0.0015027504,0.0015464349,0.0013626951,0.0011060733,0.001502468,0.0015922642,0.0024351173,0.00057784276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023137578,0.00001596716,0.00008774488,0.000023872135,0.000015807149,0.000025654477,0.000009816539,0.9906624,0.00016193237,0.0071597835,0.00029262327,0.0015212863],"study_design_scores_gemma":[0.000005873553,0.000010978707,0.000032797623,0.0000021682383,0.000005088991,0.0000032059766,0.000001803644,0.99841523,0.000028424365,0.0013817636,0.00010932104,0.0000033259178],"about_ca_topic_score_codex":0.013949938,"about_ca_topic_score_gemma":0.0098739425,"teacher_disagreement_score":0.013949938,"about_ca_system_score_codex":0.0018020169,"about_ca_system_score_gemma":0.0023568156,"threshold_uncertainty_score":0.027737439},"labels":[],"label_agreement":null},{"id":"W4403241901","doi":"10.1016/j.ifacol.2024.09.072","title":"Optimal Selective Maintenance for Complex Systems Under Stochastic Maintenance and Break Durations","year":2024,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Optimal maintenance; Computer science; Reliability engineering; Engineering","score_opus":0.0129056576962211,"score_gpt":0.23691947024066978,"score_spread":0.2240138125444487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403241901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48939466,0.00082862505,0.5064545,0.0003192306,0.000025180072,0.00005579293,0.00012914895,0.00020797309,0.0025849],"genre_scores_gemma":[0.989565,0.00016029322,0.009680199,0.000014662686,0.0000077574,0.000026934315,0.0000453048,0.000018359515,0.00048138024],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975663,0.00008548076,0.000011365078,0.0000478746,0.00004975114,0.000049028706],"domain_scores_gemma":[0.99856526,0.0009743576,0.00023396818,0.000059649683,0.00009836905,0.00006849397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009997055,0.0006885447,0.00075218105,0.0004367747,0.00026761828,0.00045023448,0.00051657966,0.0005182995,0.00081522146],"category_scores_gemma":[0.0024921505,0.00035960294,0.00034502204,0.00038701337,0.00061370904,0.00063967984,0.0006626838,0.00045503103,0.000073863994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009042465,0.000021353724,0.00031975182,0.000033626755,0.000013710898,0.000041940548,0.000023529388,0.98964334,0.0015605381,0.0022665248,0.00017883381,0.0058064326],"study_design_scores_gemma":[0.0000150235655,0.000059130492,0.00034007817,0.000002400394,0.0000067074693,0.000011793201,0.000012481026,0.9969138,0.00032026577,0.0022462765,0.00006974608,0.0000023598777],"about_ca_topic_score_codex":0.0033374398,"about_ca_topic_score_gemma":0.0022552512,"teacher_disagreement_score":0.0033374398,"about_ca_system_score_codex":0.0006237942,"about_ca_system_score_gemma":0.0006378988,"threshold_uncertainty_score":0.0066360235},"labels":[],"label_agreement":null},{"id":"W4403280268","doi":"10.1016/j.ifacol.2024.09.074","title":"Combination Warranty Optimization Model Using Reconditioned Parts Under Age Uncertainty","year":2024,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Warranty; Reliability engineering; Computer science; Econometrics; Engineering; Economics; Political science","score_opus":0.022148297410945064,"score_gpt":0.24677342993608622,"score_spread":0.22462513252514116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403280268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08050967,0.0027806265,0.8810837,0.0012114721,0.00019034672,0.00026545388,0.0009623868,0.00040773346,0.03258855],"genre_scores_gemma":[0.93791085,0.0012804635,0.038306132,0.00013393068,0.000056230514,0.00041703903,0.0004473075,0.00008673462,0.021361269],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886215,0.00033691633,0.000058539466,0.00025135803,0.00027569098,0.00021530487],"domain_scores_gemma":[0.9984132,0.0009002228,0.000304505,0.000046667927,0.0002590054,0.00007644899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021750582,0.0018808114,0.002498941,0.001296047,0.00067384844,0.0024735301,0.0023349847,0.0029091437,0.0053494074],"category_scores_gemma":[0.00280079,0.0012666947,0.0015581822,0.0013387526,0.001030456,0.0014095461,0.0011006397,0.0017599679,0.0005193744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019556757,0.000011156291,0.00009973714,0.00003010912,0.00001302366,0.00006043378,0.000012026781,0.9958794,0.00020649507,0.002328959,0.00018112705,0.0011579193],"study_design_scores_gemma":[0.0000065367426,0.000016724649,0.00008825192,0.0000058350065,0.000010341619,0.0000084595695,0.000007092969,0.99881315,0.00006804229,0.0007734243,0.0001974173,0.0000047164813],"about_ca_topic_score_codex":0.0157926,"about_ca_topic_score_gemma":0.008559326,"teacher_disagreement_score":0.0157926,"about_ca_system_score_codex":0.0023313332,"about_ca_system_score_gemma":0.002186264,"threshold_uncertainty_score":0.031401396},"labels":[],"label_agreement":null},{"id":"W4403821230","doi":"10.1002/qre.3673","title":"Joint modeling of degradation signals and time‐to‐event data for the prediction of remaining useful life","year":2024,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Event (particle physics); Data mining; Hyperparameter; Joint (building); Artificial neural network; Bayesian inference; Bayesian probability; Reliability engineering; Artificial intelligence; Real-time computing; Engineering","score_opus":0.05123528661738318,"score_gpt":0.2774122798629017,"score_spread":0.2261769932455185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403821230","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11124464,0.00027187608,0.8871845,0.000152105,0.000038542803,0.00003298914,0.00013519524,0.00030185748,0.00063836196],"genre_scores_gemma":[0.9827352,0.00015676649,0.016131569,0.000021148242,0.000018310833,0.00003706344,0.00012670638,0.000013929549,0.00075935357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956626,0.000111480724,0.000033311575,0.00009055673,0.00014646069,0.000051923016],"domain_scores_gemma":[0.9986093,0.0007924915,0.00025779102,0.00010293145,0.00019179503,0.000045648812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013849958,0.00082542584,0.0007715116,0.0006499487,0.0002078603,0.0006524626,0.0009315046,0.0009024368,0.0005840306],"category_scores_gemma":[0.0035254147,0.00040746471,0.0005775297,0.0006141379,0.00045775837,0.0011623462,0.00062101986,0.0011970447,0.00015101314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043936347,0.000032467797,0.0018146462,0.00002372642,0.000019429535,0.00005733993,0.000023578492,0.9849685,0.0013732637,0.001201727,0.000120775716,0.010320617],"study_design_scores_gemma":[6.9084234e-7,0.0000050734275,0.00017514545,6.5530384e-7,0.000001497163,0.0000032351365,8.5919345e-7,0.9995253,0.00010696803,0.00016107774,0.000017967146,0.0000014681156],"about_ca_topic_score_codex":0.008119092,"about_ca_topic_score_gemma":0.005578346,"teacher_disagreement_score":0.008119092,"about_ca_system_score_codex":0.00040302926,"about_ca_system_score_gemma":0.00069895637,"threshold_uncertainty_score":0.01614368},"labels":[],"label_agreement":null},{"id":"W4404254146","doi":"10.3390/machines12110795","title":"Optimal Inspection and Maintenance Policy: Integrating a Continuous-Time Markov Chain into a Homing Problem","year":2024,"lang":"en","type":"article","venue":"Machines","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain; Homing (biology); Computer science; Real-time computing; Machine learning; Physics","score_opus":0.0021311155577109406,"score_gpt":0.20073637830169355,"score_spread":0.1986052627439826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404254146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040615954,0.00025247232,0.9555193,0.00043167578,0.00003528829,0.00005195672,0.00007488606,0.00015385916,0.0028646798],"genre_scores_gemma":[0.9055644,0.00045104747,0.08777348,0.000103492675,0.00006270459,0.00017686245,0.00013667614,0.00007217323,0.0056591565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999203,0.0003160725,0.000025629708,0.00018152957,0.00010658829,0.0001671978],"domain_scores_gemma":[0.9959908,0.0032177414,0.0002893335,0.00010040569,0.00019210967,0.00020955261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021287845,0.0007538778,0.001301157,0.0006673902,0.0004511119,0.0013399962,0.001544374,0.0016441717,0.003095291],"category_scores_gemma":[0.005081623,0.0008491085,0.0008311587,0.0007521667,0.0018896905,0.0016439456,0.0012551771,0.001499253,0.00023979155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054078344,0.000036725658,0.00035877494,0.000023352333,0.000013661467,0.00005765741,0.000030797455,0.97668445,0.0003356874,0.018789278,0.00021752188,0.0033980554],"study_design_scores_gemma":[0.0000074754103,0.000013881201,0.000064006555,0.0000029106736,0.000004616971,0.0000057243956,0.000004831028,0.9938257,0.00007194645,0.005921584,0.00007386667,0.0000034768395],"about_ca_topic_score_codex":0.010975601,"about_ca_topic_score_gemma":0.0058480836,"teacher_disagreement_score":0.010975601,"about_ca_system_score_codex":0.0018169509,"about_ca_system_score_gemma":0.0024278422,"threshold_uncertainty_score":0.021823406},"labels":[],"label_agreement":null},{"id":"W4404687812","doi":"10.1063/5.0243214","title":"Integrated assessment framework of climate impacts on buildings using FMEA and Bayesian networks: Study on indoor overheating risk","year":2024,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Overheating (electricity); Bayesian network; Environmental science; Computer science; Reliability engineering; Engineering; Artificial intelligence","score_opus":0.01339534345620303,"score_gpt":0.2732031710602165,"score_spread":0.2598078276040135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404687812","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11557648,0.000913476,0.87717676,0.000417356,0.000035423567,0.00005452188,0.00019609438,0.00010812468,0.0055218525],"genre_scores_gemma":[0.9630715,0.00050500274,0.033476133,0.00004039126,0.000040328174,0.00005461494,0.00014085759,0.000025697815,0.00264548],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990932,0.000383218,0.000025800757,0.00017757622,0.00020057398,0.00011966955],"domain_scores_gemma":[0.99893636,0.0006490245,0.00011178585,0.000053114658,0.00020551604,0.000044165066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002638347,0.00091288326,0.0009560908,0.001430917,0.00041899233,0.0014611997,0.0012567999,0.001187919,0.0013662662],"category_scores_gemma":[0.0033955327,0.00054660015,0.0013828764,0.0011412655,0.0006880501,0.0020479583,0.00092996296,0.0008452394,0.00008657986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013642354,0.000020937865,0.00079297996,0.000014843629,0.00004407768,0.000024894463,0.0000175871,0.9849296,0.00023784986,0.008633115,0.00009993527,0.005170541],"study_design_scores_gemma":[0.0000024568506,0.000014317904,0.00059216935,0.0000053122303,0.00002372893,0.00001167289,0.000012412461,0.9932827,0.00009879834,0.005829314,0.000120636185,0.000006486899],"about_ca_topic_score_codex":0.022255655,"about_ca_topic_score_gemma":0.013539407,"teacher_disagreement_score":0.022255655,"about_ca_system_score_codex":0.0015458909,"about_ca_system_score_gemma":0.0015872183,"threshold_uncertainty_score":0.044252217},"labels":[],"label_agreement":null},{"id":"W4405487992","doi":"10.4310/sii.241023043033","title":"Testing for a change in the failure time distribution using combined data from an incident&amp;nbsp;and a prevalent cohort","year":2024,"lang":"en","type":"article","venue":"Statistics and Its Interface","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Regina","funders":"","keywords":"Statistics; Cohort; Medicine; Econometrics; Computer science; Mathematics","score_opus":0.058948332272952805,"score_gpt":0.3223544706821025,"score_spread":0.26340613840914967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405487992","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9969445,0.00003264622,0.0016033351,0.00008273223,0.000026432925,0.000014403878,0.0009162834,0.000017452412,0.00036224475],"genre_scores_gemma":[0.99776804,0.000014849289,0.0005470125,0.000026978514,0.000021706974,0.000027090591,0.001150719,0.000014377499,0.0004293704],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98285496,0.007524348,0.0011113348,0.004522607,0.0022903536,0.001696454],"domain_scores_gemma":[0.94187254,0.03635963,0.007068737,0.009087307,0.0026463459,0.0029655169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019864084,0.0007426388,0.0008354321,0.00194911,0.00093060464,0.0018877697,0.0028150026,0.0014776088,0.0048826756],"category_scores_gemma":[0.03723119,0.0005323143,0.0025715346,0.0018102292,0.0009935105,0.0020892264,0.0013909169,0.0022635832,0.00062014477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006912384,0.00013778266,0.995494,0.000007781962,0.00069759524,0.000067548244,0.00011529531,0.00030427473,0.0006485445,0.00010541961,0.0001743998,0.0015559888],"study_design_scores_gemma":[0.000069079666,0.0014815817,0.98434967,0.000011243998,0.00088148663,0.00048254844,0.0009099292,0.010191788,0.00065077253,0.00028803185,0.0006649367,0.000018889346],"about_ca_topic_score_codex":0.010020592,"about_ca_topic_score_gemma":0.008049658,"teacher_disagreement_score":0.019864084,"about_ca_system_score_codex":0.0005848603,"about_ca_system_score_gemma":0.001724771,"threshold_uncertainty_score":0.10505259},"labels":[],"label_agreement":null},{"id":"W4405517347","doi":"10.1109/tr.2024.3509446","title":"Reliability Analysis of Cyclic Accelerated Life Test Data Using Log-Location-Scale Family of Distributions Under Censoring With Application to Solder Joint Data","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Science Foundation of Jiangsu Province","keywords":"Censoring (clinical trials); Reliability theory; Reliability (semiconductor); Reliability engineering; Joint (building); Scale (ratio); Statistics; Test data; Computer science; Mathematics; Engineering; Structural engineering; Failure rate","score_opus":0.05877577352516636,"score_gpt":0.29085701656650376,"score_spread":0.2320812430413374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405517347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02924423,0.0005187685,0.96921086,0.000111454865,0.000021407537,0.00005405052,0.000183492,0.00028363586,0.00037213074],"genre_scores_gemma":[0.79424167,0.00231208,0.1974609,0.00015805218,0.00019844633,0.0006946779,0.0021575284,0.00022074471,0.0025560246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956542,0.0024658388,0.00023120944,0.00066161994,0.000824183,0.00016287129],"domain_scores_gemma":[0.95604116,0.034306258,0.0031934006,0.0036226679,0.0026075547,0.00022888259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012498365,0.0010007503,0.0010384307,0.001977757,0.0004854001,0.00074404635,0.0021683667,0.0011114776,0.0015700894],"category_scores_gemma":[0.047603272,0.00047649315,0.0018254832,0.0024888145,0.0012889605,0.0013076619,0.0010250708,0.0018461823,0.00044860542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025774696,0.00012980499,0.024108695,0.0007787991,0.0006379369,0.0011487759,0.00092464447,0.73313195,0.007075915,0.09306114,0.0024704274,0.13627408],"study_design_scores_gemma":[0.00000884979,0.00007460964,0.0041964087,0.000035561883,0.000038125414,0.00018783713,0.000051406987,0.9744436,0.0010685044,0.018895345,0.0009709472,0.000028815784],"about_ca_topic_score_codex":0.004104936,"about_ca_topic_score_gemma":0.0024476966,"teacher_disagreement_score":0.012498365,"about_ca_system_score_codex":0.00075695093,"about_ca_system_score_gemma":0.0009022567,"threshold_uncertainty_score":0.06609845},"labels":[],"label_agreement":null},{"id":"W4405766477","doi":"10.1002/qre.3714","title":"Joint Optimization of Condition‐Based Maintenance and Production Rate Using Reinforcement Learning Algorithms","year":2024,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Kermanshah University of Technology","keywords":"Reinforcement learning; Markov decision process; Production (economics); Computer science; Time horizon; Production planning; Scheduling (production processes); Mathematical optimization; Q-learning; Production control; Hyperparameter; Preventive maintenance; Dynamic programming; Industrial engineering; Operations research; Markov process; Engineering; Algorithm; Reliability engineering; Machine learning; Mathematics","score_opus":0.017490954589299397,"score_gpt":0.2587951566424163,"score_spread":0.24130420205311692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405766477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066737495,0.0003097569,0.9290148,0.0003705447,0.0000375315,0.00009875333,0.0000448416,0.00035783247,0.0030283327],"genre_scores_gemma":[0.96108377,0.000086646505,0.037364777,0.000061671926,0.000017469114,0.00010289468,0.00004395397,0.000026486117,0.0012123835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992254,0.0003319547,0.000037154958,0.00014401857,0.00013989568,0.00012165749],"domain_scores_gemma":[0.9954151,0.0034094343,0.0004579784,0.00011897025,0.00045297682,0.00014559673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025413432,0.0008468041,0.0015078146,0.0006560151,0.0003149585,0.0009237336,0.0010830846,0.0012508694,0.0017282193],"category_scores_gemma":[0.006221552,0.00056911324,0.00051939127,0.00038524522,0.0010893433,0.00095414335,0.00072828116,0.0013092327,0.00020061395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027191429,0.00003778156,0.00027017636,0.000014360613,0.000012026592,0.0000119226825,0.000008193113,0.9930177,0.00023257207,0.001218791,0.00012831963,0.005020956],"study_design_scores_gemma":[0.0000051042757,0.0000109250395,0.000034005978,0.0000014240031,0.0000017915952,0.000001499354,7.9441986e-7,0.99944955,0.000058936257,0.00041300993,0.000021777194,0.0000011756573],"about_ca_topic_score_codex":0.008753279,"about_ca_topic_score_gemma":0.0044387165,"teacher_disagreement_score":0.008753279,"about_ca_system_score_codex":0.0013500256,"about_ca_system_score_gemma":0.0017539004,"threshold_uncertainty_score":0.017404616},"labels":[],"label_agreement":null},{"id":"W4406320207","doi":"10.1016/j.cie.2025.110870","title":"Multi-Objective optimization of selective maintenance process considering profitability and personnel energy consumption","year":2025,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Profitability index; Energy consumption; Process (computing); Consumption (sociology); Reliability engineering; Energy (signal processing); Business; Operations management; Risk analysis (engineering); Computer science; Engineering; Mathematics; Statistics; Finance","score_opus":0.014983661204249836,"score_gpt":0.2168617762622665,"score_spread":0.20187811505801664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406320207","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48138452,0.0016275191,0.5061367,0.0005017151,0.00011909427,0.00019063044,0.00032961206,0.00024238865,0.009467873],"genre_scores_gemma":[0.9810095,0.00024534765,0.01574646,0.000029095921,0.000020536027,0.00009580832,0.000103219645,0.000034438635,0.0027154235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942017,0.00019284323,0.00001982827,0.00009690092,0.000107191234,0.00016307234],"domain_scores_gemma":[0.9989705,0.0006118373,0.000157822,0.000036306432,0.00014004474,0.00008356092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015889284,0.0012101948,0.002195061,0.0013596766,0.0006259934,0.0017763468,0.0014431318,0.0019064277,0.0022089058],"category_scores_gemma":[0.0020666213,0.00080944377,0.0013303796,0.0012788281,0.00057011977,0.00093422877,0.0008509528,0.00092696445,0.0002165587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050070663,0.000040304374,0.00023461439,0.000044673292,0.000028328694,0.000040708594,0.000010442138,0.9955397,0.0006626725,0.0004855696,0.00009340374,0.002769514],"study_design_scores_gemma":[0.000007154471,0.00004810998,0.00026158977,0.0000031144407,0.000012614459,0.0000072187167,0.000008305936,0.9991874,0.0002104695,0.00020460835,0.000046657104,0.0000026644507],"about_ca_topic_score_codex":0.008786967,"about_ca_topic_score_gemma":0.0047296574,"teacher_disagreement_score":0.008786967,"about_ca_system_score_codex":0.0012366675,"about_ca_system_score_gemma":0.0014570354,"threshold_uncertainty_score":0.017471671},"labels":[],"label_agreement":null},{"id":"W4406331954","doi":"10.1080/15732479.2025.2451277","title":"Reliability-based reinforcement learning driven maintenance policy optimization","year":2025,"lang":"en","type":"article","venue":"Structure and Infrastructure Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Reliability (semiconductor); Reinforcement; Reliability engineering; Reinforcement learning; Computer science; Preventive maintenance; Engineering; Structural engineering; Artificial intelligence","score_opus":0.0013279533385156248,"score_gpt":0.1783856397812691,"score_spread":0.17705768644275346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406331954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04880013,0.00027116676,0.94648635,0.00028645917,0.00005078714,0.00010491514,0.000045821926,0.0003197512,0.0036346663],"genre_scores_gemma":[0.93294585,0.0001047063,0.06451191,0.00009932443,0.00002500446,0.00013180051,0.000057970523,0.000036833233,0.002086503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947864,0.00020623977,0.000020917703,0.0000932162,0.00012289012,0.00007811952],"domain_scores_gemma":[0.997361,0.0018066074,0.00026706848,0.00007655114,0.0004019437,0.0000868039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015204505,0.00064749754,0.00097123143,0.00050554087,0.00026770734,0.0005340483,0.0010441899,0.0008481704,0.0016563319],"category_scores_gemma":[0.005171591,0.00035404798,0.00034609617,0.000300645,0.0006058862,0.0006216271,0.00056543277,0.0008197766,0.00020183573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004243392,0.00004815056,0.00044898593,0.00002927118,0.00001843661,0.000027997667,0.000018853027,0.9827419,0.0005579318,0.002398613,0.000284835,0.013382582],"study_design_scores_gemma":[0.0000070665737,0.00001923253,0.000047983824,0.000001940404,0.000002740226,0.000004305422,0.000001527311,0.99910045,0.00010106036,0.00063245784,0.0000798385,0.000001373024],"about_ca_topic_score_codex":0.004956727,"about_ca_topic_score_gemma":0.0030130965,"teacher_disagreement_score":0.004956727,"about_ca_system_score_codex":0.00096512487,"about_ca_system_score_gemma":0.0013098451,"threshold_uncertainty_score":0.009855747},"labels":[],"label_agreement":null},{"id":"W4406627786","doi":"10.1080/09617353.2024.2441545","title":"Availability-based maintenance prioritization for data centres: a dynamic programming approach","year":2025,"lang":"en","type":"article","venue":"Safety and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Prioritization; Computer science; Dynamic programming; Reliability engineering; Operations research; Engineering; Management science; Algorithm","score_opus":0.007298120864131907,"score_gpt":0.23393216716217277,"score_spread":0.22663404629804088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406627786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021266846,0.0005138819,0.9684995,0.00074258656,0.00007158183,0.00014919003,0.00018277125,0.0001608821,0.008412667],"genre_scores_gemma":[0.757677,0.0009809973,0.2293515,0.0003071273,0.00011706714,0.0005511736,0.0003112878,0.00014893037,0.010554871],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992834,0.0002745552,0.000024760708,0.00013252726,0.00013662751,0.00014809475],"domain_scores_gemma":[0.99827707,0.001271569,0.00015096333,0.000025640413,0.00017699006,0.00009777927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016411797,0.0013185455,0.0014322611,0.0011451221,0.0005227659,0.0019309242,0.0018048638,0.0017463762,0.0041416194],"category_scores_gemma":[0.0033549196,0.0012666705,0.0010814179,0.0009672527,0.00087056775,0.0012210475,0.0012579958,0.00199788,0.00032388206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014984425,0.000019490106,0.000112328555,0.000029184517,0.000010889213,0.000025055835,0.000015368412,0.99324083,0.00017435507,0.0030310436,0.00023640462,0.0030901935],"study_design_scores_gemma":[0.000004866683,0.0000137680745,0.000043513206,0.0000060773527,0.000005415639,0.000006408718,0.000010404857,0.9977416,0.000066588946,0.0018614461,0.00023670236,0.0000031485322],"about_ca_topic_score_codex":0.013747725,"about_ca_topic_score_gemma":0.011167442,"teacher_disagreement_score":0.013747725,"about_ca_system_score_codex":0.0020606765,"about_ca_system_score_gemma":0.0024824962,"threshold_uncertainty_score":0.027335405},"labels":[],"label_agreement":null},{"id":"W4407527988","doi":"10.1002/qre.3743","title":"Remaining Useful Life Prediction Through the Derivation of Acceleration Factors Based on Intermittent Inspection Data","year":2025,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"Defense Acquisition Program Administration","keywords":"Reliability (semiconductor); Reliability engineering; Acceleration; Product (mathematics); Process (computing); Computer science; Field (mathematics); Product lifecycle; Data mining; Engineering; New product development; Mathematics; Power (physics)","score_opus":0.042295149530753216,"score_gpt":0.28632454827525816,"score_spread":0.24402939874450494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407527988","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31620744,0.00013566509,0.6810425,0.00003674372,0.000009349381,0.000048595513,0.00024075722,0.000780354,0.0014986783],"genre_scores_gemma":[0.96172667,0.00004713896,0.037771333,0.0000035908845,0.0000033400956,0.0000315879,0.00012559019,0.000017034674,0.000273615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980813,0.00003548589,0.000012716611,0.00003444886,0.00009503347,0.000014159115],"domain_scores_gemma":[0.9989661,0.00052460557,0.00020093672,0.000101512414,0.00018509798,0.00002183065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042502678,0.0004825962,0.00027670554,0.0010431802,0.00012723713,0.00036640506,0.00041748426,0.0002611856,0.00057071267],"category_scores_gemma":[0.0021295403,0.00017856222,0.00025015575,0.0003333312,0.00016239741,0.0004517042,0.00019690808,0.0002967952,0.0001959243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011054044,0.00011978631,0.023610314,0.000095489035,0.00003215907,0.00015350271,0.00008660025,0.8059872,0.028526446,0.0017063553,0.0005109296,0.13906065],"study_design_scores_gemma":[0.000002257464,0.0000395833,0.0027690213,0.000004542979,0.000005284724,0.000023811908,0.0000052192113,0.9936838,0.003011313,0.00030613886,0.00014352257,0.0000054210223],"about_ca_topic_score_codex":0.0026358867,"about_ca_topic_score_gemma":0.0019573085,"teacher_disagreement_score":0.0026358867,"about_ca_system_score_codex":0.00028423246,"about_ca_system_score_gemma":0.00041381206,"threshold_uncertainty_score":0.005241096},"labels":[],"label_agreement":null},{"id":"W4408063058","doi":"10.5220/0013090100003893","title":"A Discrete Event Simulation Tool for Conducting a Fleet Mix Study","year":2025,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Discrete event simulation; Computer science; Event (particle physics); Simulation","score_opus":0.01988566399221958,"score_gpt":0.2976279441668963,"score_spread":0.2777422801746767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408063058","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052216932,0.000101906255,0.91469455,0.00023086077,0.0001655369,0.00042228296,0.0037896328,0.013442791,0.014935508],"genre_scores_gemma":[0.54658514,0.00025665035,0.44005832,0.00016382193,0.000050952378,0.0013540844,0.003355094,0.0015531465,0.006622778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996785,0.00011414202,0.000029628007,0.000040627932,0.00009297796,0.0000441007],"domain_scores_gemma":[0.9966882,0.0024864383,0.00013876177,0.00024449092,0.00029194306,0.00015015349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012933213,0.0011069049,0.0011208209,0.0010128563,0.00067119877,0.0010533575,0.0020750535,0.0014810925,0.01654859],"category_scores_gemma":[0.0033369134,0.00088496256,0.0010644251,0.0008416926,0.00037731283,0.00092109037,0.00081408536,0.0016318175,0.0013387512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083239436,0.00012101119,0.00080630166,0.00008410987,0.000057114336,0.00008160305,0.00005246817,0.9844974,0.0009859169,0.0045130495,0.0017347332,0.006982999],"study_design_scores_gemma":[0.000032445034,0.000018368033,0.000081373255,0.00000646343,0.0000104540795,0.000013528029,0.000008666918,0.9969311,0.0004129028,0.0011667359,0.0013108352,0.0000071028676],"about_ca_topic_score_codex":0.010953686,"about_ca_topic_score_gemma":0.011317856,"teacher_disagreement_score":0.01654859,"about_ca_system_score_codex":0.0007549932,"about_ca_system_score_gemma":0.0014796646,"threshold_uncertainty_score":0.055360615},"labels":[],"label_agreement":null},{"id":"W4408096879","doi":"10.1080/03610926.2025.2464079","title":"Reliability inference for dual stress factors accelerated degradation test based on the nonlinear Wiener process with three-source variability","year":2025,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Social Science Fund of China","keywords":"Reliability (semiconductor); Degradation (telecommunications); Inference; Stress (linguistics); Reliability engineering; Nonlinear system; Computer science; Dual (grammatical number); Process (computing); Wiener process; Engineering; Mathematics; Statistics; Artificial intelligence; Physics","score_opus":0.02660757068725326,"score_gpt":0.3487597902785443,"score_spread":0.32215221959129103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408096879","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04703716,0.00017966657,0.95208603,0.0000702316,0.000010330206,0.000015620788,0.00003708959,0.00009636542,0.00046759893],"genre_scores_gemma":[0.9398491,0.0002302427,0.058124784,0.000037359157,0.000025887904,0.000052754946,0.00017805949,0.000036721147,0.0014650811],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900085,0.00037534133,0.000043092874,0.00023907893,0.0002677059,0.00007386922],"domain_scores_gemma":[0.99635065,0.0026349649,0.00043275033,0.00017217657,0.00034161977,0.00006785519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025988915,0.0008490907,0.00097335776,0.00080383045,0.00022349968,0.0007570346,0.00092842994,0.0007170233,0.00076463475],"category_scores_gemma":[0.0074530067,0.0004623589,0.0011241013,0.00056680496,0.00077961304,0.0011417401,0.0009626967,0.0012452228,0.00017158088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017496296,0.000055109384,0.0046817497,0.000094192386,0.00011297815,0.00020109212,0.00009242279,0.9421513,0.005253797,0.015527033,0.00029035023,0.031364948],"study_design_scores_gemma":[0.0000024944575,0.000016304783,0.00045101345,0.0000022408055,0.0000072189987,0.000021154254,0.0000035561006,0.9966112,0.00043160623,0.0023963552,0.000051057435,0.00000572966],"about_ca_topic_score_codex":0.0029225922,"about_ca_topic_score_gemma":0.0024190948,"teacher_disagreement_score":0.0029225922,"about_ca_system_score_codex":0.0006468244,"about_ca_system_score_gemma":0.00077083247,"threshold_uncertainty_score":0.013744414},"labels":[],"label_agreement":null},{"id":"W4410291515","doi":"10.1016/j.cie.2025.111179","title":"An optimal degradation-based burn-in and warranty policy for repairable products","year":2025,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Canada Research Chairs; Government of Canada","keywords":"Warranty; Burn-in; Degradation (telecommunications); Reliability engineering; Forensic engineering; Business; Computer science; Engineering; Law; Political science; Telecommunications","score_opus":0.01275178393408294,"score_gpt":0.22251038130910422,"score_spread":0.20975859737502128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410291515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34373882,0.0026408483,0.6339352,0.0025806243,0.00023040283,0.00047823304,0.00049817865,0.0013679392,0.014529828],"genre_scores_gemma":[0.971684,0.00030412516,0.025284234,0.00011338725,0.000039519407,0.00004992511,0.00009162662,0.00006884377,0.002364363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988852,0.00030998554,0.00006291591,0.0002107599,0.0002613035,0.00026995988],"domain_scores_gemma":[0.9970757,0.0013953091,0.0004943162,0.0001889275,0.0005600791,0.000285712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002819446,0.0013203141,0.0019422246,0.0014817463,0.0006655133,0.0019545644,0.0017732371,0.001974773,0.0027939274],"category_scores_gemma":[0.0076102186,0.00089809374,0.0005612958,0.00071276654,0.0010213348,0.002594643,0.0009775581,0.001708091,0.0004062535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093988155,0.00025826093,0.0008501396,0.0002486093,0.00004056818,0.0001655375,0.000105281055,0.9305388,0.013431556,0.012123254,0.0024818326,0.03881632],"study_design_scores_gemma":[0.00003251839,0.00015891992,0.00076012866,0.000032107586,0.000022492892,0.000039814793,0.000041883697,0.9886403,0.002280061,0.0075340103,0.0004411002,0.00001673201],"about_ca_topic_score_codex":0.0037808584,"about_ca_topic_score_gemma":0.0033394296,"teacher_disagreement_score":0.0037808584,"about_ca_system_score_codex":0.0024497432,"about_ca_system_score_gemma":0.0024943668,"threshold_uncertainty_score":0.017774165},"labels":[],"label_agreement":null},{"id":"W4410851774","doi":"10.1016/j.ress.2025.111193","title":"Joint reliability of linear two-dimensional consecutive <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si127.svg\" display=\"inline\" id=\"d1e146\"> <mml:mi>k</mml:mi> </mml:math> -type systems with shared components","year":2025,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Beijing Municipality; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Scalable Vector Graphics; Reliability (semiconductor); Joint (building); Mathematics; Computer science; Algorithm; Engineering; Physics; World Wide Web; Thermodynamics","score_opus":0.009463173377817099,"score_gpt":0.20841822666245685,"score_spread":0.19895505328463975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410851774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42372698,0.000640231,0.55260575,0.00043081812,0.00015613716,0.00008996361,0.0018092039,0.0013062162,0.019234689],"genre_scores_gemma":[0.9820633,0.000082703125,0.011272221,0.000024474477,0.000026360176,0.000060836705,0.00082036643,0.0001237253,0.0055259806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989452,0.00023287136,0.000051435505,0.00024256256,0.00034998514,0.0001779929],"domain_scores_gemma":[0.994786,0.0021816357,0.00044001604,0.0006456878,0.0017091093,0.00023761998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001932948,0.00065700524,0.00086968514,0.001172846,0.00043374227,0.0017639156,0.0009757213,0.00065484823,0.0075641754],"category_scores_gemma":[0.007040063,0.0004411175,0.0007626353,0.00085522764,0.0010637677,0.0016162827,0.0010931627,0.00061340816,0.0013112138],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007349255,0.00013891175,0.011646961,0.00032521912,0.0001942524,0.0002509286,0.00029784272,0.88154495,0.0063537178,0.043598495,0.0064317286,0.048482146],"study_design_scores_gemma":[0.000009153202,0.00008958746,0.0034696525,0.000011049801,0.000026260013,0.000050036615,0.000057507106,0.98148096,0.0026785994,0.011502003,0.0005930076,0.000032092805],"about_ca_topic_score_codex":0.0033395754,"about_ca_topic_score_gemma":0.0030125652,"teacher_disagreement_score":0.0075641754,"about_ca_system_score_codex":0.0010416246,"about_ca_system_score_gemma":0.0009304034,"threshold_uncertainty_score":0.025304735},"labels":[],"label_agreement":null},{"id":"W4410976732","doi":"10.1016/j.ress.2025.111265","title":"Constrained optimal maintenance strategies for <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si5.svg\" display=\"inline\" id=\"d1e14341\"> <mml:mi>k</mml:mi> </mml:math> -out-of- <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si6.svg\" display=\"inline\" id=\"d1e14346\"> <mml:mi>n</mml:mi> </mml:math> systems with dependent components and mission duration","year":2025,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Ministero dello Sviluppo Economico; Government of Ontario","keywords":"Scalable Vector Graphics; Computer science; Mathematics; Algorithm; Discrete mathematics; World Wide Web","score_opus":0.010993962589936781,"score_gpt":0.22253404908000465,"score_spread":0.21154008649006786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410976732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22447577,0.0014011143,0.7297207,0.001479018,0.00009054693,0.00039200654,0.0014207797,0.00059188314,0.040428184],"genre_scores_gemma":[0.9296649,0.00036236725,0.05937146,0.00011768786,0.000023475228,0.00023713754,0.0005187459,0.00013773756,0.0095664635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967,0.00010832123,0.000016055677,0.00007151026,0.000058336143,0.00007571934],"domain_scores_gemma":[0.99885046,0.00069146353,0.00014023732,0.000056422825,0.00017289126,0.00008850453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009298167,0.001003794,0.0007542659,0.0008929827,0.00042904523,0.0011101352,0.0010816286,0.0010733722,0.0070837997],"category_scores_gemma":[0.0035633047,0.00047273253,0.0005808909,0.0004952302,0.00040320202,0.0009523288,0.00080157584,0.0007730088,0.00067313504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001710469,0.000082202525,0.00046876754,0.00012637021,0.00003816121,0.00006650827,0.000060188675,0.9559563,0.0015646925,0.010940297,0.0026850663,0.027840406],"study_design_scores_gemma":[0.000028524179,0.00008703537,0.00045880955,0.000025141744,0.000025430782,0.000020211804,0.000038905695,0.9904364,0.00065905304,0.0074030706,0.00080815575,0.00000938726],"about_ca_topic_score_codex":0.008934492,"about_ca_topic_score_gemma":0.011841442,"teacher_disagreement_score":0.008934492,"about_ca_system_score_codex":0.0014672211,"about_ca_system_score_gemma":0.0015579818,"threshold_uncertainty_score":0.023697674},"labels":[],"label_agreement":null},{"id":"W4411102111","doi":"10.1007/s43684-025-00099-9","title":"A hybrid Bi-LSTM model for data-driven maintenance planning","year":2025,"lang":"en","type":"article","venue":"Autonomous Intelligent Systems","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"USable; Computer science; Reliability (semiconductor); Scalability; Predictive maintenance; Dropout (neural networks); Reliability engineering; Parametric statistics; Monte Carlo method; Estimator; Mathematical optimization; Machine learning; Engineering","score_opus":0.03649822259413802,"score_gpt":0.2742112900713538,"score_spread":0.2377130674772158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411102111","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038667925,0.0010509382,0.95090896,0.00077175343,0.00013702283,0.000050903185,0.0007220354,0.0024719941,0.0052184374],"genre_scores_gemma":[0.8891744,0.00043442252,0.10220374,0.00037485597,0.000073057054,0.00022203944,0.0010956955,0.00011530585,0.0063065914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998597,0.00002231303,0.0000086376895,0.000050624138,0.000031546188,0.000027129832],"domain_scores_gemma":[0.99974126,0.00013073909,0.000026700305,0.000015532529,0.00006806753,0.000017749906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003881727,0.0007147252,0.00058132684,0.0004079799,0.000261085,0.0006614922,0.0014112843,0.00115696,0.002786678],"category_scores_gemma":[0.00098232,0.00040998406,0.000519314,0.000545326,0.0003513778,0.00083166844,0.00057477877,0.0013042961,0.0006049812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065729124,0.000044062926,0.00048288846,0.000043468503,0.00002480254,0.000042272302,0.000024472518,0.9482668,0.0012288147,0.0024457902,0.0015911047,0.0457398],"study_design_scores_gemma":[0.0000017586923,0.0000057425423,0.000035776236,0.0000017354021,0.0000024522808,0.0000036048325,0.0000011274807,0.99912244,0.00012939273,0.00057917944,0.00011557173,0.0000012915737],"about_ca_topic_score_codex":0.0159767,"about_ca_topic_score_gemma":0.020840855,"teacher_disagreement_score":0.0159767,"about_ca_system_score_codex":0.00085121434,"about_ca_system_score_gemma":0.0010767571,"threshold_uncertainty_score":0.031767428},"labels":[],"label_agreement":null},{"id":"W4411642381","doi":"10.1016/j.ymssp.2025.112970","title":"An improved exponential model for machine remaining useful life prediction using empirical Bayes","year":2025,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; Northwestern Polytechnical University; National Natural Science Foundation of China","keywords":"Bayes' theorem; Exponential function; Naive Bayes classifier; Bayesian probability; Computer science; Mathematics; Econometrics; Machine learning; Artificial intelligence; Statistics; Support vector machine","score_opus":0.027694704691907532,"score_gpt":0.27265241690738906,"score_spread":0.24495771221548152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411642381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022629922,0.00092881423,0.974533,0.00019667283,0.00006598612,0.000030130828,0.00017746216,0.00042100358,0.0010169687],"genre_scores_gemma":[0.7706193,0.0012382863,0.21662368,0.00027225027,0.000259929,0.000249039,0.0010776166,0.00020734614,0.009452526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992519,0.00027747807,0.00005589301,0.00018013087,0.00016260854,0.00007195871],"domain_scores_gemma":[0.9956617,0.0032465274,0.0001611899,0.00022607432,0.00064953027,0.000055090884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003001742,0.0006958204,0.0017766334,0.0007739666,0.00045662702,0.0010015335,0.002637992,0.001198496,0.0031494624],"category_scores_gemma":[0.0072817043,0.0007118162,0.00077322463,0.0009941757,0.0005399225,0.0022337523,0.00081664504,0.0017534748,0.0011034255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013745834,0.00007878851,0.0010575476,0.00007100036,0.000054459466,0.000059624996,0.000045541183,0.9242038,0.00072860124,0.01088841,0.0015509066,0.061123822],"study_design_scores_gemma":[0.0000021406695,0.0000036194574,0.000058104113,0.0000024115775,0.0000025946404,0.0000037417844,8.3127645e-7,0.99818486,0.000050068196,0.0016094466,0.00008033354,0.0000018899216],"about_ca_topic_score_codex":0.008745,"about_ca_topic_score_gemma":0.0065562283,"teacher_disagreement_score":0.008745,"about_ca_system_score_codex":0.0007329377,"about_ca_system_score_gemma":0.00087287807,"threshold_uncertainty_score":0.017388225},"labels":[],"label_agreement":null},{"id":"W4412170743","doi":"10.1109/msmc.2024.3509826","title":"Condition-Based Maintenance Scheduling Using Probability Distribution Function and Agglomerative Hierarchical Clustering Approaches: AI-Driven Predictive Maintenance Mapping","year":2025,"lang":"en","type":"article","venue":"IEEE Systems Man and Cybernetics Magazine","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Hierarchical clustering; Cluster analysis; Scheduling (production processes); Probability density function; Predictive maintenance; Artificial intelligence; Machine learning; Mathematics; Mathematical optimization; Statistics; Engineering; Reliability engineering","score_opus":0.016900075758106425,"score_gpt":0.2169797555675964,"score_spread":0.20007967980948999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412170743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037020743,0.00016014751,0.9607226,0.00015221718,0.000018087801,0.000054572265,0.00007578314,0.00033294724,0.001462846],"genre_scores_gemma":[0.890066,0.00014762083,0.1083323,0.00004793159,0.00002254903,0.000112410184,0.00013920429,0.000046844078,0.0010850608],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971074,0.000080480444,0.000016383105,0.00007006006,0.00008115491,0.000041154024],"domain_scores_gemma":[0.99925977,0.0004251596,0.000112734655,0.000041515337,0.00013095856,0.000029966153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072097336,0.00056453934,0.0005768891,0.0009980606,0.00040112963,0.0007150188,0.001144784,0.00058725884,0.0009403672],"category_scores_gemma":[0.002080235,0.00040763355,0.00066619326,0.00085218006,0.0003792625,0.0007327714,0.00048583816,0.0006477501,0.00017786381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013833161,0.000017949596,0.00043844403,0.000014703264,0.000015696445,0.000016109876,0.000029793411,0.9825806,0.000313525,0.0015846615,0.00016638648,0.014808194],"study_design_scores_gemma":[6.6115473e-7,0.0000030289923,0.00007731157,9.520555e-7,0.0000011432065,0.0000016197604,0.000002551694,0.9991732,0.00006467192,0.00064021634,0.000033370514,0.000001219386],"about_ca_topic_score_codex":0.024701897,"about_ca_topic_score_gemma":0.012012901,"teacher_disagreement_score":0.024701897,"about_ca_system_score_codex":0.0012664481,"about_ca_system_score_gemma":0.0012173535,"threshold_uncertainty_score":0.049116254},"labels":[],"label_agreement":null},{"id":"W4412422424","doi":"10.1016/j.ress.2025.111358","title":"Joint selective maintenance and mission abort decisions for mission-critical systems","year":2025,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Abort; Joint (building); Aeronautics; Computer science; Operations research; Engineering; Systems engineering; Reliability engineering; Operating system; Civil engineering","score_opus":0.0074508268211682955,"score_gpt":0.22843129501113918,"score_spread":0.22098046818997089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412422424","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79413915,0.0005610295,0.19876745,0.000681347,0.000094105635,0.00018979915,0.00011476571,0.00036651603,0.00508584],"genre_scores_gemma":[0.9932273,0.000033271226,0.005985167,0.000019487798,0.00001333894,0.000016570653,0.000030193392,0.000010340182,0.00066420087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941707,0.00015460106,0.000022226839,0.00006283845,0.00013897923,0.0002042056],"domain_scores_gemma":[0.99768674,0.0014284012,0.00031555133,0.00010857375,0.0002788233,0.00018202564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014731971,0.00068130647,0.00088097097,0.0006263801,0.0006232422,0.0006417833,0.00079595856,0.0005957323,0.0018861721],"category_scores_gemma":[0.0041977246,0.0003652324,0.00037634035,0.00027302004,0.00055556465,0.0006980095,0.00075481733,0.00063639623,0.000133779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002302388,0.00027928295,0.004813305,0.00016060706,0.0001002031,0.00028641056,0.00015612794,0.8829529,0.0105452975,0.005260354,0.0023230151,0.09082016],"study_design_scores_gemma":[0.00005046913,0.00028490773,0.002172671,0.000006239406,0.000043475386,0.000041941777,0.00006949911,0.9918332,0.0024649163,0.0028032102,0.00022144309,0.000008077472],"about_ca_topic_score_codex":0.004534558,"about_ca_topic_score_gemma":0.0069137476,"teacher_disagreement_score":0.004534558,"about_ca_system_score_codex":0.00063951436,"about_ca_system_score_gemma":0.0020314422,"threshold_uncertainty_score":0.009016335},"labels":[],"label_agreement":null},{"id":"W4412735999","doi":"10.1108/jm2-01-2025-0003","title":"An integrated two-dimensional warranty framework for second-hand equipment considering condition-based maintenance, upgrades, and past life","year":2025,"lang":"en","type":"article","venue":"Journal of Modelling in Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Warranty; Computer science; Business; Operations management; Process management; Risk analysis (engineering); Reliability engineering; Economics; Engineering","score_opus":0.012507329712476666,"score_gpt":0.248347070711899,"score_spread":0.23583974099942231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412735999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14694342,0.0021580108,0.81828254,0.0015518381,0.00020803827,0.00036266746,0.000869464,0.00046056992,0.029163415],"genre_scores_gemma":[0.9459391,0.00067829527,0.043922663,0.00006623858,0.00006670838,0.00023191677,0.00023109416,0.00007062446,0.008793374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841607,0.00042695567,0.00009015919,0.00033570177,0.0004889057,0.00024212284],"domain_scores_gemma":[0.9975485,0.0011630028,0.00053219247,0.00015897327,0.00044520744,0.00015208664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023937456,0.001603522,0.001213069,0.0014872404,0.00059114693,0.0026967202,0.002681126,0.0020776314,0.0055926717],"category_scores_gemma":[0.004456782,0.0009004055,0.001230956,0.0006923526,0.0011660923,0.0026508889,0.0013206023,0.001655528,0.0004360138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059774175,0.00007282759,0.00088746834,0.0001616449,0.000030861058,0.00018159306,0.00008801783,0.94756114,0.0018264281,0.039916623,0.00050781143,0.008705842],"study_design_scores_gemma":[0.000015847687,0.00011293997,0.0011802298,0.000036538408,0.000042928663,0.00007044481,0.00007289977,0.98622686,0.00036931937,0.010023286,0.0018249947,0.000023721956],"about_ca_topic_score_codex":0.014257895,"about_ca_topic_score_gemma":0.009438184,"teacher_disagreement_score":0.014257895,"about_ca_system_score_codex":0.0027596492,"about_ca_system_score_gemma":0.0030623844,"threshold_uncertainty_score":0.028349817},"labels":[],"label_agreement":null},{"id":"W4412749571","doi":"10.1016/j.cie.2025.111438","title":"Optimization of a bi-objective reliability redundancy allocation problem with heterogeneous components and strategy selection","year":2025,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Selection (genetic algorithm); Redundancy (engineering); Reliability engineering; Computer science; Reliability (semiconductor); Mathematical optimization; Engineering; Mathematics; Artificial intelligence","score_opus":0.0093349960262908,"score_gpt":0.19088626255730895,"score_spread":0.18155126653101816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412749571","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12895952,0.0014644851,0.858399,0.0004951847,0.000086242275,0.00023566952,0.0001910976,0.00023805081,0.009930762],"genre_scores_gemma":[0.92550564,0.0005320687,0.06904259,0.000104191226,0.00003628049,0.00035715968,0.00014103582,0.00004232612,0.004238677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987387,0.000606984,0.00004602623,0.00020008296,0.00018788043,0.00022025421],"domain_scores_gemma":[0.99871004,0.0008545885,0.00015980004,0.00003686339,0.00014152355,0.00009720568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022374634,0.0018818731,0.0021452643,0.0012836196,0.00048074842,0.0015259316,0.0013567868,0.0018508389,0.0022174919],"category_scores_gemma":[0.0024901358,0.000892176,0.0010917152,0.0011964297,0.00076684175,0.0009821862,0.0012312984,0.0010745212,0.00023214857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004676184,0.00004182931,0.0002122346,0.00006192663,0.000046692236,0.00008169593,0.00001877969,0.9916237,0.0004992889,0.0027584345,0.00020237685,0.004406199],"study_design_scores_gemma":[0.000013043635,0.000042805485,0.000089226545,0.0000046303917,0.000012474641,0.000012659952,0.0000092607,0.9988299,0.00010547722,0.00077923434,0.000097886346,0.0000035007738],"about_ca_topic_score_codex":0.006152161,"about_ca_topic_score_gemma":0.0030542007,"teacher_disagreement_score":0.006152161,"about_ca_system_score_codex":0.0011686638,"about_ca_system_score_gemma":0.001758577,"threshold_uncertainty_score":0.012232661},"labels":[],"label_agreement":null},{"id":"W4412998675","doi":"10.1017/s0269964825100053","title":"On a new family of <i>r</i>-modified reliability systems","year":2025,"lang":"en","type":"article","venue":"Probability in the Engineering and Informational Sciences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Reliability engineering; Reliability (semiconductor); Computer science; Mathematics; Engineering; Physics; Thermodynamics","score_opus":0.010364687075831557,"score_gpt":0.20809791940810674,"score_spread":0.1977332323322752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412998675","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10734433,0.00071277557,0.87477857,0.00064267125,0.00008245392,0.000066779525,0.00024849773,0.0003012526,0.015822679],"genre_scores_gemma":[0.9176086,0.0005847765,0.0751739,0.00020866941,0.00014857581,0.00011054651,0.00021868471,0.00012855713,0.0058177747],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990701,0.0003294798,0.000045628476,0.00020166143,0.0002491557,0.00010403188],"domain_scores_gemma":[0.9972216,0.0011280559,0.00066840515,0.0002938817,0.0005404275,0.00014760201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014637427,0.00073540566,0.00061441545,0.0011224893,0.00053257437,0.0009859011,0.0011164619,0.0009250133,0.0030186882],"category_scores_gemma":[0.00489635,0.0003081629,0.0008517039,0.0006815,0.0009521795,0.0016151097,0.00071165373,0.0010998484,0.00037805774],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005741334,0.000039376348,0.0017287593,0.00008476876,0.000052955653,0.00065885996,0.00021907911,0.40361932,0.007820336,0.5659725,0.0032877685,0.016458822],"study_design_scores_gemma":[0.0000063573166,0.000032646436,0.00042573927,0.0000119794795,0.0000072580265,0.00025554796,0.000018798653,0.9182084,0.00057590904,0.07779433,0.0026446553,0.000018388488],"about_ca_topic_score_codex":0.0013623207,"about_ca_topic_score_gemma":0.0005830603,"teacher_disagreement_score":0.0030186882,"about_ca_system_score_codex":0.00094032724,"about_ca_system_score_gemma":0.00048201325,"threshold_uncertainty_score":0.010098457},"labels":[],"label_agreement":null},{"id":"W4413309253","doi":"10.1007/s43684-025-00104-1","title":"Optimizing predictive maintenance and mission assignment to enhance fleet readiness under uncertainty","year":2025,"lang":"en","type":"article","venue":"Autonomous Intelligent Systems","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Predictive maintenance; Computer science; Operations research; Reliability engineering; Engineering","score_opus":0.008878740522137751,"score_gpt":0.2512997223482635,"score_spread":0.24242098182612573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413309253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16730851,0.00050688034,0.8285296,0.00030847167,0.00003186899,0.000045077737,0.00012484333,0.00040141193,0.0027432693],"genre_scores_gemma":[0.9746202,0.000104652645,0.024126252,0.00003715979,0.000012058576,0.000038941118,0.00009576386,0.000027590226,0.0009373303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997428,0.00006947267,0.000009503937,0.000062292354,0.000055287375,0.000060534723],"domain_scores_gemma":[0.99907744,0.0005995479,0.00013207606,0.000038193226,0.00010096854,0.000051738396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008756045,0.00075638056,0.0007117015,0.00045038215,0.00022585307,0.0006169892,0.0008345124,0.0007281333,0.001066515],"category_scores_gemma":[0.0027155587,0.00043201246,0.00040432977,0.00038586414,0.00045026856,0.0008244495,0.00063592294,0.0008977297,0.0001140097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015717784,0.000009969636,0.00023598285,0.000009726437,0.0000062687786,0.000009620578,0.000007814801,0.994575,0.00026910997,0.00066499336,0.000095394804,0.004100411],"study_design_scores_gemma":[0.000001391169,0.000008574542,0.000081700215,0.0000011710194,0.0000022540237,0.0000022509519,0.000002344033,0.99924767,0.00008528273,0.0005294553,0.0000370608,8.131753e-7],"about_ca_topic_score_codex":0.008283901,"about_ca_topic_score_gemma":0.00700998,"teacher_disagreement_score":0.008283901,"about_ca_system_score_codex":0.00083142164,"about_ca_system_score_gemma":0.0012046978,"threshold_uncertainty_score":0.016471386},"labels":[],"label_agreement":null},{"id":"W4413360646","doi":"10.1109/icse-seip66354.2025.00023","title":"On the Diagnosis of Flaky Job Failures: Understanding and Prioritizing Failure Categories","year":2025,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Telus (Canada); École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reliability engineering; Engineering","score_opus":0.013297073497763837,"score_gpt":0.208806746648095,"score_spread":0.1955096731503312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413360646","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8169364,0.0016436143,0.17345317,0.0011550466,0.00008198443,0.000341282,0.0013947092,0.0009210362,0.0040727574],"genre_scores_gemma":[0.9474817,0.00025815686,0.050264996,0.00008262102,0.000031548894,0.00005185753,0.00087354163,0.000060115523,0.00089559273],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998014,0.0003659527,0.00017279475,0.0004248404,0.00067172345,0.00035075584],"domain_scores_gemma":[0.9854021,0.0067428425,0.0036082424,0.0006173908,0.0029638503,0.00066546514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025909573,0.0013312693,0.0009578245,0.011029543,0.0010332996,0.0022476376,0.001356013,0.0011158463,0.0013651159],"category_scores_gemma":[0.015005794,0.00036750897,0.0006410699,0.003756554,0.00086274755,0.0022342422,0.0013849396,0.0011826245,0.00057956035],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000716647,0.0007169382,0.5481548,0.00064943027,0.00020421633,0.0008327343,0.0022983763,0.14245485,0.013997423,0.0047220667,0.0051024347,0.28015015],"study_design_scores_gemma":[0.000027077893,0.00045401137,0.26308846,0.00022434247,0.0000916395,0.0007124865,0.0038166689,0.7090361,0.007877952,0.010491617,0.0040541687,0.00012545253],"about_ca_topic_score_codex":0.026334452,"about_ca_topic_score_gemma":0.03604463,"teacher_disagreement_score":0.026334452,"about_ca_system_score_codex":0.0015562264,"about_ca_system_score_gemma":0.0021052614,"threshold_uncertainty_score":0.052362382},"labels":[],"label_agreement":null},{"id":"W4413450610","doi":"10.1002/9781394345731.ch5","title":"An Analytic Toolbox for Optimizing Condition Based Maintenance (CBM) Decisions","year":2025,"lang":"en","type":"other","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Toolbox; Computer science; Reliability engineering; Engineering; Programming language","score_opus":0.009859646644166553,"score_gpt":0.25324403645934,"score_spread":0.24338438981517346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413450610","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015890163,0.0000943118,0.99197257,0.000077214514,0.000016549371,0.000033169857,0.00016845013,0.0019924697,0.004056304],"genre_scores_gemma":[0.14718862,0.0006519822,0.8450239,0.00008138295,0.000054403998,0.00033713042,0.00060834223,0.00071146694,0.0053427545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993456,0.00020465597,0.000037382182,0.00006885235,0.0003055309,0.00003790249],"domain_scores_gemma":[0.99834967,0.0010012237,0.00013280292,0.00015320563,0.00032212213,0.000040941944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016670163,0.0014037832,0.00080998393,0.0014483375,0.00051168085,0.0012068811,0.0010934486,0.0007457038,0.009561733],"category_scores_gemma":[0.005062188,0.0005471647,0.0007224022,0.00087444973,0.00044991827,0.0010248496,0.0012045612,0.0010988038,0.0019477949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005625156,0.00008358103,0.00061723165,0.00020577732,0.000045102344,0.000097459626,0.00008183804,0.75849116,0.0030879069,0.041797053,0.007098069,0.18833858],"study_design_scores_gemma":[0.000010866918,0.000022175978,0.00007781082,0.000019333085,0.000009554457,0.000032574077,0.000009661054,0.9807793,0.0012429755,0.0116188945,0.006167283,0.00000952349],"about_ca_topic_score_codex":0.0030491697,"about_ca_topic_score_gemma":0.0034703906,"teacher_disagreement_score":0.009561733,"about_ca_system_score_codex":0.00072425616,"about_ca_system_score_gemma":0.0017031976,"threshold_uncertainty_score":0.03198725},"labels":[],"label_agreement":null},{"id":"W4413872563","doi":"10.5267/j.ijiec.2025.8.013","title":"Optimal grouping preventive maintenance strategy for the multi-part series system under the extended warranty condition","year":2025,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Warranty; Preventive maintenance; Series (stratigraphy); Reliability engineering; Computer science; Condition-based maintenance; Optimal maintenance; Engineering; Biology","score_opus":0.024162410896961008,"score_gpt":0.26931225787759716,"score_spread":0.24514984698063616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413872563","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31498054,0.0013564072,0.6748594,0.00034005733,0.00008885022,0.00019412133,0.00014421692,0.00062243,0.00741399],"genre_scores_gemma":[0.9830656,0.000121548175,0.01574346,0.000018296527,0.000016174077,0.000035379384,0.00004393158,0.000015207316,0.00094029703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995907,0.00009167318,0.000027311182,0.000098701166,0.000110589724,0.000081066115],"domain_scores_gemma":[0.9995764,0.00010765705,0.0001438772,0.000040440562,0.00008946925,0.000042195832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006432738,0.0010313931,0.0009830914,0.0007332094,0.0005506977,0.0007311225,0.0011683166,0.00067712937,0.0017474565],"category_scores_gemma":[0.0010245449,0.0003621698,0.0006056998,0.0005562015,0.00033978265,0.00072441454,0.00042259527,0.00044306516,0.00018626466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000276202,0.00012873,0.0017604117,0.00024023956,0.00007189366,0.00031196466,0.00014719229,0.90749174,0.0138430195,0.0041320426,0.0017391383,0.06985749],"study_design_scores_gemma":[0.000030429024,0.000209386,0.0013279145,0.000008314074,0.00004446277,0.00009754203,0.000038829818,0.9949132,0.0014596957,0.0013865305,0.00047415716,0.000009566575],"about_ca_topic_score_codex":0.006072515,"about_ca_topic_score_gemma":0.004647692,"teacher_disagreement_score":0.006072515,"about_ca_system_score_codex":0.00077884115,"about_ca_system_score_gemma":0.00076083693,"threshold_uncertainty_score":0.012074351},"labels":[],"label_agreement":null},{"id":"W4414568454","doi":"10.1016/j.ifacol.2025.09.022","title":"Optimizing Maintenance Planning for Marine Energy Generators","year":2025,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cost of electricity by source; Maintenance engineering; Integer programming; Production (economics); Submarine pipeline; Renewable energy; Linear programming; Energy (signal processing); Interval (graph theory)","score_opus":0.0065109404188904785,"score_gpt":0.2227390945010865,"score_spread":0.21622815408219603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414568454","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35868087,0.0008981708,0.6266672,0.0006103016,0.00004159092,0.000115343966,0.00028292564,0.00025878262,0.012444776],"genre_scores_gemma":[0.9665069,0.00016362811,0.031169496,0.000022487637,0.000009104718,0.00004405024,0.00008167898,0.000026322017,0.0019764395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998418,0.00006661137,0.0000051626585,0.000025434578,0.000031845448,0.000029139757],"domain_scores_gemma":[0.9997036,0.00018558084,0.000050463543,0.000012253757,0.000029854868,0.000018172228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042864017,0.00049562927,0.0003987274,0.0002963671,0.000209521,0.00053275336,0.00045438172,0.00051643373,0.0013050498],"category_scores_gemma":[0.001133663,0.00028935197,0.00026131736,0.00036784683,0.00023612885,0.00043474569,0.0002896448,0.0004270019,0.00013031819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031562045,0.000012966991,0.0002781846,0.000023152961,0.0000060181696,0.000028133747,0.000010097586,0.9883117,0.00079251005,0.0014633122,0.00023245631,0.008809877],"study_design_scores_gemma":[0.0000070181877,0.00003646939,0.00029160173,0.000003380388,0.0000044368303,0.000012304483,0.000010766769,0.9978974,0.00036180427,0.0011270932,0.0002456418,0.000002047751],"about_ca_topic_score_codex":0.0040787384,"about_ca_topic_score_gemma":0.0048169107,"teacher_disagreement_score":0.0040787384,"about_ca_system_score_codex":0.00059597316,"about_ca_system_score_gemma":0.00057519367,"threshold_uncertainty_score":0.008109987},"labels":[],"label_agreement":null},{"id":"W4415042415","doi":"10.3850/978-981-94-3281-3_esrel-sra-e2025-p6336-cd","title":"Tracking Reliability and Updating the Overhaul Interval of Engineering Components: Bayesian Approach","year":2025,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University Network of Excellence in Nuclear Engineering","keywords":"Interval (graph theory); Reliability (semiconductor); Tracking (education); Bayesian probability; Interval arithmetic; Interval estimation","score_opus":0.006391451037670458,"score_gpt":0.19735471997020282,"score_spread":0.19096326893253235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415042415","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013540957,0.00046473343,0.9845664,0.00023035448,0.00003071563,0.00001846251,0.00006556067,0.00010529422,0.000977482],"genre_scores_gemma":[0.7767909,0.0015998553,0.21429154,0.0002802733,0.00030348773,0.00020037469,0.0004238356,0.00017153281,0.0059382482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868685,0.00045533027,0.000065054046,0.00035776626,0.00030449583,0.00013049724],"domain_scores_gemma":[0.99075186,0.0071955645,0.0007184596,0.00038102613,0.00079823256,0.00015482119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047830944,0.0011846765,0.0026942743,0.0020451497,0.00061598624,0.002097513,0.0025939285,0.0021953993,0.0018317978],"category_scores_gemma":[0.021048918,0.0019028455,0.0012038822,0.0018565191,0.001510315,0.0038085156,0.0014296849,0.0026015886,0.0003667414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006353517,0.000032487125,0.00073914597,0.000054742595,0.00006299494,0.00002460022,0.000049723723,0.95922,0.00043320179,0.016985618,0.0004515304,0.021882318],"study_design_scores_gemma":[0.0000056888525,0.000009661675,0.00018512363,0.000010006495,0.000012885347,0.000009536007,0.000003882818,0.9898643,0.00010604025,0.009649061,0.0001352678,0.00000849843],"about_ca_topic_score_codex":0.012735694,"about_ca_topic_score_gemma":0.010507389,"teacher_disagreement_score":0.012735694,"about_ca_system_score_codex":0.0016205474,"about_ca_system_score_gemma":0.0013991485,"threshold_uncertainty_score":0.025323153},"labels":[],"label_agreement":null},{"id":"W4415171299","doi":"10.1080/15732479.2025.2572501","title":"Unified modelling of infrastructure asset performance deterioration – a bounded gamma process approach","year":2025,"lang":"en","type":"article","venue":"Structure and Infrastructure Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Process (computing); Bounded function; Asset management; Asset (computer security); Key (lock); Mathematical model","score_opus":0.003499601453016598,"score_gpt":0.1768210970015857,"score_spread":0.1733214955485691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415171299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032025952,0.00026089328,0.96333563,0.00016877502,0.000026223193,0.000039924707,0.00011183447,0.00016487196,0.0038658986],"genre_scores_gemma":[0.9530026,0.00078030647,0.041301187,0.000056552442,0.000035677247,0.00012706667,0.00017940134,0.000055751338,0.0044615534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990758,0.0003339217,0.000048164922,0.0001961198,0.00020283993,0.0001431885],"domain_scores_gemma":[0.9987256,0.00070196204,0.00021989369,0.000089650755,0.0002143243,0.000048626476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001963495,0.001175289,0.0009296582,0.0012173511,0.00028849035,0.0016487129,0.0018712371,0.0013985371,0.001777833],"category_scores_gemma":[0.004133752,0.0004546571,0.0013892051,0.0012343081,0.0010160615,0.0019558838,0.0012275892,0.0015788865,0.00033069708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011952673,0.00001122702,0.00051286246,0.000021424661,0.000013122036,0.000062017134,0.00005951425,0.97908,0.00058361,0.014025942,0.00012356573,0.0054946705],"study_design_scores_gemma":[0.0000018885029,0.000010781789,0.00014793965,0.0000034887962,0.0000059119793,0.000012370449,0.000010526876,0.9945262,0.00010929484,0.0049913,0.00017610929,0.000004147721],"about_ca_topic_score_codex":0.009597407,"about_ca_topic_score_gemma":0.0042350306,"teacher_disagreement_score":0.009597407,"about_ca_system_score_codex":0.0011423631,"about_ca_system_score_gemma":0.0011751372,"threshold_uncertainty_score":0.019083142},"labels":[],"label_agreement":null},{"id":"W4415551264","doi":"10.36001/phmconf.2025.v17i1.4315","title":"Maintenance, Engineering, and Operational Decision-Making Metrics Derived from Simple Maintenance and Aircraft Datasets","year":2025,"lang":"","type":"article","venue":"Annual Conference of the PHM Society","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Unavailability; Reliability (semiconductor); Component (thermodynamics); Set (abstract data type); Maintenance actions; Downtime; Predictive maintenance; Dependability; Maintenance engineering","score_opus":0.00887361509470956,"score_gpt":0.2367908826973616,"score_spread":0.22791726760265205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415551264","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9261014,0.00022160276,0.036858644,0.00030703095,0.000035323497,0.0004449565,0.031897336,0.0006662411,0.0034674788],"genre_scores_gemma":[0.92586917,0.00009965375,0.038008515,0.000034473855,0.000017791117,0.00027172244,0.035039287,0.000032188404,0.00062720396],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847513,0.0003156056,0.0002360267,0.0002960136,0.0005484353,0.00012867904],"domain_scores_gemma":[0.9922537,0.0039935587,0.0011398674,0.00089751836,0.001476007,0.00023929153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028993133,0.0005665819,0.0004722729,0.0034525718,0.0003243236,0.0011628113,0.0005101879,0.0006028472,0.0009261329],"category_scores_gemma":[0.015040878,0.00014221985,0.0006278633,0.0023710874,0.00026069052,0.0009222742,0.0005429035,0.0005402162,0.00039284126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007608379,0.0009727052,0.49568084,0.00070646836,0.00038913754,0.00040245042,0.00042524264,0.31018463,0.009718888,0.003332747,0.007576054,0.16984995],"study_design_scores_gemma":[0.000033629065,0.00062105624,0.36349952,0.000090169844,0.000101705475,0.000287875,0.0004265167,0.61765206,0.008765562,0.003302681,0.005141855,0.00007743929],"about_ca_topic_score_codex":0.0072836624,"about_ca_topic_score_gemma":0.010589361,"teacher_disagreement_score":0.0072836624,"about_ca_system_score_codex":0.0010332632,"about_ca_system_score_gemma":0.00095287367,"threshold_uncertainty_score":0.015333235},"labels":[],"label_agreement":null},{"id":"W4415762040","doi":"10.1002/cjce.70038","title":"Automatic parameter update algorithm for regression models to estimate remaining useful life: Application in an offshore natural gas dehydration unit","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Petrobras; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Natural gas; Submarine pipeline; Nonlinear system; Bayesian probability; Function (biology); Work (physics); Linear regression; Computation","score_opus":0.010005148912747655,"score_gpt":0.2379533946473721,"score_spread":0.22794824573462447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415762040","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023235543,0.00016693491,0.9748649,0.00009242253,0.000016956868,0.000034287088,0.000042659005,0.0009987765,0.00054753444],"genre_scores_gemma":[0.5863569,0.000213751,0.41007861,0.00010538501,0.000043750635,0.00029984224,0.00030577532,0.0001966044,0.0023993282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999509,0.00017516698,0.0000354347,0.00012946106,0.000101758385,0.00004924071],"domain_scores_gemma":[0.99826306,0.0010992743,0.00016576776,0.00008162485,0.00035756055,0.000032729327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015800248,0.0008803399,0.0010853135,0.0007910603,0.00043106324,0.00076202775,0.0011019837,0.0011761838,0.0016010801],"category_scores_gemma":[0.005378498,0.00054691546,0.0006508652,0.00055414677,0.00029020346,0.0006690169,0.00071277196,0.0016187215,0.000639803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000115536604,0.00010595664,0.0021702258,0.000059565216,0.0000728617,0.00006648232,0.00007787268,0.7871148,0.0039743143,0.0017207452,0.00085136003,0.2036704],"study_design_scores_gemma":[0.000004027955,0.000009703249,0.00016019076,0.0000023222085,0.0000030279193,0.0000061388887,0.000002653685,0.99922895,0.00028892225,0.00018390095,0.000107273285,0.0000029660032],"about_ca_topic_score_codex":0.013084091,"about_ca_topic_score_gemma":0.010890096,"teacher_disagreement_score":0.013084091,"about_ca_system_score_codex":0.0005717068,"about_ca_system_score_gemma":0.0011622288,"threshold_uncertainty_score":0.026015878},"labels":[],"label_agreement":null},{"id":"W4416081152","doi":"10.1007/978-3-032-00167-2_66","title":"Data Driven Decision Support for Nickel Smelting Furnaces","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hatch (Canada)","funders":"","keywords":"Asset (computer security); Asset management; Process (computing); Electric arc furnace; Decision support system; Smelting","score_opus":0.025757465431083558,"score_gpt":0.2589794282416195,"score_spread":0.23322196281053595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416081152","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013784322,0.0016348124,0.94587195,0.00076022465,0.00030453055,0.0001025191,0.0020487988,0.012221134,0.023271784],"genre_scores_gemma":[0.35935444,0.0022397158,0.5754086,0.0005029139,0.00018613033,0.00024184072,0.005642675,0.0014732092,0.054950483],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968326,0.00005986974,0.000021164169,0.000060226048,0.00014832876,0.000027111055],"domain_scores_gemma":[0.9992343,0.00055179716,0.000025471394,0.00006949377,0.00009994914,0.000018939169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005463231,0.0008870748,0.0005793792,0.0003227945,0.00023007877,0.0017766781,0.0014041481,0.00061164424,0.012338006],"category_scores_gemma":[0.0015619365,0.00035636284,0.0004734094,0.0004732643,0.00018915639,0.0013206394,0.0005989399,0.0011881064,0.0030726444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046772312,0.00023496838,0.0007580685,0.0004898128,0.00008252816,0.0003711926,0.00016603713,0.26067325,0.016032925,0.02513406,0.05843858,0.6371508],"study_design_scores_gemma":[0.000029125897,0.000045638386,0.00029560353,0.00006561469,0.000017664352,0.00013080375,0.000058457284,0.91693527,0.012810899,0.03626171,0.033329908,0.000019326573],"about_ca_topic_score_codex":0.002360067,"about_ca_topic_score_gemma":0.0035575172,"teacher_disagreement_score":0.012338006,"about_ca_system_score_codex":0.00044318422,"about_ca_system_score_gemma":0.0004131727,"threshold_uncertainty_score":0.041274786},"labels":[],"label_agreement":null},{"id":"W4416617212","doi":"10.1007/s11009-025-10229-8","title":"Redundancy in Two-series-parallel and Two-parallel-series Systems with Independent Components Randomly Chosen from Two Batches","year":2025,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Redundancy (engineering); Component (thermodynamics); Triple modular redundancy; Minimum redundancy feature selection","score_opus":0.02737317342005456,"score_gpt":0.27018718822966226,"score_spread":0.24281401480960768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416617212","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7575389,0.0004063172,0.23560578,0.00044979074,0.000066364075,0.000078031946,0.00019599577,0.00024886135,0.005409963],"genre_scores_gemma":[0.99184316,0.000045568962,0.00631965,0.000016722257,0.000017434655,0.000024721201,0.000053818752,0.000016320622,0.0016626663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99951947,0.00019531415,0.000026452011,0.00010155315,0.00007454884,0.000082686856],"domain_scores_gemma":[0.9924678,0.0057655303,0.0007390732,0.00031093942,0.000545788,0.000170799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019297538,0.00051569514,0.0010788075,0.00056750904,0.00040269055,0.0005428383,0.0010464176,0.0007097607,0.0018455241],"category_scores_gemma":[0.006575566,0.00041177252,0.0006494902,0.00046711648,0.0009337194,0.00089289586,0.0004930273,0.00048273907,0.0001293445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057916064,0.000069943766,0.0012821167,0.00010228717,0.000039862414,0.0002811633,0.00006657496,0.9731416,0.002104029,0.016226985,0.00062363694,0.0054826434],"study_design_scores_gemma":[0.000013408332,0.00003366122,0.00044425257,0.000002225012,0.000011786748,0.00002399793,0.000008934745,0.9969176,0.00020028863,0.0023090893,0.000030258618,0.000004550649],"about_ca_topic_score_codex":0.0025680477,"about_ca_topic_score_gemma":0.0020771162,"teacher_disagreement_score":0.0025680477,"about_ca_system_score_codex":0.00072672573,"about_ca_system_score_gemma":0.0005125004,"threshold_uncertainty_score":0.010205686},"labels":[],"label_agreement":null},{"id":"W4416722204","doi":"10.25259/jksus_96_2025","title":"Integrated just-in-time production and imperfect maintenance management considering random quality degradation","year":2025,"lang":"en","type":"article","venue":"Journal of King Saud University - Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Production (economics); Sensitivity (control systems); Process (computing); Imperfect; Quality (philosophy); Maintenance actions; Stochastic process; Material flow; Control (management); Stochastic modelling","score_opus":0.008694745159071647,"score_gpt":0.2196284300306544,"score_spread":0.21093368487158276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416722204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24126795,0.0004927924,0.7533637,0.00034166782,0.000052923107,0.000077833414,0.000122057405,0.00023636685,0.004044699],"genre_scores_gemma":[0.99140924,0.000103993836,0.0073669227,0.000014770578,0.0000075291873,0.000028909873,0.000022338583,0.0000095971545,0.0010365966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948263,0.00012437881,0.000023705039,0.0001179886,0.0001390618,0.00011219728],"domain_scores_gemma":[0.9991177,0.00036283274,0.0002713506,0.000063248226,0.00012861262,0.000056301535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009147231,0.0007808465,0.00108868,0.00042073103,0.00036995256,0.0011266168,0.0011757095,0.000911484,0.0009243887],"category_scores_gemma":[0.0017349694,0.00043421122,0.00060355716,0.0003794106,0.0007771993,0.0009410864,0.0007387235,0.00056396367,0.000084792526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022389957,0.000017557746,0.0002612485,0.000012688629,0.00000967058,0.000036425383,0.000008964072,0.9952969,0.00078358006,0.0016021034,0.00004542391,0.0019030349],"study_design_scores_gemma":[0.0000037600998,0.000024098985,0.00013512885,0.0000012981031,0.000006340732,0.00000826638,0.0000030826288,0.9989982,0.00015485894,0.00062698283,0.000035741374,0.0000021487917],"about_ca_topic_score_codex":0.0065260255,"about_ca_topic_score_gemma":0.0041327686,"teacher_disagreement_score":0.0065260255,"about_ca_system_score_codex":0.0011252176,"about_ca_system_score_gemma":0.001208723,"threshold_uncertainty_score":0.01297605},"labels":[],"label_agreement":null},{"id":"W4416817342","doi":"10.1142/s0218539325500561","title":"Interval-Dependent Maintenance Effect Modeling for Optimization of Multiple Preventive Maintenance on a Repairable System: A Virtual Age-Based Approach","year":2025,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Providence Health Care","funders":"","keywords":"Preventive maintenance; Particle swarm optimization; Simulated annealing; Optimal maintenance; Maintenance engineering; Scheduling (production processes); Interval (graph theory); Robustness (evolution); Maintenance actions; Optimization problem","score_opus":0.009856209793523927,"score_gpt":0.24585352051479548,"score_spread":0.23599731072127156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416817342","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026756093,0.0002465861,0.9705154,0.000105141866,0.000021645603,0.000032589327,0.00006948625,0.00017093247,0.002082093],"genre_scores_gemma":[0.9320896,0.00035894607,0.06530477,0.00006151101,0.000032907803,0.00013183097,0.000107944994,0.000072974,0.0018395655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955744,0.00016550772,0.000016961554,0.00009454885,0.000106550055,0.000059052836],"domain_scores_gemma":[0.99880755,0.00079802726,0.00018555265,0.00005430846,0.000111198475,0.000043426677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015323792,0.0009360727,0.0010847197,0.00092135544,0.0002447685,0.00088324153,0.001363941,0.0010805,0.0015684225],"category_scores_gemma":[0.0028140978,0.00062195805,0.001180305,0.0004979384,0.00059894926,0.0008351078,0.0007756146,0.0010232121,0.00013886236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004393976,0.0000051133916,0.00008898438,0.000006869248,0.000005701588,0.000006562194,0.000004555241,0.99781895,0.0001394286,0.0009302672,0.000024860303,0.0009644085],"study_design_scores_gemma":[0.0000010216075,0.000005972802,0.00003663956,0.0000011590206,0.0000032368373,0.0000014723137,0.0000011532475,0.99942917,0.000047094203,0.0004276776,0.000044312696,0.0000011096545],"about_ca_topic_score_codex":0.0065604197,"about_ca_topic_score_gemma":0.0033064727,"teacher_disagreement_score":0.0065604197,"about_ca_system_score_codex":0.00091932726,"about_ca_system_score_gemma":0.000893138,"threshold_uncertainty_score":0.0130444765},"labels":[],"label_agreement":null},{"id":"W4416829569","doi":"10.5206/mase/22276","title":"A two-stage bulk-service queueing model with rework, closedown and multiple vacations","year":2025,"lang":"en","type":"article","venue":"Mathematics in Applied Sciences and Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Rework; Queue; Queueing system; Queueing theory; Bulk queue; Quality (philosophy); Service (business); State (computer science)","score_opus":0.010038989156621096,"score_gpt":0.21299532027131404,"score_spread":0.20295633111469294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416829569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21112336,0.005156008,0.73207545,0.0050639883,0.0012669537,0.0006739143,0.0037772234,0.0010825178,0.039780598],"genre_scores_gemma":[0.9211505,0.0021702414,0.028528059,0.00050936727,0.00041585538,0.0005836019,0.00085443043,0.0001331939,0.04565486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779534,0.00059509184,0.000097389886,0.0005474275,0.00025907852,0.00070563337],"domain_scores_gemma":[0.99540484,0.0024441585,0.0006128333,0.00015150935,0.00079087116,0.00059577223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031422293,0.003250195,0.0037405556,0.0015132265,0.0015690334,0.003380654,0.0063390774,0.004873736,0.0128797665],"category_scores_gemma":[0.005521148,0.0013920419,0.0019217044,0.0018186261,0.0022912377,0.0029829326,0.0020179986,0.0032054393,0.001764391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044752323,0.00029238328,0.00143866,0.00026783402,0.000100794525,0.0007976086,0.00025707335,0.9029927,0.0022030808,0.08371856,0.0033315467,0.0041522784],"study_design_scores_gemma":[0.00008687582,0.00007509619,0.00018927168,0.000012549629,0.00004093541,0.000039075745,0.00004516451,0.99247026,0.000097742246,0.0064118356,0.00050613144,0.000025124296],"about_ca_topic_score_codex":0.038410157,"about_ca_topic_score_gemma":0.016743856,"teacher_disagreement_score":0.038410157,"about_ca_system_score_codex":0.0045078727,"about_ca_system_score_gemma":0.003019172,"threshold_uncertainty_score":0.07637316},"labels":[],"label_agreement":null},{"id":"W4416884873","doi":"10.37665/srjyovr63054","title":"Improving Product Reliability Using Accelerated Stress Testing","year":2012,"lang":"","type":"article","venue":"Soldering and Reliability Conferences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sciex (Canada); Spinal Cord Injury BC; North Toronto Eye Care","funders":"","keywords":"Reliability (semiconductor); Accelerated life testing; Failure rate; Field (mathematics); Component (thermodynamics); Stress (linguistics); Stress testing (software); Failure mode and effects analysis","score_opus":0.04721270029100482,"score_gpt":0.25558304566570167,"score_spread":0.20837034537469684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416884873","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8702959,0.0005911946,0.12472004,0.00014522845,0.00004157577,0.00012194744,0.000088652094,0.0013685909,0.0026268691],"genre_scores_gemma":[0.9542849,0.00014813992,0.044612814,0.00001818164,0.000010742616,0.000032489832,0.000068400266,0.000039654748,0.00078470784],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906224,0.0003041606,0.0000489262,0.00008618533,0.0004390006,0.000059474427],"domain_scores_gemma":[0.9979297,0.00045676148,0.00023651747,0.00034644746,0.000981214,0.000049403163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010675306,0.00056276325,0.00038486434,0.001077165,0.0001861931,0.0004458047,0.00057442335,0.0002660169,0.0012111414],"category_scores_gemma":[0.0021304616,0.00021335634,0.00027862217,0.00049942837,0.00022805744,0.00057838025,0.0004576683,0.00028263972,0.0002900491],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028585133,0.00029648756,0.0132125225,0.00015041172,0.000051360807,0.00016200243,0.00022967071,0.03364766,0.7692207,0.0008581832,0.0006078113,0.18127738],"study_design_scores_gemma":[0.00014345885,0.007938982,0.03808255,0.000051606436,0.00014136058,0.0007262154,0.00017343512,0.31861016,0.6229736,0.001742632,0.009306587,0.00010940243],"about_ca_topic_score_codex":0.0007631308,"about_ca_topic_score_gemma":0.0009096933,"teacher_disagreement_score":0.0012111414,"about_ca_system_score_codex":0.00022847683,"about_ca_system_score_gemma":0.000286109,"threshold_uncertainty_score":0.005645752},"labels":[],"label_agreement":null},{"id":"W4416885136","doi":"10.37665/srdtrdj30791","title":"Laser Projector Field Reliability Dashboard: An Effective Tool to Monitor Product Reliability","year":2018,"lang":"","type":"article","venue":"Soldering and Reliability Conferences","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Christie (Canada)","funders":"","keywords":"Reliability (semiconductor); Field (mathematics); Visualization; Parametric statistics; Product (mathematics); Dashboard; Projector","score_opus":0.009722184997990352,"score_gpt":0.25216910281337845,"score_spread":0.2424469178153881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416885136","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13526286,0.0017969048,0.6092618,0.0025848222,0.0010619467,0.0019129402,0.008527243,0.21466632,0.024925182],"genre_scores_gemma":[0.44840023,0.0010957922,0.52652264,0.0007498311,0.00034636556,0.0020604548,0.0050085443,0.0049123843,0.010903654],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99299216,0.002204846,0.0006195237,0.0007275343,0.0032500846,0.00020588619],"domain_scores_gemma":[0.96218234,0.014887172,0.0056938487,0.0050295955,0.010682092,0.001524878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009826746,0.0021029136,0.0010009024,0.0077534895,0.0006439445,0.0028990004,0.0017042527,0.0012178667,0.010470039],"category_scores_gemma":[0.025550038,0.0007446056,0.00040009225,0.0026788153,0.000514027,0.004328338,0.002363097,0.0015864181,0.0041711],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013266177,0.0007657465,0.026343057,0.0014448556,0.00022702702,0.00074205257,0.002585938,0.011290218,0.03160277,0.0034970162,0.11905048,0.8011242],"study_design_scores_gemma":[0.0007440556,0.0042337864,0.100799866,0.0025249573,0.00059583795,0.0017779855,0.0053427103,0.40635127,0.1347498,0.014706947,0.3267689,0.0014039206],"about_ca_topic_score_codex":0.0008477762,"about_ca_topic_score_gemma":0.0007939224,"teacher_disagreement_score":0.010470039,"about_ca_system_score_codex":0.00058838446,"about_ca_system_score_gemma":0.00084664114,"threshold_uncertainty_score":0.05196947},"labels":[],"label_agreement":null},{"id":"W4416922306","doi":"10.1109/tr.2025.3635002","title":"Reliability Analysis of Limited Failure One-Shot Devices","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Covariate; Context (archaeology); Set (abstract data type); Monte Carlo method; Reliability theory; Population","score_opus":0.01674256929456665,"score_gpt":0.2531267228847429,"score_spread":0.23638415359017625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416922306","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7794548,0.0012898768,0.21628866,0.0002962652,0.000027218082,0.00007384868,0.0008199679,0.00023749987,0.0015118361],"genre_scores_gemma":[0.9919151,0.00015210151,0.006762879,0.000020434993,0.0000166389,0.000043637116,0.00046164944,0.000012124595,0.00061529514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995584,0.00013884784,0.000019710042,0.000140392,0.000098161974,0.00004445727],"domain_scores_gemma":[0.996482,0.0024529072,0.00035315243,0.0003192935,0.0003117823,0.00008081993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020758219,0.00048561365,0.00090379803,0.0008928899,0.0002341106,0.00052897277,0.0014105473,0.0007696768,0.00068472873],"category_scores_gemma":[0.0057174973,0.00025500424,0.0007626907,0.00044432998,0.00064220914,0.0007937888,0.0004786781,0.00057569495,0.000107817374],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012998897,0.00008165646,0.012243185,0.0001572592,0.00010901787,0.00039309458,0.00012282518,0.9470474,0.007151419,0.01051202,0.0010695125,0.020982528],"study_design_scores_gemma":[0.000007829542,0.00008085458,0.006372996,0.000008417225,0.000017431967,0.00010671665,0.00002748728,0.9866225,0.0011665828,0.005309212,0.00026585656,0.000014097987],"about_ca_topic_score_codex":0.0019326758,"about_ca_topic_score_gemma":0.001128043,"teacher_disagreement_score":0.0020758219,"about_ca_system_score_codex":0.0005948665,"about_ca_system_score_gemma":0.00028368505,"threshold_uncertainty_score":0.010978103},"labels":[],"label_agreement":null},{"id":"W4417118172","doi":"10.1007/s10696-025-09647-0","title":"Coordinated production and opportunistic-preventive maintenance control policy in deteriorating hybrid manufacturing–remanufacturing systems","year":2025,"lang":"en","type":"article","venue":"Flexible Services and Manufacturing Journal","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; École de Technologie Supérieure; Université du Québec à Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preventive maintenance; Production (economics); Remanufacturing; Control (management); Production planning; Point (geometry); Production control; Control system; Total cost","score_opus":0.004910103905209298,"score_gpt":0.21197387804988244,"score_spread":0.20706377414467314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417118172","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5772046,0.00067159464,0.4167524,0.00057243,0.00009215533,0.000120759294,0.0001969482,0.00039394372,0.003995162],"genre_scores_gemma":[0.9978709,0.000023949764,0.0016870237,0.000014776736,0.000005996881,0.000009474316,0.000012856702,0.0000041162302,0.00037085102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989532,0.00027413672,0.00005067925,0.0002548783,0.00014508811,0.00032201453],"domain_scores_gemma":[0.9974438,0.0012699559,0.0005965086,0.00015073492,0.00036512746,0.00017376308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019732267,0.0007927635,0.0013705264,0.0006177662,0.0007564487,0.0011899695,0.0016813643,0.0010877339,0.0013356749],"category_scores_gemma":[0.0030859283,0.00052557257,0.00046663694,0.000609888,0.00084761705,0.00091647,0.0012304016,0.00064793497,0.000106684514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021996006,0.00005493215,0.0011749032,0.000040119674,0.00004751887,0.0001433336,0.000047429545,0.98838764,0.001484654,0.0023558908,0.00026286786,0.0057806834],"study_design_scores_gemma":[0.000009728899,0.000057896163,0.0006004048,0.0000023949806,0.000015473475,0.000016619579,0.000018546953,0.9981396,0.00018988908,0.0008951019,0.000049948467,0.0000043369723],"about_ca_topic_score_codex":0.010200943,"about_ca_topic_score_gemma":0.007605931,"teacher_disagreement_score":0.010200943,"about_ca_system_score_codex":0.0011640049,"about_ca_system_score_gemma":0.0010671883,"threshold_uncertainty_score":0.020283163},"labels":[],"label_agreement":null},{"id":"W4417248992","doi":"10.1109/tr.2025.3639363","title":"Smart Maintenance Optimization for Large Scale Parallel Systems Using Deep Reinforcement Learning","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Reinforcement learning; Markov decision process; Scalability; Markov process; Maintenance engineering; Component (thermodynamics); Process (computing); Scale (ratio); Optimal maintenance","score_opus":0.009912774379318114,"score_gpt":0.2370802353446643,"score_spread":0.22716746096534618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417248992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18103002,0.0009416816,0.81280804,0.00089576194,0.00009261972,0.00006426345,0.00009171788,0.0010184608,0.0030573441],"genre_scores_gemma":[0.9722816,0.00012448804,0.026141647,0.00014627192,0.000019635632,0.00004279158,0.0000694058,0.00003565353,0.0011385245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997745,0.000065451466,0.000010212753,0.00005397628,0.000042065643,0.000053763593],"domain_scores_gemma":[0.99896085,0.00067321735,0.00012613146,0.00004101095,0.0001238107,0.00007492723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009313209,0.0008710087,0.0009010659,0.00027912203,0.0003074167,0.00056054484,0.0009199038,0.00096683536,0.0011538266],"category_scores_gemma":[0.0024998554,0.00042557492,0.0003931303,0.0002470533,0.0007221757,0.000746947,0.0007219404,0.0014007251,0.0001354611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024255649,0.000021423548,0.00037423134,0.000015386731,0.000008321841,0.000017777624,0.000008137225,0.9922063,0.00023626426,0.00057005975,0.00018874157,0.0063291756],"study_design_scores_gemma":[0.0000024981205,0.0000060445905,0.000028414644,9.099989e-7,0.0000011141101,0.0000014234065,0.0000011619079,0.99947196,0.000052057436,0.00040700103,0.000026774722,6.444876e-7],"about_ca_topic_score_codex":0.013348371,"about_ca_topic_score_gemma":0.009376857,"teacher_disagreement_score":0.013348371,"about_ca_system_score_codex":0.0012525992,"about_ca_system_score_gemma":0.0013987848,"threshold_uncertainty_score":0.026541352},"labels":[],"label_agreement":null},{"id":"W4417517369","doi":"10.1142/s0218539325500615","title":"Fleet-Wide Interval-Dependent Nonparametric Modeling and Optimization of Multi-Level Preventive Maintenance Effectiveness: Application to Hybrid (AC) LHD Trucks in a Mine","year":2025,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Providence Health Care","funders":"","keywords":"Interval (graph theory); Schedule; Truck; Reliability (semiconductor); Preventive maintenance; Sensitivity (control systems); Parametric statistics; Poisson distribution; Optimal maintenance","score_opus":0.011934270949719175,"score_gpt":0.2718502630188464,"score_spread":0.25991599206912724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417517369","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.696693,0.00018501528,0.3004203,0.0002677751,0.000013232417,0.000046485584,0.00026030283,0.00018717296,0.0019268531],"genre_scores_gemma":[0.9914302,0.00003637534,0.008040094,0.000011090721,0.0000034620248,0.000020692114,0.000063359,0.000014363468,0.00038044885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969757,0.0001512273,0.000010156199,0.000060526094,0.00003198581,0.000048596343],"domain_scores_gemma":[0.99709105,0.002222272,0.00036862947,0.00012685229,0.00011764837,0.00007352213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017042892,0.0005050657,0.0005614172,0.00034744764,0.00021407506,0.0006107588,0.0009139011,0.0008779705,0.0007025763],"category_scores_gemma":[0.0043459767,0.00040612413,0.00066653173,0.0003962468,0.0007084143,0.0005558779,0.0006748983,0.00092580286,0.000064474174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010968933,0.000009472246,0.00045587614,0.0000036499787,0.0000050670874,0.000008020023,0.0000047767094,0.99838674,0.000109852685,0.00034168272,0.000018508264,0.0006453034],"study_design_scores_gemma":[0.0000017761972,0.000009849142,0.00021675721,5.707489e-7,0.0000019085512,0.0000023078744,0.0000033520823,0.9994677,0.00006088212,0.00021730486,0.000015761927,0.0000017801026],"about_ca_topic_score_codex":0.01287442,"about_ca_topic_score_gemma":0.0066764187,"teacher_disagreement_score":0.01287442,"about_ca_system_score_codex":0.00095998193,"about_ca_system_score_gemma":0.0006919041,"threshold_uncertainty_score":0.025599003},"labels":[],"label_agreement":null},{"id":"W568861270","doi":"","title":"Strategy to maximize maintenance operation","year":2005,"lang":"en","type":"dissertation","venue":"Summit (Simon Fraser University)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Simon Fraser University","keywords":"Reliability engineering; Computer science; Risk analysis (engineering); Business; Operations management; Engineering","score_opus":0.00751431393233596,"score_gpt":0.19639681146994034,"score_spread":0.1888824975376044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W568861270","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34480008,0.0010208457,0.3261357,0.0030806474,0.00006332125,0.0005387385,0.00024313225,0.00036062914,0.3237569],"genre_scores_gemma":[0.9660371,0.00033207156,0.019766461,0.0000879268,0.0000080838,0.000079367004,0.0000385789,0.00004212538,0.013608326],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992399,0.00021254225,0.000017574901,0.000098816345,0.00021605182,0.00021505296],"domain_scores_gemma":[0.9996692,0.00010356263,0.00005323083,0.000029579594,0.000096099546,0.000048296413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007230462,0.00081276736,0.0004060237,0.0011319752,0.0009033839,0.0019792647,0.00068687054,0.00078349776,0.005378287],"category_scores_gemma":[0.0015908662,0.0002300009,0.0003423659,0.0008371996,0.00091836764,0.0010225782,0.0009264934,0.00055718236,0.0005406105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024182624,0.00020216267,0.0061313994,0.00039150022,0.00018712528,0.0005447041,0.0016018418,0.2592587,0.015297572,0.5430151,0.0070988117,0.1660293],"study_design_scores_gemma":[0.00013685504,0.0012136085,0.016887687,0.00031182898,0.00033024524,0.0013314807,0.005538484,0.44130343,0.014590736,0.4194921,0.09873661,0.0001269135],"about_ca_topic_score_codex":0.010553297,"about_ca_topic_score_gemma":0.0251016,"teacher_disagreement_score":0.010553297,"about_ca_system_score_codex":0.0036634353,"about_ca_system_score_gemma":0.004775648,"threshold_uncertainty_score":0.026580215},"labels":[],"label_agreement":null},{"id":"W617407927","doi":"10.1287/opre.2013.1171","title":"Joint Optimization of Sampling and Control of Partially Observable Failing Systems","year":2013,"lang":"en","type":"article","venue":"Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Partially observable Markov decision process; Schedule; Computer science; Sampling (signal processing); Markov decision process; Mathematical optimization; Operations research; Joint (building); Optimization problem; Reliability (semiconductor); Control (management); Markov process; Observable; Engineering; Mathematics; Statistics; Artificial intelligence","score_opus":0.06817649623033104,"score_gpt":0.2949681273271667,"score_spread":0.22679163109683567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W617407927","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21682705,0.00049888226,0.77615964,0.0007798831,0.000048558777,0.000106532076,0.00020621101,0.00027444723,0.005098783],"genre_scores_gemma":[0.98707354,0.00013065079,0.011386185,0.000031479725,0.000012848211,0.00006284604,0.00006211386,0.000019737889,0.0012205881],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985079,0.000619872,0.00006305815,0.00022920636,0.00025342827,0.00032643467],"domain_scores_gemma":[0.99367464,0.0047925655,0.00072784134,0.0001813284,0.00036556373,0.00025818704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028929366,0.0008990587,0.0011462338,0.0005683703,0.0003281732,0.0013697983,0.0008180747,0.0009813523,0.0016067061],"category_scores_gemma":[0.009159399,0.0005294371,0.0005504387,0.00053782895,0.0017975654,0.0010933101,0.0010185,0.0010234041,0.00013049364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011028913,0.000029192457,0.0005549249,0.000031159758,0.00001756823,0.000045882756,0.00003084594,0.97984844,0.000764374,0.0146761555,0.00017489237,0.0037162802],"study_design_scores_gemma":[0.0000101766855,0.000023807475,0.00019584569,0.0000034703833,0.0000039271226,0.000004259316,0.000005771269,0.99405515,0.00027065494,0.005372716,0.000050333703,0.000003887342],"about_ca_topic_score_codex":0.008055039,"about_ca_topic_score_gemma":0.0034669654,"teacher_disagreement_score":0.008055039,"about_ca_system_score_codex":0.001733028,"about_ca_system_score_gemma":0.0016565346,"threshold_uncertainty_score":0.016016304},"labels":[],"label_agreement":null},{"id":"W621182484","doi":"","title":"CURRENT REGULATORY STATUS IN REGARD TO MAINTENANCE RESOURCE MANAGEMENT","year":2001,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Aviation; Business; European union; Resource (disambiguation); Administration (probate law); Relation (database); Operations management; Political science; Engineering; International trade; Computer science; Law","score_opus":0.006824338479255657,"score_gpt":0.21041321268168386,"score_spread":0.20358887420242822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W621182484","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053906746,0.33375442,0.03301549,0.14253189,0.010251481,0.0002836264,0.0012454219,0.0010278809,0.42398295],"genre_scores_gemma":[0.57780075,0.24151924,0.034747068,0.08979916,0.013041476,0.0009249603,0.003194499,0.00053131685,0.038441546],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9701057,0.005258547,0.002852214,0.002906719,0.017204685,0.0016721581],"domain_scores_gemma":[0.86285293,0.084193096,0.01563861,0.0046375007,0.030302238,0.002375594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030747036,0.00067726366,0.0010896033,0.003545073,0.0024224056,0.008207244,0.0061966595,0.008085817,0.0077257757],"category_scores_gemma":[0.070693396,0.00044342323,0.001008494,0.0044830036,0.0062910733,0.0042074732,0.0020756007,0.0056488845,0.0022565478],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029778556,0.00045407139,0.00845362,0.005969538,0.000087993176,0.00043638825,0.0017731609,0.002540963,0.0060011554,0.23000227,0.077838816,0.66614425],"study_design_scores_gemma":[0.00006808017,0.0005481652,0.02187732,0.012630051,0.00030882438,0.0009666776,0.002155174,0.0013003387,0.004627981,0.033401523,0.92197025,0.00014571562],"about_ca_topic_score_codex":0.010174675,"about_ca_topic_score_gemma":0.008922386,"teacher_disagreement_score":0.030747036,"about_ca_system_score_codex":0.0033838153,"about_ca_system_score_gemma":0.012301302,"threshold_uncertainty_score":0.16260785},"labels":[],"label_agreement":null},{"id":"W645581224","doi":"10.1299/jsmeicone.2003.16","title":"ICONE11-36262 Initiating Stochastic Maintenance Optimization at Candu Power Plants","year":2003,"lang":"en","type":"article","venue":"The Proceedings of the International Conference on Nuclear Engineering (ICONE)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bruce Power (Canada)","funders":"","keywords":"Reliability engineering; Computer science; Preventive maintenance; Boom; Phase (matter); Component (thermodynamics); Operations research; Risk analysis (engineering); Engineering; Business","score_opus":0.013051496425649735,"score_gpt":0.1862269737241433,"score_spread":0.17317547729849356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W645581224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28521436,0.0010771555,0.5104704,0.0018507609,0.00030149677,0.000793102,0.0033510153,0.008526923,0.18841471],"genre_scores_gemma":[0.794355,0.0002469044,0.15519592,0.00009762478,0.0000602704,0.00026013746,0.0023501,0.0003829747,0.04705109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913114,0.00026501203,0.000020292884,0.00009544017,0.00041190893,0.000076335025],"domain_scores_gemma":[0.998572,0.00055029633,0.00008502033,0.00021616514,0.0004454462,0.00013104628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028166587,0.00057088025,0.00051426154,0.0006146081,0.00045291407,0.0009919291,0.0007685236,0.0006418304,0.012965181],"category_scores_gemma":[0.003272304,0.00023674108,0.0002810034,0.000664478,0.00030183516,0.00043823122,0.0006392536,0.00075045414,0.0020592639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015963237,0.00048684012,0.004384817,0.00021112013,0.000057364738,0.00019795167,0.00012571226,0.6535285,0.02418799,0.014714704,0.035285763,0.2652229],"study_design_scores_gemma":[0.00020238201,0.00076393475,0.003689102,0.00002893631,0.00001782293,0.000057821453,0.000048438505,0.92808926,0.023222096,0.0033339497,0.040499542,0.000046782214],"about_ca_topic_score_codex":0.00994473,"about_ca_topic_score_gemma":0.016223164,"teacher_disagreement_score":0.012965181,"about_ca_system_score_codex":0.0015172036,"about_ca_system_score_gemma":0.0017473974,"threshold_uncertainty_score":0.04337281},"labels":[],"label_agreement":null},{"id":"W6892822876","doi":"10.5281/zenodo.12188254","title":"Net Zero City Complexity, a Study of Emerging Trends from Net Zero Cities","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sociotechnical system; Sustainability; Corporate governance; Adaptation (eye); Indigenous; Climate governance; Underpinning; Supply chain; Systems thinking; Top-down and bottom-up design","score_opus":0.0437239472157149,"score_gpt":0.25095190714871135,"score_spread":0.20722795993299645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892822876","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9856947,0.00017878333,0.00032986497,0.002635386,0.000024199615,0.00005803968,0.00032623124,0.000007743355,0.010745015],"genre_scores_gemma":[0.99723405,0.00016794517,0.00015569288,0.00017058878,0.000012678768,0.00004618884,0.0002375283,0.000012348198,0.0019628922],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985185,0.0004274636,0.00008117114,0.00019202007,0.00043379783,0.00034703178],"domain_scores_gemma":[0.99557847,0.0011427382,0.0010934763,0.00024117235,0.0009722167,0.0009719404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016347917,0.00020755868,0.00038746282,0.0031562094,0.0064376723,0.00796355,0.0011944387,0.00089923094,0.006251269],"category_scores_gemma":[0.0070243534,0.0003133893,0.00030545954,0.005827107,0.004815453,0.0063953977,0.0072637917,0.002014601,0.00043689256],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006840322,0.00011411061,0.167701,0.00020919868,0.000025683592,0.0008625617,0.7846921,0.00026330815,0.00029143112,0.020729136,0.007340142,0.017702876],"study_design_scores_gemma":[0.0000042564675,0.000041838062,0.15481286,0.00009876109,0.00000804035,0.00012503171,0.81298745,0.0005110288,0.00012395701,0.0017677102,0.029495735,0.00002327085],"about_ca_topic_score_codex":0.10055133,"about_ca_topic_score_gemma":0.17259353,"teacher_disagreement_score":0.10055133,"about_ca_system_score_codex":0.01005374,"about_ca_system_score_gemma":0.006557064,"threshold_uncertainty_score":0.1999321},"labels":[],"label_agreement":null},{"id":"W6925020217","doi":"10.15468/dl.vr7dh5","title":"Occurrence Download","year":2023,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Download; Matching (statistics); Alien; Range (aeronautics)","score_opus":0.009544422784039977,"score_gpt":0.19118864662626514,"score_spread":0.18164422384222517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6925020217","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006281812,0.000030326437,0.00006861597,0.000036250778,0.000010940197,0.0000061536966,0.99865377,0.0005284278,0.0006026466],"genre_scores_gemma":[0.00021105823,0.00003272669,0.00025986365,0.00003685394,0.0000029191158,0.000040834228,0.99889874,0.00012660661,0.00039049165],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99907196,0.00012646346,0.000117596,0.00032549346,0.00022255226,0.00013590226],"domain_scores_gemma":[0.9976705,0.0006720349,0.00020547301,0.00067460217,0.0005671896,0.00021022554],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009871908,0.0020660353,0.0014215655,0.0039270218,0.0008337926,0.002192345,0.0029147672,0.0021401718,0.09761436],"category_scores_gemma":[0.005652432,0.00080436526,0.0012942385,0.007907445,0.00040027726,0.0018235319,0.0018997424,0.0018462278,0.15305455],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033692573,0.000018121764,0.00049718714,0.0005526074,0.000019298779,0.000015347403,0.000019643523,0.00033423334,0.00013111148,0.00042232158,0.99598557,0.0019708076],"study_design_scores_gemma":[0.000098848825,0.000013331463,0.0025886046,0.00021663519,0.00001949222,0.000041948857,0.00006651527,0.00045155708,0.0002937342,0.0012115162,0.99497557,0.000022262724],"about_ca_topic_score_codex":0.02134566,"about_ca_topic_score_gemma":0.0357276,"teacher_disagreement_score":0.90238565,"about_ca_system_score_codex":0.001523602,"about_ca_system_score_gemma":0.001980139,"threshold_uncertainty_score":0.32655257},"labels":[],"label_agreement":null},{"id":"W6926495703","doi":"10.25384/sage.c.6759298.v1","title":"Overlap syndrome of anti-aquaporin-4 positive neuromyelitis optica spectrum disorder and systemic lupus erythematosus: A systematic review of individual patient data","year":2023,"lang":"en","type":"other","venue":"Sage Journals Data","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Alberta","funders":"","keywords":"Neuromyelitis optica; Spectrum disorder; Transverse myelitis; Multiple sclerosis; Myelitis; Overlap syndrome; Cerebrospinal fluid; Demyelinating Disorder","score_opus":0.017007240289646612,"score_gpt":0.2443966933807813,"score_spread":0.22738945309113467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6926495703","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00753755,0.98789155,0.00020567012,0.00020933733,0.000082438666,0.00025431722,0.0034507213,0.000011250264,0.00035715866],"genre_scores_gemma":[0.061771292,0.9326339,0.0011660518,0.0007587948,0.000119834105,0.0007111751,0.0026846342,0.000015977042,0.0001383131],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99144757,0.001417161,0.005027313,0.0008599152,0.0010822671,0.00016584502],"domain_scores_gemma":[0.9624676,0.022274464,0.01082809,0.00079279405,0.0032423837,0.0003945435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004616712,0.00086828205,0.0051246136,0.01779517,0.000539063,0.0015086663,0.0014205627,0.0012803193,0.0023224289],"category_scores_gemma":[0.026374903,0.00061168737,0.0032967112,0.01897319,0.0008192991,0.0017723967,0.001333062,0.0005425078,0.0002661724],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003147166,0.000030273779,0.01738794,0.90934324,0.008040736,0.0014734534,0.0006682729,0.00009804037,0.0005081116,0.00025176245,0.0036732233,0.05821026],"study_design_scores_gemma":[0.00020227396,0.0003381835,0.070204444,0.8116762,0.05435478,0.010952559,0.0014339646,0.00013273791,0.0005405838,0.00040435334,0.04963313,0.00012678266],"about_ca_topic_score_codex":0.0043098317,"about_ca_topic_score_gemma":0.013936766,"teacher_disagreement_score":0.01779517,"about_ca_system_score_codex":0.001583507,"about_ca_system_score_gemma":0.00641997,"threshold_uncertainty_score":0.024415791},"labels":[],"label_agreement":null},{"id":"W6930722461","doi":"10.5281/zenodo.13850886","title":"Enhancing the Reliability of Urea Pumps in Fertilizer Production Lines through Exponential Reliability Analysis","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Switchover; Backup; Reliability (semiconductor); Fertilizer; Redundancy (engineering); Schedule; Production (economics); Production line; Condenser (optics)","score_opus":0.017926942560723375,"score_gpt":0.23455176643668593,"score_spread":0.21662482387596255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930722461","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8519519,0.00061992835,0.1456063,0.00011264354,0.00001240121,0.00002661689,0.00007409371,0.0001759859,0.0014201578],"genre_scores_gemma":[0.9931941,0.00012629676,0.0062915087,0.0000031464124,0.000002806083,0.0000070096803,0.0000365293,0.000008934484,0.0003296749],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964607,0.00010006493,0.000024193923,0.00006804599,0.00012622087,0.000035455207],"domain_scores_gemma":[0.9987179,0.00053722115,0.00027368322,0.00005961421,0.00039129038,0.000020244257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008163712,0.00033646537,0.00022622873,0.00067608093,0.00015210437,0.00041650046,0.0003906737,0.00022319684,0.00036712884],"category_scores_gemma":[0.0019438852,0.00019584585,0.00026419302,0.00035236773,0.00018758641,0.00041114356,0.00020977427,0.00023369557,0.000093598246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023592007,0.0001237576,0.031893484,0.00021778212,0.00007458782,0.0002986926,0.00019216418,0.8019601,0.056969475,0.0012169504,0.00045695368,0.10636014],"study_design_scores_gemma":[0.000005857332,0.00023996962,0.011069676,0.000014169588,0.000032736596,0.00010185421,0.00006238426,0.97658914,0.010911726,0.0004983233,0.00046228565,0.000011737852],"about_ca_topic_score_codex":0.003137983,"about_ca_topic_score_gemma":0.003659615,"teacher_disagreement_score":0.003137983,"about_ca_system_score_codex":0.00046016768,"about_ca_system_score_gemma":0.0004238783,"threshold_uncertainty_score":0.006239414},"labels":[],"label_agreement":null},{"id":"W6931072184","doi":"10.5281/zenodo.4312399","title":"Synapse XT Review: Synapse XT Ingredients - Synapse XT Amazon","year":2020,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Work (physics); Nucleofection; Ringing; Natural (archaeology)","score_opus":0.015232576672274966,"score_gpt":0.20860534001220157,"score_spread":0.19337276333992662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931072184","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00069952157,0.13342911,0.0015174013,0.033865318,0.066507615,0.0012888148,0.007901436,0.0036451528,0.75114566],"genre_scores_gemma":[0.0032924262,0.095870376,0.0011995122,0.031085799,0.014634554,0.00059005164,0.007943139,0.0010582639,0.84432584],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99879956,0.000101161626,0.000099910794,0.00012377252,0.0007820896,0.00009339108],"domain_scores_gemma":[0.9962889,0.0005488563,0.00026459727,0.00015807814,0.0022281623,0.0005113601],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00079610007,0.0008773078,0.0012308653,0.0019542323,0.00094048405,0.00422093,0.0020783027,0.0035532163,0.49659735],"category_scores_gemma":[0.0070490246,0.00051447947,0.0014127635,0.0013546228,0.00049841305,0.0027753673,0.0013380125,0.003491186,0.39943022],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000201563,0.000015509295,0.000017587514,0.0009591319,0.000006294713,0.000035998175,0.0000067725896,0.000011749477,0.0001973942,0.00030936318,0.95981693,0.038603134],"study_design_scores_gemma":[0.000024061386,0.000018951005,0.00009572104,0.0004925425,0.000007629448,0.00009241565,0.000008383745,0.00001581267,0.00010365075,0.00018122893,0.99895334,0.0000061496394],"about_ca_topic_score_codex":0.0030269772,"about_ca_topic_score_gemma":0.007233678,"teacher_disagreement_score":0.50340265,"about_ca_system_score_codex":0.0013911308,"about_ca_system_score_gemma":0.002606276,"threshold_uncertainty_score":0.71804273},"labels":[],"label_agreement":null},{"id":"W6931264976","doi":"10.5281/zenodo.3791074","title":"Gyrophaena (Gyrophaena) chippewa Seevers 1951","year":2009,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Forest Service; Natural Resources Canada","funders":"","keywords":"Aedeagus; Dorsum; Appendage; Crista; Head (geology); Seta","score_opus":0.012791033839857205,"score_gpt":0.19938590296556763,"score_spread":0.18659486912571044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931264976","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2660542,0.026061067,0.007977253,0.00096347765,0.0010019596,0.0015434421,0.006759207,0.0013370708,0.68830234],"genre_scores_gemma":[0.91588956,0.007857002,0.0054027303,0.0007778158,0.0002614621,0.00045394865,0.0035740645,0.000110319525,0.065673135],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99984324,0.000019260196,0.000013701853,0.00006787688,0.000032271153,0.00002358778],"domain_scores_gemma":[0.9998622,0.000016459639,0.000051239866,0.000018725676,0.000037310092,0.000014079735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001034835,0.0011776906,0.0004812686,0.0012211639,0.0015952537,0.00044920496,0.00074047665,0.00069380243,0.022352144],"category_scores_gemma":[0.00035104246,0.0003512269,0.00017839561,0.00072656956,0.0011015208,0.0012102235,0.0010337023,0.00054177456,0.01004606],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049982435,0.00009814363,0.02752534,0.00102547,0.00007610753,0.000912572,0.0019245292,0.00088123046,0.0193412,0.008964025,0.041954804,0.8967968],"study_design_scores_gemma":[0.00019399753,0.0003787649,0.27816436,0.00069445005,0.00009687596,0.0024237637,0.0010663894,0.00053051836,0.0023436027,0.0022518805,0.7118087,0.000046723944],"about_ca_topic_score_codex":0.019940956,"about_ca_topic_score_gemma":0.03702502,"teacher_disagreement_score":0.022352144,"about_ca_system_score_codex":0.0010135609,"about_ca_system_score_gemma":0.0004997355,"threshold_uncertainty_score":0.07477534},"labels":[],"label_agreement":null},{"id":"W6931341365","doi":"10.5281/zenodo.4775020","title":"Saudi Arabia %% (+27710158438) \" Love Spells Casters Yemen Love Spells Denmark Love Spells Love Spells California Love Spells Toronto Bring Back Lost Lover","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Witch; Spell; MAGIC (telescope); Love story; Romance; Feeling","score_opus":0.0163349023190217,"score_gpt":0.21067659318281884,"score_spread":0.19434169086379713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931341365","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026700387,0.0009648233,0.00022651585,0.0050521367,0.0043368014,0.00014372007,0.0028018819,0.0007301249,0.983074],"genre_scores_gemma":[0.0023149885,0.00032328523,0.00009824135,0.000997061,0.0001423764,0.000016794536,0.00045936683,0.00012053933,0.9955272],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996973,0.000030305366,0.000015947313,0.00004288464,0.0001101406,0.00010348124],"domain_scores_gemma":[0.99900836,0.00003847848,0.00004142241,0.000046574776,0.00046239462,0.00040277868],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003572786,0.00084277487,0.00039384395,0.00060711085,0.0030213818,0.0024724198,0.00042255805,0.001037771,0.87568367],"category_scores_gemma":[0.0009794375,0.00027859348,0.0003312218,0.000468846,0.00030308895,0.0011085919,0.0021663597,0.0011290164,0.786551],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033093376,0.00002567551,0.00040021172,0.00005949978,0.0000012997186,0.000047851838,0.000060454342,0.000020451665,0.0003176793,0.0007178673,0.94348305,0.05483287],"study_design_scores_gemma":[0.0000045709767,0.00001088507,0.0007003472,0.00003196979,6.647627e-7,0.00004513488,0.00014708951,0.00001710916,0.00009721253,0.000043217355,0.99889946,0.000002455899],"about_ca_topic_score_codex":0.011740296,"about_ca_topic_score_gemma":0.023801163,"teacher_disagreement_score":0.124316335,"about_ca_system_score_codex":0.0010888408,"about_ca_system_score_gemma":0.0014997941,"threshold_uncertainty_score":0.17732209},"labels":[],"label_agreement":null},{"id":"W6986473587","doi":"","title":"The physiological basis of intraspecific variation in egg size, quality and number in birds","year":2000,"lang":"en","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Intraspecific competition; Variation (astronomy); Quality (philosophy); Basis (linear algebra); Competition (biology)","score_opus":0.0037869150305961615,"score_gpt":0.15529939639634371,"score_spread":0.15151248136574755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6986473587","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951944,0.000765258,0.00074362115,0.0000719466,0.000009549532,0.000016066215,0.0007801493,0.000024229688,0.002394775],"genre_scores_gemma":[0.99659145,0.0002280835,0.0006792394,0.00007123987,0.000007842805,0.000018361057,0.00050976657,0.000022121847,0.0018719886],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998267,0.000026508762,0.000009219775,0.000058829093,0.00005570193,0.000023156816],"domain_scores_gemma":[0.9988337,0.00034610747,0.0002778422,0.000073586736,0.0003176209,0.00015108727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030148542,0.0001913147,0.00025992387,0.00074438856,0.00036984764,0.00060051517,0.00043130762,0.00033408648,0.001004936],"category_scores_gemma":[0.0012321761,0.00027784597,0.0001296725,0.0006146649,0.0007028945,0.00025182607,0.0002968951,0.00028987133,0.0002251485],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010848003,0.00041569205,0.74589574,0.00025983047,0.00028412283,0.00026673896,0.0008794236,0.0031309836,0.19595903,0.0007348062,0.0021960924,0.0488928],"study_design_scores_gemma":[0.0000014188686,0.000017889637,0.99924576,0.0000017171601,0.0000044880003,0.000018654544,0.000030688847,0.00019003135,0.0003779434,0.000041172752,0.000067251116,0.0000029094972],"about_ca_topic_score_codex":0.14025109,"about_ca_topic_score_gemma":0.27945197,"teacher_disagreement_score":0.14025109,"about_ca_system_score_codex":0.0013534249,"about_ca_system_score_gemma":0.00096175284,"threshold_uncertainty_score":0.2788695},"labels":[],"label_agreement":null},{"id":"W7009905057","doi":"","title":"Flexible accelerated failure time modeling of multivariable time-to-event data","year":2020,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds de Recherche du Québec - Santé; McGill University Health Centre; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Multivariable calculus; Control theory (sociology); Measure (data warehouse); Reliability (semiconductor); Interval (graph theory)","score_opus":0.023959067200289546,"score_gpt":0.23723694070480483,"score_spread":0.2132778735045153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7009905057","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08256754,0.00021418442,0.91490006,0.00023851219,0.0000500493,0.000043658027,0.0002933185,0.0003605972,0.001332084],"genre_scores_gemma":[0.9729699,0.00023963887,0.022356987,0.000033381944,0.000046278532,0.000074304444,0.0003299371,0.00006892088,0.0038805697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905103,0.0002625247,0.00004955764,0.00026423292,0.00020531297,0.00016736011],"domain_scores_gemma":[0.99348986,0.0048941537,0.00059221126,0.0003503471,0.0005445681,0.00012898071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022432746,0.0006525317,0.0010533931,0.0007325477,0.00036760166,0.0013032351,0.0016553309,0.0007097137,0.0025045504],"category_scores_gemma":[0.010554906,0.0006442033,0.0010024188,0.0009510661,0.0006190939,0.0013898873,0.0010051865,0.0014901912,0.00029619987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044192777,0.000018852237,0.00068242376,0.000029148392,0.000024728624,0.000036972964,0.00004567786,0.9829342,0.00049284287,0.008917793,0.00017835251,0.0065948726],"study_design_scores_gemma":[0.0000012434563,0.000005657154,0.00012791663,0.0000010125727,0.000002652322,0.0000034991526,0.0000018779122,0.99855965,0.00007010049,0.0011679416,0.00005677089,0.0000016905344],"about_ca_topic_score_codex":0.008852014,"about_ca_topic_score_gemma":0.0060133953,"teacher_disagreement_score":0.008852014,"about_ca_system_score_codex":0.0007112649,"about_ca_system_score_gemma":0.00079164054,"threshold_uncertainty_score":0.017600954},"labels":[],"label_agreement":null},{"id":"W7025571152","doi":"","title":"Westbridge Expands Alberta Footprint with Acquisition of 236 MW Sunnynook Solar PV Project and 100 MW of Battery Energy Storage System","year":2021,"lang":"en","type":"other","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Photovoltaic system; Battery (electricity); Footprint; Energy storage; Solar energy; Energy (signal processing)","score_opus":0.005207010961127727,"score_gpt":0.18244199887850604,"score_spread":0.17723498791737832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7025571152","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052781064,0.00041224086,0.007888277,0.004258636,0.0005681612,0.0002868178,0.013433379,0.0032379166,0.9171334],"genre_scores_gemma":[0.116003715,0.00038165087,0.009406504,0.0007767252,0.0000508706,0.0000653288,0.009768412,0.00052853813,0.8630182],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999495,0.000021008593,0.0000040163836,0.00002975902,0.00034394205,0.00010620812],"domain_scores_gemma":[0.9991093,0.000028271073,0.000010824173,0.00005041556,0.0006617846,0.00013938475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000638963,0.0006215989,0.0002251442,0.0010671776,0.0017368806,0.0014180506,0.0009363952,0.0006052984,0.076987095],"category_scores_gemma":[0.00066109013,0.00026076805,0.0003504329,0.000754447,0.00045963173,0.0005693582,0.0010293424,0.0007940646,0.011347031],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006099667,0.00035026154,0.01004627,0.0001251707,0.000036210033,0.00053828576,0.00018990804,0.007659446,0.009271033,0.014111368,0.6571528,0.29990938],"study_design_scores_gemma":[0.00012397245,0.00011325192,0.0232969,0.000043909247,0.000020170371,0.0001313263,0.00051442074,0.009300354,0.0060249693,0.0027637442,0.9576324,0.000034555866],"about_ca_topic_score_codex":0.759686,"about_ca_topic_score_gemma":0.926074,"teacher_disagreement_score":0.240314,"about_ca_system_score_codex":0.00682024,"about_ca_system_score_gemma":0.018235372,"threshold_uncertainty_score":0.48345852},"labels":[],"label_agreement":null},{"id":"W7026881064","doi":"","title":"Architecture of a Distributed, Model-Based Asset Management System","year":2001,"lang":"en","type":"article","venue":"NPARC","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Architecture; Asset management; Set (abstract data type); Systems architecture; Reference architecture; Asset (computer security); Component (thermodynamics)","score_opus":0.005075361887883173,"score_gpt":0.18364755010707984,"score_spread":0.17857218821919665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7026881064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029733002,0.00020084478,0.9441485,0.00043971694,0.00004973215,0.00024250383,0.000302635,0.014201116,0.010681898],"genre_scores_gemma":[0.45238504,0.00040698313,0.5370565,0.0002324309,0.00004989358,0.0004726779,0.001556548,0.0004918693,0.0073480066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999511,0.000087288616,0.00005188614,0.00011675262,0.00019008324,0.000043058215],"domain_scores_gemma":[0.9996136,0.000065400694,0.000028516106,0.00012346686,0.00010769576,0.00006133535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088277116,0.00036207936,0.0005420697,0.0005747966,0.0006602448,0.0021350263,0.001516857,0.0009266135,0.0039136633],"category_scores_gemma":[0.0010579258,0.0004015195,0.00035861353,0.00056510244,0.0005267544,0.0014891529,0.001270473,0.0010205986,0.0014171449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053518947,0.0007559029,0.005107681,0.00053654326,0.00020255776,0.0009239942,0.0012130232,0.42762136,0.085335486,0.13437116,0.018715223,0.32468194],"study_design_scores_gemma":[0.00018016456,0.00025970233,0.0013031912,0.000061713305,0.000114921735,0.00035158102,0.0001027583,0.913028,0.0133113125,0.028999904,0.042222813,0.0000640124],"about_ca_topic_score_codex":0.0027740614,"about_ca_topic_score_gemma":0.002118755,"teacher_disagreement_score":0.0039136633,"about_ca_system_score_codex":0.0007552753,"about_ca_system_score_gemma":0.0016397708,"threshold_uncertainty_score":0.013092518},"labels":[],"label_agreement":null},{"id":"W7032729522","doi":"","title":"Temple of Isis","year":2008,"lang":"en","type":"other","venue":"Rice Digital Scholarship Archive (Rice University)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Temple; Submersion (mathematics); Work (physics); Quarter (Canadian coin)","score_opus":0.010084355557014417,"score_gpt":0.17614382470805318,"score_spread":0.16605946915103875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7032729522","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04887098,0.00094479014,0.0022593006,0.0013232024,0.0008413081,0.00006999119,0.0063483594,0.0015620044,0.93777996],"genre_scores_gemma":[0.2648606,0.0014565848,0.0040356684,0.000349856,0.00041009823,0.000056590987,0.010263749,0.0006054053,0.71796143],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998462,0.00000987788,0.000004901961,0.00002710092,0.00006119314,0.00005070598],"domain_scores_gemma":[0.99974245,0.000016937585,0.00001851045,0.00004252719,0.000096573516,0.00008306053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023023809,0.0003795133,0.00026204236,0.0008295671,0.0014883874,0.0014540282,0.00042101546,0.0003636783,0.114060454],"category_scores_gemma":[0.00052768935,0.00014664436,0.0003113446,0.0010063152,0.00040605487,0.0005427853,0.0013139478,0.0007534472,0.02554949],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009333279,0.00014635628,0.015221201,0.00041718542,0.00004535424,0.0017775127,0.001525811,0.0035991904,0.0069068614,0.03607529,0.59626365,0.3370882],"study_design_scores_gemma":[0.000022688575,0.000058417667,0.013533538,0.00006130181,0.0000120304285,0.0004478408,0.0004966814,0.0009140327,0.0019962662,0.0021952817,0.98024714,0.000014861974],"about_ca_topic_score_codex":0.009785225,"about_ca_topic_score_gemma":0.020018216,"teacher_disagreement_score":0.114060454,"about_ca_system_score_codex":0.0012548843,"about_ca_system_score_gemma":0.0011417167,"threshold_uncertainty_score":0.38157028},"labels":[],"label_agreement":null},{"id":"W7033166823","doi":"","title":"A place for everything and everything in its place..","year":2024,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Amateur; Context (archaeology); Need to know; Quarter (Canadian coin); Wonder; Space (punctuation)","score_opus":0.004425332446005851,"score_gpt":0.1742024215330773,"score_spread":0.16977708908707145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033166823","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028149814,0.0077304766,0.004768694,0.15699202,0.021299351,0.00013037959,0.00014197193,0.00092468533,0.8051974],"genre_scores_gemma":[0.03576285,0.0025117623,0.0028711187,0.035566352,0.0026845995,0.000097666496,0.0000694101,0.0005208415,0.9199154],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99833137,0.00061840506,0.00003983756,0.00022318638,0.00043270245,0.00035449513],"domain_scores_gemma":[0.99678135,0.00033612087,0.00011924517,0.00024015474,0.00073682825,0.0017861684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001767575,0.00074368616,0.00029657458,0.00057254825,0.010356123,0.008710672,0.0010345037,0.0038059673,0.09044338],"category_scores_gemma":[0.0037395696,0.0005856976,0.00035571522,0.000322865,0.0053908383,0.006314951,0.007242505,0.0067919837,0.058509152],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014151228,0.000021197207,0.0002532271,0.00007598652,0.0000025624108,0.00018122107,0.010835291,0.000020949092,0.00041284467,0.03275492,0.89260274,0.06282489],"study_design_scores_gemma":[0.000001510115,0.000008677475,0.00024378965,0.000056601406,7.362142e-7,0.0001321096,0.0028590073,0.000011321079,0.000027916532,0.001067919,0.99558616,0.0000042612246],"about_ca_topic_score_codex":0.01242822,"about_ca_topic_score_gemma":0.029193478,"teacher_disagreement_score":0.09044338,"about_ca_system_score_codex":0.0022904116,"about_ca_system_score_gemma":0.0026229797,"threshold_uncertainty_score":0.30256325},"labels":[],"label_agreement":null},{"id":"W7099102634","doi":"","title":"Measuring the Head Tilt Illusion During Sustained Acceleration Canadian Approach to","year":2012,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Tilt (camera); Aviation; Aviation accident; Acceleration; Head (geology); Illusion; Spatial disorientation","score_opus":0.020866488926459554,"score_gpt":0.19643223257877865,"score_spread":0.17556574365231908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099102634","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.993115,0.00021146156,0.00073071604,0.00010377009,0.00004236073,0.000046696547,0.00064618624,0.000042238866,0.005061604],"genre_scores_gemma":[0.9963995,0.00028510494,0.00081413914,0.00012406825,0.00001973039,0.00002434225,0.0003378203,0.0000122087395,0.0019830475],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997557,0.000026762486,0.000010757797,0.00003933782,0.00011720417,0.000050377406],"domain_scores_gemma":[0.9993104,0.00009342129,0.00011744696,0.00003556849,0.00032027918,0.00012282726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019007048,0.00021958626,0.00012259284,0.0006499438,0.00032040945,0.00048595513,0.000255973,0.00032788864,0.0023558359],"category_scores_gemma":[0.0022471948,0.000119495184,0.00016422804,0.00035637373,0.0003463609,0.00020972926,0.00048411617,0.0004112588,0.0005326455],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032180273,0.0004186573,0.7207013,0.00015668411,0.00014270659,0.0007437386,0.0038011838,0.0006161498,0.16531502,0.00054618594,0.0071512396,0.09718913],"study_design_scores_gemma":[0.000014973474,0.00023921533,0.99321365,0.000018022673,0.000018020146,0.00037905874,0.0006354947,0.0006730344,0.0037336121,0.00007897772,0.00097246404,0.000023638766],"about_ca_topic_score_codex":0.075044766,"about_ca_topic_score_gemma":0.14142478,"teacher_disagreement_score":0.92495525,"about_ca_system_score_codex":0.00062092836,"about_ca_system_score_gemma":0.0006387481,"threshold_uncertainty_score":0.14921588},"labels":[],"label_agreement":null},{"id":"W7099265472","doi":"","title":"88Copyright © Canadian Academy of Oriental and Occidental Culture Research on Newly-Found Writing of ox Bone in Warring States","year":2014,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Islam; Government (linguistics); China; Reflection (computer programming)","score_opus":0.01712195912055827,"score_gpt":0.2871583002104198,"score_spread":0.2700363410898616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099265472","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22382572,0.008009109,0.0011687034,0.008686342,0.0012579547,0.000065259745,0.0020109115,0.00006133428,0.75491476],"genre_scores_gemma":[0.6722668,0.014953863,0.0029416995,0.0011629553,0.0002111351,0.000045071654,0.0016743052,0.000075483185,0.30666873],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99942636,0.00007361622,0.000028470497,0.000069646805,0.00028854285,0.00011335809],"domain_scores_gemma":[0.99616724,0.00046421797,0.0002601068,0.00015566521,0.002726164,0.00022653713],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010243244,0.0003868214,0.00023519275,0.0018257474,0.0040912004,0.0032727772,0.00058127334,0.00031781377,0.058230963],"category_scores_gemma":[0.002916991,0.00011619767,0.00020307697,0.00323764,0.0017873573,0.0007868644,0.0007267881,0.000530876,0.0032518283],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002728813,0.00022386167,0.15282631,0.0010426164,0.00009092496,0.0007155776,0.027531985,0.0017218288,0.0050157323,0.06772905,0.18382113,0.55900806],"study_design_scores_gemma":[0.000013832024,0.000086069354,0.33017424,0.0008099525,0.000077717305,0.00033554266,0.04910188,0.00049784867,0.0024900876,0.0029928992,0.61337733,0.00004254526],"about_ca_topic_score_codex":0.7066555,"about_ca_topic_score_gemma":0.8638529,"teacher_disagreement_score":0.94176906,"about_ca_system_score_codex":0.008892128,"about_ca_system_score_gemma":0.01611973,"threshold_uncertainty_score":0.5901441},"labels":[],"label_agreement":null},{"id":"W7100140054","doi":"","title":"Counting Spanning Out-trees in Multidigraphs","year":2000,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Spanning tree; Digraph; Undirected graph; Minimum spanning tree; Graph; Graph theory; Pathwidth; Extension (predicate logic); Trémaux tree","score_opus":0.005781193607538884,"score_gpt":0.19312999771833836,"score_spread":0.18734880411079946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100140054","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30018228,0.0013225222,0.6783469,0.00052394584,0.00015898646,0.00010968668,0.00064980955,0.0006086872,0.018097123],"genre_scores_gemma":[0.8016273,0.000714675,0.18956433,0.00019121644,0.00015829514,0.00013868877,0.0006765752,0.00019611669,0.0067328922],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99887985,0.00022149218,0.00010938953,0.00026383717,0.0003641849,0.00016129827],"domain_scores_gemma":[0.99477875,0.0028603936,0.0007791816,0.0005332703,0.00067730725,0.0003711718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000982437,0.0003597795,0.00059283426,0.0024948688,0.0009595732,0.0018991939,0.0013717323,0.00076862465,0.0032428608],"category_scores_gemma":[0.0079862345,0.0003984897,0.00039520088,0.0016253786,0.0008522535,0.004566216,0.0011083146,0.0005613016,0.00036842594],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025872135,0.00014209666,0.01676055,0.0004991269,0.000073516865,0.0006578421,0.0009750313,0.06842802,0.017274143,0.70014286,0.00581812,0.18896997],"study_design_scores_gemma":[0.000023344917,0.0001048322,0.0050306744,0.00011394501,0.00008709314,0.0010729085,0.00029835504,0.38009867,0.011279004,0.5822502,0.019602472,0.00003848232],"about_ca_topic_score_codex":0.0010784288,"about_ca_topic_score_gemma":0.0019427411,"teacher_disagreement_score":0.0032428608,"about_ca_system_score_codex":0.0010522001,"about_ca_system_score_gemma":0.00034172268,"threshold_uncertainty_score":0.010848403},"labels":[],"label_agreement":null},{"id":"W7100926693","doi":"","title":"National Defence Headquarters","year":2012,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Maintainability; Aircraft maintenance; Order (exchange); Planned maintenance; Maintenance actions; Decision support system","score_opus":0.009105469449345286,"score_gpt":0.2035097364479832,"score_spread":0.19440426699863791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100926693","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00078978617,0.0013345422,0.001292429,0.0027100772,0.0020507474,0.0001660536,0.011974665,0.002194679,0.9774869],"genre_scores_gemma":[0.0029152061,0.0007857373,0.000616947,0.00047561733,0.0001304096,0.000049235194,0.0040968335,0.0002820538,0.9906481],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99913293,0.00006374964,0.00003509561,0.00023379686,0.00041325277,0.00012105959],"domain_scores_gemma":[0.99906343,0.00007278571,0.00004416778,0.00011781298,0.00052118575,0.00018057086],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00062248413,0.0016395623,0.0008726498,0.0012225658,0.0016522487,0.0042758123,0.0012296963,0.0023021344,0.8587848],"category_scores_gemma":[0.0018237463,0.0005161832,0.0004374743,0.0012071134,0.0004785225,0.001854049,0.002103443,0.0017661906,0.7454983],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001014205,0.000038698025,0.00033931056,0.00020499073,0.000007726117,0.000110422225,0.00004668429,0.00015111033,0.0008990463,0.0043049776,0.88669676,0.10709878],"study_design_scores_gemma":[0.000011428397,0.00003219833,0.00030256313,0.00004889524,0.0000020161106,0.0000420729,0.000027823718,0.0000997892,0.0002037364,0.0004097722,0.99881387,0.0000058076803],"about_ca_topic_score_codex":0.009182447,"about_ca_topic_score_gemma":0.0139168855,"teacher_disagreement_score":0.1412152,"about_ca_system_score_codex":0.0019090289,"about_ca_system_score_gemma":0.002426113,"threshold_uncertainty_score":0.20142627},"labels":[],"label_agreement":null},{"id":"W7105981217","doi":"10.1108/jqme-04-2025-0025","title":"A semi-Markov model for reliability and cost analysis of two-unit cold standby systems considering operator fatigue, preventive maintenance and imperfect repairs","year":2025,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Downtime; Preventive maintenance; Reliability (semiconductor); Imperfect; Operator (biology); Maintenance engineering; Corrective maintenance; Quality (philosophy)","score_opus":0.017292805404390987,"score_gpt":0.2898611953423296,"score_spread":0.27256838993793864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7105981217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08702874,0.0007966331,0.9024782,0.00078927923,0.0000916676,0.00016856263,0.0007884777,0.00031617616,0.0075422237],"genre_scores_gemma":[0.96248543,0.0007679645,0.024550091,0.00011254749,0.0000663582,0.00039216867,0.00053654687,0.000058612422,0.011030423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998982,0.00035614002,0.000044552224,0.0001635142,0.00019479106,0.0002589304],"domain_scores_gemma":[0.9964217,0.0024190035,0.000493447,0.00011492631,0.00040145448,0.00014949615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021803998,0.0014081146,0.0016642064,0.0010830706,0.00066059316,0.0015909206,0.0020827132,0.0018291484,0.005833218],"category_scores_gemma":[0.0043731257,0.0009821282,0.0017153209,0.0007939708,0.0013419221,0.001506198,0.0011806998,0.0019959898,0.0006971838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043940414,0.000020990336,0.0006266207,0.000033985198,0.0000241422,0.000094236384,0.000037231366,0.9845281,0.00046435674,0.012379977,0.00026408586,0.0014822531],"study_design_scores_gemma":[0.0000049749874,0.000012391374,0.000147072,0.0000044201724,0.000007946725,0.000009654471,0.000008967105,0.9972692,0.00005195539,0.0023620871,0.00011666697,0.0000046494956],"about_ca_topic_score_codex":0.029650602,"about_ca_topic_score_gemma":0.020000199,"teacher_disagreement_score":0.029650602,"about_ca_system_score_codex":0.0024320162,"about_ca_system_score_gemma":0.0022099758,"threshold_uncertainty_score":0.058956027},"labels":[],"label_agreement":null},{"id":"W7108079376","doi":"10.1139/tcsme-2025-0100","title":"Residual life prediction method of multi-source sensing linear degradation equipment based on BP neural network","year":2025,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Residual; Reliability (semiconductor); Degradation (telecommunications); Backpropagation; Linear prediction; Process (computing)","score_opus":0.013006074516330245,"score_gpt":0.22504321746575834,"score_spread":0.2120371429494281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7108079376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09666705,0.00042212402,0.9006237,0.00009373938,0.000036940015,0.000035581354,0.000055796434,0.0008294741,0.0012355521],"genre_scores_gemma":[0.94388074,0.00022634918,0.054335114,0.000034234024,0.00002067495,0.00006522749,0.00010291331,0.00002748054,0.001307291],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997534,0.000033011587,0.0000148313775,0.000081162085,0.00009346645,0.000024169674],"domain_scores_gemma":[0.99962866,0.00012682461,0.000052743977,0.000019937084,0.00015662573,0.00001519751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046568605,0.00073562365,0.000538694,0.0006470784,0.00020705366,0.00038717868,0.0007450343,0.00052442314,0.00054602686],"category_scores_gemma":[0.0011595412,0.00024265029,0.0003999801,0.0003866988,0.00021894548,0.0006754292,0.0003533143,0.0006611317,0.000142814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014539856,0.000092982154,0.004248516,0.00009015887,0.00005902774,0.000109108165,0.00007325483,0.7899291,0.009393825,0.000614266,0.0006003929,0.19464399],"study_design_scores_gemma":[0.0000016947413,0.000010460509,0.0004904687,0.0000019040249,0.0000044367616,0.0000068621907,0.0000021074952,0.9984493,0.00088139425,0.00010750224,0.00004112268,0.0000026677844],"about_ca_topic_score_codex":0.007934961,"about_ca_topic_score_gemma":0.004313252,"teacher_disagreement_score":0.007934961,"about_ca_system_score_codex":0.0004234264,"about_ca_system_score_gemma":0.0004734572,"threshold_uncertainty_score":0.015777528},"labels":[],"label_agreement":null},{"id":"W7112692237","doi":"","title":"ANALYSIS OF MARKOV CHAIN MONTE CARLO METHODS IN MULTI-INDENTURE INVENTORY OPTIMIZATION","year":2022,"lang":"","type":"dissertation","venue":"Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Markov chain; Markov chain Monte Carlo; Spare part; Monte Carlo method; Simulated annealing; Markov process; Simple (philosophy)","score_opus":0.022026922826328,"score_gpt":0.3092462858416685,"score_spread":0.2872193630153405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7112692237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0374328,0.00094574026,0.9543937,0.0005862856,0.00006270333,0.00015328359,0.00008482935,0.00033801116,0.006002646],"genre_scores_gemma":[0.5789199,0.0011190239,0.41543323,0.00030654282,0.000113036316,0.00071737514,0.0002698073,0.00028175296,0.0028393094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971124,0.0020059168,0.00007833367,0.00017330724,0.00045561287,0.0001744145],"domain_scores_gemma":[0.9469973,0.049316514,0.0008746514,0.0007295949,0.0016990596,0.0003829529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009411087,0.0009104489,0.0013085464,0.0014678178,0.0010239801,0.0014234165,0.0012808272,0.0013970354,0.0035069012],"category_scores_gemma":[0.028812874,0.0010006368,0.0012988899,0.0010826719,0.0014845526,0.0014222147,0.0011040395,0.0021316733,0.00026685925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042365366,0.000027065087,0.0006614696,0.000039372255,0.000034697317,0.000012629251,0.000023064182,0.9821945,0.000120317425,0.011056422,0.00023159082,0.005556537],"study_design_scores_gemma":[0.0000045360835,0.000008515871,0.00006198582,0.00000748025,0.000003722587,0.0000019247311,0.0000024872165,0.9971662,0.000057085163,0.0025856295,0.000097901,0.0000025572624],"about_ca_topic_score_codex":0.017871598,"about_ca_topic_score_gemma":0.015241212,"teacher_disagreement_score":0.017871598,"about_ca_system_score_codex":0.0024681261,"about_ca_system_score_gemma":0.0037604386,"threshold_uncertainty_score":0.04977113},"labels":[],"label_agreement":null},{"id":"W7117235498","doi":"10.5267/j.ijiec.2025.9.005","title":"Cost-availability ratio modeling of two-dimensional extended warranty for multi-component systems with fault correlation","year":2025,"lang":"","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Warranty; Fault (geology); Independence (probability theory); Sensitivity (control systems); Reliability (semiconductor); Fault model; Interval (graph theory); Transmission system","score_opus":0.043323683363978266,"score_gpt":0.287709952686788,"score_spread":0.24438626932280974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117235498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13528691,0.000843201,0.85031265,0.00039485755,0.000057506164,0.00009140861,0.00022892978,0.00026255415,0.012522008],"genre_scores_gemma":[0.9818854,0.0002595495,0.014521525,0.000021191037,0.000014774286,0.00007569309,0.00010740443,0.00003170303,0.0030828032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923396,0.00019178454,0.000049063117,0.00014217803,0.0002744424,0.000108524466],"domain_scores_gemma":[0.9990978,0.00041954147,0.00019785605,0.00005593622,0.0001907103,0.00003826389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011038134,0.00093770894,0.00069267536,0.0009516222,0.0003982334,0.0010711566,0.0014752637,0.0010228632,0.002348918],"category_scores_gemma":[0.0027640264,0.00048422022,0.0009951773,0.0008053696,0.0005187243,0.00173099,0.00059100805,0.00088681746,0.00020368783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017352833,0.000011097956,0.00040334638,0.000029165834,0.000009622579,0.00006587225,0.000026182686,0.99133366,0.00051027816,0.004798432,0.00014528351,0.0026497638],"study_design_scores_gemma":[9.943773e-7,0.000007642578,0.00015450885,0.0000015115522,0.0000035329838,0.000011630302,0.000004569471,0.9990343,0.00005961592,0.00064229366,0.00007757036,0.0000017916982],"about_ca_topic_score_codex":0.013287904,"about_ca_topic_score_gemma":0.0068730805,"teacher_disagreement_score":0.013287904,"about_ca_system_score_codex":0.001478212,"about_ca_system_score_gemma":0.0009834545,"threshold_uncertainty_score":0.02642113},"labels":[],"label_agreement":null},{"id":"W7125583114","doi":"10.18280/jesa.581207","title":"Performance Assessment of Static and Adaptive Maintenance Strategies Under Stochastic Conditions with Penalty-Augmented Objectives Using a Monte Carlo Simulation Approach to Reliability and Cost Optimization","year":2025,"lang":"","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Monte Carlo method; Stochastic optimization; Preventive maintenance; Key (lock)","score_opus":0.01674582433414027,"score_gpt":0.2755846826803069,"score_spread":0.2588388583461666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125583114","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88865083,0.0006721768,0.10438227,0.00023737067,0.000035373574,0.00007317045,0.000081332575,0.00017156164,0.0056959135],"genre_scores_gemma":[0.9956728,0.000054128774,0.0039569726,0.000009689226,0.0000034858772,0.000016328453,0.000020313802,0.000009510071,0.00025689794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993507,0.00032996046,0.00002902042,0.00006398161,0.000121253535,0.00010493819],"domain_scores_gemma":[0.992515,0.0063014785,0.00040693107,0.00016558399,0.00046798232,0.00014303203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028379438,0.00078507693,0.0009728294,0.0009843872,0.00038797918,0.0010627649,0.0006994998,0.0014106319,0.00093469536],"category_scores_gemma":[0.007378715,0.0004412008,0.00063189055,0.00065638445,0.0007204291,0.00080301886,0.0005002863,0.00065432297,0.000081858496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009296203,0.000031907915,0.000303544,0.000011728465,0.000012661399,0.000010976217,0.0000059314775,0.99715936,0.0003140163,0.00046001992,0.000024152227,0.0015727564],"study_design_scores_gemma":[0.00000550213,0.000047030026,0.000208116,0.0000014707756,0.0000059244844,0.0000029423325,0.0000031196475,0.9994387,0.00015334408,0.000121206744,0.0000098839155,0.0000027149422],"about_ca_topic_score_codex":0.009939975,"about_ca_topic_score_gemma":0.004790009,"teacher_disagreement_score":0.009939975,"about_ca_system_score_codex":0.0011685337,"about_ca_system_score_gemma":0.0011402256,"threshold_uncertainty_score":0.019764245},"labels":[],"label_agreement":null},{"id":"W7126171114","doi":"10.1109/icir68135.2025.11361598","title":"How Much Confidence Do We Have for GenAI-Based Reasoning for Reliability Estimation?","year":2025,"lang":"","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Future Earth","funders":"Western New England University","keywords":"Weibull distribution; Robustness (evolution); Shape parameter; Reliability (semiconductor); Confidence interval; Scale parameter; Sample size determination; Estimation theory","score_opus":0.010138319806756698,"score_gpt":0.25809888851919227,"score_spread":0.24796056871243558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126171114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1079593,0.001293169,0.8788101,0.004839347,0.0002115678,0.00018539034,0.00060487544,0.0013382034,0.0047580176],"genre_scores_gemma":[0.7980548,0.00046032236,0.19822317,0.001347296,0.00015908475,0.0002889595,0.0006607196,0.0002596436,0.0005460284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9277167,0.044989802,0.004027348,0.009342582,0.012279463,0.0016442059],"domain_scores_gemma":[0.44431198,0.47657183,0.019653056,0.03996681,0.017162586,0.0023337712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09914777,0.002047139,0.0027673417,0.002263413,0.0011957359,0.008527362,0.0053729736,0.00367705,0.0037120027],"category_scores_gemma":[0.44394186,0.0013380864,0.0023011446,0.0017389453,0.0053841667,0.0144314775,0.0043403725,0.0071467836,0.0010837839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004856736,0.0008313025,0.073008984,0.002284274,0.0024971357,0.0005983326,0.006238855,0.4156085,0.0069549824,0.21640523,0.0062184567,0.26449725],"study_design_scores_gemma":[0.00023591264,0.0005215226,0.005267305,0.00043493533,0.00025111478,0.00026465018,0.0010604523,0.72358596,0.0043360265,0.26110944,0.002763729,0.00016894199],"about_ca_topic_score_codex":0.004261105,"about_ca_topic_score_gemma":0.003398592,"teacher_disagreement_score":0.09914777,"about_ca_system_score_codex":0.0031536722,"about_ca_system_score_gemma":0.0035984924,"threshold_uncertainty_score":0.5243498},"labels":[],"label_agreement":null},{"id":"W7132887931","doi":"","title":"An application of the CBM design analysis tool to network assets","year":2003,"lang":"","type":"dissertation","venue":"TSpace","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Bibliothèque et Archives nationales du Québec; University of Toronto","funders":"","keywords":"Reliability (semiconductor); Key (lock); Hazard; Telephone network; Network planning and design; Data modeling; Extension (predicate logic)","score_opus":0.008627466597546922,"score_gpt":0.28124432300341184,"score_spread":0.2726168564058649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132887931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015225818,0.00007206657,0.97652847,0.00013448525,0.000030929867,0.00013796336,0.00030625123,0.0023127103,0.0052513448],"genre_scores_gemma":[0.13775258,0.0000943436,0.85691714,0.000057511756,0.000012846788,0.00041046823,0.00041771302,0.00039697156,0.0039404104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950457,0.00014938659,0.000022954797,0.00006284958,0.00022086542,0.000039256065],"domain_scores_gemma":[0.99871206,0.0008032793,0.00007072236,0.00010770182,0.00028588084,0.00002044908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015598766,0.0008382741,0.0004814691,0.0009886035,0.0005415448,0.0007546555,0.0008193705,0.0006582133,0.012196924],"category_scores_gemma":[0.0034181091,0.00048095215,0.0007179453,0.0006093198,0.00028803083,0.00038578527,0.0005620766,0.00083439227,0.0009167648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001891198,0.00014447492,0.0023470116,0.0005302226,0.00006677819,0.00031748434,0.00030014443,0.5940849,0.01637988,0.024969555,0.008269576,0.3524008],"study_design_scores_gemma":[0.000035751313,0.00007812775,0.0003084236,0.000033507607,0.000014280769,0.000054287728,0.00004045232,0.9829124,0.0035295815,0.004155341,0.008828297,0.000009508663],"about_ca_topic_score_codex":0.005069198,"about_ca_topic_score_gemma":0.006133736,"teacher_disagreement_score":0.012196924,"about_ca_system_score_codex":0.0006956009,"about_ca_system_score_gemma":0.0014445735,"threshold_uncertainty_score":0.040802777},"labels":[],"label_agreement":null},{"id":"W7132919224","doi":"","title":"Systems subject to repair and maintenance actions: Modeling and optimization","year":2008,"lang":"","type":"dissertation","venue":"TSpace","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Ontario Centres of Excellence","keywords":"Context (archaeology); Outsourcing; Time horizon; Decision model; Decision support system; Optimal decision; Decision problem; Optimization problem","score_opus":0.018457927020080315,"score_gpt":0.2720665791190569,"score_spread":0.2536086520989766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132919224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021999568,0.014520738,0.91405904,0.003403749,0.0004284788,0.00024271259,0.0016760416,0.0004349977,0.043234657],"genre_scores_gemma":[0.7725097,0.024437888,0.12363241,0.00064236065,0.0009859656,0.0016492694,0.0027682076,0.00032413757,0.073050044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990613,0.0003487129,0.000051302737,0.0001741724,0.0002201466,0.000144409],"domain_scores_gemma":[0.9983298,0.0011255484,0.00023765076,0.0000539671,0.00019025919,0.00006273376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013174904,0.0018754766,0.0018467341,0.0010060339,0.000663977,0.0022846295,0.0020255435,0.0033038133,0.005297516],"category_scores_gemma":[0.0032819174,0.00089554006,0.0013990547,0.0019008414,0.00139824,0.0019123489,0.0018132222,0.0025233545,0.0012131637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019674373,0.000032115528,0.00033218876,0.00010478104,0.000028315078,0.00007398256,0.00004939269,0.9380071,0.00015174836,0.053252514,0.0017640116,0.0061841016],"study_design_scores_gemma":[0.0000072439907,0.000010949872,0.00013178407,0.000020611722,0.000010196256,0.000018512277,0.000021147876,0.97370934,0.00005228497,0.023539176,0.0024716828,0.0000071120003],"about_ca_topic_score_codex":0.019235067,"about_ca_topic_score_gemma":0.009181362,"teacher_disagreement_score":0.019235067,"about_ca_system_score_codex":0.0016402339,"about_ca_system_score_gemma":0.0017724548,"threshold_uncertainty_score":0.038246214},"labels":[],"label_agreement":null},{"id":"W7132928632","doi":"","title":"Modeling, estimation, and control of partially observable failing systems using phase method","year":2016,"lang":"","type":"dissertation","venue":"TSpace","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto","keywords":"Reliability (semiconductor); Bayesian probability; Process (computing); Posterior probability; Observable; Condition-based maintenance; Autoregressive model; State (computer science); Control theory (sociology); Partially observable Markov decision process","score_opus":0.028707227360881427,"score_gpt":0.347145103264047,"score_spread":0.3184378759031656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132928632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015270764,0.00022894003,0.9834878,0.00007221623,0.00001895052,0.00003578831,0.00005331751,0.00016071885,0.00067150715],"genre_scores_gemma":[0.90179497,0.0007205986,0.09392676,0.00004069385,0.00005665299,0.00024251662,0.00021381919,0.00007179238,0.0029322398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934036,0.0002012579,0.00003495724,0.00016656074,0.000174546,0.00008236317],"domain_scores_gemma":[0.99836487,0.001040556,0.00030719084,0.00005100743,0.00020453117,0.000031788277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014255382,0.0008803361,0.0012262207,0.00072987954,0.00041182514,0.0010258411,0.0013133967,0.0008948083,0.0012027983],"category_scores_gemma":[0.0033599779,0.00086145784,0.0010513053,0.0005740216,0.0007426466,0.00092136295,0.0007852079,0.0011392588,0.00017618365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024174127,0.000014724061,0.000439011,0.000042734115,0.000020970865,0.000024762818,0.00002565408,0.9872574,0.00077437924,0.003536091,0.000081611965,0.0077584614],"study_design_scores_gemma":[0.0000023082532,0.000008789256,0.00007775647,0.000001814051,0.0000028084648,0.000002815386,0.0000013656424,0.99910945,0.00008401669,0.00064834004,0.000057966678,0.00000257988],"about_ca_topic_score_codex":0.017706592,"about_ca_topic_score_gemma":0.00893877,"teacher_disagreement_score":0.017706592,"about_ca_system_score_codex":0.00069612055,"about_ca_system_score_gemma":0.0011601715,"threshold_uncertainty_score":0.035207093},"labels":[],"label_agreement":null},{"id":"W7133009766","doi":"","title":"Maintenance and repair contracts: Modeling and optimization","year":2007,"lang":"","type":"dissertation","venue":"TSpace","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ontario Centres of Excellence","keywords":"Negotiation; Work (physics); Key (lock); Order (exchange); Maintenance engineering","score_opus":0.009835897238642963,"score_gpt":0.2798286520276552,"score_spread":0.26999275478901225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133009766","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029603215,0.0034842503,0.92719066,0.0022463726,0.00009902532,0.00016376369,0.00034299787,0.00013424741,0.036735527],"genre_scores_gemma":[0.7100143,0.0077683493,0.24368773,0.00020274005,0.00027289565,0.000943511,0.0006260337,0.0001506389,0.036333833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872714,0.00066989067,0.000043900567,0.00016534234,0.00027074706,0.00012299814],"domain_scores_gemma":[0.9983883,0.0011830451,0.00017871772,0.00007092079,0.000114500916,0.00006446139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002095193,0.0011408618,0.0010174051,0.00082580186,0.00066679955,0.0029217075,0.0017614295,0.0025781682,0.0032805374],"category_scores_gemma":[0.0038140954,0.0007798772,0.001006648,0.0016034982,0.0015577275,0.002450664,0.0011548734,0.002003198,0.00045009307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000141765595,0.000041497096,0.00026885697,0.000051892304,0.00001889021,0.000040455336,0.00006534234,0.81455934,0.00016783229,0.17669046,0.0010341263,0.0070472276],"study_design_scores_gemma":[0.000006932199,0.000013627216,0.00010163086,0.000014822239,0.0000074519025,0.000016621634,0.000025004623,0.9283571,0.00006311036,0.06883542,0.002552092,0.000006178039],"about_ca_topic_score_codex":0.009122603,"about_ca_topic_score_gemma":0.006391579,"teacher_disagreement_score":0.009122603,"about_ca_system_score_codex":0.0028549181,"about_ca_system_score_gemma":0.0025215358,"threshold_uncertainty_score":0.020713925},"labels":[],"label_agreement":null},{"id":"W7133019043","doi":"","title":"Machine interference models: An investigation into non-Markovian failure and repair models","year":2008,"lang":"","type":"dissertation","venue":"TSpace","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto; Ontario Centres of Excellence","keywords":"Downtime; Spare part; Weibull distribution; Unavailability; Maintainability; Exponential function; Burn-in","score_opus":0.015192752933461425,"score_gpt":0.25421724562286735,"score_spread":0.23902449268940593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133019043","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12044441,0.0018384132,0.85659313,0.0029781542,0.00015748876,0.00009944131,0.00020665803,0.00022329832,0.017459018],"genre_scores_gemma":[0.94310915,0.0023089475,0.03907696,0.00039786124,0.00034080565,0.00018200498,0.00022665385,0.00009493499,0.014262763],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883336,0.00049307646,0.000038752518,0.000118944,0.0002699114,0.0002460226],"domain_scores_gemma":[0.9879677,0.009937455,0.000981169,0.00031354933,0.00054763927,0.0002523862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038821693,0.00094676984,0.0015006341,0.0010200532,0.00071369024,0.001327904,0.0027171527,0.0020201288,0.004066401],"category_scores_gemma":[0.009494916,0.0006293215,0.0016646993,0.0011355414,0.0012623348,0.0027663275,0.0011293966,0.002023318,0.00037715706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007346793,0.00013983401,0.0018800014,0.00007817368,0.000055307446,0.00017274707,0.00021531036,0.8038981,0.00051250716,0.18644746,0.0012089559,0.0053182193],"study_design_scores_gemma":[0.000009587347,0.000019801626,0.000206084,0.000006427519,0.000008659918,0.000022720309,0.000015399948,0.9801813,0.00004948016,0.01913981,0.00033495715,0.000005777074],"about_ca_topic_score_codex":0.012853094,"about_ca_topic_score_gemma":0.009464496,"teacher_disagreement_score":0.012853094,"about_ca_system_score_codex":0.0015089342,"about_ca_system_score_gemma":0.0015806804,"threshold_uncertainty_score":0.025556564},"labels":[],"label_agreement":null},{"id":"W7133026946","doi":"","title":"Optimization of critical spare parts inventories: A reliability perspective","year":2007,"lang":"","type":"dissertation","venue":"TSpace","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Ontario Centres of Excellence","keywords":"Spare part; Maintainability; Reliability (semiconductor); Service (business); Perspective (graphical); Inventory theory; Focus (optics); Inventory control","score_opus":0.015581535578648623,"score_gpt":0.34569727984834536,"score_spread":0.33011574426969675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133026946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025708547,0.0024237332,0.95696425,0.0010622443,0.00010085428,0.000090950685,0.00019023918,0.00014461264,0.013314588],"genre_scores_gemma":[0.8061972,0.0065700533,0.16350746,0.0004396843,0.00030107677,0.0003034338,0.000261588,0.00033609942,0.022083277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993,0.00026896724,0.000025596068,0.000105998595,0.00018871728,0.00011065429],"domain_scores_gemma":[0.9986337,0.0009067804,0.00016768265,0.00007991574,0.00013283196,0.00007897273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014572331,0.0018012921,0.0011352337,0.0011842573,0.00037880903,0.0016938541,0.0015925545,0.0013858237,0.0044516944],"category_scores_gemma":[0.0037787524,0.0009164739,0.0011976338,0.0012092653,0.0010802731,0.0020877516,0.0010607783,0.0015567807,0.0004494894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016308082,0.00002765181,0.00015524973,0.000065105916,0.000015137225,0.000037084807,0.000025304093,0.9701431,0.00085183163,0.022924684,0.00047764217,0.0052608885],"study_design_scores_gemma":[0.000006146792,0.00005523636,0.00013724454,0.000025402,0.000012235249,0.000028990886,0.00002383968,0.9813059,0.00039579935,0.017037574,0.0009625669,0.000009083885],"about_ca_topic_score_codex":0.003979438,"about_ca_topic_score_gemma":0.002653119,"teacher_disagreement_score":0.0044516944,"about_ca_system_score_codex":0.0018732524,"about_ca_system_score_gemma":0.0018852879,"threshold_uncertainty_score":0.014892399},"labels":[],"label_agreement":null},{"id":"W7133481307","doi":"10.5281/zenodo.18858963","title":"Evaluation of Quasi-Experimental Design in Assessing System Reliability Within Ghana's Regional Monitoring Networks","year":2007,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Reliability (semiconductor); Data collection; Baseline (sea); Estimation; Systems design","score_opus":0.05717764079719118,"score_gpt":0.2806429224763191,"score_spread":0.22346528167912794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133481307","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70495236,0.00039161564,0.24149704,0.00045864834,0.0005661029,0.046211414,0.00036970933,0.00026674624,0.005286391],"genre_scores_gemma":[0.69464415,0.00018777473,0.2134887,0.0002638751,0.00008107205,0.08953323,0.000122665,0.000039108436,0.0016394279],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.83668375,0.15316728,0.002610463,0.0030689477,0.0033838218,0.0010856788],"domain_scores_gemma":[0.78660387,0.17803851,0.015676485,0.012608539,0.005926774,0.0011458717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.099314086,0.0012930145,0.0013502346,0.0005654314,0.0010125437,0.0011304456,0.001692367,0.0014020256,0.0034243602],"category_scores_gemma":[0.13029218,0.00095454114,0.0009612124,0.0005769878,0.0021792473,0.0013265238,0.0013026487,0.0013193418,0.0004181576],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.24680275,0.11753287,0.05919693,0.006814968,0.0029840153,0.00067801523,0.02154505,0.107254885,0.030949974,0.051936295,0.0032001238,0.35110408],"study_design_scores_gemma":[0.056971163,0.6080903,0.07366019,0.0007762449,0.0016594203,0.00022484183,0.0037187766,0.19418834,0.024943065,0.016691018,0.018735217,0.00034141442],"about_ca_topic_score_codex":0.0026246519,"about_ca_topic_score_gemma":0.0022116348,"teacher_disagreement_score":0.099314086,"about_ca_system_score_codex":0.0022180027,"about_ca_system_score_gemma":0.0038327693,"threshold_uncertainty_score":0.52522933},"labels":[],"label_agreement":null},{"id":"W7133537606","doi":"10.5281/zenodo.18858964","title":"Evaluation of Quasi-Experimental Design in Assessing System Reliability Within Ghana's Regional Monitoring Networks","year":2007,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Reliability (semiconductor); Data collection; Baseline (sea); Estimation; Systems design","score_opus":0.05717764079719118,"score_gpt":0.2806429224763191,"score_spread":0.22346528167912794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133537606","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70495236,0.00039161564,0.24149704,0.00045864834,0.0005661029,0.046211414,0.00036970933,0.00026674624,0.005286391],"genre_scores_gemma":[0.69464415,0.00018777473,0.2134887,0.0002638751,0.00008107205,0.08953323,0.000122665,0.000039108436,0.0016394279],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.83668375,0.15316728,0.002610463,0.0030689477,0.0033838218,0.0010856788],"domain_scores_gemma":[0.78660387,0.17803851,0.015676485,0.012608539,0.005926774,0.0011458717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.099314086,0.0012930145,0.0013502346,0.0005654314,0.0010125437,0.0011304456,0.001692367,0.0014020256,0.0034243602],"category_scores_gemma":[0.13029218,0.00095454114,0.0009612124,0.0005769878,0.0021792473,0.0013265238,0.0013026487,0.0013193418,0.0004181576],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.24680275,0.11753287,0.05919693,0.006814968,0.0029840153,0.00067801523,0.02154505,0.107254885,0.030949974,0.051936295,0.0032001238,0.35110408],"study_design_scores_gemma":[0.056971163,0.6080903,0.07366019,0.0007762449,0.0016594203,0.00022484183,0.0037187766,0.19418834,0.024943065,0.016691018,0.018735217,0.00034141442],"about_ca_topic_score_codex":0.0026246519,"about_ca_topic_score_gemma":0.0022116348,"teacher_disagreement_score":0.099314086,"about_ca_system_score_codex":0.0022180027,"about_ca_system_score_gemma":0.0038327693,"threshold_uncertainty_score":0.52522933},"labels":[],"label_agreement":null},{"id":"W7135750291","doi":"","title":"An innovative incentive contract to improve supplier reliability","year":2023,"lang":"en","type":"article","venue":"Research Portal (Queen's University Belfast)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Queen's University","funders":"","keywords":"Reliability (semiconductor); Incentive; Supplier relationship management; Production (economics); Component (thermodynamics); Control (management); Quality (philosophy)","score_opus":0.011181863530596874,"score_gpt":0.26610985626987893,"score_spread":0.25492799273928207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7135750291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06436,0.00040570003,0.86872476,0.0030169229,0.00117979,0.0010367988,0.00053404045,0.0015526715,0.059189275],"genre_scores_gemma":[0.7546383,0.00016965599,0.20856303,0.0005555069,0.00040215807,0.00056928373,0.00025184514,0.00022501827,0.034625217],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99428314,0.0028710137,0.00020365689,0.00048230623,0.001766538,0.00039339132],"domain_scores_gemma":[0.9850633,0.007822109,0.0009438041,0.0024875673,0.002339518,0.0013437438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010437138,0.00086848275,0.0012006894,0.0010948355,0.0011725918,0.0021319585,0.0036561969,0.0029985413,0.016843855],"category_scores_gemma":[0.022041544,0.00057589414,0.00075562415,0.0012422759,0.0011854114,0.0039041128,0.0028803989,0.002614782,0.001246993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017868784,0.0026685016,0.002571074,0.0005732325,0.00010232483,0.00030673383,0.0003828827,0.22518268,0.013291,0.32967022,0.039625812,0.38383865],"study_design_scores_gemma":[0.0009464144,0.0016697712,0.0019618561,0.00009110231,0.00008522417,0.00035554092,0.00012552938,0.8534701,0.004149189,0.09615752,0.040871046,0.0001167902],"about_ca_topic_score_codex":0.0010051898,"about_ca_topic_score_gemma":0.001086319,"teacher_disagreement_score":0.016843855,"about_ca_system_score_codex":0.0015837401,"about_ca_system_score_gemma":0.004847863,"threshold_uncertainty_score":0.056348324},"labels":[],"label_agreement":null},{"id":"W7139818958","doi":"","title":"Strategic Decision Making Model in Maintenance Management: Is it right time to learn from failures?","year":2015,"lang":"en","type":"article","venue":"Research Explorer (The University of Manchester)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Refinery; Asset management; Asset (computer security); Oil refinery; Process (computing); Strategic planning; European union; Petroleum industry","score_opus":0.07547419541442758,"score_gpt":0.27824020980793057,"score_spread":0.202766014393503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7139818958","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28937396,0.0047006006,0.6070909,0.023564968,0.0002802966,0.0010012777,0.0005731005,0.00018372214,0.07323114],"genre_scores_gemma":[0.930948,0.0013597228,0.06501184,0.00025684788,0.00005302489,0.00035524025,0.00013060859,0.000011336431,0.0018732722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9942163,0.004023424,0.00024042152,0.00034833336,0.0008259909,0.00034546686],"domain_scores_gemma":[0.98896116,0.008819748,0.0008062136,0.00014865868,0.0008267086,0.00043758625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007795674,0.0009351439,0.0010308803,0.002114384,0.0013381081,0.004087786,0.0014320449,0.002198577,0.0031084097],"category_scores_gemma":[0.01350459,0.00044046578,0.0009346317,0.0024547623,0.0019710662,0.0036742382,0.0015978866,0.002108583,0.00035518],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003360295,0.00048240804,0.013013869,0.001205048,0.0003061787,0.0010799854,0.0049367896,0.5387981,0.0013244743,0.31246224,0.0038773022,0.1221777],"study_design_scores_gemma":[0.00009613246,0.0004818161,0.0050996076,0.0006453238,0.0001504857,0.00024102445,0.0057737706,0.6984475,0.0005430966,0.27928647,0.009134613,0.00010016826],"about_ca_topic_score_codex":0.00646367,"about_ca_topic_score_gemma":0.012574245,"teacher_disagreement_score":0.007795674,"about_ca_system_score_codex":0.004951713,"about_ca_system_score_gemma":0.0070925155,"threshold_uncertainty_score":0.041227996},"labels":[],"label_agreement":null},{"id":"W7142932870","doi":"10.5281/zenodo.19321805","title":"Accelerated Life Testing Strategies for Competing Risks under Linear Degradation and Tampered Failure Rates","year":2021,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Degradation (telecommunications); Failure rate; Accelerated life testing; Maximum likelihood; Function (biology); Stress (linguistics); Stress testing (software); Reliability (semiconductor); Simple (philosophy)","score_opus":0.13373448200990873,"score_gpt":0.2945506233432144,"score_spread":0.16081614133330568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7142932870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008640007,0.00034777247,0.98993254,0.00017299618,0.00002648374,0.00008338053,0.000039815724,0.00012426791,0.0006327825],"genre_scores_gemma":[0.52329963,0.0010656933,0.46483225,0.00027267548,0.00020649817,0.0011522674,0.00034457314,0.00025657064,0.008569954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941391,0.0042975913,0.0001407363,0.0005446348,0.0005678726,0.00031004313],"domain_scores_gemma":[0.9736355,0.022344537,0.0016288871,0.00072575547,0.0012068714,0.0004584237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01410814,0.002956553,0.0026632966,0.0019688378,0.00065815746,0.001363154,0.0048339334,0.0020653296,0.0055817408],"category_scores_gemma":[0.025342101,0.0012800473,0.0020531563,0.00093010464,0.0018257414,0.002716468,0.0028069627,0.0028424193,0.00065863685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014656773,0.00008137466,0.0010040685,0.00014963579,0.0001666181,0.00033373854,0.0001783878,0.9013663,0.00092597265,0.07427029,0.0005322964,0.020844761],"study_design_scores_gemma":[0.00001698023,0.00006835376,0.00013538137,0.000011906625,0.00001732222,0.00005072334,0.000009990533,0.9848295,0.00018095705,0.0143449195,0.00032047322,0.000013478444],"about_ca_topic_score_codex":0.00388039,"about_ca_topic_score_gemma":0.002203999,"teacher_disagreement_score":0.01410814,"about_ca_system_score_codex":0.0016473603,"about_ca_system_score_gemma":0.0019642678,"threshold_uncertainty_score":0.07461184},"labels":[],"label_agreement":null},{"id":"W7142977502","doi":"10.5281/zenodo.19321806","title":"Accelerated Life Testing Strategies for Competing Risks under Linear Degradation and Tampered Failure Rates","year":2021,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Degradation (telecommunications); Failure rate; Accelerated life testing; Maximum likelihood; Function (biology); Stress (linguistics); Stress testing (software); Reliability (semiconductor); Simple (philosophy)","score_opus":0.13373448200990873,"score_gpt":0.2945506233432144,"score_spread":0.16081614133330568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7142977502","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008640007,0.00034777247,0.98993254,0.00017299618,0.00002648374,0.00008338053,0.000039815724,0.00012426791,0.0006327825],"genre_scores_gemma":[0.52329963,0.0010656933,0.46483225,0.00027267548,0.00020649817,0.0011522674,0.00034457314,0.00025657064,0.008569954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941391,0.0042975913,0.0001407363,0.0005446348,0.0005678726,0.00031004313],"domain_scores_gemma":[0.9736355,0.022344537,0.0016288871,0.00072575547,0.0012068714,0.0004584237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01410814,0.002956553,0.0026632966,0.0019688378,0.00065815746,0.001363154,0.0048339334,0.0020653296,0.0055817408],"category_scores_gemma":[0.025342101,0.0012800473,0.0020531563,0.00093010464,0.0018257414,0.002716468,0.0028069627,0.0028424193,0.00065863685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014656773,0.00008137466,0.0010040685,0.00014963579,0.0001666181,0.00033373854,0.0001783878,0.9013663,0.00092597265,0.07427029,0.0005322964,0.020844761],"study_design_scores_gemma":[0.00001698023,0.00006835376,0.00013538137,0.000011906625,0.00001732222,0.00005072334,0.000009990533,0.9848295,0.00018095705,0.0143449195,0.00032047322,0.000013478444],"about_ca_topic_score_codex":0.00388039,"about_ca_topic_score_gemma":0.002203999,"teacher_disagreement_score":0.01410814,"about_ca_system_score_codex":0.0016473603,"about_ca_system_score_gemma":0.0019642678,"threshold_uncertainty_score":0.07461184},"labels":[],"label_agreement":null},{"id":"W7143370358","doi":"10.5281/zenodo.19332422","title":"Accelerated Life Testing Strategies for Competing Risks under Linear Degradation and Tampered Failure Rates","year":2024,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Degradation (telecommunications); Failure rate; Accelerated life testing; Maximum likelihood; Function (biology); Stress (linguistics); Stress testing (software); Reliability (semiconductor); Simple (philosophy)","score_opus":0.1294503113769297,"score_gpt":0.3013755378370059,"score_spread":0.17192522646007619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7143370358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008658401,0.00034684333,0.9899131,0.00017300136,0.000026537704,0.00008333557,0.000039661234,0.0001248548,0.0006342603],"genre_scores_gemma":[0.5250215,0.0010596367,0.4631223,0.00027333916,0.00020685089,0.001147594,0.00034324618,0.00025607625,0.008569485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941658,0.0042748135,0.00014032221,0.00054222817,0.00056742254,0.0003093692],"domain_scores_gemma":[0.9737594,0.02222814,0.0016259189,0.00072327734,0.0012037831,0.00045944934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014100602,0.0029483512,0.0026558824,0.0019666653,0.0006553265,0.0013577124,0.00483944,0.0020620634,0.00559917],"category_scores_gemma":[0.025278633,0.0012755485,0.0020494012,0.00092559075,0.001817212,0.0027043754,0.002800265,0.0028351087,0.0006609109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014724051,0.000081516664,0.0010031341,0.00014917475,0.0001659931,0.00033423246,0.00017786615,0.90185577,0.0009280539,0.07367498,0.00053291314,0.020949088],"study_design_scores_gemma":[0.000017001708,0.00006835685,0.00013479604,0.000011855519,0.00001725848,0.0000507418,0.000009915808,0.9849502,0.00018060526,0.01422701,0.00031887798,0.000013430079],"about_ca_topic_score_codex":0.003874007,"about_ca_topic_score_gemma":0.0021987401,"teacher_disagreement_score":0.014100602,"about_ca_system_score_codex":0.0016444128,"about_ca_system_score_gemma":0.0019640902,"threshold_uncertainty_score":0.07457197},"labels":[],"label_agreement":null},{"id":"W7143398819","doi":"10.5281/zenodo.19326167","title":"Accelerated Life Testing Strategies for Competing Risks under Linear Degradation and Tampered Failure Rates","year":2022,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Degradation (telecommunications); Failure rate; Accelerated life testing; Maximum likelihood; Function (biology); Stress (linguistics); Stress testing (software); Reliability (semiconductor); Simple (philosophy)","score_opus":0.12864817809268475,"score_gpt":0.2884275289869296,"score_spread":0.15977935089424483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7143398819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008650326,0.00034871217,0.9899176,0.00017319166,0.000026579917,0.00008348844,0.000039773226,0.00012490134,0.0006353708],"genre_scores_gemma":[0.52468,0.0010646739,0.46344185,0.00027314454,0.00020691298,0.001150343,0.00034346845,0.00025627998,0.008583289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99414897,0.0042872666,0.00014062594,0.00054409925,0.0005691208,0.00030994072],"domain_scores_gemma":[0.9736907,0.022287393,0.0016291186,0.0007252974,0.0012081133,0.00045951508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01413116,0.0029551042,0.0026585928,0.0019678785,0.0006564451,0.001362158,0.0048404667,0.0020625198,0.005600423],"category_scores_gemma":[0.02532071,0.0012799199,0.0020520398,0.00092727103,0.0018197689,0.002714211,0.002805071,0.002842076,0.00066096446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014724482,0.000081593396,0.0010047992,0.00014989701,0.00016694001,0.0003355583,0.00017886356,0.9012837,0.0009287911,0.07425637,0.00053408014,0.02093211],"study_design_scores_gemma":[0.000017044673,0.00006825376,0.00013491425,0.000011888765,0.000017322218,0.00005080002,0.00000996754,0.9848726,0.00018069096,0.014303048,0.00031998908,0.000013464619],"about_ca_topic_score_codex":0.0038726842,"about_ca_topic_score_gemma":0.0021984722,"teacher_disagreement_score":0.01413116,"about_ca_system_score_codex":0.0016454408,"about_ca_system_score_gemma":0.0019655733,"threshold_uncertainty_score":0.074733615},"labels":[],"label_agreement":null},{"id":"W7143403393","doi":"10.5281/zenodo.19326166","title":"Accelerated Life Testing Strategies for Competing Risks under Linear Degradation and Tampered Failure Rates","year":2022,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Degradation (telecommunications); Failure rate; Accelerated life testing; Maximum likelihood; Function (biology); Stress (linguistics); Stress testing (software); Reliability (semiconductor); Simple (philosophy)","score_opus":0.12864817809268475,"score_gpt":0.2884275289869296,"score_spread":0.15977935089424483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7143403393","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008650326,0.00034871217,0.9899176,0.00017319166,0.000026579917,0.00008348844,0.000039773226,0.00012490134,0.0006353708],"genre_scores_gemma":[0.52468,0.0010646739,0.46344185,0.00027314454,0.00020691298,0.001150343,0.00034346845,0.00025627998,0.008583289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99414897,0.0042872666,0.00014062594,0.00054409925,0.0005691208,0.00030994072],"domain_scores_gemma":[0.9736907,0.022287393,0.0016291186,0.0007252974,0.0012081133,0.00045951508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01413116,0.0029551042,0.0026585928,0.0019678785,0.0006564451,0.001362158,0.0048404667,0.0020625198,0.005600423],"category_scores_gemma":[0.02532071,0.0012799199,0.0020520398,0.00092727103,0.0018197689,0.002714211,0.002805071,0.002842076,0.00066096446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014724482,0.000081593396,0.0010047992,0.00014989701,0.00016694001,0.0003355583,0.00017886356,0.9012837,0.0009287911,0.07425637,0.00053408014,0.02093211],"study_design_scores_gemma":[0.000017044673,0.00006825376,0.00013491425,0.000011888765,0.000017322218,0.00005080002,0.00000996754,0.9848726,0.00018069096,0.014303048,0.00031998908,0.000013464619],"about_ca_topic_score_codex":0.0038726842,"about_ca_topic_score_gemma":0.0021984722,"teacher_disagreement_score":0.01413116,"about_ca_system_score_codex":0.0016454408,"about_ca_system_score_gemma":0.0019655733,"threshold_uncertainty_score":0.074733615},"labels":[],"label_agreement":null},{"id":"W7143505943","doi":"10.5281/zenodo.19332423","title":"Accelerated Life Testing Strategies for Competing Risks under Linear Degradation and Tampered Failure Rates","year":2024,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Degradation (telecommunications); Failure rate; Accelerated life testing; Maximum likelihood; Function (biology); Stress (linguistics); Stress testing (software); Reliability (semiconductor); Simple (philosophy)","score_opus":0.1294503113769297,"score_gpt":0.3013755378370059,"score_spread":0.17192522646007619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7143505943","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008658401,0.00034684333,0.9899131,0.00017300136,0.000026537704,0.00008333557,0.000039661234,0.0001248548,0.0006342603],"genre_scores_gemma":[0.5250215,0.0010596367,0.4631223,0.00027333916,0.00020685089,0.001147594,0.00034324618,0.00025607625,0.008569485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941658,0.0042748135,0.00014032221,0.00054222817,0.00056742254,0.0003093692],"domain_scores_gemma":[0.9737594,0.02222814,0.0016259189,0.00072327734,0.0012037831,0.00045944934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014100602,0.0029483512,0.0026558824,0.0019666653,0.0006553265,0.0013577124,0.00483944,0.0020620634,0.00559917],"category_scores_gemma":[0.025278633,0.0012755485,0.0020494012,0.00092559075,0.001817212,0.0027043754,0.002800265,0.0028351087,0.0006609109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014724051,0.000081516664,0.0010031341,0.00014917475,0.0001659931,0.00033423246,0.00017786615,0.90185577,0.0009280539,0.07367498,0.00053291314,0.020949088],"study_design_scores_gemma":[0.000017001708,0.00006835685,0.00013479604,0.000011855519,0.00001725848,0.0000507418,0.000009915808,0.9849502,0.00018060526,0.01422701,0.00031887798,0.000013430079],"about_ca_topic_score_codex":0.003874007,"about_ca_topic_score_gemma":0.0021987401,"teacher_disagreement_score":0.014100602,"about_ca_system_score_codex":0.0016444128,"about_ca_system_score_gemma":0.0019640902,"threshold_uncertainty_score":0.07457197},"labels":[],"label_agreement":null},{"id":"W71450748","doi":"10.1007/978-0-85729-215-5_5","title":"Optimal Schedules of Two Periodic Imperfect Preventive Maintenance Policies and Their Comparison","year":2011,"lang":"en","type":"book-chapter","venue":"Springer series in reliability engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Imperfect; Schedule; Unit (ring theory); Action (physics); Preventive maintenance; Mathematical optimization; Computer science; Mathematics; Reliability engineering; Engineering; Physics","score_opus":0.008136437167440927,"score_gpt":0.2052332785097276,"score_spread":0.1970968413422867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W71450748","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38961303,0.0041508954,0.5612907,0.0005488221,0.0004900003,0.00028277104,0.00065558153,0.00090298115,0.042065255],"genre_scores_gemma":[0.92111367,0.0015541257,0.072198026,0.000061035185,0.0001227633,0.00019492795,0.00032432843,0.00018945317,0.004241626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994287,0.00021475067,0.000029321305,0.0000560287,0.00018407019,0.00008714377],"domain_scores_gemma":[0.9966928,0.002225683,0.00031348615,0.00022174466,0.00039096852,0.00015535703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017851314,0.0007150985,0.00090113585,0.001279826,0.0003111075,0.0009726468,0.0008489806,0.00078032847,0.003567033],"category_scores_gemma":[0.006855803,0.00047140193,0.00047563392,0.0010629001,0.0005495968,0.00072657684,0.0003881673,0.0005535912,0.00034011778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00106273,0.0001679491,0.00048738523,0.00020945621,0.000050595918,0.000053104635,0.000110811685,0.86491233,0.0027524284,0.044061508,0.0023843215,0.083747365],"study_design_scores_gemma":[0.000114519265,0.00036267197,0.0009163031,0.000025703013,0.000046757017,0.000044603265,0.000042864893,0.96913034,0.0012959134,0.026550254,0.0014532689,0.00001679957],"about_ca_topic_score_codex":0.0019466764,"about_ca_topic_score_gemma":0.0013654219,"teacher_disagreement_score":0.003567033,"about_ca_system_score_codex":0.0012388877,"about_ca_system_score_gemma":0.0018168463,"threshold_uncertainty_score":0.0119329095},"labels":[],"label_agreement":null},{"id":"W7147401233","doi":"10.5281/zenodo.19342910","title":"Accelerated Life Testing Strategies for Competing Risks under Linear Degradation and Tampered Failure Rates","year":2021,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Degradation (telecommunications); Failure rate; Accelerated life testing; Maximum likelihood; Function (biology); Stress (linguistics); Stress testing (software); Reliability (semiconductor); Simple (philosophy)","score_opus":0.13373448200990873,"score_gpt":0.2945506233432144,"score_spread":0.16081614133330568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7147401233","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008640007,0.00034777247,0.98993254,0.00017299618,0.00002648374,0.00008338053,0.000039815724,0.00012426791,0.0006327825],"genre_scores_gemma":[0.52329963,0.0010656933,0.46483225,0.00027267548,0.00020649817,0.0011522674,0.00034457314,0.00025657064,0.008569954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941391,0.0042975913,0.0001407363,0.0005446348,0.0005678726,0.00031004313],"domain_scores_gemma":[0.9736355,0.022344537,0.0016288871,0.00072575547,0.0012068714,0.0004584237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01410814,0.002956553,0.0026632966,0.0019688378,0.00065815746,0.001363154,0.0048339334,0.0020653296,0.0055817408],"category_scores_gemma":[0.025342101,0.0012800473,0.0020531563,0.00093010464,0.0018257414,0.002716468,0.0028069627,0.0028424193,0.00065863685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014656773,0.00008137466,0.0010040685,0.00014963579,0.0001666181,0.00033373854,0.0001783878,0.9013663,0.00092597265,0.07427029,0.0005322964,0.020844761],"study_design_scores_gemma":[0.00001698023,0.00006835376,0.00013538137,0.000011906625,0.00001732222,0.00005072334,0.000009990533,0.9848295,0.00018095705,0.0143449195,0.00032047322,0.000013478444],"about_ca_topic_score_codex":0.00388039,"about_ca_topic_score_gemma":0.002203999,"teacher_disagreement_score":0.01410814,"about_ca_system_score_codex":0.0016473603,"about_ca_system_score_gemma":0.0019642678,"threshold_uncertainty_score":0.07461184},"labels":[],"label_agreement":null},{"id":"W7147521147","doi":"10.5281/zenodo.19342911","title":"Accelerated Life Testing Strategies for Competing Risks under Linear Degradation and Tampered Failure Rates","year":2021,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Degradation (telecommunications); Failure rate; Accelerated life testing; Maximum likelihood; Function (biology); Stress (linguistics); Stress testing (software); Reliability (semiconductor); Simple (philosophy)","score_opus":0.13373448200990873,"score_gpt":0.2945506233432144,"score_spread":0.16081614133330568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7147521147","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008640007,0.00034777247,0.98993254,0.00017299618,0.00002648374,0.00008338053,0.000039815724,0.00012426791,0.0006327825],"genre_scores_gemma":[0.52329963,0.0010656933,0.46483225,0.00027267548,0.00020649817,0.0011522674,0.00034457314,0.00025657064,0.008569954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941391,0.0042975913,0.0001407363,0.0005446348,0.0005678726,0.00031004313],"domain_scores_gemma":[0.9736355,0.022344537,0.0016288871,0.00072575547,0.0012068714,0.0004584237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01410814,0.002956553,0.0026632966,0.0019688378,0.00065815746,0.001363154,0.0048339334,0.0020653296,0.0055817408],"category_scores_gemma":[0.025342101,0.0012800473,0.0020531563,0.00093010464,0.0018257414,0.002716468,0.0028069627,0.0028424193,0.00065863685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014656773,0.00008137466,0.0010040685,0.00014963579,0.0001666181,0.00033373854,0.0001783878,0.9013663,0.00092597265,0.07427029,0.0005322964,0.020844761],"study_design_scores_gemma":[0.00001698023,0.00006835376,0.00013538137,0.000011906625,0.00001732222,0.00005072334,0.000009990533,0.9848295,0.00018095705,0.0143449195,0.00032047322,0.000013478444],"about_ca_topic_score_codex":0.00388039,"about_ca_topic_score_gemma":0.002203999,"teacher_disagreement_score":0.01410814,"about_ca_system_score_codex":0.0016473603,"about_ca_system_score_gemma":0.0019642678,"threshold_uncertainty_score":0.07461184},"labels":[],"label_agreement":null},{"id":"W787858845","doi":"10.1080/1023697x.2008.10668126","title":"Designing Reliable Transport Networks: The Multiple Network Spoiler Approach","year":2008,"lang":"en","type":"article","venue":"HKIE Transactions","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"National University of Singapore","keywords":"Reliability (semiconductor); Network planning and design; Computer science; Flow network; Transport network; Scheme (mathematics); Network simulation; Traffic flow (computer networking); Computer network; Distributed computing; Reliability engineering; Engineering; Mathematical optimization","score_opus":0.013407804385055825,"score_gpt":0.1705527980380868,"score_spread":0.15714499365303097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W787858845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016957544,0.00020228488,0.9808193,0.00022061716,0.000020553203,0.00003207912,0.000021817057,0.00007117417,0.0016546036],"genre_scores_gemma":[0.78148305,0.00086253695,0.21331239,0.000096699805,0.00007882148,0.00020012085,0.00007898642,0.00010965718,0.003777711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887973,0.00059864763,0.000028903301,0.00014416687,0.00024374596,0.00010480544],"domain_scores_gemma":[0.99858236,0.0008322562,0.00020909186,0.00012855181,0.00017347866,0.00007431583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020272955,0.0010014203,0.00078634184,0.00088942004,0.00046730353,0.0011230631,0.0014273388,0.0012578089,0.0018341219],"category_scores_gemma":[0.004119987,0.00072579074,0.00076478015,0.00060636573,0.0012720017,0.0022516074,0.001399597,0.0013905587,0.0001922099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031552205,0.000012264426,0.00019319462,0.00003444761,0.00001536137,0.00005725546,0.000043746782,0.97549987,0.00073412724,0.016375558,0.00017404828,0.0068285745],"study_design_scores_gemma":[0.000006855115,0.000032678825,0.000043551518,0.0000057918082,0.000005844035,0.000018189114,0.000013816068,0.9896757,0.0002666541,0.009433213,0.0004924974,0.000005141074],"about_ca_topic_score_codex":0.0021619238,"about_ca_topic_score_gemma":0.001738098,"teacher_disagreement_score":0.0021619238,"about_ca_system_score_codex":0.0010443068,"about_ca_system_score_gemma":0.00094586273,"threshold_uncertainty_score":0.010721445},"labels":[],"label_agreement":null},{"id":"W868687346","doi":"","title":"Guest Editorial: Stochastic models in reliability engineering, life sciences and operations management","year":2011,"lang":"en","type":"editorial","venue":"Journal | MESA","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"China; Presentation (obstetrics); Library science; Inclusion (mineral); Work (physics); Political science; Diversity (politics); Social science; Engineering; Sociology; Law; Computer science; Medicine; Mechanical engineering","score_opus":0.008330934718970113,"score_gpt":0.21609677200706884,"score_spread":0.20776583728809872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W868687346","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000114736504,0.007149536,0.0005716875,0.029137658,0.96014917,0.000014802305,0.00007282741,0.00005011836,0.0027395682],"genre_scores_gemma":[0.0016885909,0.008808498,0.00028523672,0.005378833,0.9662267,0.000021232105,0.0000756099,0.00008354517,0.017431682],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972683,0.00047760544,0.00031057742,0.00036194432,0.0013979351,0.00018363431],"domain_scores_gemma":[0.98959863,0.004089333,0.0005787827,0.00023190683,0.0043253163,0.0011760041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004302841,0.0037071686,0.00236777,0.0027504377,0.0018528054,0.005156557,0.0022663018,0.005780471,0.012321562],"category_scores_gemma":[0.011524939,0.000750062,0.0020881621,0.001249623,0.0012658758,0.0032159507,0.0009735567,0.009187567,0.0071788295],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026600057,0.0000115793855,0.000026861917,0.0001355538,0.0000123346,0.000091778646,0.000012438005,0.00010349482,0.00005626229,0.00075933203,0.99433064,0.00443306],"study_design_scores_gemma":[0.000039062717,0.00003794008,0.00034605854,0.00035945277,0.000042739714,0.0003374422,0.000054135242,0.0006597331,0.00017622848,0.0026300773,0.99529904,0.00001809472],"about_ca_topic_score_codex":0.0007198776,"about_ca_topic_score_gemma":0.0021159148,"teacher_disagreement_score":0.012321562,"about_ca_system_score_codex":0.0020872331,"about_ca_system_score_gemma":0.0020197923,"threshold_uncertainty_score":0.04121971},"labels":[],"label_agreement":null}]}